{"id": "P17-1001", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1001.flac", "type": "audio", "duration": 1081.216}, {"from": "Assistant", "value": "Neural network models have shown their promising opportunities for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant features. However, in most existing approaches, the extracted shared features are prone to be contaminated by task-specific features or the noise brought by other tasks. In this paper, we propose an adversarial multi-task learning framework, alleviating the shared and private latent feature spaces from interfering with each other. We conduct extensive experiments on 16 different text classification tasks, which demonstrates the benefits of our approach. Besides, we show that the shared knowledge learned by our proposed model can be regarded as off-the-shelf knowledge and easily transferred to new tasks. The datasets of all 16 tasks are publicly available athttp://nlp.fudan.edu.cn/data/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1002", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1002.flac", "type": "audio", "duration": 1005.312}, {"from": "Assistant", "value": "We investigate neural techniques for end-to-end computational argumentation mining (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiLSTMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem. Moreover, we find that jointly learning ‘natural’ subtasks, in a multi-task learning setup, improves performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1003", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1003.flac", "type": "audio", "duration": 1011.712}, {"from": "Assistant", "value": "Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic Machine, which contains (a) a neural “programmer”, i.e., a sequence-to-sequence model that maps language utterances to programs and utilizes a key-variable memory to handle compositionality (b) a symbolic “computer”, i.e., a Lisp interpreter that performs program execution, and helps find good programs by pruning the search space. We apply REINFORCE to directly optimize the task reward of this structured prediction problem. To train with weak supervision and improve the stability of REINFORCE, we augment it with an iterative maximum-likelihood training process. NSM outperforms the state-of-the-art on the WebQuestionsSP dataset when trained from question-answer pairs only, without requiring any feature engineering or domain-specific knowledge.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1004", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1004.flac", "type": "audio", "duration": 1212.672}, {"from": "Assistant", "value": "Relation extraction has been widely used for finding unknown relational facts from plain text. Most existing methods focus on exploiting mono-lingual data for relation extraction, ignoring massive information from the texts in various languages. To address this issue, we introduce a multi-lingual neural relation extraction framework, which employs mono-lingual attention to utilize the information within mono-lingual texts and further proposes cross-lingual attention to consider the information consistency and complementarity among cross-lingual texts. Experimental results on real-world datasets show that, our model can take advantage of multi-lingual texts and consistently achieve significant improvements on relation extraction as compared with baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1005", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1005.flac", "type": "audio", "duration": 1095.403}, {"from": "Assistant", "value": "We introduce a neural semantic parser which is interpretable and scalable. Our model converts natural language utterances to intermediate, domain-general natural language representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. The semantic parser is trained end-to-end using annotated logical forms or their denotations. We achieve the state of the art on SPADES and GRAPHQUESTIONS and obtain competitive results on GEOQUERY and WEBQUESTIONS. The induced predicate-argument structures shed light on the types of representations useful for semantic parsing and how these are different from linguistically motivated ones.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1006", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1006.flac", "type": "audio", "duration": 1133.739}, {"from": "Assistant", "value": "Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures. These effects are detrimental for language understanding systems, which may infer that ‘inexpensive’ is a rephrasing for ‘expensive’ or may not associate ‘acquire’ with ‘acquires’. In this work, we propose a novel morph-fitting procedure which moves past the use of curated semantic lexicons for improving distributional vector spaces. Instead, our method injects morphological constraints generated using simple language-specific rules, pulling inflectional forms of the same word close together and pushing derivational antonyms far apart. In intrinsic evaluation over four languages, we show that our approach: 1) improves low-frequency word estimates; and 2) boosts the semantic quality of the entire word vector collection. Finally, we show that morph-fitted vectors yield large gains in the downstream task of dialogue state tracking, highlighting the importance of morphology for tackling long-tail phenomena in language understanding tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1007", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1007.flac", "type": "audio", "duration": 1015.509}, {"from": "Assistant", "value": "In recent years word-embedding models have gained great popularity due to their remarkable performance on several tasks, including word analogy questions and caption generation. An unexpected “side-effect” of such models is that their vectors often exhibit compositionality, i.e.,addingtwo word-vectors results in a vector that is only a small angle away from the vector of a word representing the semantic composite of the original words, e.g., “man” + “royal” = “king”. This work provides a theoretical justification for the presence of additive compositionality in word vectors learned using the Skip-Gram model. In particular, it shows that additive compositionality holds in an even stricter sense (small distance rather than small angle) under certain assumptions on the process generating the corpus. As a corollary, it explains the success of vector calculus in solving word analogies. When these assumptions do not hold, this work describes the correct non-linear composition operator. Finally, this work establishes a connection between the Skip-Gram model and the Sufficient Dimensionality Reduction (SDR) framework of Globerson and Tishby: the parameters of SDR models can be obtained from those of Skip-Gram models simply by adding information on symbol frequencies. This shows that Skip-Gram embeddings are optimal in the sense of Globerson and Tishby and, further, implies that the heuristics commonly used to approximately fit Skip-Gram models can be used to fit SDR models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1008", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1008.flac", "type": "audio", "duration": 1333.248}, {"from": "Assistant", "value": "Semantic representation is receiving growing attention in NLP in the past few years, and many proposals for semantic schemes (e.g., AMR, UCCA, GMB, UDS) have been put forth. Yet, little has been done to assess the achievements and the shortcomings of these new contenders, compare them with syntactic schemes, and clarify the general goals of research on semantic representation. We address these gaps by critically surveying the state of the art in the field.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1009", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1009.flac", "type": "audio", "duration": 908.352}, {"from": "Assistant", "value": "While joint models have been developed for many NLP tasks, the vast majority of event coreference resolvers, including the top-performing resolvers competing in the recent TAC KBP 2016 Event Nugget Detection and Coreference task, are pipeline-based, where the propagation of errors from the trigger detection component to the event coreference component is a major performance limiting factor. To address this problem, we propose a model for jointly learning event coreference, trigger detection, and event anaphoricity. Our joint model is novel in its choice of tasks and its features for capturing cross-task interactions. To our knowledge, this is the first attempt to train a mention-ranking model and employ event anaphoricity for event coreference. Our model achieves the best results to date on the KBP 2016 English and Chinese datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1010", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1010.flac", "type": "audio", "duration": 872.021}, {"from": "Assistant", "value": "Most existing approaches for zero pronoun resolution are heavily relying on annotated data, which is often released by shared task organizers. Therefore, the lack of annotated data becomes a major obstacle in the progress of zero pronoun resolution task. Also, it is expensive to spend manpower on labeling the data for better performance. To alleviate the problem above, in this paper, we propose a simple but novel approach to automatically generate large-scale pseudo training data for zero pronoun resolution. Furthermore, we successfully transfer the cloze-style reading comprehension neural network model into zero pronoun resolution task and propose a two-step training mechanism to overcome the gap between the pseudo training data and the real one. Experimental results show that the proposed approach significantly outperforms the state-of-the-art systems with an absolute improvements of 3.1% F-score on OntoNotes 5.0 data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1011", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1011.flac", "type": "audio", "duration": 1309.12}, {"from": "Assistant", "value": "Discourse modes play an important role in writing composition and evaluation. This paper presents a study on the manual and automatic identification of narration,exposition, description, argument and emotion expressing sentences in narrative essays. We annotate a corpus to study the characteristics of discourse modes and describe a neural sequence labeling model for identification. Evaluation results show that discourse modes can be identified automatically with an average F1-score of 0.7. We further demonstrate that discourse modes can be used as features that improve automatic essay scoring (AES). The impacts of discourse modes for AES are also discussed.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1012", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1012.flac", "type": "audio", "duration": 1236.245}, {"from": "Assistant", "value": "The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence. We present a faster and simpler architecture based on a succession of convolutional layers. This allows to encode the source sentence simultaneously compared to recurrent networks for which computation is constrained by temporal dependencies. On WMT’16 English-Romanian translation we achieve competitive accuracy to the state-of-the-art and on WMT’15 English-German we outperform several recently published results. Our models obtain almost the same accuracy as a very deep LSTM setup on WMT’14 English-French translation. We speed up CPU decoding by more than two times at the same or higher accuracy as a strong bi-directional LSTM.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1013", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1013.flac", "type": "audio", "duration": 1060.651}, {"from": "Assistant", "value": "Deep Neural Networks (DNNs) have provably enhanced the state-of-the-art Neural Machine Translation (NMT) with its capability in modeling complex functions and capturing complex linguistic structures. However NMT with deep architecture in its encoder or decoder RNNs often suffer from severe gradient diffusion due to the non-linear recurrent activations, which often makes the optimization much more difficult. To address this problem we propose a novel linear associative units (LAU) to reduce the gradient propagation path inside the recurrent unit. Different from conventional approaches (LSTM unit and GRU), LAUs uses linear associative connections between input and output of the recurrent unit, which allows unimpeded information flow through both space and time The model is quite simple, but it is surprisingly effective. Our empirical study on Chinese-English translation shows that our model with proper configuration can improve by 11.7 BLEU upon Groundhog and the best reported on results in the same setting. On WMT14 English-German task and a larger WMT14 English-French task, our model achieves comparable results with the state-of-the-art.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1014", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1014.flac", "type": "audio", "duration": 197.568}, {"from": "Assistant", "value": "Sequence-to-sequence models have shown strong performance across a broad range of applications. However, their application to parsing and generating text using Abstract Meaning Representation (AMR) has been limited, due to the relatively limited amount of labeled data and the non-sequential nature of the AMR graphs. We present a novel training procedure that can lift this limitation using millions of unlabeled sentences and careful preprocessing of the AMR graphs. For AMR parsing, our model achieves competitive results of 62.1 SMATCH, the current best score reported without significant use of external semantic resources. For AMR generation, our model establishes a new state-of-the-art performance of BLEU 33.8. We present extensive ablative and qualitative analysis including strong evidence that sequence-based AMR models are robust against ordering variations of graph-to-sequence conversions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1015", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1015.flac", "type": "audio", "duration": 1145.536}, {"from": "Assistant", "value": "Solving algebraic word problems requires executing a series of arithmetic operations—a program—to obtain a final answer. However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge. To make this task more feasible, we solve these problems by generating answer rationales, sequences of natural language and human-readable mathematical expressions that derive the final answer through a series of small steps. Although rationales do not explicitly specify programs, they provide a scaffolding for their structure via intermediate milestones. To evaluate our approach, we have created a new 100,000-sample dataset of questions, answers and rationales. Experimental results show that indirect supervision of program learning via answer rationales is a promising strategy for inducing arithmetic programs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1016", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1016.flac", "type": "audio", "duration": 1035.947}, {"from": "Assistant", "value": "We propose two novel methodologies for the automatic generation of rhythmic poetry in a variety of forms. The first approach uses a neural language model trained on a phonetic encoding to learn an implicit representation of both the form and content of English poetry. This model can effectively learn common poetic devices such as rhyme, rhythm and alliteration. The second approach considers poetry generation as a constraint satisfaction problem where a generative neural language model is tasked with learning a representation of content, and a discriminative weighted finite state machine constrains it on the basis of form. By manipulating the constraints of the latter model, we can generate coherent poetry with arbitrary forms and themes. A large-scale extrinsic evaluation demonstrated that participants consider machine-generated poems to be written by humans 54% of the time. In addition, participants rated a machine-generated poem to be the best amongst all evaluated.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1017", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1017.flac", "type": "audio", "duration": 1129.216}, {"from": "Assistant", "value": "In this paper, we present a novel framework for semi-automatically creating linguistically challenging micro-planning data-to-text corpora from existing Knowledge Bases. Because our method pairs data of varying size and shape with texts ranging from simple clauses to short texts, a dataset created using this framework provides a challenging benchmark for microplanning. Another feature of this framework is that it can be applied to any large scale knowledge base and can therefore be used to train and learn KB verbalisers. We apply our framework to DBpedia data and compare the resulting dataset with Wen et al. 2016’s. We show that while Wen et al.’s dataset is more than twice larger than ours, it is less diverse both in terms of input and in terms of text. We thus propose our corpus generation framework as a novel method for creating challenging data sets from which NLG models can be learned which are capable of handling the complex interactions occurring during in micro-planning between lexicalisation, aggregation, surface realisation, referring expression generation and sentence segmentation. To encourage researchers to take up this challenge, we made available a dataset of 21,855 data/text pairs created using this framework in the context of the WebNLG shared task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1022", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1022.flac", "type": "audio", "duration": 1082.389}, {"from": "Assistant", "value": "Several approaches have recently been proposed for learning decentralized deep multiagent policies that coordinate via a differentiable communication channel. While these policies are effective for many tasks, interpretation of their induced communication strategies has remained a challenge. Here we propose to interpret agents’ messages by translating them. Unlike in typical machine translation problems, we have no parallel data to learn from. Instead we develop a translation model based on the insight that agent messages and natural language strings mean the same thing if they induce the same belief about the world in a listener. We present theoretical guarantees and empirical evidence that our approach preserves both the semantics and pragmatics of messages by ensuring that players communicating through a translation layer do not suffer a substantial loss in reward relative to players with a common language.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1023", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1023.flac", "type": "audio", "duration": 952.683}, {"from": "Assistant", "value": "We investigate object naming, which is an important sub-task of referring expression generation on real-world images. As opposed to mutually exclusive labels used in object recognition, object names are more flexible, subject to communicative preferences and semantically related to each other. Therefore, we investigate models of referential word meaning that link visual to lexical information which we assume to be given through distributional word embeddings. We present a model that learns individual predictors for object names that link visual and distributional aspects of word meaning during training. We show that this is particularly beneficial for zero-shot learning, as compared to projecting visual objects directly into the distributional space. In a standard object naming task, we find that different ways of combining lexical and visual information achieve very similar performance, though experiments on model combination suggest that they capture complementary aspects of referential meaning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1024", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1024.flac", "type": "audio", "duration": 896.107}, {"from": "Assistant", "value": "In this paper, we aim to understand whether current language and vision (LaVi) models truly grasp the interaction between the two modalities. To this end, we propose an extension of the MS-COCO dataset, FOIL-COCO, which associates images with both correct and ‘foil’ captions, that is, descriptions of the image that are highly similar to the original ones, but contain one single mistake (‘foil word’). We show that current LaVi models fall into the traps of this data and perform badly on three tasks: a) caption classification (correct vs. foil); b) foil word detection; c) foil word correction. Humans, in contrast, have near-perfect performance on those tasks. We demonstrate that merely utilising language cues is not enough to model FOIL-COCO and that it challenges the state-of-the-art by requiring a fine-grained understanding of the relation between text and image.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1025", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1025.flac", "type": "audio", "duration": 1115.584}, {"from": "Assistant", "value": "Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., “My house is bigger than me.” However, while rarely stated explicitly, this trivial everyday knowledge does influence the way people talk about the world, which provides indirect clues to reason about the world. For example, a statement like, “Tyler entered his house” implies that his house is bigger than Tyler. In this paper, we present an approach to infer relative physical knowledge of actions and objects along five dimensions (e.g., size, weight, and strength) from unstructured natural language text. We frame knowledge acquisition as joint inference over two closely related problems: learning (1) relative physical knowledge of object pairs and (2) physical implications of actions when applied to those object pairs. Empirical results demonstrate that it is possible to extract knowledge of actions and objects from language and that joint inference over different types of knowledge improves performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1026", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1026.flac", "type": "audio", "duration": 1016.64}, {"from": "Assistant", "value": "We propose a new A* CCG parsing model in which the probability of a tree is decomposed into factors of CCG categories and its syntactic dependencies both defined on bi-directional LSTMs. Our factored model allows the precomputation of all probabilities and runs very efficiently, while modeling sentence structures explicitly via dependencies. Our model achieves the state-of-the-art results on English and Japanese CCG parsing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1027", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1027.flac", "type": "audio", "duration": 1115.712}, {"from": "Assistant", "value": "Restricted non-monotonicity has been shown beneficial for the projective arc-eager dependency parser in previous research, as posterior decisions can repair mistakes made in previous states due to the lack of information. In this paper, we propose a novel, fully non-monotonic transition system based on the non-projective Covington algorithm. As a non-monotonic system requires exploration of erroneous actions during the training process, we develop several non-monotonic variants of the recently defined dynamic oracle for the Covington parser, based on tight approximations of the loss. Experiments on datasets from the CoNLL-X and CoNLL-XI shared tasks show that a non-monotonic dynamic oracle outperforms the monotonic version in the majority of languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1028", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1028.flac", "type": "audio", "duration": 1006.315}, {"from": "Assistant", "value": "Despite sequences being core to NLP, scant work has considered how to handle noisy sequence labels from multiple annotators for the same text. Given such annotations, we consider two complementary tasks: (1) aggregating sequential crowd labels to infer a best single set of consensus annotations; and (2) using crowd annotations as training data for a model that can predict sequences in unannotated text. For aggregation, we propose a novel Hidden Markov Model variant. To predict sequences in unannotated text, we propose a neural approach using Long Short Term Memory. We evaluate a suite of methods across two different applications and text genres: Named-Entity Recognition in news articles and Information Extraction from biomedical abstracts. Results show improvement over strong baselines. Our source code and data are available online.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1029", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1029.flac", "type": "audio", "duration": 1030.656}, {"from": "Assistant", "value": "Labeled sequence transduction is a task of transforming one sequence into another sequence that satisfies desiderata specified by a set of labels. In this paper we propose multi-space variational encoder-decoders, a new model for labeled sequence transduction with semi-supervised learning. The generative model can use neural networks to handle both discrete and continuous latent variables to exploit various features of data. Experiments show that our model provides not only a powerful supervised framework but also can effectively take advantage of the unlabeled data. On the SIGMORPHON morphological inflection benchmark, our model outperforms single-model state-of-art results by a large margin for the majority of languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1030", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1030.flac", "type": "audio", "duration": 1009.152}, {"from": "Assistant", "value": "Recurrent neural networks (RNNs) have shown promising performance for language modeling. However, traditional training of RNNs using back-propagation through time often suffers from overfitting. One reason for this is that stochastic optimization (used for large training sets) does not provide good estimates of model uncertainty. This paper leverages recent advances in stochastic gradient Markov Chain Monte Carlo (also appropriate for large training sets) to learn weight uncertainty in RNNs. It yields a principled Bayesian learning algorithm, adding gradient noise during training (enhancing exploration of the model-parameter space) and model averaging when testing. Extensive experiments on various RNN models and across a broad range of applications demonstrate the superiority of the proposed approach relative to stochastic optimization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1031", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1031.flac", "type": "audio", "duration": 1180.736}, {"from": "Assistant", "value": "Automated processing of historical texts often relies on pre-normalization to modern word forms. Training encoder-decoder architectures to solve such problems typically requires a lot of training data, which is not available for the named task. We address this problem by using several novel encoder-decoder architectures, including a multi-task learning (MTL) architecture using a grapheme-to-phoneme dictionary as auxiliary data, pushing the state-of-the-art by an absolute 2% increase in performance. We analyze the induced models across 44 different texts from Early New High German. Interestingly, we observe that, as previously conjectured, multi-task learning can learn to focus attention during decoding, in ways remarkably similar to recently proposed attention mechanisms. This, we believe, is an important step toward understanding how MTL works.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1032", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1032.flac", "type": "audio", "duration": 1034.517}, {"from": "Assistant", "value": "Kernel methods enable the direct usage of structured representations of textual data during language learning and inference tasks. Expressive kernels, such as Tree Kernels, achieve excellent performance in NLP. On the other side, deep neural networks have been demonstrated effective in automatically learning feature representations during training. However, their input is tensor data, i.e., they can not manage rich structured information. In this paper, we show that expressive kernels and deep neural networks can be combined in a common framework in order to (i) explicitly model structured information and (ii) learn non-linear decision functions. We show that the input layer of a deep architecture can be pre-trained through the application of the Nystrom low-rank approximation of kernel spaces. The resulting “kernelized” neural network achieves state-of-the-art accuracy in three different tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1033", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1033.flac", "type": "audio", "duration": 1048.405}, {"from": "Assistant", "value": "Language models are typically applied at the sentence level, without access to the broader document context. We present a neural language model that incorporates document context in the form of a topic model-like architecture, thus providing a succinct representation of the broader document context outside of the current sentence. Experiments over a range of datasets demonstrate that our model outperforms a pure sentence-based model in terms of language model perplexity, and leads to topics that are potentially more coherent than those produced by a standard LDA topic model. Our model also has the ability to generate related sentences for a topic, providing another way to interpret topics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1034", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1034.flac", "type": "audio", "duration": 885.909}, {"from": "Assistant", "value": "Solving cold-start problem in review spam detection is an urgent and significant task. It can help the on-line review websites to relieve the damage of spammers in time, but has never been investigated by previous work. This paper proposes a novel neural network model to detect review spam for cold-start problem, by learning to represent the new reviewers’ review with jointly embedded textual and behavioral information. Experimental results prove the proposed model achieves an effective performance and possesses preferable domain-adaptability. It is also applicable to a large scale dataset in an unsupervised way.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1035", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1035.flac", "type": "audio", "duration": 1030.315}, {"from": "Assistant", "value": "Cognitive NLP systems- i.e., NLP systems that make use of behavioral data - augment traditional text-based features with cognitive features extracted from eye-movement patterns, EEG signals, brain-imaging etc. Such extraction of features is typically manual. We contend that manual extraction of features may not be the best way to tackle text subtleties that characteristically prevail in complex classification tasks like Sentiment Analysis and Sarcasm Detection, and that even the extraction and choice of features should be delegated to the learning system. We introduce a framework to automatically extract cognitive features from the eye-movement/gaze data of human readers reading the text and use them as features along with textual features for the tasks of sentiment polarity and sarcasm detection. Our proposed framework is based on Convolutional Neural Network (CNN). The CNN learns features from both gaze and text and uses them to classify the input text. We test our technique on published sentiment and sarcasm labeled datasets, enriched with gaze information, to show that using a combination of automatically learned text and gaze features often yields better classification performance over (i) CNN based systems that rely on text input alone and (ii) existing systems that rely on handcrafted gaze and textual features.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1036", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1036.flac", "type": "audio", "duration": 1233.579}, {"from": "Assistant", "value": "Aspect extraction is an important and challenging task in aspect-based sentiment analysis. Existing works tend to apply variants of topic models on this task. While fairly successful, these methods usually do not produce highly coherent aspects. In this paper, we present a novel neural approach with the aim of discovering coherent aspects. The model improves coherence by exploiting the distribution of word co-occurrences through the use of neural word embeddings. Unlike topic models which typically assume independently generated words, word embedding models encourage words that appear in similar contexts to be located close to each other in the embedding space. In addition, we use an attention mechanism to de-emphasize irrelevant words during training, further improving the coherence of aspects. Experimental results on real-life datasets demonstrate that our approach discovers more meaningful and coherent aspects, and substantially outperforms baseline methods on several evaluation tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1041", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1041.flac", "type": "audio", "duration": 1020.544}, {"from": "Assistant", "value": "We consider the problem of parsing natural language descriptions into source code written in a general-purpose programming language like Python. Existing data-driven methods treat this problem as a language generation task without considering the underlying syntax of the target programming language. Informed by previous work in semantic parsing, in this paper we propose a novel neural architecture powered by a grammar model to explicitly capture the target syntax as prior knowledge. Experiments find this an effective way to scale up to generation of complex programs from natural language descriptions, achieving state-of-the-art results that well outperform previous code generation and semantic parsing approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1042", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1042.flac", "type": "audio", "duration": 1101.269}, {"from": "Assistant", "value": "Most methods to learn bilingual word embeddings rely on large parallel corpora, which is difficult to obtain for most language pairs. This has motivated an active research line to relax this requirement, with methods that use document-aligned corpora or bilingual dictionaries of a few thousand words instead. In this work, we further reduce the need of bilingual resources using a very simple self-learning approach that can be combined with any dictionary-based mapping technique. Our method exploits the structural similarity of embedding spaces, and works with as little bilingual evidence as a 25 word dictionary or even an automatically generated list of numerals, obtaining results comparable to those of systems that use richer resources.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1043", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1043.flac", "type": "audio", "duration": 1084.949}, {"from": "Assistant", "value": "We present a system which parses sentences into Abstract Meaning Representations, improving state-of-the-art results for this task by more than 5%. AMR graphs represent semantic content using linguistic properties such as semantic roles, coreference, negation, and more. The AMR parser does not rely on a syntactic pre-parse, or heavily engineered features, and uses five recurrent neural networks as the key architectural components for inferring AMR graphs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1044", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1044.flac", "type": "audio", "duration": 1146.603}, {"from": "Assistant", "value": "We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations. We use a deep highway BiLSTM architecture with constrained decoding, while observing a number of recent best practices for initialization and regularization. Our 8-layer ensemble model achieves 83.2 F1 on theCoNLL 2005 test set and 83.4 F1 on CoNLL 2012, roughly a 10% relative error reduction over the previous state of the art. Extensive empirical analysis of these gains show that (1) deep models excel at recovering long-distance dependencies but can still make surprisingly obvious errors, and (2) that there is still room for syntactic parsers to improve these results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1045", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1045.flac", "type": "audio", "duration": 1097.451}, {"from": "Assistant", "value": "This paper proposes KB-InfoBot - a multi-turn dialogue agent which helps users search Knowledge Bases (KBs) without composing complicated queries. Such goal-oriented dialogue agents typically need to interact with an external database to access real-world knowledge. Previous systems achieved this by issuing a symbolic query to the KB to retrieve entries based on their attributes. However, such symbolic operations break the differentiability of the system and prevent end-to-end training of neural dialogue agents. In this paper, we address this limitation by replacing symbolic queries with an induced “soft” posterior distribution over the KB that indicates which entities the user is interested in. Integrating the soft retrieval process with a reinforcement learner leads to higher task success rate and reward in both simulations and against real users. We also present a fully neural end-to-end agent, trained entirely from user feedback, and discuss its application towards personalized dialogue agents.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1046", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1046.flac", "type": "audio", "duration": 1024.939}, {"from": "Assistant", "value": "We study response selection for multi-turn conversation in retrieval based chatbots. Existing work either concatenates utterances in context or matches a response with a highly abstract context vector finally, which may lose relationships among the utterances or important information in the context. We propose a sequential matching network (SMN) to address both problems. SMN first matches a response with each utterance in the context on multiple levels of granularity, and distills important matching information from each pair as a vector with convolution and pooling operations. The vectors are then accumulated in a chronological order through a recurrent neural network (RNN) which models relationships among the utterances. The final matching score is calculated with the hidden states of the RNN. Empirical study on two public data sets shows that SMN can significantly outperform state-of-the-art methods for response selection in multi-turn conversation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1047", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1047.flac", "type": "audio", "duration": 1180.437}, {"from": "Assistant", "value": "Given a collection of images and spoken audio captions, we present a method for discovering word-like acoustic units in the continuous speech signal and grounding them to semantically relevant image regions. For example, our model is able to detect spoken instances of the word ‘lighthouse’ within an utterance and associate them with image regions containing lighthouses. We do not use any form of conventional automatic speech recognition, nor do we use any text transcriptions or conventional linguistic annotations. Our model effectively implements a form of spoken language acquisition, in which the computer learns not only to recognize word categories by sound, but also to enrich the words it learns with semantics by grounding them in images.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1048", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1048.flac", "type": "audio", "duration": 1152.32}, {"from": "Assistant", "value": "End-to-end automatic speech recognition (ASR) has become a popular alternative to conventional DNN/HMM systems because it avoids the need for linguistic resources such as pronunciation dictionary, tokenization, and context-dependency trees, leading to a greatly simplified model-building process. There are two major types of end-to-end architectures for ASR: attention-based methods use an attention mechanism to perform alignment between acoustic frames and recognized symbols, and connectionist temporal classification (CTC), uses Markov assumptions to efficiently solve sequential problems by dynamic programming. This paper proposes joint decoding algorithm for end-to-end ASR with a hybrid CTC/attention architecture, which effectively utilizes both advantages in decoding. We have applied the proposed method to two ASR benchmarks (spontaneous Japanese and Mandarin Chinese), and showing the comparable performance to conventional state-of-the-art DNN/HMM ASR systems without linguistic resources.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1050", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1050.flac", "type": "audio", "duration": 1025.856}, {"from": "Assistant", "value": "A fundamental question in language learning concerns the role of a speaker’s first language in second language acquisition. We present a novel methodology for studying this question: analysis of eye-movement patterns in second language reading of free-form text. Using this methodology, we demonstrate for the first time that the native language of English learners can be predicted from their gaze fixations when reading English. We provide analysis of classifier uncertainty and learned features, which indicates that differences in English reading are likely to be rooted in linguistic divergences across native languages. The presented framework complements production studies and offers new ground for advancing research on multilingualism.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1051", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1051.flac", "type": "audio", "duration": 1058.048}, {"from": "Assistant", "value": "We present in this paper a novel framework for morpheme segmentation which uses the morpho-syntactic regularities preserved by word representations, in addition to orthographic features, to segment words into morphemes. This framework is the first to consider vocabulary-wide syntactico-semantic information for this task. We also analyze the deficiencies of available benchmarking datasets and introduce our own dataset that was created on the basis of compositionality. We validate our algorithm across datasets and present state-of-the-art results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1052", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1052.flac", "type": "audio", "duration": 1222.869}, {"from": "Assistant", "value": "This paper proposes a low-complexity word-level deep convolutional neural network (CNN) architecture for text categorization that can efficiently represent long-range associations in text. In the literature, several deep and complex neural networks have been proposed for this task, assuming availability of relatively large amounts of training data. However, the associated computational complexity increases as the networks go deeper, which poses serious challenges in practical applications. Moreover, it was shown recently that shallow word-level CNNs are more accurate and much faster than the state-of-the-art very deep nets such as character-level CNNs even in the setting of large training data. Motivated by these findings, we carefully studied deepening of word-level CNNs to capture global representations of text, and found a simple network architecture with which the best accuracy can be obtained by increasing the network depth without increasing computational cost by much. We call it deep pyramid CNN. The proposed model with 15 weight layers outperforms the previous best models on six benchmark datasets for sentiment classification and topic categorization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1053", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1053.flac", "type": "audio", "duration": 1065.045}, {"from": "Assistant", "value": "Relation detection is a core component of many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning which detects KB relations given an input question. Our method uses deep residual bidirectional LSTMs to compare questions and relation names via different levels of abstraction. Additionally, we propose a simple KBQA system that integrates entity linking and our proposed relation detector to make the two components enhance each other. Our experimental results show that our approach not only achieves outstanding relation detection performance, but more importantly, it helps our KBQA system achieve state-of-the-art accuracy for both single-relation (SimpleQuestions) and multi-relation (WebQSP) QA benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1054", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1054.flac", "type": "audio", "duration": 1357.675}, {"from": "Assistant", "value": "Keyphrase provides highly-summative information that can be effectively used for understanding, organizing and retrieving text content. Though previous studies have provided many workable solutions for automated keyphrase extraction, they commonly divided the to-be-summarized content into multiple text chunks, then ranked and selected the most meaningful ones. These approaches could neither identify keyphrases that do not appear in the text, nor capture the real semantic meaning behind the text. We propose a generative model for keyphrase prediction with an encoder-decoder framework, which can effectively overcome the above drawbacks. We name it asdeep keyphrase generationsince it attempts to capture the deep semantic meaning of the content with a deep learning method. Empirical analysis on six datasets demonstrates that our proposed model not only achieves a significant performance boost on extracting keyphrases that appear in the source text, but also can generate absent keyphrases based on the semantic meaning of the text. Code and dataset are available athttps://github.com/memray/seq2seq-keyphrase.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1055", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1055.flac", "type": "audio", "duration": 921.451}, {"from": "Assistant", "value": "Cloze-style reading comprehension is a representative problem in mining relationship between document and query. In this paper, we present a simple but novel model called attention-over-attention reader for better solving cloze-style reading comprehension task. The proposed model aims to place another attention mechanism over the document-level attention and induces “attended attention” for final answer predictions. One advantage of our model is that it is simpler than related works while giving excellent performance. In addition to the primary model, we also propose an N-best re-ranking strategy to double check the validity of the candidates and further improve the performance. Experimental results show that the proposed methods significantly outperform various state-of-the-art systems by a large margin in public datasets, such as CNN and Children’s Book Test.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1056", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1056.flac", "type": "audio", "duration": 1178.304}, {"from": "Assistant", "value": "Cultural fit is widely believed to affect the success of individuals and the groups to which they belong. Yet it remains an elusive, poorly measured construct. Recent research draws on computational linguistics to measure cultural fit but overlooks asymmetries in cultural adaptation. By contrast, we develop a directed, dynamic measure of cultural fit based on linguistic alignment, which estimates the influence of one person’s word use on another’s and distinguishes between two enculturation mechanisms: internalization and self-regulation. We use this measure to trace employees’ enculturation trajectories over a large, multi-year corpus of corporate emails and find that patterns of alignment in the first six months of employment are predictive of individuals’ downstream outcomes, especially involuntary exit. Further predictive analyses suggest referential alignment plays an overlooked role in linguistic alignment.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1057", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1057.flac", "type": "audio", "duration": 967.083}, {"from": "Assistant", "value": "We present a visually grounded model of speech perception which projects spoken utterances and images to a joint semantic space. We use a multi-layer recurrent highway network to model the temporal nature of spoken speech, and show that it learns to extract both form and meaning-based linguistic knowledge from the input signal. We carry out an in-depth analysis of the representations used by different components of the trained model and show that encoding of semantic aspects tends to become richer as we go up the hierarchy of layers, whereas encoding of form-related aspects of the language input tends to initially increase and then plateau or decrease.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1058", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1058.flac", "type": "audio", "duration": 1006.443}, {"from": "Assistant", "value": "We propose a perspective on dialogue that focuses on relative information contributions of conversation partners as a key to successful communication. We predict the success of collaborative task in English and Danish corpora of task-oriented dialogue. Two features are extracted from the frequency domain representations of the lexical entropy series of each interlocutor, power spectrum overlap (PSO) and relative phase (RP). We find that PSO is a negative predictor of task success, while RP is a positive one. An SVM with these features significantly improved on previous task success prediction models. Our findings suggest that the strategic distribution of information density between interlocutors is relevant to task success.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1059", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1059.flac", "type": "audio", "duration": 1185.109}, {"from": "Assistant", "value": "Human verbal communication includes affective messages which are conveyed through use of emotionally colored words. There has been a lot of research effort in this direction but the problem of integrating state-of-the-art neural language models with affective information remains an area ripe for exploration. In this paper, we propose an extension to an LSTM (Long Short-Term Memory) language model for generation of conversational text, conditioned on affect categories. Our proposed model, Affect-LM enables us to customize the degree of emotional content in generated sentences through an additional design parameter. Perception studies conducted using Amazon Mechanical Turk show that Affect-LM can generate naturally looking emotional sentences without sacrificing grammatical correctness. Affect-LM also learns affect-discriminative word representations, and perplexity experiments show that additional affective information in conversational text can improve language model prediction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1060", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1060.flac", "type": "audio", "duration": 1051.285}, {"from": "Assistant", "value": "An important problem in domain adaptation is to quickly generalize to a new domain with limited supervision given K existing domains. One approach is to retrain a global model across all K + 1 domains using standard techniques, for instance Daumé III (2009). However, it is desirable to adapt without having to re-estimate a global model from scratch each time a new domain with potentially new intents and slots is added. We describe a solution based on attending an ensemble of domain experts. We assume K domain specific intent and slot models trained on respective domains. When given domain K + 1, our model uses a weighted combination of the K domain experts’ feedback along with its own opinion to make predictions on the new domain. In experiments, the model significantly outperforms baselines that do not use domain adaptation and also performs better than the full retraining approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1061", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1061.flac", "type": "audio", "duration": 1095.637}, {"from": "Assistant", "value": "While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses. Unlike past work that has focused on diversifying the output of the decoder from word-level to alleviate this problem, we present a novel framework based on conditional variational autoencoders that capture the discourse-level diversity in the encoder. Our model uses latent variables to learn a distribution over potential conversational intents and generates diverse responses using only greedy decoders. We have further developed a novel variant that is integrated with linguistic prior knowledge for better performance. Finally, the training procedure is improved through introducing a bag-of-word loss. Our proposed models have been validated to generate significantly more diverse responses than baseline approaches and exhibit competence of discourse-level decision-making.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1062", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1062.flac", "type": "audio", "duration": 1103.851}, {"from": "Assistant", "value": "End-to-end learning of recurrent neural networks (RNNs) is an attractive solution for dialog systems; however, current techniques are data-intensive and require thousands of dialogs to learn simple behaviors. We introduce Hybrid Code Networks (HCNs), which combine an RNN with domain-specific knowledge encoded as software and system action templates. Compared to existing end-to-end approaches, HCNs considerably reduce the amount of training data required, while retaining the key benefit of inferring a latent representation of dialog state. In addition, HCNs can be optimized with supervised learning, reinforcement learning, or a mixture of both. HCNs attain state-of-the-art performance on the bAbI dialog dataset (Bordes and Weston, 2016), and outperform two commercially deployed customer-facing dialog systems at our company.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1063", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1063.flac", "type": "audio", "duration": 1019.605}, {"from": "Assistant", "value": "The referring expressions (REs) produced by a natural language generation (NLG) system can be misunderstood by the hearer, even when they are semantically correct. In an interactive setting, the NLG system can try to recognize such misunderstandings and correct them. We present an algorithm for generating corrective REs that use contrastive focus (“no, the BLUE button”) to emphasize the information the hearer most likely misunderstood. We show empirically that these contrastive REs are preferred over REs without contrast marking.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1064", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1064.flac", "type": "audio", "duration": 1172.011}, {"from": "Assistant", "value": "Even though a linguistics-free sequence to sequence model in neural machine translation (NMT) has certain capability of implicitly learning syntactic information of source sentences, this paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements. Specifically, we linearize parse trees of source sentences to obtain structural label sequences. On the basis, we propose three different sorts of encoders to incorporate source syntax into NMT: 1) Parallel RNN encoder that learns word and label annotation vectors parallelly; 2) Hierarchical RNN encoder that learns word and label annotation vectors in a two-level hierarchy; and 3) Mixed RNN encoder that stitchingly learns word and label annotation vectors over sequences where words and labels are mixed. Experimentation on Chinese-to-English translation demonstrates that all the three proposed syntactic encoders are able to improve translation accuracy. It is interesting to note that the simplest RNN encoder, i.e., Mixed RNN encoder yields the best performance with an significant improvement of 1.4 BLEU points. Moreover, an in-depth analysis from several perspectives is provided to reveal how source syntax benefits NMT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1065", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1065.flac", "type": "audio", "duration": 1047.552}, {"from": "Assistant", "value": "Nowadays a typical Neural Machine Translation (NMT) model generates translations from left to right as a linear sequence, during which latent syntactic structures of the target sentences are not explicitly concerned. Inspired by the success of using syntactic knowledge of target language for improving statistical machine translation, in this paper we propose a novel Sequence-to-Dependency Neural Machine Translation (SD-NMT) method, in which the target word sequence and its corresponding dependency structure are jointly constructed and modeled, and this structure is used as context to facilitate word generations. Experimental results show that the proposed method significantly outperforms state-of-the-art baselines on Chinese-English and Japanese-English translation tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1066", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1066.flac", "type": "audio", "duration": 291.307}, {"from": "Assistant", "value": "How fake news goes viral via social media? How does its propagation pattern differ from real stories? In this paper, we attempt to address the problem of identifying rumors, i.e., fake information, out of microblog posts based on their propagation structure. We firstly model microblog posts diffusion with propagation trees, which provide valuable clues on how an original message is transmitted and developed over time. We then propose a kernel-based method called Propagation Tree Kernel, which captures high-order patterns differentiating different types of rumors by evaluating the similarities between their propagation tree structures. Experimental results on two real-world datasets demonstrate that the proposed kernel-based approach can detect rumors more quickly and accurately than state-of-the-art rumor detection models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1067", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1067.flac", "type": "audio", "duration": 1245.419}, {"from": "Assistant", "value": "Accurate detection of emotion from natural language has applications ranging from building emotional chatbots to better understanding individuals and their lives. However, progress on emotion detection has been hampered by the absence of large labeled datasets. In this work, we build a very large dataset for fine-grained emotions and develop deep learning models on it. We achieve a new state-of-the-art on 24 fine-grained types of emotions (with an average accuracy of 87.58%). We also extend the task beyond emotion types to model Robert Plutick’s 8 primary emotion dimensions, acquiring a superior accuracy of 95.68%.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1068", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1068.flac", "type": "audio", "duration": 1059.584}, {"from": "Assistant", "value": "Automatic political orientation prediction from social media posts has to date proven successful only in distinguishing between publicly declared liberals and conservatives in the US. This study examines users’ political ideology using a seven-point scale which enables us to identify politically moderate and neutral users – groups which are of particular interest to political scientists and pollsters. Using a novel data set with political ideology labels self-reported through surveys, our goal is two-fold: a) to characterize the groups of politically engaged users through language use on Twitter; b) to build a fine-grained model that predicts political ideology of unseen users. Our results identify differences in both political leaning and engagement and the extent to which each group tweets using political keywords. Finally, we demonstrate how to improve ideology prediction accuracy by exploiting the relationships between the user groups.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1069", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1069.flac", "type": "audio", "duration": 1104.107}, {"from": "Assistant", "value": "Framing is a political strategy in which politicians carefully word their statements in order to control public perception of issues. Previous works exploring political framing typically analyze frame usage in longer texts, such as congressional speeches. We present a collection of weakly supervised models which harness collective classification to predict the frames used in political discourse on the microblogging platform, Twitter. Our global probabilistic models show that by combining both lexical features of tweets and network-based behavioral features of Twitter, we are able to increase the average, unsupervised F1 score by 21.52 points over a lexical baseline alone.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1070", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1070.flac", "type": "audio", "duration": 1082.752}, {"from": "Assistant", "value": "Grammatical error correction (GEC) systems strive to correct both global errors inword order and usage, and local errors inspelling and inflection. Further developing upon recent work on neural machine translation, we propose a new hybrid neural model with nested attention layers for GEC.Experiments show that the new model can effectively correct errors of both types by incorporating word and character-level information, and that the model significantly outperforms previous neural models for GEC as measured on the standard CoNLL-14 benchmark dataset. Further analysis also shows that the superiority of the proposed model can be largely attributed to the use of the nested attention mechanism, which has proven particularly effective incorrecting local errors that involve small edits in orthography.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1071", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1071.flac", "type": "audio", "duration": 675.008}, {"from": "Assistant", "value": "Text similarity measures are used in multiple tasks such as plagiarism detection, information ranking and recognition of paraphrases and textual entailment. While recent advances in deep learning highlighted the relevance of sequential models in natural language generation, existing similarity measures do not fully exploit the sequential nature of language. Examples of such similarity measures include n-grams and skip-grams overlap which rely on distinct slices of the input texts. In this paper we present a novel text similarity measure inspired from a common representation in DNA sequence alignment algorithms. The new measure, called TextFlow, represents input text pairs as continuous curves and uses both the actual position of the words and sequence matching to compute the similarity value. Our experiments on 8 different datasets show very encouraging results in paraphrase detection, textual entailment recognition and ranking relevance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1072", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1072.flac", "type": "audio", "duration": 1163.285}, {"from": "Assistant", "value": "Understanding how ideas relate to each other is a fundamental question in many domains, ranging from intellectual history to public communication. Because ideas are naturally embedded in texts, we propose the first framework to systematically characterize the relations between ideas based on their occurrence in a corpus of documents, independent of how these ideas are represented. Combining two statistics—cooccurrence within documents and prevalence correlation over time—our approach reveals a number of different ways in which ideas can cooperate and compete. For instance, two ideas can closely track each other’s prevalence over time, and yet rarely cooccur, almost like a “cold war” scenario. We observe that pairwise cooccurrence and prevalence correlation exhibit different distributions. We further demonstrate that our approach is able to uncover intriguing relations between ideas through in-depth case studies on news articles and research papers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1073", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1073.flac", "type": "audio", "duration": 1088.043}, {"from": "Assistant", "value": "The paper presents a procedure of building an evaluation dataset. for the validation of compositional distributional semantics models estimated for languages other than English. The procedure generally builds on steps designed to assemble the SICK corpus, which contains pairs of English sentences annotated for semantic relatedness and entailment, because we aim at building a comparable dataset. However, the implementation of particular building steps significantly differs from the original SICK design assumptions, which is caused by both lack of necessary extraneous resources for an investigated language and the need for language-specific transformation rules. The designed procedure is verified on Polish, a fusional language with a relatively free word order, and contributes to building a Polish evaluation dataset. The resource consists of 10K sentence pairs which are human-annotated for semantic relatedness and entailment. The dataset may be used for the evaluation of compositional distributional semantics models of Polish.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1074", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1074.flac", "type": "audio", "duration": 945.472}, {"from": "Assistant", "value": "Until now, error type performance for Grammatical Error Correction (GEC) systems could only be measured in terms of recall because system output is not annotated. To overcome this problem, we introduce ERRANT, a grammatical ERRor ANnotation Toolkit designed to automatically extract edits from parallel original and corrected sentences and classify them according to a new, dataset-agnostic, rule-based framework. This not only facilitates error type evaluation at different levels of granularity, but can also be used to reduce annotator workload and standardise existing GEC datasets. Human experts rated the automatic edits as “Good” or “Acceptable” in at least 95% of cases, so we applied ERRANT to the system output of the CoNLL-2014 shared task to carry out a detailed error type analysis for the first time.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1075", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1075.flac", "type": "audio", "duration": 1123.477}, {"from": "Assistant", "value": "Knowing the quality of reading comprehension (RC) datasets is important for the development of natural-language understanding systems. In this study, two classes of metrics were adopted for evaluating RC datasets: prerequisite skills and readability. We applied these classes to six existing datasets, including MCTest and SQuAD, and highlighted the characteristics of the datasets according to each metric and the correlation between the two classes. Our dataset analysis suggests that the readability of RC datasets does not directly affect the question difficulty and that it is possible to create an RC dataset that is easy to read but difficult to answer.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1076", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1076.flac", "type": "audio", "duration": 1069.141}, {"from": "Assistant", "value": "In this work, we present a minimal neural model for constituency parsing based on independent scoring of labels and spans. We show that this model is not only compatible with classical dynamic programming techniques, but also admits a novel greedy top-down inference algorithm based on recursive partitioning of the input. We demonstrate empirically that both prediction schemes are competitive with recent work, and when combined with basic extensions to the scoring model are capable of achieving state-of-the-art single-model performance on the Penn Treebank (91.79 F1) and strong performance on the French Treebank (82.23 F1).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1077", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1077.flac", "type": "audio", "duration": 1114.709}, {"from": "Assistant", "value": "We model a dependency graph as a book, a particular kind of topological space, for semantic dependency parsing. The spine of the book is made up of a sequence of words, and each page contains a subset of noncrossing arcs. To build a semantic graph for a given sentence, we design new Maximum Subgraph algorithms to generate noncrossing graphs on each page, and a Lagrangian Relaxation-based algorithm tocombine pages into a book. Experiments demonstrate the effectiveness of the bookembedding framework across a wide range of conditions. Our parser obtains comparable results with a state-of-the-art transition-based parser.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1078", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1078.flac", "type": "audio", "duration": 1040.683}, {"from": "Assistant", "value": "Neural word segmentation research has benefited from large-scale raw texts by leveraging them for pretraining character and word embeddings. On the other hand, statistical segmentation research has exploited richer sources of external information, such as punctuation, automatic segmentation and POS. We investigate the effectiveness of a range of external training sources for neural word segmentation by building a modular segmentation model, pretraining the most important submodule using rich external sources. Results show that such pretraining significantly improves the model, leading to accuracies competitive to the best methods on six benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1079", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1079.flac", "type": "audio", "duration": 1060.523}, {"from": "Assistant", "value": "In this paper, we propose a new method for calculating the output layer in neural machine translation systems. The method is based on predicting a binary code for each word and can reduce computation time/memory requirements of the output layer to be logarithmic in vocabulary size in the best case. In addition, we also introduce two advanced approaches to improve the robustness of the proposed model: using error-correcting codes and combining softmax and binary codes. Experiments on two English-Japanese bidirectional translation tasks show proposed models achieve BLEU scores that approach the softmax, while reducing memory usage to the order of less than 1/10 and improving decoding speed on CPUs by x5 to x10.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1080", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1080.flac", "type": "audio", "duration": 1033.707}, {"from": "Assistant", "value": "Neural machine translation (MT) models obtain state-of-the-art performance while maintaining a simple, end-to-end architecture. However, little is known about what these models learn about source and target languages during the training process. In this work, we analyze the representations learned by neural MT models at various levels of granularity and empirically evaluate the quality of the representations for learning morphology through extrinsic part-of-speech and morphological tagging tasks. We conduct a thorough investigation along several parameters: word-based vs. character-based representations, depth of the encoding layer, the identity of the target language, and encoder vs. decoder representations. Our data-driven, quantitative evaluation sheds light on important aspects in the neural MT system and its ability to capture word structure.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1081", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1081.flac", "type": "audio", "duration": 1108.779}, {"from": "Assistant", "value": "Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos. Current research considers utterances as independent entities, i.e., ignores the interdependencies and relations among the utterances of a video. In this paper, we propose a LSTM-based model that enables utterances to capture contextual information from their surroundings in the same video, thus aiding the classification process. Our method shows 5-10% performance improvement over the state of the art and high robustness to generalizability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1082", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1082.flac", "type": "audio", "duration": 1126.571}, {"from": "Assistant", "value": "The sociolinguistic construct of stancetaking describes the activities through which discourse participants create and signal relationships to their interlocutors, to the topic of discussion, and to the talk itself. Stancetaking underlies a wide range of interactional phenomena, relating to formality, politeness, affect, and subjectivity. We present a computational approach to stancetaking, in which we build a theoretically-motivated lexicon of stance markers, and then use multidimensional analysis to identify a set of underlying stance dimensions. We validate these dimensions intrinscially and extrinsically, showing that they are internally coherent, match pre-registered hypotheses, and correlate with social phenomena.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1083", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1083.flac", "type": "audio", "duration": 964.949}, {"from": "Assistant", "value": "Interactive topic models are powerful tools for those seeking to understand large collections of text. However, existing sampling-based interactive topic modeling approaches scale poorly to large data sets. Anchor methods, which use a single word to uniquely identify a topic, offer the speed needed for interactive work but lack both a mechanism to inject prior knowledge and lack the intuitive semantics needed for user-facing applications. We propose combinations of words as anchors, going beyond existing single word anchor algorithms—an approach we call “Tandem Anchors”. We begin with a synthetic investigation of this approach then apply the approach to interactive topic modeling in a user study and compare it to interactive and non-interactive approaches. Tandem anchors are faster and more intuitive than existing interactive approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1084", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1084.flac", "type": "audio", "duration": 1076.203}, {"from": "Assistant", "value": "Understanding common entities and their attributes is a primary requirement for any system that comprehends natural language. In order to enable learning about common entities, we introduce a novel machine comprehension task, GuessTwo: given a short paragraph comparing different aspects of two real-world semantically-similar entities, a system should guess what those entities are. Accomplishing this task requires deep language understanding which enables inference, connecting each comparison paragraph to different levels of knowledge about world entities and their attributes. So far we have crowdsourced a dataset of more than 14K comparison paragraphs comparing entities from a variety of categories such as fruits and animals. We have designed two schemes for evaluation: open-ended, and binary-choice prediction. For benchmarking further progress in the task, we have collected a set of paragraphs as the test set on which human can accomplish the task with an accuracy of 94.2% on open-ended prediction. We have implemented various models for tackling the task, ranging from semantic-driven to neural models. The semantic-driven approach outperforms the neural models, however, the results indicate that the task is very challenging across the models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1085", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1085.flac", "type": "audio", "duration": 1026.155}, {"from": "Assistant", "value": "We present a novel attention-based recurrent neural network for joint extraction of entity mentions and relations. We show that attention along with long short term memory (LSTM) network can extract semantic relations between entity mentions without having access to dependency trees. Experiments on Automatic Content Extraction (ACE) corpora show that our model significantly outperforms feature-based joint model by Li and Ji (2014). We also compare our model with an end-to-end tree-based LSTM model (SPTree) by Miwa and Bansal (2016) and show that our model performs within 1% on entity mentions and 2% on relations. Our fine-grained analysis also shows that our model performs significantly better on Agent-Artifact relations, while SPTree performs better on Physical and Part-Whole relations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1086", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1086.flac", "type": "audio", "duration": 1110.037}, {"from": "Assistant", "value": "Our goal is to create a convenient natural language interface for performing well-specified but complex actions such as analyzing data, manipulating text, and querying databases. However, existing natural language interfaces for such tasks are quite primitive compared to the power one wields with a programming language. To bridge this gap, we start with a core programming language and allow users to “naturalize” the core language incrementally by defining alternative, more natural syntax and increasingly complex concepts in terms of compositions of simpler ones. In a voxel world, we show that a community of users can simultaneously teach a common system a diverse language and use it to build hundreds of complex voxel structures. Over the course of three days, these users went from using only the core language to using the naturalized language in 85.9% of the last 10K utterances.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1087", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1087.flac", "type": "audio", "duration": 944.256}, {"from": "Assistant", "value": "Vector space representations of words capture many aspects of word similarity, but such methods tend to produce vector spaces in which antonyms (as well as synonyms) are close to each other. For spectral clustering using such word embeddings, words are points in a vector space where synonyms are linked with positive weights, while antonyms are linked with negative weights. We present a new signed spectral normalized graph cut algorithm,signed clustering, that overlays existing thesauri upon distributionally derived vector representations of words, so that antonym relationships between word pairs are represented by negative weights. Our signed clustering algorithm produces clusters of words that simultaneously capture distributional and synonym relations. By using randomized spectral decomposition (Halko et al., 2011) and sparse matrices, our method is both fast and scalable. We validate our clusters using datasets containing human judgments of word pair similarities and show the benefit of using our word clusters for sentiment prediction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1089", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1089.flac", "type": "audio", "duration": 1105.941}, {"from": "Assistant", "value": "We present an approach to rapidly and easily build natural language interfaces to databases for new domains, whose performance improves over time based on user feedback, and requires minimal intervention. To achieve this, we adapt neural sequence models to map utterances directly to SQL with its full expressivity, bypassing any intermediate meaning representations. These models are immediately deployed online to solicit feedback from real users to flag incorrect queries. Finally, the popularity of SQL facilitates gathering annotations for incorrect predictions using the crowd, which is directly used to improve our models. This complete feedback loop, without intermediate representations or database specific engineering, opens up new ways of building high quality semantic parsers. Experiments suggest that this approach can be deployed quickly for any new target domain, as we show by learning a semantic parser for an online academic database from scratch.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1090", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1090.flac", "type": "audio", "duration": 1000.747}, {"from": "Assistant", "value": "We present a joint modeling approach to identify salient discussion points in spoken meetings as well as to label the discourse relations between speaker turns. A variation of our model is also discussed when discourse relations are treated as latent variables. Experimental results on two popular meeting corpora show that our joint model can outperform state-of-the-art approaches for both phrase-based content selection and discourse relation prediction tasks. We also evaluate our model on predicting the consistency among team members’ understanding of their group decisions. Classifiers trained with features constructed from our model achieve significant better predictive performance than the state-of-the-art.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1091", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1091.flac", "type": "audio", "duration": 1095.488}, {"from": "Assistant", "value": "We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20% of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentative relation prediction. Moreover, our model supports SVM and RNN parametrizations, can enforce structure constraints (e.g., transitivity), and can express dependencies between adjacent relations and propositions. Our approaches outperform unstructured baselines in both web comments and argumentative essay datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1092", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1092.flac", "type": "audio", "duration": 1227.008}, {"from": "Assistant", "value": "We show that discourse structure, as defined by Rhetorical Structure Theory and provided by an existing discourse parser, benefits text categorization. Our approach uses a recursive neural network and a newly proposed attention mechanism to compute a representation of the text that focuses on salient content, from the perspective of both RST and the task. Experiments consider variants of the approach and illustrate its strengths and weaknesses.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1093", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1093.flac", "type": "audio", "duration": 1070.72}, {"from": "Assistant", "value": "Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition. We propose a feature imitation framework in which an implicit relation network is driven to learn from another neural network with access to connectives, and thus encouraged to extract similarly salient features for accurate classification. We develop an adversarial model to enable an adaptive imitation scheme through competition between the implicit network and a rival feature discriminator. Our method effectively transfers discriminability of connectives to the implicit features, and achieves state-of-the-art performance on the PDTB benchmark.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1094", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1094.flac", "type": "audio", "duration": 1082.709}, {"from": "Assistant", "value": "An interesting aspect of structured prediction is the evaluation of an output structure against the gold standard. Especially in the loss-augmented setting, the need of finding the max-violating constraint has severely limited the expressivity of effective loss functions. In this paper, we trade off exact computation for enabling the use and study of more complex loss functions for coreference resolution. Most interestingly, we show that such functions can be (i) automatically learned also from controversial but commonly accepted coreference measures, e.g., MELA, and (ii) successfully used in learning algorithms. The accurate model comparison on the standard CoNLL-2012 setting shows the benefit of more expressive loss functions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1095", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1095.flac", "type": "audio", "duration": 941.12}, {"from": "Assistant", "value": "Lexical resources such as dictionaries and gazetteers are often used as auxiliary data for tasks such as part-of-speech induction and named-entity recognition. However, discriminative training with lexical features requires annotated data to reliably estimate the lexical feature weights and may result in overfitting the lexical features at the expense of features which generalize better. In this paper, we investigate a more robust approach: we stipulate that the lexicon is the result of an assumed generative process. Practically, this means that we may treat the lexical resources as observations under the proposed generative model. The lexical resources provide training data for the generative model without requiring separate data to estimate lexical feature weights. We evaluate the proposed approach in two settings: part-of-speech induction and low-resource named-entity recognition.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1096", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1096.flac", "type": "audio", "duration": 923.52}, {"from": "Assistant", "value": "We study the problem of semi-supervised question answering—utilizing unlabeled text to boost the performance of question answering models. We propose a novel training framework, theGenerative Domain-Adaptive Nets. In this framework, we train a generative model to generate questions based on the unlabeled text, and combine model-generated questions with human-generated questions for training question answering models. We develop novel domain adaptation algorithms, based on reinforcement learning, to alleviate the discrepancy between the model-generated data distribution and the human-generated data distribution. Experiments show that our proposed framework obtains substantial improvement from unlabeled text.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1097", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1097.flac", "type": "audio", "duration": 948.181}, {"from": "Assistant", "value": "Our goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, but not the program itself. Consequently, we must search the space of programs for those that output the correct result, while not being misled byspurious programs: incorrect programs that coincidentally output the correct result. We connect two common learning paradigms, reinforcement learning (RL) and maximum marginal likelihood (MML), and then present a new learning algorithm that combines the strengths of both. The new algorithm guards against spurious programs by combining the systematic search traditionally employed in MML with the randomized exploration of RL, and by updating parameters such that probability is spread more evenly across consistent programs. We apply our learning algorithm to a new neural semantic parser and show significant gains over existing state-of-the-art results on a recent context-dependent semantic parsing task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1098", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1098.flac", "type": "audio", "duration": 967.253}, {"from": "Assistant", "value": "Abstractive summarization aims to generate a shorter version of the document covering all the salient points in a compact and coherent fashion. On the other hand, query-based summarization highlights those points that are relevant in the context of a given query. The encode-attend-decode paradigm has achieved notable success in machine translation, extractive summarization, dialog systems, etc. But it suffers from the drawback of generation of repeated phrases. In this work we propose a model for the query-based summarization task based on the encode-attend-decode paradigm with two key additions (i) a query attention model (in addition to document attention model) which learns to focus on different portions of the query at different time steps (instead of using a static representation for the query) and (ii) a new diversity based attention model which aims to alleviate the problem of repeating phrases in the summary. In order to enable the testing of this model we introduce a new query-based summarization dataset building on debatepedia. Our experiments show that with these two additions the proposed model clearly outperforms vanilla encode-attend-decode models with a gain of 28% (absolute) in ROUGE-L scores.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1099", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1099.flac", "type": "audio", "duration": 891.541}, {"from": "Assistant", "value": "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1100", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1100.flac", "type": "audio", "duration": 1037.973}, {"from": "Assistant", "value": "We present a new supervised framework that learns to estimate automatic Pyramid scores and uses them for optimization-based extractive multi-document summarization. For learning automatic Pyramid scores, we developed a method for automatic training data generation which is based on a genetic algorithm using automatic Pyramid as the fitness function. Our experimental evaluation shows that our new framework significantly outperforms strong baselines regarding automatic Pyramid, and that there is much room for improvement in comparison with the upper-bound for automatic Pyramid.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1101", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1101.flac", "type": "audio", "duration": 1076.48}, {"from": "Assistant", "value": "We propose a selective encoding model to extend the sequence-to-sequence framework for abstractive sentence summarization. It consists of a sentence encoder, a selective gate network, and an attention equipped decoder. The sentence encoder and decoder are built with recurrent neural networks. The selective gate network constructs a second level sentence representation by controlling the information flow from encoder to decoder. The second level representation is tailored for sentence summarization task, which leads to better performance. We evaluate our model on the English Gigaword, DUC 2004 and MSR abstractive sentence summarization datasets. The experimental results show that the proposed selective encoding model outperforms the state-of-the-art baseline models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1103", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1103.flac", "type": "audio", "duration": 1079.936}, {"from": "Assistant", "value": "Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and correlate very poorly with human judgements of response quality (Liu et al., 2016). Yet having an accurate automatic evaluation procedure is crucial for dialogue research, as it allows rapid prototyping and testing of new models with fewer expensive human evaluations. In response to this challenge, we formulate automatic dialogue evaluation as a learning problem. We present an evaluation model (ADEM)that learns to predict human-like scores to input responses, using a new dataset of human response scores. We show that the ADEM model’s predictions correlate significantly, and at a level much higher than word-overlap metrics such as BLEU, with human judgements at both the utterance and system-level. We also show that ADEM can generalize to evaluating dialogue mod-els unseen during training, an important step for automatic dialogue evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1104", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1104.flac", "type": "audio", "duration": 1053.888}, {"from": "Assistant", "value": "We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. To our knowledge, the conjunction of these formal properties is not supported by any existing parser. Our transition-based parser, which uses a novel transition set and features based on bidirectional LSTMs, has value not just for UCCA parsing: its ability to handle more general graph structures can inform the development of parsers for other semantic DAG structures, and in languages that frequently use discontinuous structures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1105", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1105.flac", "type": "audio", "duration": 942.613}, {"from": "Assistant", "value": "Tasks like code generation and semantic parsing require mapping unstructured (or partially structured) inputs to well-formed, executable outputs. We introduce abstract syntax networks, a modeling framework for these problems. The outputs are represented as abstract syntax trees (ASTs) and constructed by a decoder with a dynamically-determined modular structure paralleling the structure of the output tree. On the benchmark Hearthstone dataset for code generation, our model obtains 79.2 BLEU and 22.7% exact match accuracy, compared to previous state-of-the-art values of 67.1 and 6.1%. Furthermore, we perform competitively on the Atis, Jobs, and Geo semantic parsing datasets with no task-specific engineering.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1106", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1106.flac", "type": "audio", "duration": 987.179}, {"from": "Assistant", "value": "While neural machine translation (NMT) has made remarkable progress in recent years, it is hard to interpret its internal workings due to the continuous representations and non-linearity of neural networks. In this work, we propose to use layer-wise relevance propagation (LRP) to compute the contribution of each contextual word to arbitrary hidden states in the attention-based encoder-decoder framework. We show that visualization with LRP helps to interpret the internal workings of NMT and analyze translation errors.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1108", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1108.flac", "type": "audio", "duration": 990.421}, {"from": "Assistant", "value": "Abstractive summarization is the ultimate goal of document summarization research, but previously it is less investigated due to the immaturity of text generation techniques. Recently impressive progress has been made to abstractive sentence summarization using neural models. Unfortunately, attempts on abstractive document summarization are still in a primitive stage, and the evaluation results are worse than extractive methods on benchmark datasets. In this paper, we review the difficulties of neural abstractive document summarization, and propose a novel graph-based attention mechanism in the sequence-to-sequence framework. The intuition is to address the saliency factor of summarization, which has been overlooked by prior works. Experimental results demonstrate our model is able to achieve considerable improvement over previous neural abstractive models. The data-driven neural abstractive method is also competitive with state-of-the-art extractive methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1109", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1109.flac", "type": "audio", "duration": 1152.939}, {"from": "Assistant", "value": "Linguistic typology studies the range of structures present in human language. The main goal of the field is to discover which sets of possible phenomena are universal, and which are merely frequent. For example, all languages have vowels, while most—but not all—languages have an /u/ sound. In this paper we present the first probabilistic treatment of a basic question in phonological typology: What makes a natural vowel inventory? We introduce a series of deep stochastic point processes, and contrast them with previous computational, simulation-based approaches. We provide a comprehensive suite of experiments on over 200 distinct languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1110", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1110.flac", "type": "audio", "duration": 1187.904}, {"from": "Assistant", "value": "Different linguistic perspectives causes many diverse segmentation criteria for Chinese word segmentation (CWS). Most existing methods focus on improve the performance for each single criterion. However, it is interesting to exploit these different criteria and mining their common underlying knowledge. In this paper, we propose adversarial multi-criteria learning for CWS by integrating shared knowledge from multiple heterogeneous segmentation criteria. Experiments on eight corpora with heterogeneous segmentation criteria show that the performance of each corpus obtains a significant improvement, compared to single-criterion learning. Source codes of this paper are available on Github.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1111", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1111.flac", "type": "audio", "duration": 1040.64}, {"from": "Assistant", "value": "We present neural network-based joint models for Chinese word segmentation, POS tagging and dependency parsing. Our models are the first neural approaches for fully joint Chinese analysis that is known to prevent the error propagation problem of pipeline models. Although word embeddings play a key role in dependency parsing, they cannot be applied directly to the joint task in the previous work. To address this problem, we propose embeddings of character strings, in addition to words. Experiments show that our models outperform existing systems in Chinese word segmentation and POS tagging, and perform preferable accuracies in dependency parsing. We also explore bi-LSTM models with fewer features.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1113", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1113.flac", "type": "audio", "duration": 1090.069}, {"from": "Assistant", "value": "Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem.. Then, based on our tagging scheme, we study different end-to-end models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What’s more, the end-to-end model proposed in this paper, achieves the best results on the public dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1114", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1114.flac", "type": "audio", "duration": 1056.875}, {"from": "Assistant", "value": "In this paper, we study a novel approach for named entity recognition (NER) and mention detection (MD) in natural language processing. Instead of treating NER as a sequence labeling problem, we propose a new local detection approach, which relies on the recent fixed-size ordinally forgetting encoding (FOFE) method to fully encode each sentence fragment and its left/right contexts into a fixed-size representation. Subsequently, a simple feedforward neural network (FFNN) is learned to either reject or predict entity label for each individual text fragment. The proposed method has been evaluated in several popular NER and MD tasks, including CoNLL 2003 NER task and TAC-KBP2015 and TAC-KBP2016 Tri-lingual Entity Discovery and Linking (EDL) tasks. Our method has yielded pretty strong performance in all of these examined tasks. This local detection approach has shown many advantages over the traditional sequence labeling methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1115", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1115.flac", "type": "audio", "duration": 1099.947}, {"from": "Assistant", "value": "Named entities are frequently used in a metonymic manner. They serve as references to related entities such as people and organisations. Accurate identification and interpretation of metonymy can be directly beneficial to various NLP applications, such as Named Entity Recognition and Geographical Parsing. Until now, metonymy resolution (MR) methods mainly relied on parsers, taggers, dictionaries, external word lists and other handcrafted lexical resources. We show how a minimalist neural approach combined with a novel predicate window method can achieve competitive results on the SemEval 2007 task on Metonymy Resolution. Additionally, we contribute with a new Wikipedia-based MR dataset called RelocaR, which is tailored towards locations as well as improving previous deficiencies in annotation guidelines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1116", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1116.flac", "type": "audio", "duration": 911.275}, {"from": "Assistant", "value": "We propose a novel geolocation prediction model using a complex neural network. Geolocation prediction in social media has attracted many researchers to use information of various types. Our model unifies text, metadata, and user network representations with an attention mechanism to overcome previous ensemble approaches. In an evaluation using two open datasets, the proposed model exhibited a maximum 3.8% increase in accuracy and a maximum of 6.6% increase in accuracy@161 against previous models. We further analyzed several intermediate layers of our model, which revealed that their states capture some statistical characteristics of the datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1117", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1117.flac", "type": "audio", "duration": 1236.203}, {"from": "Assistant", "value": "Video captioning, the task of describing the content of a video, has seen some promising improvements in recent years with sequence-to-sequence models, but accurately learning the temporal and logical dynamics involved in the task still remains a challenge, especially given the lack of sufficient annotated data. We improve video captioning by sharing knowledge with two related directed-generation tasks: a temporally-directed unsupervised video prediction task to learn richer context-aware video encoder representations, and a logically-directed language entailment generation task to learn better video-entailing caption decoder representations. For this, we present a many-to-many multi-task learning model that shares parameters across the encoders and decoders of the three tasks. We achieve significant improvements and the new state-of-the-art on several standard video captioning datasets using diverse automatic and human evaluations. We also show mutual multi-task improvements on the entailment generation task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-1121", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-1121.flac", "type": "audio", "duration": 932.032}, {"from": "Assistant", "value": "We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2001", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2001.flac", "type": "audio", "duration": 870.037}, {"from": "Assistant", "value": "Temporal relation classification is becoming an active research field. Lots of methods have been proposed, while most of them focus on extracting features from external resources. Less attention has been paid to a significant advance in a closely related task: relation extraction. In this work, we borrow a state-of-the-art method in relation extraction by adopting bidirectional long short-term memory (Bi-LSTM) along dependency paths (DP). We make a “common root” assumption to extend DP representations of cross-sentence links. In the final comparison to two state-of-the-art systems on TimeBank-Dense, our model achieves comparable performance, without using external knowledge, as well as manually annotated attributes of entities (class, tense, polarity, etc.).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2002", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2002.flac", "type": "audio", "duration": 545.28}, {"from": "Assistant", "value": "This paper addresses the task of AMR-to-text generation by leveraging synchronous node replacement grammar. During training, graph-to-string rules are learned using a heuristic extraction algorithm. At test time, a graph transducer is applied to collapse input AMRs and generate output sentences. Evaluated on a standard benchmark, our method gives the state-of-the-art result.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2003", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2003.flac", "type": "audio", "duration": 798.912}, {"from": "Assistant", "value": "Lexical features are a major source of information in state-of-the-art coreference resolvers. Lexical features implicitly model some of the linguistic phenomena at a fine granularity level. They are especially useful for representing the context of mentions. In this paper we investigate a drawback of using many lexical features in state-of-the-art coreference resolvers. We show that if coreference resolvers mainly rely on lexical features, they can hardly generalize to unseen domains. Furthermore, we show that the current coreference resolution evaluation is clearly flawed by only evaluating on a specific split of a specific dataset in which there is a notable overlap between the training, development and test sets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2004", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2004.flac", "type": "audio", "duration": 1014.144}, {"from": "Assistant", "value": "MT evaluation metrics are tested for correlation with human judgments either at the sentence- or the corpus-level. Trained metrics ignore corpus-level judgments and are trained for high sentence-level correlation only. We show that training only for one objective (sentence or corpus level), can not only harm the performance on the other objective, but it can also be suboptimal for the objective being optimized. To this end we present a metric trained for corpus-level and show empirical comparison against a metric trained for sentence-level exemplifying how their performance may vary per language pair, type and level of judgment. Subsequently we propose a model trained to optimize both objectives simultaneously and show that it is far more stable than–and on average outperforms–both models on both objectives.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2005", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2005.flac", "type": "audio", "duration": 906.603}, {"from": "Assistant", "value": "We present a new framework for evaluating extractive summarizers, which is based on a principled representation as optimization problem. We prove that every extractive summarizer can be decomposed into an objective function and an optimization technique. We perform a comparative analysis and evaluation of several objective functions embedded in well-known summarizers regarding their correlation with human judgments. Our comparison of these correlations across two datasets yields surprising insights into the role and performance of objective functions in the different summarizers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2007", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2007.flac", "type": "audio", "duration": 610.069}, {"from": "Assistant", "value": "In this paper, we address semantic parsing in a multilingual context. We train one multilingual model that is capable of parsing natural language sentences from multiple different languages into their corresponding formal semantic representations. We extend an existing sequence-to-tree model to a multi-task learning framework which shares the decoder for generating semantic representations. We report evaluation results on the multilingual GeoQuery corpus and introduce a new multilingual version of the ATIS corpus.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2008", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2008.flac", "type": "audio", "duration": 692.907}, {"from": "Assistant", "value": "There is a growing demand for automatic assessment of spoken English proficiency. These systems need to handle large variations in input data owing to the wide range of candidate skill levels and L1s, and errors from ASR. Some candidates will be a poor match to the training data set, undermining the validity of the predicted grade. For high stakes tests it is essential for such systems not only to grade well, but also to provide a measure of their uncertainty in their predictions, enabling rejection to human graders. Previous work examined Gaussian Process (GP) graders which, though successful, do not scale well with large data sets. Deep Neural Network (DNN) may also be used to provide uncertainty using Monte-Carlo Dropout (MCD). This paper proposes a novel method to yield uncertainty and compares it to GPs and DNNs with MCD. The proposed approach explicitly teaches a DNN to have low uncertainty on training data and high uncertainty on generated artificial data. On experiments conducted on data from the Business Language Testing Service (BULATS), the proposed approach is found to outperform GPs and DNNs with MCD in uncertainty-based rejection whilst achieving comparable grading performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2009", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2009.flac", "type": "audio", "duration": 799.851}, {"from": "Assistant", "value": "Language identification (LID) is a critical first step for processing multilingual text. Yet most LID systems are not designed to handle the linguistic diversity of global platforms like Twitter, where local dialects and rampant code-switching lead language classifiers to systematically miss minority dialect speakers and multilingual speakers. We propose a new dataset and a character-based sequence-to-sequence model for LID designed to support dialectal and multilingual language varieties. Our model achieves state-of-the-art performance on multiple LID benchmarks. Furthermore, in a case study using Twitter for health tracking, our method substantially increases the availability of texts written by underrepresented populations, enabling the development of “socially inclusive” NLP tools.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2010", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2010.flac", "type": "audio", "duration": 811.008}, {"from": "Assistant", "value": "Traditionally, compound splitters are evaluated intrinsically on gold-standard data or extrinsically on the task of statistical machine translation. We explore a novel way for the extrinsic evaluation of compound splitters, namely recognizing textual entailment. Compound splitting has great potential for this novel task that is both transparent and well-defined. Moreover, we show that it addresses certain aspects that are either ignored in intrinsic evaluations or compensated for by taskinternal mechanisms in statistical machine translation. We show significant improvements using different compound splitting methods on a German textual entailment dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2011", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2011.flac", "type": "audio", "duration": 979.52}, {"from": "Assistant", "value": "A large amount of recent research has focused on tasks that combine language and vision, resulting in a proliferation of datasets and methods. One such task is action recognition, whose applications include image annotation, scene understanding and image retrieval. In this survey, we categorize the existing approaches based on how they conceptualize this problem and provide a detailed review of existing datasets, highlighting their diversity as well as advantages and disadvantages. We focus on recently developed datasets which link visual information with linguistic resources and provide a fine-grained syntactic and semantic analysis of actions in images.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2012", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2012.flac", "type": "audio", "duration": 1052.736}, {"from": "Assistant", "value": "There has been relatively little attention to incorporating linguistic prior to neural machine translation. Much of the previous work was further constrained to considering linguistic prior on the source side. In this paper, we propose a hybrid model, called NMT+RNNG, that learns to parse and translate by combining the recurrent neural network grammar into the attention-based neural machine translation. Our approach encourages the neural machine translation model to incorporate linguistic prior during training, and lets it translate on its own afterward. Extensive experiments with four language pairs show the effectiveness of the proposed NMT+RNNG.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2013", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2013.flac", "type": "audio", "duration": 956.949}, {"from": "Assistant", "value": "Natural language processing has increasingly moved from modeling documents and words toward studying the people behind the language. This move to working with data at the user or community level has presented the field with different characteristics of linguistic data. In this paper, we empirically characterize various lexical distributions at different levels of analysis, showing that, while most features are decidedly sparse and non-normal at the message-level (as with traditional NLP), they follow the central limit theorem to become much more Log-normal or even Normal at the user- and county-levels. Finally, we demonstrate that modeling lexical features for the correct level of analysis leads to marked improvements in common social scientific prediction tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2014", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2014.flac", "type": "audio", "duration": 981.269}, {"from": "Assistant", "value": "We present the first attempt at using sequence to sequence neural networks to model text simplification (TS). Unlike the previously proposed automated TS systems, our neural text simplification (NTS) systems are able to simultaneously perform lexical simplification and content reduction. An extensive human evaluation of the output has shown that NTS systems achieve almost perfect grammaticality and meaning preservation of output sentences and higher level of simplification than the state-of-the-art automated TS systems", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2015", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2015.flac", "type": "audio", "duration": 1088.555}, {"from": "Assistant", "value": "This paper highlights challenges in industrial research related to translating research in natural language processing into commercial products. While the interest in natural language processing from industry is significant, the transfer of research to commercial products is non-trivial and its challenges are often unknown to or underestimated by many researchers. I discuss current obstacles and provide suggestions for increasing the chances for translating research to commercial success based on my experience in industrial research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2016", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2016.flac", "type": "audio", "duration": 865.707}, {"from": "Assistant", "value": "We provide several methods for sentence-alignment of texts with different complexity levels. Using the best of them, we sentence-align the Newsela corpora, thus providing large training materials for automatic text simplification (ATS) systems. We show that using this dataset, even the standard phrase-based statistical machine translation models for ATS can outperform the state-of-the-art ATS systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2017", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2017.flac", "type": "audio", "duration": 733.013}, {"from": "Assistant", "value": "Linguistically diverse datasets are critical for training and evaluating robust machine learning systems, but data collection is a costly process that often requires experts. Crowdsourcing the process of paraphrase generation is an effective means of expanding natural language datasets, but there has been limited analysis of the trade-offs that arise when designing tasks. In this paper, we present the first systematic study of the key factors in crowdsourcing paraphrase collection. We consider variations in instructions, incentives, data domains, and workflows. We manually analyzed paraphrases for correctness, grammaticality, and linguistic diversity. Our observations provide new insight into the trade-offs between accuracy and diversity in crowd responses that arise as a result of task design, providing guidance for future paraphrase generation procedures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2018", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2018.flac", "type": "audio", "duration": 756.501}, {"from": "Assistant", "value": "Transition-based dependency parsers often need sequences of local shift and reduce operations to produce certain attachments. Correct individual decisions hence require global information about the sentence context and mistakes cause error propagation. This paper proposes a novel transition system, arc-swift, that enables direct attachments between tokens farther apart with a single transition. This allows the parser to leverage lexical information more directly in transition decisions. Hence, arc-swift can achieve significantly better performance with a very small beam size. Our parsers reduce error by 3.7–7.6% relative to those using existing transition systems on the Penn Treebank dependency parsing task and English Universal Dependencies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2019", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2019.flac", "type": "audio", "duration": 749.653}, {"from": "Assistant", "value": "Generative models defining joint distributions over parse trees and sentences are useful for parsing and language modeling, but impose restrictions on the scope of features and are often outperformed by discriminative models. We propose a framework for parsing and language modeling which marries a generative model with a discriminative recognition model in an encoder-decoder setting. We provide interpretations of the framework based on expectation maximization and variational inference, and show that it enables parsing and language modeling within a single implementation. On the English Penn Treen-bank, our framework obtains competitive performance on constituency parsing while matching the state-of-the-art single-model language modeling score.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2020", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2020.flac", "type": "audio", "duration": 694.037}, {"from": "Assistant", "value": "Recently, the neural machine translation systems showed their promising performance and surpassed the phrase-based systems for most translation tasks. Retreating into conventional concepts machine translation while utilizing effective neural models is vital for comprehending the leap accomplished by neural machine translation over phrase-based methods. This work proposes a direct HMM with neural network-based lexicon and alignment models, which are trained jointly using the Baum-Welch algorithm. The direct HMM is applied to rerank the n-best list created by a state-of-the-art phrase-based translation system and it provides improvements by up to 1.0% Bleu scores on two different translation tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2021", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2021.flac", "type": "audio", "duration": 1072.619}, {"from": "Assistant", "value": "We present a simple method to incorporate syntactic information about the target language in a neural machine translation system by translating into linearized, lexicalized constituency trees. An experiment on the WMT16 German-English news translation task resulted in an improved BLEU score when compared to a syntax-agnostic NMT baseline trained on the same dataset. An analysis of the translations from the syntax-aware system shows that it performs more reordering during translation in comparison to the baseline. A small-scale human evaluation also showed an advantage to the syntax-aware system.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2022", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2022.flac", "type": "audio", "duration": 814.72}, {"from": "Assistant", "value": "Informal first-person narratives are a unique resource for computational models of everyday events and people’s affective reactions to them. People blogging about their day tend not to explicitly say I am happy. Instead they describe situations from which other humans can readily infer their affective reactions. However current sentiment dictionaries are missing much of the information needed to make similar inferences. We build on recent work that models affect in terms of lexical predicate functions and affect on the predicate’s arguments. We present a method to learn proxies for these functions from first-person narratives. We construct a novel fine-grained test set, and show that the patterns we learn improve our ability to predict first-person affective reactions to everyday events, from a Stanford sentiment baseline of .67F to .75F.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2023", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2023.flac", "type": "audio", "duration": 1001.109}, {"from": "Assistant", "value": "This paper makes a focused contribution to supervised aspect extraction. It shows that if the system has performed aspect extraction from many past domains and retained their results as knowledge, Conditional Random Fields (CRF) can leverage this knowledge in a lifelong learning manner to extract in a new domain markedly better than the traditional CRF without using this prior knowledge. The key innovation is that even after CRF training, the model can still improve its extraction with experiences in its applications.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2024", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2024.flac", "type": "audio", "duration": 730.453}, {"from": "Assistant", "value": "A fundamental advantage of neural models for NLP is their ability to learn representations from scratch. However, in practice this often means ignoring existing external linguistic resources, e.g., WordNet or domain specific ontologies such as the Unified Medical Language System (UMLS). We propose a general, novel method for exploiting such resources via weight sharing. Prior work on weight sharing in neural networks has considered it largely as a means of model compression. In contrast, we treat weight sharing as a flexible mechanism for incorporating prior knowledge into neural models. We show that this approach consistently yields improved performance on classification tasks compared to baseline strategies that do not exploit weight sharing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2025", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2025.flac", "type": "audio", "duration": 1027.584}, {"from": "Assistant", "value": "Recent work has proposed several generative neural models for constituency parsing that achieve state-of-the-art results. Since direct search in these generative models is difficult, they have primarily been used to rescore candidate outputs from base parsers in which decoding is more straightforward. We first present an algorithm for direct search in these generative models. We then demonstrate that the rescoring results are at least partly due to implicit model combination rather than reranking effects. Finally, we show that explicit model combination can improve performance even further, resulting in new state-of-the-art numbers on the PTB of 94.25 F1 when training only on gold data and 94.66 F1 when using external data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2026", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2026.flac", "type": "audio", "duration": 639.275}, {"from": "Assistant", "value": "In this paper we define a measure of dependency between two random variables, based on the Jensen-Shannon (JS) divergence between their joint distribution and the product of their marginal distributions. Then, we show that word2vec’s skip-gram with negative sampling embedding algorithm finds the optimal low-dimensional approximation of this JS dependency measure between the words and their contexts. The gap between the optimal score and the low-dimensional approximation is demonstrated on a standard text corpus.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2027", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2027.flac", "type": "audio", "duration": 1035.051}, {"from": "Assistant", "value": "In this work, we propose a novel, implicitly-defined neural network architecture and describe a method to compute its components. The proposed architecture forgoes the causality assumption used to formulate recurrent neural networks and instead couples the hidden states of the network, allowing improvement on problems with complex, long-distance dependencies. Initial experiments demonstrate the new architecture outperforms both the Stanford Parser and baseline bidirectional networks on the Penn Treebank Part-of-Speech tagging task and a baseline bidirectional network on an additional artificial random biased walk task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2028", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2028.flac", "type": "audio", "duration": 773.248}, {"from": "Assistant", "value": "This study explores the role of speech register and prosody for the task of word segmentation. Since these two factors are thought to play an important role in early language acquisition, we aim to quantify their contribution for this task. We study a Japanese corpus containing both infant- and adult-directed speech and we apply four different word segmentation models, with and without knowledge of prosodic boundaries. The results showed that the difference between registers is smaller than previously reported and that prosodic boundary information helps more adult- than infant-directed speech.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2029", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2029.flac", "type": "audio", "duration": 726.549}, {"from": "Assistant", "value": "Previous work introduced transition-based algorithms to form a unified architecture of parsing rhetorical structures (including span, nuclearity and relation), but did not achieve satisfactory performance. In this paper, we propose that transition-based model is more appropriate for parsing the naked discourse tree (i.e., identifying span and nuclearity) due to data sparsity. At the same time, we argue that relation labeling can benefit from naked tree structure and should be treated elaborately with consideration of three kinds of relations including within-sentence, across-sentence and across-paragraph relations. Thus, we design a pipelined two-stage parsing method for generating an RST tree from text. Experimental results show that our method achieves state-of-the-art performance, especially on span and nuclearity identification.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2030", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2030.flac", "type": "audio", "duration": 868.181}, {"from": "Assistant", "value": "We propose a new dependency parsing scheme which jointly parses a sentence and repairs grammatical errors by extending the non-directional transition-based formalism of Goldberg and Elhadad (2010) with three additional actions: SUBSTITUTE, DELETE, INSERT. Because these actions may cause an infinite loop in derivation, we also introduce simple constraints that ensure the parser termination. We evaluate our model with respect to dependency accuracy and grammaticality improvements for ungrammatical sentences, demonstrating the robustness and applicability of our scheme.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2031", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2031.flac", "type": "audio", "duration": 701.845}, {"from": "Assistant", "value": "Modeling attention in neural multi-source sequence-to-sequence learning remains a relatively unexplored area, despite its usefulness in tasks that incorporate multiple source languages or modalities. We propose two novel approaches to combine the outputs of attention mechanisms over each source sequence, flat and hierarchical. We compare the proposed methods with existing techniques and present results of systematic evaluation of those methods on the WMT16 Multimodal Translation and Automatic Post-editing tasks. We show that the proposed methods achieve competitive results on both tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2033", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2033.flac", "type": "audio", "duration": 745.237}, {"from": "Assistant", "value": "We propose a simple yet effective text-based user geolocation model based on a neural network with one hidden layer, which achieves state of the art performance over three Twitter benchmark geolocation datasets, in addition to producing word and phrase embeddings in the hidden layer that we show to be useful for detecting dialectal terms. As part of our analysis of dialectal terms, we release DAREDS, a dataset for evaluating dialect term detection methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-2034", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-2034.flac", "type": "audio", "duration": 800.768}, {"from": "Assistant", "value": "We present a new visual reasoning language dataset, containing 92,244 pairs of examples of natural statements grounded in synthetic images with 3,962 unique sentences. We describe a method of crowdsourcing linguistically-diverse data, and present an analysis of our data. The data demonstrates a broad set of linguistic phenomena, requiring visual and set-theoretic reasoning. We experiment with various models, and show the data presents a strong challenge for future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-5003", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-5003.flac", "type": "audio", "duration": 11885.76}, {"from": "Assistant", "value": "Learning representation to model the meaning of text has been a core problem in NLP. The last several years have seen extensive interests on distributional approaches, in which text spans of different granularities are encoded as vectors of numerical values. If properly learned, such representation has showed to achieve the state-of-the-art performance on a wide range of NLP problems.In this tutorial, we will cover the fundamentals and the state-of-the-art research on neural network-based modeling for semantic composition, which aims to learn distributed representation for different granularities of text, e.g., phrases, sentences, or even documents, from their sub-component meaning representation, e.g., word embedding.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-5004", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-5004.flac", "type": "audio", "duration": 10617.771}, {"from": "Assistant", "value": "In the past decade, goal-oriented spoken dialogue systems have been the most prominent component in today's virtual personal assistants. The classic dialogue systems have rather complex and/or modular pipelines. The advance of deep learning technologies has recently risen the applications of neural models to dialogue modeling. However, how to successfully apply deep learning based approaches to a dialogue system is still challenging. Hence, this tutorial is designed to focus on an overview of the dialogue system development while describing most recent research for building dialogue systems and summarizing the challenges, in order to allow researchers to study the potential improvements of the state-of-the-art dialogue systems. The tutorial material is available athttp://deepdialogue.miulab.tw.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P17-5006", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P17-5006.flac", "type": "audio", "duration": 5159.957}, {"from": "Assistant", "value": "Over the last decade, crowdsourcing has been used to harness the power of human computation to solve tasks that are notoriously difficult to solve with computers alone, such as determining whether or not an image contains a tree, rating the relevance of a website, or verifying the phone number of a business. The natural language processing community was early to embrace crowdsourcing as a tool for quickly and inexpensively obtaining annotated data to train NLP systems. Once this data is collected, it can be handed off to algorithms that learn to perform basic NLP tasks such as translation or parsing. Usually this handoff is where interaction with the crowd ends. The crowd provides the data, but the ultimate goal is to eventually take humans out of the loop. Are there better ways to make use of the crowd?In this tutorial, I will begin with a showcase of innovative uses of crowdsourcing that go beyond data collection and annotation. I will discuss applications to natural language processing and machine learning, hybrid intelligence or “human in the loop” AI systems that leverage the complementary strengths of humans and machines in order to achieve more than either could achieve alone, and large scale studies of human behavior online. I will then spend the majority of the tutorial diving into recent research aimed at understanding who crowdworkers are, how they behave, and what this should teach us about best practices for interacting with the crowd.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1001", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1001.flac", "type": "audio", "duration": 1296.491}, {"from": "Assistant", "value": "We introduce Probabilistic FastText, a new model for word embeddings that can capture multiple word senses, sub-word structure, and uncertainty information. In particular, we represent each word with a Gaussian mixture density, where the mean of a mixture component is given by the sum of n-grams. This representation allows the model to share the “strength” across sub-word structures (e.g. Latin roots), producing accurate representations of rare, misspelt, or even unseen words. Moreover, each component of the mixture can capture a different word sense. Probabilistic FastText outperforms both FastText, which has no probabilistic model, and dictionary-level probabilistic embeddings, which do not incorporate subword structures, on several word-similarity benchmarks, including English RareWord and foreign language datasets. We also achieve state-of-art performance on benchmarks that measure ability to discern different meanings. Thus, our model is the first to achieve best of both the worlds: multi-sense representations while having enriched semantics on rare words.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1002", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1002.flac", "type": "audio", "duration": 1504.0}, {"from": "Assistant", "value": "Motivations like domain adaptation, transfer learning, and feature learning have fueled interest in inducing embeddings for rare or unseen words, n-grams, synsets, and other textual features. This paper introduces a la carte embedding, a simple and general alternative to the usual word2vec-based approaches for building such representations that is based upon recent theoretical results for GloVe-like embeddings. Our method relies mainly on a linear transformation that is efficiently learnable using pretrained word vectors and linear regression. This transform is applicable on the fly in the future when a new text feature or rare word is encountered, even if only a single usage example is available. We introduce a new dataset showing how the a la carte method requires fewer examples of words in context to learn high-quality embeddings and we obtain state-of-the-art results on a nonce task and some unsupervised document classification tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1003", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1003.flac", "type": "audio", "duration": 1321.963}, {"from": "Assistant", "value": "Word embedding models such as GloVe rely on co-occurrence statistics to learn vector representations of word meaning. While we may similarly expect that co-occurrence statistics can be used to capture rich information about the relationships between different words, existing approaches for modeling such relationships are based on manipulating pre-trained word vectors. In this paper, we introduce a novel method which directly learns relation vectors from co-occurrence statistics. To this end, we first introduce a variant of GloVe, in which there is an explicit connection between word vectors and PMI weighted co-occurrence vectors. We then show how relation vectors can be naturally embedded into the resulting vector space.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1004", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1004.flac", "type": "audio", "duration": 1909.653}, {"from": "Assistant", "value": "Semantic specialization of distributional word vectors, referred to as retrofitting, is a process of fine-tuning word vectors using external lexical knowledge in order to better embed some semantic relation. Existing retrofitting models integrate linguistic constraints directly into learning objectives and, consequently, specialize only the vectors of words from the constraints. In this work, in contrast, we transform external lexico-semantic relations into training examples which we use to learn an explicit retrofitting model (ER). The ER model allows us to learn a global specialization function and specialize the vectors of words unobserved in the training data as well. We report large gains over original distributional vector spaces in (1) intrinsic word similarity evaluation and on (2) two downstream tasks − lexical simplification and dialog state tracking. Finally, we also successfully specialize vector spaces of new languages (i.e., unseen in the training data) by coupling ER with shared multilingual distributional vector spaces.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1005", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1005.flac", "type": "audio", "duration": 1115.819}, {"from": "Assistant", "value": "Unsupervised neural machine translation (NMT) is a recently proposed approach for machine translation which aims to train the model without using any labeled data. The models proposed for unsupervised NMT often use only one shared encoder to map the pairs of sentences from different languages to a shared-latent space, which is weak in keeping the unique and internal characteristics of each language, such as the style, terminology, and sentence structure. To address this issue, we introduce an extension by utilizing two independent encoders but sharing some partial weights which are responsible for extracting high-level representations of the input sentences. Besides, two different generative adversarial networks (GANs), namely the local GAN and global GAN, are proposed to enhance the cross-language translation. With this new approach, we achieve significant improvements on English-German, English-French and Chinese-to-English translation tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1006", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1006.flac", "type": "audio", "duration": 1492.971}, {"from": "Assistant", "value": "Neural Machine Translation (NMT) performs poor on the low-resource language pair (X,Z), especially when Z is a rare language. By introducing another rich language Y, we propose a novel triangular training architecture (TA-NMT) to leverage bilingual data (Y,Z) (may be small) and (X,Y) (can be rich) to improve the translation performance of low-resource pairs. In this triangular architecture, Z is taken as the intermediate latent variable, and translation models of Z are jointly optimized with an unified bidirectional EM algorithm under the goal of maximizing the translation likelihood of (X,Y). Empirical results demonstrate that our method significantly improves the translation quality of rare languages on MultiUN and IWSLT2012 datasets, and achieves even better performance combining back-translation methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1007", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1007.flac", "type": "audio", "duration": 1432.213}, {"from": "Assistant", "value": "Subword units are an effective way to alleviate the open vocabulary problems in neural machine translation (NMT). While sentences are usually converted into unique subword sequences, subword segmentation is potentially ambiguous and multiple segmentations are possible even with the same vocabulary. The question addressed in this paper is whether it is possible to harness the segmentation ambiguity as a noise to improve the robustness of NMT. We present a simple regularization method, subword regularization, which trains the model with multiple subword segmentations probabilistically sampled during training. In addition, for better subword sampling, we propose a new subword segmentation algorithm based on a unigram language model. We experiment with multiple corpora and report consistent improvements especially on low resource and out-of-domain settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1008", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1008.flac", "type": "audio", "duration": 1508.288}, {"from": "Assistant", "value": "The past year has witnessed rapid advances in sequence-to-sequence (seq2seq) modeling for Machine Translation (MT). The classic RNN-based approaches to MT were first out-performed by the convolutional seq2seq model, which was then out-performed by the more recent Transformer model. Each of these new approaches consists of a fundamental architecture accompanied by a set of modeling and training techniques that are in principle applicable to other seq2seq architectures. In this paper, we tease apart the new architectures and their accompanying techniques in two ways. First, we identify several key modeling and training techniques, and apply them to the RNN architecture, yielding a new RNMT+ model that outperforms all of the three fundamental architectures on the benchmark WMT’14 English to French and English to German tasks. Second, we analyze the properties of each fundamental seq2seq architecture and devise new hybrid architectures intended to combine their strengths. Our hybrid models obtain further improvements, outperforming the RNMT+ model on both benchmark datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1009", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1009.flac", "type": "audio", "duration": 1453.888}, {"from": "Assistant", "value": "We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at large scale: head words, which indicate the type of the noun phrases they appear in. We show that these ultra-fine types can be crowd-sourced, and introduce new evaluation sets that are much more diverse and fine-grained than existing benchmarks. We present a model that can predict ultra-fine types, and is trained using a multitask objective that pools our new head-word supervision with prior supervision from entity linking. Experimental results demonstrate that our model is effective in predicting entity types at varying granularity; it achieves state of the art performance on an existing fine-grained entity typing benchmark, and sets baselines for our newly-introduced datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1010", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1010.flac", "type": "audio", "duration": 1285.611}, {"from": "Assistant", "value": "Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchies over types and entities contained in curated ontologies. Previous attempts to incorporate hierarchical structure have yielded little benefit and are restricted to shallow ontologies. This paper presents new methods using real and complex bilinear mappings for integrating hierarchical information, yielding substantial improvement over flat predictions in entity linking and fine-grained entity typing, and achieving new state-of-the-art results for end-to-end models on the benchmark FIGER dataset. We also present two new human-annotated datasets containing wide and deep hierarchies which we will release to the community to encourage further research in this direction:MedMentions, a collection of PubMed abstracts in which 246k mentions have been mapped to the massive UMLS ontology; andTypeNet, which aligns Freebase types with the WordNet hierarchy to obtain nearly 2k entity types. In experiments on all three datasets we show substantial gains from hierarchy-aware training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1011", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1011.flac", "type": "audio", "duration": 1343.403}, {"from": "Assistant", "value": "Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Early works performed this task via simple models developed over KG triples. Recent attempts focused on either designing more complicated triple scoring models, or incorporating extra information beyond triples. This paper, by contrast, investigates the potential of using very simple constraints to improve KG embedding. We examine non-negativity constraints on entity representations and approximate entailment constraints on relation representations. The former help to learn compact and interpretable representations for entities. The latter further encode regularities of logical entailment between relations into their distributed representations. These constraints impose prior beliefs upon the structure of the embedding space, without negative impacts on efficiency or scalability. Evaluation on WordNet, Freebase, and DBpedia shows that our approach is simple yet surprisingly effective, significantly and consistently outperforming competitive baselines. The constraints imposed indeed improve model interpretability, leading to a substantially increased structuring of the embedding space. Code and data are available athttps://github.com/iieir-km/ComplEx-NNE_AER.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1012", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1012.flac", "type": "audio", "duration": 1220.48}, {"from": "Assistant", "value": "Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding methods. These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space. Despite this popularity and effectiveness of KG embeddings in various tasks (e.g., link prediction), geometric understanding of such embeddings (i.e., arrangement of entity and relation vectors in vector space) is unexplored – we fill this gap in the paper. We initiate a study to analyze the geometry of KG embeddings and correlate it with task performance and other hyperparameters. To the best of our knowledge, this is the first study of its kind. Through extensive experiments on real-world datasets, we discover several insights. For example, we find that there are sharp differences between the geometry of embeddings learnt by different classes of KG embeddings methods. We hope that this initial study will inspire other follow-up research on this important but unexplored problem.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1013", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1013.flac", "type": "audio", "duration": 1130.603}, {"from": "Assistant", "value": "We propose a unified model combining the strength of extractive and abstractive summarization. On the one hand, a simple extractive model can obtain sentence-level attention with high ROUGE scores but less readable. On the other hand, a more complicated abstractive model can obtain word-level dynamic attention to generate a more readable paragraph. In our model, sentence-level attention is used to modulate the word-level attention such that words in less attended sentences are less likely to be generated. Moreover, a novel inconsistency loss function is introduced to penalize the inconsistency between two levels of attentions. By end-to-end training our model with the inconsistency loss and original losses of extractive and abstractive models, we achieve state-of-the-art ROUGE scores while being the most informative and readable summarization on the CNN/Daily Mail dataset in a solid human evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1014", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1014.flac", "type": "audio", "duration": 1193.579}, {"from": "Assistant", "value": "We present a new neural sequence-to-sequence model for extractive summarization called SWAP-NET (Sentences and Words from Alternating Pointer Networks). Extractive summaries comprising a salient subset of input sentences, often also contain important key words. Guided by this principle, we design SWAP-NET that models the interaction of key words and salient sentences using a new two-level pointer network based architecture. SWAP-NET identifies both salient sentences and key words in an input document, and then combines them to form the extractive summary. Experiments on large scale benchmark corpora demonstrate the efficacy of SWAP-NET that outperforms state-of-the-art extractive summarizers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1015", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1015.flac", "type": "audio", "duration": 1081.003}, {"from": "Assistant", "value": "Most previous seq2seq summarization systems purely depend on the source text to generate summaries, which tends to work unstably. Inspired by the traditional template-based summarization approaches, this paper proposes to use existing summaries as soft templates to guide the seq2seq model. To this end, we use a popular IR platform to Retrieve proper summaries as candidate templates. Then, we extend the seq2seq framework to jointly conduct template Reranking and template-aware summary generation (Rewriting). Experiments show that, in terms of informativeness, our model significantly outperforms the state-of-the-art methods, and even soft templates themselves demonstrate high competitiveness. In addition, the import of high-quality external summaries improves the stability and readability of generated summaries.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1016", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1016.flac", "type": "audio", "duration": 1176.171}, {"from": "Assistant", "value": "Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used in this situation. Previous application of Machine Translation for simplification suffers from a considerable disadvantage in that they are over-conservative, often failing to modify the source in any way. Splitting based on semantic parsing, as proposed here, alleviates this issue. Extensive automatic and human evaluation shows that the proposed method compares favorably to the state-of-the-art in combined lexical and structural simplification.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1017", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1017.flac", "type": "audio", "duration": 1341.12}, {"from": "Assistant", "value": "Words play a central role in language and thought. Factor analysis studies have shown that the primary dimensions of meaning are valence, arousal, and dominance (VAD). We present the NRC VAD Lexicon, which has human ratings of valence, arousal, and dominance for more than 20,000 English words. We use Best–Worst Scaling to obtain fine-grained scores and address issues of annotation consistency that plague traditional rating scale methods of annotation. We show that the ratings obtained are vastly more reliable than those in existing lexicons. We also show that there exist statistically significant differences in the shared understanding of valence, arousal, and dominance across demographic variables such as age, gender, and personality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1018", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1018.flac", "type": "audio", "duration": 1490.453}, {"from": "Assistant", "value": "Semantic relations are often signaled with prepositional or possessive marking—but extreme polysemy bedevils their analysis and automatic interpretation. We introduce a new annotation scheme, corpus, and task for the disambiguation of prepositions and possessives in English. Unlike previous approaches, our annotations are comprehensive with respect to types and tokens of these markers; use broadly applicable supersense classes rather than fine-grained dictionary definitions; unite prepositions and possessives under the same class inventory; and distinguish between a marker’s lexical contribution and the role it marks in the context of a predicate or scene. Strong interannotator agreement rates, as well as encouraging disambiguation results with established supervised methods, speak to the viability of the scheme and task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1019", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1019.flac", "type": "audio", "duration": 1390.4}, {"from": "Assistant", "value": "We present a corpus of 5,000 richly annotated abstracts of medical articles describing clinical randomized controlled trials. Annotations include demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured (the ‘PICO’ elements). These spans are further annotated at a more granular level, e.g., individual interventions within them are marked and mapped onto a structured medical vocabulary. We acquired annotations from a diverse set of workers with varying levels of expertise and cost. We describe our data collection process and the corpus itself in detail. We then outline a set of challenging NLP tasks that would aid searching of the medical literature and the practice of evidence-based medicine.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1020", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1020.flac", "type": "audio", "duration": 903.851}, {"from": "Assistant", "value": "We describe a novel method for efficiently eliciting scalar annotations for dataset construction and system quality estimation by human judgments. We contrast direct assessment (annotators assign scores to items directly), online pairwise ranking aggregation (scores derive from annotator comparison of items), and a hybrid approach (EASL: Efficient Annotation of Scalar Labels) proposed here. Our proposal leads to increased correlation with ground truth, at far greater annotator efficiency, suggesting this strategy as an improved mechanism for dataset creation and manual system evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1021", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1021.flac", "type": "audio", "duration": 1325.483}, {"from": "Assistant", "value": "High quality arguments are essential elements for human reasoning and decision-making processes. However, effective argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose an encoder-decoder style neural network-based argument generation model enriched with externally retrieved evidence from Wikipedia. Our model first generates a set of talking point phrases as intermediate representation, followed by a separate decoder producing the final argument based on both input and the keyphrases. Experiments on a large-scale dataset collected from Reddit show that our model constructs arguments with more topic-relevant content than popular sequence-to-sequence generation models according to automatic evaluation and human assessments.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1022", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1022.flac", "type": "audio", "duration": 1417.045}, {"from": "Assistant", "value": "We report on a comparative style analysis of hyperpartisan (extremely one-sided) news and fake news. A corpus of 1,627 articles from 9 political publishers, three each from the mainstream, the hyperpartisan left, and the hyperpartisan right, have been fact-checked by professional journalists at BuzzFeed: 97% of the 299 fake news articles identified are also hyperpartisan. We show how a style analysis can distinguish hyperpartisan news from the mainstream (F1 = 0.78), and satire from both (F1 = 0.81). But stylometry is no silver bullet as style-based fake news detection does not work (F1 = 0.46). We further reveal that left-wing and right-wing news share significantly more stylistic similarities than either does with the mainstream. This result is robust: it has been confirmed by three different modeling approaches, one of which employs Unmasking in a novel way. Applications of our results include partisanship detection and pre-screening for semi-automatic fake news detection.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1023", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1023.flac", "type": "audio", "duration": 1377.728}, {"from": "Assistant", "value": "Given any argument on any controversial topic, how to counter it? This question implies the challenging retrieval task of finding the best counterargument. Since prior knowledge of a topic cannot be expected in general, we hypothesize the best counterargument to invoke the same aspects as the argument while having the opposite stance. To operationalize our hypothesis, we simultaneously model the similarity and dissimilarity of pairs of arguments, based on the words and embeddings of the arguments’ premises and conclusions. A salient property of our model is its independence from the topic at hand, i.e., it applies to arbitrary arguments. We evaluate different model variations on millions of argument pairs derived from the web portal idebate.org. Systematic ranking experiments suggest that our hypothesis is true for many arguments: For 7.6 candidates with opposing stance on average, we rank the best counterargument highest with 60% accuracy. Even among all 2801 test set pairs as candidates, we still find the best one about every third time.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1068", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1068.flac", "type": "audio", "duration": 1458.283}, {"from": "Assistant", "value": "Semantic parsing aims at mapping natural language utterances into structured meaning representations. In this work, we propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. Given an input utterance, we first generate a rough sketch of its meaning, where low-level information (such as variable names and arguments) is glossed over. Then, we fill in missing details by taking into account the natural language input and the sketch itself. Experimental results on four datasets characteristic of different domains and meaning representations show that our approach consistently improves performance, achieving competitive results despite the use of relatively simple decoders.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1070", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1070.flac", "type": "audio", "duration": 1429.163}, {"from": "Assistant", "value": "Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures. Annotating NL utterances with their corresponding MRs is expensive and time-consuming, and thus the limited availability of labeled data often becomes the bottleneck of data-driven, supervised models. We introduce StructVAE, a variational auto-encoding model for semi-supervised semantic parsing, which learns both from limited amounts of parallel data, and readily-available unlabeled NL utterances. StructVAE models latent MRs not observed in the unlabeled data as tree-structured latent variables. Experiments on semantic parsing on the ATIS domain and Python code generation show that with extra unlabeled data, StructVAE outperforms strong supervised models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1071", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1071.flac", "type": "audio", "duration": 1472.939}, {"from": "Assistant", "value": "This paper proposes a neural semantic parsing approach – Sequence-to-Action, which models semantic parsing as an end-to-end semantic graph generation process. Our method simultaneously leverages the advantages from two recent promising directions of semantic parsing. Firstly, our model uses a semantic graph to represent the meaning of a sentence, which has a tight-coupling with knowledge bases. Secondly, by leveraging the powerful representation learning and prediction ability of neural network models, we propose a RNN model which can effectively map sentences to action sequences for semantic graph generation. Experiments show that our method achieves state-of-the-art performance on Overnight dataset and gets competitive performance on Geo and Atis datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1072", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1072.flac", "type": "audio", "duration": 1430.613}, {"from": "Assistant", "value": "Unsupervised machine translation - i.e., not assuming any cross-lingual supervision signal, whether a dictionary, translations, or comparable corpora - seems impossible, but nevertheless, Lample et al. (2017) recently proposed a fully unsupervised machine translation (MT) model. The model relies heavily on an adversarial, unsupervised cross-lingual word embedding technique for bilingual dictionary induction (Conneau et al., 2017), which we examine here. Our results identify the limitations of current unsupervised MT: unsupervised bilingual dictionary induction performs much worse on morphologically rich languages that are not dependent marking, when monolingual corpora from different domains or different embedding algorithms are used. We show that a simple trick, exploiting a weak supervision signal from identical words, enables more robust induction and establish a near-perfect correlation between unsupervised bilingual dictionary induction performance and a previously unexplored graph similarity metric.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1073", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1073.flac", "type": "audio", "duration": 1404.736}, {"from": "Assistant", "value": "Recent work has managed to learn cross-lingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, their evaluation has focused on favorable conditions, using comparable corpora or closely-related languages, and we show that they often fail in more realistic scenarios. This work proposes an alternative approach based on a fully unsupervised initialization that explicitly exploits the structural similarity of the embeddings, and a robust self-learning algorithm that iteratively improves this solution. Our method succeeds in all tested scenarios and obtains the best published results in standard datasets, even surpassing previous supervised systems. Our implementation is released as an open source project athttps://github.com/artetxem/vecmap.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1074", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1074.flac", "type": "audio", "duration": 1363.093}, {"from": "Assistant", "value": "We propose a multi-lingual multi-task architecture to develop supervised models with a minimal amount of labeled data for sequence labeling. In this new architecture, we combine various transfer models using two layers of parameter sharing. On the first layer, we construct the basis of the architecture to provide universal word representation and feature extraction capability for all models. On the second level, we adopt different parameter sharing strategies for different transfer schemes. This architecture proves to be particularly effective for low-resource settings, when there are less than 200 training sentences for the target task. Using Name Tagging as a target task, our approach achieved 4.3%-50.5% absolute F-score gains compared to the mono-lingual single-task baseline model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1075", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1075.flac", "type": "audio", "duration": 1195.563}, {"from": "Assistant", "value": "Bilingual tasks, such as bilingual lexicon induction and cross-lingual classification, are crucial for overcoming data sparsity in the target language. Resources required for such tasks are often out-of-domain, thus domain adaptation is an important problem here. We make two contributions. First, we test a delightfully simple method for domain adaptation of bilingual word embeddings. We evaluate these embeddings on two bilingual tasks involving different domains: cross-lingual twitter sentiment classification and medical bilingual lexicon induction. Second, we tailor a broadly applicable semi-supervised classification method from computer vision to these tasks. We show that this method also helps in low-resource setups. Using both methods together we achieve large improvements over our baselines, by using only additional unlabeled data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1076", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1076.flac", "type": "audio", "duration": 1284.16}, {"from": "Assistant", "value": "We introduce a neural reading comprehension model that integrates external commonsense knowledge, encoded as a key-value memory, in a cloze-style setting. Instead of relying only on document-to-question interaction or discrete features as in prior work, our model attends to relevant external knowledge and combines this knowledge with the context representation before inferring the answer. This allows the model to attract and imply knowledge from an external knowledge source that is not explicitly stated in the text, but that is relevant for inferring the answer. Our model improves results over a very strong baseline on a hard Common Nouns dataset, making it a strong competitor of much more complex models. By including knowledge explicitly, our model can also provide evidence about the background knowledge used in the RC process.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1077", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1077.flac", "type": "audio", "duration": 1285.973}, {"from": "Assistant", "value": "Question Answering (QA), as a research field, has primarily focused on either knowledge bases (KBs) or free text as a source of knowledge. These two sources have historically shaped the kinds of questions that are asked over these sources, and the methods developed to answer them. In this work, we look towards a practical use-case of QA over user-instructed knowledge that uniquely combines elements of both structured QA over knowledge bases, and unstructured QA over narrative, introducing the task of multi-relational QA over personal narrative. As a first step towards this goal, we make three key contributions: (i) we generate and release TextWorldsQA, a set of five diverse datasets, where each dataset contains dynamic narrative that describes entities and relations in a simulated world, paired with variably compositional questions over that knowledge, (ii) we perform a thorough evaluation and analysis of several state-of-the-art QA models and their variants at this task, and (iii) we release a lightweight Python-based framework we call TextWorlds for easily generating arbitrary additional worlds and narrative, with the goal of allowing the community to create and share a growing collection of diverse worlds as a test-bed for this task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1078", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1078.flac", "type": "audio", "duration": 1082.411}, {"from": "Assistant", "value": "We introduce a method of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Most current question answering models cannot scale to document or multi-document input, and naively applying these models to each paragraph independently often results in them being distracted by irrelevant text. We show that it is possible to significantly improve performance by using a modified training scheme that teaches the model to ignore non-answer containing paragraphs. Our method involves sampling multiple paragraphs from each document, and using an objective function that requires the model to produce globally correct output. We additionally identify and improve upon a number of other design decisions that arise when working with document-level data. Experiments on TriviaQA and SQuAD shows our method advances the state of the art, including a 10 point gain on TriviaQA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1079", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1079.flac", "type": "audio", "duration": 1637.056}, {"from": "Assistant", "value": "Complex machine learning models for NLP are often brittle, making different predictions for input instances that are extremely similar semantically. To automatically detect this behavior for individual instances, we present semantically equivalent adversaries (SEAs) – semantic-preserving perturbations that induce changes in the model’s predictions. We generalize these adversaries into semantically equivalent adversarial rules (SEARs) – simple, universal replacement rules that induce adversaries on many instances. We demonstrate the usefulness and flexibility of SEAs and SEARs by detecting bugs in black-box state-of-the-art models for three domains: machine comprehension, visual question-answering, and sentiment analysis. Via user studies, we demonstrate that we generate high-quality local adversaries for more instances than humans, and that SEARs induce four times as many mistakes as the bugs discovered by human experts. SEARs are also actionable: retraining models using data augmentation significantly reduces bugs, while maintaining accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1080", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1080.flac", "type": "audio", "duration": 1315.733}, {"from": "Assistant", "value": "Style transfer is the task of rephrasing the text to contain specific stylistic properties without changing the intent or affect within the context. This paper introduces a new method for automatic style transfer. We first learn a latent representation of the input sentence which is grounded in a language translation model in order to better preserve the meaning of the sentence while reducing stylistic properties. Then adversarial generation techniques are used to make the output match the desired style. We evaluate this technique on three different style transformations: sentiment, gender and political slant. Compared to two state-of-the-art style transfer modeling techniques we show improvements both in automatic evaluation of style transfer and in manual evaluation of meaning preservation and fluency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1081", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1081.flac", "type": "audio", "duration": 1372.779}, {"from": "Assistant", "value": "While large-scale knowledge graphs provide vast amounts of structured facts about entities, a short textual description can often be useful to succinctly characterize an entity and its type. Unfortunately, many knowledge graphs entities lack such textual descriptions. In this paper, we introduce a dynamic memory-based network that generates a short open vocabulary description of an entity by jointly leveraging induced fact embeddings as well as the dynamic context of the generated sequence of words. We demonstrate the ability of our architecture to discern relevant information for more accurate generation of type description by pitting the system against several strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1082", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1082.flac", "type": "audio", "duration": 1504.576}, {"from": "Assistant", "value": "We explore story generation: creative systems that can build coherent and fluent passages of text about a topic. We collect a large dataset of 300K human-written stories paired with writing prompts from an online forum. Our dataset enables hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text. We gain further improvements with a novel form of model fusion that improves the relevance of the story to the prompt, and adding a new gated multi-scale self-attention mechanism to model long-range context. Experiments show large improvements over strong baselines on both automated and human evaluations. Human judges prefer stories generated by our approach to those from a strong non-hierarchical model by a factor of two to one.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1083", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1083.flac", "type": "audio", "duration": 1177.365}, {"from": "Assistant", "value": "Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challenges to behavioral cloning algorithms. Furthermore, due to the limitations of automatic metrics on evaluating story quality, reinforcement learning methods with hand-crafted rewards also face difficulties in gaining an overall performance boost. Therefore, we propose an Adversarial REward Learning (AREL) framework to learn an implicit reward function from human demonstrations, and then optimize policy search with the learned reward function. Though automatic evaluation indicates slight performance boost over state-of-the-art (SOTA) methods in cloning expert behaviors, human evaluation shows that our approach achieves significant improvement in generating more human-like stories than SOTA systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1084", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1084.flac", "type": "audio", "duration": 1442.88}, {"from": "Assistant", "value": "We present a deep neural network that leverages images to improve bilingual text embeddings. Relying on bilingual image tags and descriptions, our approach conditions text embedding induction on the shared visual information for both languages, producing highly correlated bilingual embeddings. In particular, we propose a novel model based on Partial Canonical Correlation Analysis (PCCA). While the original PCCA finds linear projections of two views in order to maximize their canonical correlation conditioned on a shared third variable, we introduce a non-linear Deep PCCA (DPCCA) model, and develop a new stochastic iterative algorithm for its optimization. We evaluate PCCA and DPCCA on multilingual word similarity and cross-lingual image description retrieval. Our models outperform a large variety of previous methods, despite not having access to any visual signal during test time inference.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1086", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1086.flac", "type": "audio", "duration": 1457.856}, {"from": "Assistant", "value": "Despite recent advances in knowledge representation, automated reasoning, and machine learning, artificial agents still lack the ability to understand basic action-effect relations regarding the physical world, for example, the action of cutting a cucumber most likely leads to the state where the cucumber is broken apart into smaller pieces. If artificial agents (e.g., robots) ever become our partners in joint tasks, it is critical to empower them with such action-effect understanding so that they can reason about the state of the world and plan for actions. Towards this goal, this paper introduces a new task on naive physical action-effect prediction, which addresses the relations between concrete actions (expressed in the form of verb-noun pairs) and their effects on the state of the physical world as depicted by images. We collected a dataset for this task and developed an approach that harnesses web image data through distant supervision to facilitate learning for action-effect prediction. Our empirical results have shown that web data can be used to complement a small number of seed examples (e.g., three examples for each action) for model learning. This opens up possibilities for agents to learn physical action-effect relations for tasks at hand through communication with humans with a few examples.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1087", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1087.flac", "type": "audio", "duration": 1168.363}, {"from": "Assistant", "value": "Target-oriented sentiment classification aims at classifying sentiment polarities over individual opinion targets in a sentence. RNN with attention seems a good fit for the characteristics of this task, and indeed it achieves the state-of-the-art performance. After re-examining the drawbacks of attention mechanism and the obstacles that block CNN to perform well in this classification task, we propose a new model that achieves new state-of-the-art results on a few benchmarks. Instead of attention, our model employs a CNN layer to extract salient features from the transformed word representations originated from a bi-directional RNN layer. Between the two layers, we propose a component which first generates target-specific representations of words in the sentence, and then incorporates a mechanism for preserving the original contextual information from the RNN layer.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1088", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1088.flac", "type": "audio", "duration": 1469.44}, {"from": "Assistant", "value": "Aspect sentiment classification (ASC) is a fundamental task in sentiment analysis. Given an aspect/target and a sentence, the task classifies the sentiment polarity expressed on the target in the sentence. Memory networks (MNs) have been used for this task recently and have achieved state-of-the-art results. In MNs, attention mechanism plays a crucial role in detecting the sentiment context for the given target. However, we found an important problem with the current MNs in performing the ASC task. Simply improving the attention mechanism will not solve it. The problem is referred to as target-sensitive sentiment, which means that the sentiment polarity of the (detected) context is dependent on the given target and it cannot be inferred from the context alone. To tackle this problem, we propose the target-sensitive memory networks (TMNs). Several alternative techniques are designed for the implementation of TMNs and their effectiveness is experimentally evaluated.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1089", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1089.flac", "type": "audio", "duration": 1434.005}, {"from": "Assistant", "value": "Getting manually labeled data in each domain is always an expensive and a time consuming task. Cross-domain sentiment analysis has emerged as a demanding concept where a labeled source domain facilitates a sentiment classifier for an unlabeled target domain. However, polarity orientation (positive or negative) and the significance of a word to express an opinion often differ from one domain to another domain. Owing to these differences, cross-domain sentiment classification is still a challenging task. In this paper, we propose that words that do not change their polarity and significance represent the transferable (usable) information across domains for cross-domain sentiment classification. We present a novel approach based on χ2 test and cosine-similarity between context vector of words to identify polarity preserving significant words across domains. Furthermore, we show that a weighted ensemble of the classifiers enhances the cross-domain classification performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1090", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1090.flac", "type": "audio", "duration": 1577.493}, {"from": "Assistant", "value": "The goal of sentiment-to-sentiment “translation” is to change the underlying sentiment of a sentence while keeping its content. The main challenge is the lack of parallel data. To solve this problem, we propose a cycled reinforcement learning method that enables training on unpaired data by collaboration between a neutralization module and an emotionalization module. We evaluate our approach on two review datasets, Yelp and Amazon. Experimental results show that our approach significantly outperforms the state-of-the-art systems. Especially, the proposed method substantially improves the content preservation performance. The BLEU score is improved from 1.64 to 22.46 and from 0.56 to 14.06 on the two datasets, respectively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1091", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1091.flac", "type": "audio", "duration": 1293.333}, {"from": "Assistant", "value": "Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is one of the most important problems in natural language processing. It requires to infer the logical relationship between two given sentences. While current approaches mostly focus on the interaction architectures of the sentences, in this paper, we propose to transfer knowledge from some important discourse markers to augment the quality of the NLI model. We observe that people usually use some discourse markers such as “so” or “but” to represent the logical relationship between two sentences. These words potentially have deep connections with the meanings of the sentences, thus can be utilized to help improve the representations of them. Moreover, we use reinforcement learning to optimize a new objective function with a reward defined by the property of the NLI datasets to make full use of the labels information. Experiments show that our method achieves the state-of-the-art performance on several large-scale datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1092", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1092.flac", "type": "audio", "duration": 973.653}, {"from": "Assistant", "value": "During the last years, there has been a lot of interest in achieving some kind of complex reasoning using deep neural networks. To do that, models like Memory Networks (MemNNs) have combined external memory storages and attention mechanisms. These architectures, however, lack of more complex reasoning mechanisms that could allow, for instance, relational reasoning. Relation Networks (RNs), on the other hand, have shown outstanding results in relational reasoning tasks. Unfortunately, their computational cost grows quadratically with the number of memories, something prohibitive for larger problems. To solve these issues, we introduce the Working Memory Network, a MemNN architecture with a novel working memory storage and reasoning module. Our model retains the relational reasoning abilities of the RN while reducing its computational complexity from quadratic to linear. We tested our model on the text QA dataset bAbI and the visual QA dataset NLVR. In the jointly trained bAbI-10k, we set a new state-of-the-art, achieving a mean error of less than 0.5%. Moreover, a simple ensemble of two of our models solves all 20 tasks in the joint version of the benchmark.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1093", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1093.flac", "type": "audio", "duration": 1050.219}, {"from": "Assistant", "value": "Sarcasm is a sophisticated speech act which commonly manifests on social communities such as Twitter and Reddit. The prevalence of sarcasm on the social web is highly disruptive to opinion mining systems due to not only its tendency of polarity flipping but also usage of figurative language. Sarcasm commonly manifests with a contrastive theme either between positive-negative sentiments or between literal-figurative scenarios. In this paper, we revisit the notion of modeling contrast in order to reason with sarcasm. More specifically, we propose an attention-based neural model that looks in-between instead of across, enabling it to explicitly model contrast and incongruity. We conduct extensive experiments on six benchmark datasets from Twitter, Reddit and the Internet Argument Corpus. Our proposed model not only achieves state-of-the-art performance on all datasets but also enjoys improved interpretability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1094", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1094.flac", "type": "audio", "duration": 1434.773}, {"from": "Assistant", "value": "Learning by contrasting positive and negative samples is a general strategy adopted by many methods. Noise contrastive estimation (NCE) for word embeddings and translating embeddings for knowledge graphs are examples in NLP employing this approach. In this work, we view contrastive learning as an abstraction of all such methods and augment the negative sampler into a mixture distribution containing an adversarially learned sampler. The resulting adaptive sampler finds harder negative examples, which forces the main model to learn a better representation of the data. We evaluate our proposal on learning word embeddings, order embeddings and knowledge graph embeddings and observe both faster convergence and improved results on multiple metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1095", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1095.flac", "type": "audio", "duration": 1456.896}, {"from": "Assistant", "value": "This paper focuses on detection tasks in information extraction, where positive instances are sparsely distributed and models are usually evaluated using F-measure on positive classes. These characteristics often result in deficient performance of neural network based detection models. In this paper, we proposeadaptive scaling, an algorithm which can handle the positive sparsity problem and directly optimize over F-measure via dynamic cost-sensitive learning. To this end, we borrow the idea of marginal utility from economics and propose a theoretical framework for instance importance measuring without introducing any additional hyper-parameters. Experiments show that our algorithm leads to a more effective and stable training of neural network based detection models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1096", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1096.flac", "type": "audio", "duration": 1584.256}, {"from": "Assistant", "value": "Novel neural models have been proposed in recent years for learning under domain shift. Most models, however, only evaluate on a single task, on proprietary datasets, or compare to weak baselines, which makes comparison of models difficult. In this paper, we re-evaluate classic general-purpose bootstrapping approaches in the context of neural networks under domain shifts vs. recent neural approaches and propose a novel multi-task tri-training method that reduces the time and space complexity of classic tri-training. Extensive experiments on two benchmarks for part-of-speech tagging and sentiment analysis are negative: while our novel method establishes a new state-of-the-art for sentiment analysis, it does not fare consistently the best. More importantly, we arrive at the somewhat surprising conclusion that classic tri-training, with some additions, outperforms the state-of-the-art for NLP. Hence classic approaches constitute an important and strong baseline.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1097", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1097.flac", "type": "audio", "duration": 1556.608}, {"from": "Assistant", "value": "Most of the neural sequence-to-sequence (seq2seq) models for grammatical error correction (GEC) have two limitations: (1) a seq2seq model may not be well generalized with only limited error-corrected data; (2) a seq2seq model may fail to completely correct a sentence with multiple errors through normal seq2seq inference. We attempt to address these limitations by proposing a fluency boost learning and inference mechanism. Fluency boosting learning generates fluency-boost sentence pairs during training, enabling the error correction model to learn how to improve a sentence’s fluency from more instances, while fluency boosting inference allows the model to correct a sentence incrementally with multiple inference steps until the sentence’s fluency stops increasing. Experiments show our approaches improve the performance of seq2seq models for GEC, achieving state-of-the-art results on both CoNLL-2014 and JFLEG benchmark datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1098", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1098.flac", "type": "audio", "duration": 1446.805}, {"from": "Assistant", "value": "The International Classification of Diseases (ICD) provides a hierarchy of diagnostic codes for classifying diseases. Medical coding – which assigns a subset of ICD codes to a patient visit – is a mandatory process that is crucial for patient care and billing. Manual coding is time-consuming, expensive, and error prone. In this paper, we build a neural architecture for automated coding. It takes the diagnosis descriptions (DDs) of a patient as inputs and selects the most relevant ICD codes. This architecture contains four major ingredients: (1) tree-of-sequences LSTM encoding of code descriptions (CDs), (2) adversarial learning for reconciling the different writing styles of DDs and CDs, (3) isotonic constraints for incorporating the importance order among the assigned codes, and (4) attentional matching for performing many-to-one and one-to-many mappings from DDs to CDs. We demonstrate the effectiveness of the proposed methods on a clinical datasets with 59K patient visits.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1099", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1099.flac", "type": "audio", "duration": 1315.051}, {"from": "Assistant", "value": "The success of deep neural networks (DNNs) is heavily dependent on the availability of labeled data. However, obtaining labeled data is a big challenge in many real-world problems. In such scenarios, a DNN model can leverage labeled and unlabeled data from a related domain, but it has to deal with the shift in data distributions between the source and the target domains. In this paper, we study the problem of classifying social media posts during a crisis event (e.g., Earthquake). For that, we use labeled and unlabeled data from past similar events (e.g., Flood) and unlabeled data for the current event. We propose a novel model that performs adversarial learning based domain adaptation to deal with distribution drifts and graph based semi-supervised learning to leverage unlabeled data within a single unified deep learning framework. Our experiments with two real-world crisis datasets collected from Twitter demonstrate significant improvements over several baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1100", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1100.flac", "type": "audio", "duration": 1479.445}, {"from": "Assistant", "value": "Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To close this gap, a two-stage deep neural network (TDNN) is proposed. In particular, in the first stage, using the rated essays for non-target prompts as the training data, a shallow model is learned to select essays with an extreme quality for the target prompt, serving as pseudo training data; in the second stage, an end-to-end hybrid deep model is proposed to learn a prompt-dependent rating model consuming the pseudo training data from the first step. Evaluation of the proposed TDNN on the standard ASAP dataset demonstrates a promising improvement for the prompt-independent AES task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1101", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1101.flac", "type": "audio", "duration": 1359.573}, {"from": "Assistant", "value": "The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence representation learning method that can integrate with any existing encoder-decoder dialog models for interpretable response generation. Building upon variational autoencoders (VAEs), we present two novel models, DI-VAE and DI-VST that improve VAEs and can discover interpretable semantics via either auto encoding or context predicting. Our methods have been validated on real-world dialog datasets to discover semantic representations and enhance encoder-decoder models with interpretable generation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1102", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1102.flac", "type": "audio", "duration": 1203.093}, {"from": "Assistant", "value": "In conversation, a general response (e.g., “I don’t know”) could correspond to a large variety of input utterances. Previous generative conversational models usually employ a single model to learn the relationship between different utterance-response pairs, thus tend to favor general and trivial responses which appear frequently. To address this problem, we propose a novel controlled response generation mechanism to handle different utterance-response relationships in terms of specificity. Specifically, we introduce an explicit specificity control variable into a sequence-to-sequence model, which interacts with the usage representation of words through a Gaussian Kernel layer, to guide the model to generate responses at different specificity levels. We describe two ways to acquire distant labels for the specificity control variable in learning. Empirical studies show that our model can significantly outperform the state-of-the-art response generation models under both automatic and human evaluations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1103", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1103.flac", "type": "audio", "duration": 1196.16}, {"from": "Assistant", "value": "Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context. In this paper, we investigate matching a response with its multi-turn context using dependency information based entirely on attention. Our solution is inspired by the recently proposed Transformer in machine translation (Vaswani et al., 2017) and we extend the attention mechanism in two ways. First, we construct representations of text segments at different granularities solely with stacked self-attention. Second, we try to extract the truly matched segment pairs with attention across the context and response. We jointly introduce those two kinds of attention in one uniform neural network. Experiments on two large-scale multi-turn response selection tasks show that our proposed model significantly outperforms the state-of-the-art models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1104", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1104.flac", "type": "audio", "duration": 1605.163}, {"from": "Assistant", "value": "Generating emotional language is a key step towards building empathetic natural language processing agents. However, a major challenge for this line of research is the lack of large-scale labeled training data, and previous studies are limited to only small sets of human annotated sentiment labels. Additionally, explicitly controlling the emotion and sentiment of generated text is also difficult. In this paper, we take a more radical approach: we exploit the idea of leveraging Twitter data that are naturally labeled with emojis. We collect a large corpus of Twitter conversations that include emojis in the response and assume the emojis convey the underlying emotions of the sentence. We investigate several conditional variational autoencoders training on these conversations, which allow us to use emojis to control the emotion of the generated text. Experimentally, we show in our quantitative and qualitative analyses that the proposed models can successfully generate high-quality abstractive conversation responses in accordance with designated emotions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1105", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1105.flac", "type": "audio", "duration": 1492.331}, {"from": "Assistant", "value": "Taylor’s law describes the fluctuation characteristics underlying a system in which the variance of an event within a time span grows by a power law with respect to the mean. Although Taylor’s law has been applied in many natural and social systems, its application for language has been scarce. This article describes a new way to quantify Taylor’s law in natural language and conducts Taylor analysis of over 1100 texts across 14 languages. We found that the Taylor exponents of natural language written texts exhibit almost the same value. The exponent was also compared for other language-related data, such as the child-directed speech, music, and programming languages. The results show how the Taylor exponent serves to quantify the fundamental structural complexity underlying linguistic time series. The article also shows the applicability of these findings in evaluating language models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1106", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1106.flac", "type": "audio", "duration": 1388.16}, {"from": "Assistant", "value": "Language variation and change are driven both by individuals’ internal cognitive processes and by the social structures through which language propagates. A wide range of computational frameworks have been proposed to connect these drivers. We compare the strengths and weaknesses of existing approaches and propose a new analytic framework which combines previous network models’ ability to capture realistic social structure with practically and more elegant computational properties. The framework privileges the process of language acquisition and embeds learners in a social network but is modular so that population structure can be combined with different acquisition models. We demonstrate two applications for the framework: a test of practical concerns that arise when modeling acquisition in a population setting and an application of the framework to recent work on phonological mergers in progress.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1107", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1107.flac", "type": "audio", "duration": 1018.411}, {"from": "Assistant", "value": "We show that an epsilon-free, chain-free synchronous context-free grammar (SCFG) can be converted into a weakly equivalent synchronous tree-adjoining grammar (STAG) which is prefix lexicalized. This transformation at most doubles the grammar’s rank and cubes its size, but we show that in practice the size increase is only quadratic. Our results extend Greibach normal form from CFGs to SCFGs and prove new formal properties about SCFG, a formalism with many applications in natural language processing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1108", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1108.flac", "type": "audio", "duration": 1274.325}, {"from": "Assistant", "value": "In this work, we propose a novel constituency parsing scheme. The model first predicts a real-valued scalar, named syntactic distance, for each split position in the sentence. The topology of grammar tree is then determined by the values of syntactic distances. Compared to traditional shift-reduce parsing schemes, our approach is free from the potentially disastrous compounding error. It is also easier to parallelize and much faster. Our model achieves the state-of-the-art single model F1 score of 92.1 on PTB and 86.4 on CTB dataset, which surpasses the previous single model results by a large margin.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1109", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1109.flac", "type": "audio", "duration": 1303.168}, {"from": "Assistant", "value": "We introduce Latent Vector Grammars (LVeGs), a new framework that extends latent variable grammars such that each nonterminal symbol is associated with a continuous vector space representing the set of (infinitely many) subtypes of the nonterminal. We show that previous models such as latent variable grammars and compositional vector grammars can be interpreted as special cases of LVeGs. We then present Gaussian Mixture LVeGs (GM-LVeGs), a new special case of LVeGs that uses Gaussian mixtures to formulate the weights of production rules over subtypes of nonterminals. A major advantage of using Gaussian mixtures is that the partition function and the expectations of subtype rules can be computed using an extension of the inside-outside algorithm, which enables efficient inference and learning. We apply GM-LVeGs to part-of-speech tagging and constituency parsing and show that GM-LVeGs can achieve competitive accuracies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1110", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1110.flac", "type": "audio", "duration": 1272.64}, {"from": "Assistant", "value": "We revisit domain adaptation for parsers in the neural era. First we show that recent advances in word representations greatly diminish the need for domain adaptation when the target domain is syntactically similar to the source domain. As evidence, we train a parser on the Wall Street Journal alone that achieves over 90% F1 on the Brown corpus. For more syntactically distant domains, we provide a simple way to adapt a parser using only dozens of partial annotations. For instance, we increase the percentage of error-free geometry-domain parses in a held-out set from 45% to 73% using approximately five dozen training examples. In the process, we demonstrate a new state-of-the-art single model result on the Wall Street Journal test set of 94.3%. This is an absolute increase of 1.7% over the previous state-of-the-art of 92.6%.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1111", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1111.flac", "type": "audio", "duration": 1266.048}, {"from": "Assistant", "value": "Revealing the implicit semantic relation between the constituents of a noun-compound is important for many NLP applications. It has been addressed in the literature either as a classification task to a set of pre-defined relations or by producing free text paraphrases explicating the relations. Most existing paraphrasing methods lack the ability to generalize, and have a hard time interpreting infrequent or new noun-compounds. We propose a neural model that generalizes better by representing paraphrases in a continuous space, generalizing for both unseen noun-compounds and rare paraphrases. Our model helps improving performance on both the noun-compound paraphrasing and classification tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1112", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1112.flac", "type": "audio", "duration": 1500.011}, {"from": "Assistant", "value": "We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this to be the case indeed. Moreover, based on the discovery of the outsized role that sentiment words play on subjectivity-sensitive tasks such as sentiment classification, we develop a novel word embedding SentiVec which is infused with sentiment information from a lexical resource, and is shown to outperform baselines on such tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1113", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1113.flac", "type": "audio", "duration": 1713.365}, {"from": "Assistant", "value": "Metaphoric expressions are widespread in natural language, posing a significant challenge for various natural language processing tasks such as Machine Translation. Current word embedding based metaphor identification models cannot identify the exact metaphorical words within a sentence. In this paper, we propose an unsupervised learning method that identifies and interprets metaphors at word-level without any preprocessing, outperforming strong baselines in the metaphor identification task. Our model extends to interpret the identified metaphors, paraphrasing them into their literal counterparts, so that they can be better translated by machines. We evaluated this with two popular translation systems for English to Chinese, showing that our model improved the systems significantly.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1114", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1114.flac", "type": "audio", "duration": 1172.779}, {"from": "Assistant", "value": "Traditional word embedding approaches learn semantic information at word level while ignoring the meaningful internal structures of words like morphemes. Furthermore, existing morphology-based models directly incorporate morphemes to train word embeddings, but still neglect the latent meanings of morphemes. In this paper, we explore to employ the latent meanings of morphological compositions of words to train and enhance word embeddings. Based on this purpose, we propose three Latent Meaning Models (LMMs), named LMM-A, LMM-S and LMM-M respectively, which adopt different strategies to incorporate the latent meanings of morphemes during the training process. Experiments on word similarity, syntactic analogy and text classification are conducted to validate the feasibility of our models. The results demonstrate that our models outperform the baselines on five word similarity datasets. On Wordsim-353 and RG-65 datasets, our models nearly achieve 5% and 7% gains over the classic CBOW model, respectively. For the syntactic analogy and text classification tasks, our models also surpass all the baselines including a morphology-based model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1115", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1115.flac", "type": "audio", "duration": 1413.611}, {"from": "Assistant", "value": "The process of translation is ambiguous, in that there are typically many valid translations for a given sentence. This gives rise to significant variation in parallel corpora, however, most current models of machine translation do not account for this variation, instead treating the problem as a deterministic process. To this end, we present a deep generative model of machine translation which incorporates a chain of latent variables, in order to account for local lexical and syntactic variation in parallel corpora. We provide an in-depth analysis of the pitfalls encountered in variational inference for training deep generative models. Experiments on several different language pairs demonstrate that the model consistently improves over strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1116", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1116.flac", "type": "audio", "duration": 1408.128}, {"from": "Assistant", "value": "Tree-based neural machine translation (NMT) approaches, although achieved impressive performance, suffer from a major drawback: they only use the 1-best parse tree to direct the translation, which potentially introduces translation mistakes due to parsing errors. For statistical machine translation (SMT), forest-based methods have been proven to be effective for solving this problem, while for NMT this kind of approach has not been attempted. This paper proposes a forest-based NMT method that translates a linearized packed forest under a simple sequence-to-sequence framework (i.e., a forest-to-sequence NMT model). The BLEU score of the proposed method is higher than that of the sequence-to-sequence NMT, tree-based NMT, and forest-based SMT systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1117", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1117.flac", "type": "audio", "duration": 1404.011}, {"from": "Assistant", "value": "Standard machine translation systems process sentences in isolation and hence ignore extra-sentential information, even though extended context can both prevent mistakes in ambiguous cases and improve translation coherence. We introduce a context-aware neural machine translation model designed in such way that the flow of information from the extended context to the translation model can be controlled and analyzed. We experiment with an English-Russian subtitles dataset, and observe that much of what is captured by our model deals with improving pronoun translation. We measure correspondences between induced attention distributions and coreference relations and observe that the model implicitly captures anaphora. It is consistent with gains for sentences where pronouns need to be gendered in translation. Beside improvements in anaphoric cases, the model also improves in overall BLEU, both over its context-agnostic version (+0.7) and over simple concatenation of the context and source sentences (+0.6).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1118", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1118.flac", "type": "audio", "duration": 1119.616}, {"from": "Assistant", "value": "We present a document-level neural machine translation model which takes both source and target document context into account using memory networks. We model the problem as a structured prediction problem with interdependencies among the observed and hidden variables, i.e., the source sentences and their unobserved target translations in the document. The resulting structured prediction problem is tackled with a neural translation model equipped with two memory components, one each for the source and target side, to capture the documental interdependencies. We train the model end-to-end, and propose an iterative decoding algorithm based on block coordinate descent. Experimental results of English translations from French, German, and Estonian documents show that our model is effective in exploiting both source and target document context, and statistically significantly outperforms the previous work in terms of BLEU and METEOR.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1119", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1119.flac", "type": "audio", "duration": 1207.403}, {"from": "Assistant", "value": "The purpose of text geolocation is to associate geographic information contained in a document with a set (or sets) of coordinates, either implicitly by using linguistic features and/or explicitly by using geographic metadata combined with heuristics. We introduce a geocoder (location mention disambiguator) that achieves state-of-the-art (SOTA) results on three diverse datasets by exploiting the implicit lexical clues. Moreover, we propose a new method for systematic encoding of geographic metadata to generate two distinct views of the same text. To that end, we introduce the Map Vector (MapVec), a sparse representation obtained by plotting prior geographic probabilities, derived from population figures, on a World Map. We then integrate the implicit (language) and explicit (map) features to significantly improve a range of metrics. We also introduce an open-source dataset for geoparsing of news events covering global disease outbreaks and epidemics to help future evaluation in geoparsing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1120", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1120.flac", "type": "audio", "duration": 1379.093}, {"from": "Assistant", "value": "People go to different places to engage in activities that reflect their goals. For example, people go to restaurants to eat, libraries to study, and churches to pray. We refer to an activity that represents a common reason why people typically go to a location as a prototypical goal activity (goal-act). Our research aims to learn goal-acts for specific locations using a text corpus and semi-supervised learning. First, we extract activities and locations that co-occur in goal-oriented syntactic patterns. Next, we create an activity profile matrix and apply a semi-supervised label propagation algorithm to iteratively revise the activity strengths for different locations using a small set of labeled data. We show that this approach outperforms several baseline methods when judged against goal-acts identified by human annotators.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1121", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1121.flac", "type": "audio", "duration": 1221.931}, {"from": "Assistant", "value": "Acronyms are abbreviations formed from the initial components of words or phrases. In enterprises, people often use acronyms to make communications more efficient. However, acronyms could be difficult to understand for people who are not familiar with the subject matter (new employees, etc.), thereby affecting productivity. To alleviate such troubles, we study how to automatically resolve the true meanings of acronyms in a given context. Acronym disambiguation for enterprises is challenging for several reasons. First, acronyms may be highly ambiguous since an acronym used in the enterprise could have multiple internal and external meanings. Second, there are usually no comprehensive knowledge bases such as Wikipedia available in enterprises. Finally, the system should be generic to work for any enterprise. In this work we propose an end-to-end framework to tackle all these challenges. The framework takes the enterprise corpus as input and produces a high-quality acronym disambiguation system as output. Our disambiguation models are trained via distant supervised learning, without requiring any manually labeled training examples. Therefore, our proposed framework can be deployed to any enterprise to support high-quality acronym disambiguation. Experimental results on real world data justified the effectiveness of our system.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1122", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1122.flac", "type": "audio", "duration": 1338.731}, {"from": "Assistant", "value": "Existing temporal relation (TempRel) annotation schemes often have low inter-annotator agreements (IAA) even between experts, suggesting that the current annotation task needs a better definition. This paper proposes a new multi-axis modeling to better capture the temporal structure of events. In addition, we identify that event end-points are a major source of confusion in annotation, so we also propose to annotate TempRels based on start-points only. A pilot expert annotation effort using the proposed scheme shows significant improvement in IAA from the conventional 60’s to 80’s (Cohen’s Kappa). This better-defined annotation scheme further enables the use of crowdsourcing to alleviate the labor intensity for each annotator. We hope that this work can foster more interesting studies towards event understanding.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1123", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1123.flac", "type": "audio", "duration": 1280.363}, {"from": "Assistant", "value": "In this paper we present the Exemplar Encoder-Decoder network (EED), a novel conversation model that learns to utilizesimilarexamples from training data to generate responses. Similar conversation examples (context-response pairs) from training data are retrieved using a traditional TF-IDF based retrieval model and the corresponding responses are used by our decoder to generate the ground truth response. The contribution of each retrieved response is weighed by the similarity of corresponding context with the input context. As a result, our model learns to assign higher similarity scores to those retrieved contexts whose responses are crucial for generating the final response. We present detailed experiments on two large data sets and we find that our method out-performs state of the art sequence to sequence generative models on several recently proposed evaluation metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1124", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1124.flac", "type": "audio", "duration": 1212.608}, {"from": "Assistant", "value": "The recent advance in deep learning and semantic parsing has significantly improved the translation accuracy of natural language questions to structured queries. However, further improvement of the existing approaches turns out to be quite challenging. Rather than solely relying on algorithmic innovations, in this work, we introduce DialSQL, a dialogue-based structured query generation framework that leverages human intelligence to boost the performance of existing algorithms via user interaction. DialSQL is capable of identifying potential errors in a generated SQL query and asking users for validation via simple multi-choice questions. User feedback is then leveraged to revise the query. We design a generic simulator to bootstrap synthetic training dialogues and evaluate the performance of DialSQL on the WikiSQL dataset. Using SQLNet as a black box query generation tool, DialSQL improves its performance from 61.3% to 69.0% using only 2.4 validation questions per dialogue.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1125", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1125.flac", "type": "audio", "duration": 1516.693}, {"from": "Assistant", "value": "One of the main challenges online social systems face is the prevalence of antisocial behavior, such as harassment and personal attacks. In this work, we introduce the task of predicting from the very start of a conversation whether it will get out of hand. As opposed to detecting undesirable behavior after the fact, this task aims to enable early, actionable prediction at a time when the conversation might still be salvaged. To this end, we develop a framework for capturing pragmatic devices—such as politeness strategies and rhetorical prompts—used to start a conversation, and analyze their relation to its future trajectory. Applying this framework in a controlled setting, we demonstrate the feasibility of detecting early warning signs of antisocial behavior in online discussions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1126", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1126.flac", "type": "audio", "duration": 1286.613}, {"from": "Assistant", "value": "One of possible ways of obtaining continuous-space sentence representations is by training neural machine translation (NMT) systems. The recent attention mechanism however removes the single point in the neural network from which the source sentence representation can be extracted. We propose several variations of the attentive NMT architecture bringing this meeting point back. Empirical evaluation suggests that the better the translation quality, the worse the learned sentence representations serve in a wide range of classification and similarity tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1127", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1127.flac", "type": "audio", "duration": 1330.283}, {"from": "Assistant", "value": "Metric validation in Grammatical Error Correction (GEC) is currently done by observing the correlation between human and metric-induced rankings. However, such correlation studies are costly, methodologically troublesome, and suffer from low inter-rater agreement. We propose MAEGE, an automatic methodology for GEC metric validation, that overcomes many of the difficulties in the existing methodology. Experiments with MAEGE shed a new light on metric quality, showing for example that the standardM2metric fares poorly on corpus-level ranking. Moreover, we use MAEGE to perform a detailed analysis of metric behavior, showing that some types of valid edits are consistently penalized by existing metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1128", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1128.flac", "type": "audio", "duration": 1440.405}, {"from": "Assistant", "value": "Statistical significance testing is a standard statistical tool designed to ensure that experimental results are not coincidental. In this opinion/ theoretical paper we discuss the role of statistical significance testing in Natural Language Processing (NLP) research. We establish the fundamental concepts of significance testing and discuss the specific aspects of NLP tasks, experimental setups and evaluation measures that affect the choice of significance tests in NLP research. Based on this discussion we propose a simple practical protocol for statistical significance test selection in NLP setups and accompany this protocol with a brief survey of the most relevant tests. We then survey recent empirical papers published in ACL and TACL during 2017 and show that while our community assigns great value to experimental results, statistical significance testing is often ignored or misused. We conclude with a brief discussion of open issues that should be properly addressed so that this important tool can be applied. in NLP research in a statistically sound manner.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1129", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1129.flac", "type": "audio", "duration": 1413.483}, {"from": "Assistant", "value": "Many natural language processing tasks can be modeled into structured prediction and solved as a search problem. In this paper, we distill an ensemble of multiple models trained with different initialization into a single model. In addition to learning to match the ensemble’s probability output on the reference states, we also use the ensemble to explore the search space and learn from the encountered states in the exploration. Experimental results on two typical search-based structured prediction tasks – transition-based dependency parsing and neural machine translation show that distillation can effectively improve the single model’s performance and the final model achieves improvements of 1.32 in LAS and 2.65 in BLEU score on these two tasks respectively over strong baselines and it outperforms the greedy structured prediction models in previous literatures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1130", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1130.flac", "type": "audio", "duration": 1511.808}, {"from": "Assistant", "value": "We introduce a novel architecture for dependency parsing: stack-pointer networks (StackPtr). Combining pointer networks (Vinyals et al., 2015) with an internal stack, the proposed model first reads and encodes the whole sentence, then builds the dependency tree top-down (from root-to-leaf) in a depth-first fashion. The stack tracks the status of the depth-first search and the pointer networks select one child for the word at the top of the stack at each step. The StackPtr parser benefits from the information of whole sentence and all previously derived subtree structures, and removes the left-to-right restriction in classical transition-based parsers. Yet the number of steps for building any (non-projective) parse tree is linear in the length of the sentence just as other transition-based parsers, yielding an efficient decoding algorithm withO(n2)time complexity. We evaluate our model on 29 treebanks spanning 20 languages and different dependency annotation schemas, and achieve state-of-the-art performances on 21 of them", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1131", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1131.flac", "type": "audio", "duration": 1443.2}, {"from": "Assistant", "value": "Due to the presence of both Twitter-specific conventions and non-standard and dialectal language, Twitter presents a significant parsing challenge to current dependency parsing tools. We broaden English dependency parsing to handle social media English, particularly social media African-American English (AAE), by developing and annotating a new dataset of 500 tweets, 250 of which are in AAE, within the Universal Dependencies 2.0 framework. We describe our standards for handling Twitter- and AAE-specific features and evaluate a variety of cross-domain strategies for improving parsing with no, or very little, in-domain labeled data, including a new data synthesis approach. We analyze these methods’ impact on performance disparities between AAE and Mainstream American English tweets, and assess parsing accuracy for specific AAE lexical and syntactic features. Our annotated data and a parsing model are available at:http://slanglab.cs.umass.edu/TwitterAAE/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1132", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1132.flac", "type": "audio", "duration": 1515.925}, {"from": "Assistant", "value": "Language exhibits hierarchical structure, but recent work using a subject-verb agreement diagnostic argued that state-of-the-art language models, LSTMs, fail to learn long-range syntax sensitive dependencies. Using the same diagnostic, we show that, in fact, LSTMs do succeed in learning such dependencies—provided they have enough capacity. We then explore whether models that have access to explicit syntactic information learn agreement more effectively, and how the way in which this structural information is incorporated into the model impacts performance. We find that the mere presence of syntactic information does not improve accuracy, but when model architecture is determined by syntax, number agreement is improved. Further, we find that the choice of how syntactic structure is built affects how well number agreement is learned: top-down construction outperforms left-corner and bottom-up variants in capturing non-local structural dependencies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1168", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1168.flac", "type": "audio", "duration": 1463.339}, {"from": "Assistant", "value": "Training semantic parsers from weak supervision (denotations) rather than strong supervision (programs) complicates training in two ways. First, a large search space of potential programs needs to be explored at training time to find a correct program. Second, spurious programs that accidentally lead to a correct denotation add noise to training. In this work we propose that in closed worlds with clear semantic types, one can substantially alleviate these problems by utilizing an abstract representation, where tokens in both the language utterance and program are lifted to an abstract form. We show that these abstractions can be defined with a handful of lexical rules and that they result in sharing between different examples that alleviates the difficulties in training. To test our approach, we develop the first semantic parser for CNLVR, a challenging visual reasoning dataset, where the search space is large and overcoming spuriousness is critical, because denotations are either TRUE or FALSE, and thus random programs are likely to lead to a correct denotation. Our method substantially improves performance, and reaches 82.5% accuracy, a 14.7% absolute accuracy improvement compared to the best reported accuracy so far.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1169", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1169.flac", "type": "audio", "duration": 1235.371}, {"from": "Assistant", "value": "Counterfactual learning from human bandit feedback describes a scenario where user feedback on the quality of outputs of a historic system is logged and used to improve a target system. We show how to apply this learning framework to neural semantic parsing. From a machine learning perspective, the key challenge lies in a proper reweighting of the estimator so as to avoid known degeneracies in counterfactual learning, while still being applicable to stochastic gradient optimization. To conduct experiments with human users, we devise an easy-to-use interface to collect human feedback on semantic parses. Our work is the first to show that semantic parsers can be improved significantly by counterfactual learning from logged human feedback data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1170", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1170.flac", "type": "audio", "duration": 1518.763}, {"from": "Assistant", "value": "We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system. We present two approximative decoding algorithms, which achieve state-of-the-art accuracy and outperform strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1171", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1171.flac", "type": "audio", "duration": 1077.973}, {"from": "Assistant", "value": "In this paper, we present a sequence-to-sequence based approach for mapping natural language sentences to AMR semantic graphs. We transform the sequence to graph mapping problem to a word sequence to transition action sequence problem using a special transition system called a cache transition system. To address the sparsity issue of neural AMR parsing, we feed feature embeddings from the transition state to provide relevant local information for each decoder state. We present a monotonic hard attention model for the transition framework to handle the strictly left-to-right alignment between each transition state and the current buffer input focus. We evaluate our neural transition model on the AMR parsing task, and our parser outperforms other sequence-to-sequence approaches and achieves competitive results in comparison with the best-performing models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1172", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1172.flac", "type": "audio", "duration": 1560.768}, {"from": "Assistant", "value": "Stochastic Gradient Descent (SGD) with negative sampling is the most prevalent approach to learn word representations. However, it is known that sampling methods are biased especially when the sampling distribution deviates from the true data distribution. Besides, SGD suffers from dramatic fluctuation due to the one-sample learning scheme. In this work, we propose AllVec that uses batch gradient learning to generate word representations from all training samples. Remarkably, the time complexity of AllVec remains at the same level as SGD, being determined by the number of positive samples rather than all samples. We evaluate AllVec on several benchmark tasks. Experiments show that AllVec outperforms sampling-based SGD methods with comparable efficiency, especially for small training corpora.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1173", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1173.flac", "type": "audio", "duration": 1470.699}, {"from": "Assistant", "value": "We introduce structured projection of intermediate gradients (SPIGOT), a new method for backpropagating through neural networks that include hard-decision structured predictions (e.g., parsing) in intermediate layers. SPIGOT requires no marginal inference, unlike structured attention networks and reinforcement learning-inspired solutions. Like so-called straight-through estimators, SPIGOT defines gradient-like quantities associated with intermediate nondifferentiable operations, allowing backpropagation before and after them; SPIGOT’s proxy aims to ensure that, after a parameter update, the intermediate structure will remain well-formed. We experiment on two structured NLP pipelines: syntactic-then-semantic dependency parsing, and semantic parsing followed by sentiment classification. We show that training with SPIGOT leads to a larger improvement on the downstream task than a modularly-trained pipeline, the straight-through estimator, and structured attention, reaching a new state of the art on semantic dependency parsing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1174", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1174.flac", "type": "audio", "duration": 1374.443}, {"from": "Assistant", "value": "Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary. We introduce a method that learns an AL “policy” using “imitation learning” (IL). Our IL-based approach makes use of an efficient and effective “algorithmic expert”, which provides the policy learner with good actions in the encountered AL situations. The AL strategy is then learned with a feedforward network, mapping situations to most informative query datapoints. We evaluate our method on two different tasks: text classification and named entity recognition. Experimental results show that our IL-based AL strategy is more effective than strong previous methods using heuristics and reinforcement learning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1175", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1175.flac", "type": "audio", "duration": 1617.259}, {"from": "Assistant", "value": "Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. A semantic parser converts these explanations into programmatic labeling functions that generate noisy labels for an arbitrary amount of unlabeled data, which is used to train a classifier. On three relation extraction tasks, we find that users are able to train classifiers with comparable F1 scores from 5-100 faster by providing explanations instead of just labels. Furthermore, given the inherent imperfection of labeling functions, we find that a simple rule-based semantic parser suffices.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1176", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1176.flac", "type": "audio", "duration": 1169.173}, {"from": "Assistant", "value": "We analyze state-of-the-art deep learning models for three tasks: question answering on (1) images, (2) tables, and (3) passages of text. Using the notion of “attribution” (word importance), we find that these deep networks often ignore important question terms. Leveraging such behavior, we perturb questions to craft a variety of adversarial examples. Our strongest attacks drop the accuracy of a visual question answering model from 61.1% to 19%, and that of a tabular question answering model from 33.5% to 3.3%. Additionally, we show how attributions can strengthen attacks proposed by Jia and Liang (2017) on paragraph comprehension models. Our results demonstrate that attributions can augment standard measures of accuracy and empower investigation of model performance. When a model is accurate but for the wrong reasons, attributions can surface erroneous logic in the model that indicates inadequacies in the test data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1177", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1177.flac", "type": "audio", "duration": 1574.699}, {"from": "Assistant", "value": "We study the task of generating from Wikipedia articles question-answer pairs that cover content beyond a single sentence. We propose a neural network approach that incorporates coreference knowledge via a novel gating mechanism. As compared to models that only take into account sentence-level information (Heilman and Smith, 2010; Du et al., 2017; Zhou et al., 2017), we find that the linguistic knowledge introduced by the coreference representation aids question generation significantly, producing models that outperform the current state-of-the-art. We apply our system (composed of an answer span extraction system and the passage-level QG system) to the 10,000 top ranking Wikipedia articles and create a corpus of over one million question-answer pairs. We provide qualitative analysis for the this large-scale generated corpus from Wikipedia.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1178", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1178.flac", "type": "audio", "duration": 1366.016}, {"from": "Assistant", "value": "Machine reading comprehension (MRC) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine. Compared with MRC on a single passage, multi-passage MRC is more challenging, since we are likely to get multiple confusing answer candidates from different passages. To address this problem, we propose an end-to-end neural model that enables those answer candidates from different passages to verify each other based on their content representations. Specifically, we jointly train three modules that can predict the final answer based on three factors: the answer boundary, the answer content and the cross-passage answer verification. The experimental results show that our method outperforms the baseline by a large margin and achieves the state-of-the-art performance on the English MS-MARCO dataset and the Chinese DuReader dataset, both of which are designed for MRC in real-world settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1179", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1179.flac", "type": "audio", "duration": 1469.803}, {"from": "Assistant", "value": "A DAG automaton is a formal device for manipulating graphs. By augmenting a DAG automaton with transduction rules, a DAG transducer has potential applications in fundamental NLP tasks. In this paper, we propose a novel DAG transducer to perform graph-to-program transformation. The target structure of our transducer is a program licensed by a declarative programming language rather than linguistic structures. By executing such a program, we can easily get a surface string. Our transducer is designed especially for natural language generation (NLG) from type-logical semantic graphs. Taking Elementary Dependency Structures, a format of English Resource Semantics, as input, our NLG system achieves a BLEU-4 score of 68.07. This remarkable result demonstrates the feasibility of applying a DAG transducer to resolve NLG, as well as the effectiveness of our design.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1180", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1180.flac", "type": "audio", "duration": 1523.648}, {"from": "Assistant", "value": "Modeling derivational morphology to generate words with particular semantics is useful in many text generation tasks, such as machine translation or abstractive question answering. In this work, we tackle the task of derived word generation. That is, we attempt to generate the word “runner” for “someone who runs.” We identify two key problems in generating derived words from root words and transformations. We contribute a novel aggregation model of derived word generation that learns derivational transformations both as orthographic functions using sequence-to-sequence models and as functions in distributional word embedding space. The model then learns to choose between the hypothesis of each system. We also present two ways of incorporating corpus information into derived word generation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1181", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1181.flac", "type": "audio", "duration": 1387.563}, {"from": "Assistant", "value": "In this paper, we propose a joint architecture that captures language, rhyme and meter for sonnet modelling. We assess the quality of generated poems using crowd and expert judgements. The stress and rhyme models perform very well, as generated poems are largely indistinguishable from human-written poems. Expert evaluation, however, reveals that a vanilla language model captures meter implicitly, and that machine-generated poems still underperform in terms of readability and emotion. Our research shows the importance expert evaluation for poetry generation, and that future research should look beyond rhyme/meter and focus on poetic language.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1182", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1182.flac", "type": "audio", "duration": 1317.376}, {"from": "Assistant", "value": "Traditionally, Referring Expression Generation (REG) models first decide on the form and then on the content of references to discourse entities in text, typically relying on features such as salience and grammatical function. In this paper, we present a new approach (NeuralREG), relying on deep neural networks, which makes decisions about form and content in one go without explicit feature extraction. Using a delexicalized version of the WebNLG corpus, we show that the neural model substantially improves over two strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1183", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1183.flac", "type": "audio", "duration": 1339.413}, {"from": "Assistant", "value": "Stock movement prediction is a challenging problem: the market is highly stochastic, and we make temporally-dependent predictions from chaotic data. We treat these three complexities and present a novel deep generative model jointly exploiting text and price signals for this task. Unlike the case with discriminative or topic modeling, our model introduces recurrent, continuous latent variables for a better treatment of stochasticity, and uses neural variational inference to address the intractable posterior inference. We also provide a hybrid objective with temporal auxiliary to flexibly capture predictive dependencies. We demonstrate the state-of-the-art performance of our proposed model on a new stock movement prediction dataset which we collected.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1184", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1184.flac", "type": "audio", "duration": 1442.603}, {"from": "Assistant", "value": "Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based on a bottom-up and a top-down tree-structured neural networks for rumor representation learning and classification, which naturally conform to the propagation layout of tweets. Results on two public Twitter datasets demonstrate that our recursive neural models 1) achieve much better performance than state-of-the-art approaches; 2) demonstrate superior capacity on detecting rumors at very early stage.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1185", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1185.flac", "type": "audio", "duration": 1105.685}, {"from": "Assistant", "value": "Everyday billions of multimodal posts containing both images and text are shared in social media sites such as Snapchat, Twitter or Instagram. This combination of image and text in a single message allows for more creative and expressive forms of communication, and has become increasingly common in such sites. This new paradigm brings new challenges for natural language understanding, as the textual component tends to be shorter, more informal, and often is only understood if combined with the visual context. In this paper, we explore the task of name tagging in multimodal social media posts. We start by creating two new multimodal datasets: the first based on Twitter posts and the second based on Snapchat captions (exclusively submitted to public and crowd-sourced stories). We then propose a novel model architecture based on Visual Attention that not only provides deeper visual understanding on the decisions of the model, but also significantly outperforms other state-of-the-art baseline methods for this task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1186", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1186.flac", "type": "audio", "duration": 1045.525}, {"from": "Assistant", "value": "We introduce the new Multimodal Named Entity Disambiguation (MNED) task for multimodal social media posts such as Snapchat or Instagram captions, which are composed of short captions with accompanying images. Social media posts bring significant challenges for disambiguation tasks because 1) ambiguity not only comes from polysemous entities, but also from inconsistent or incomplete notations, 2) very limited context is provided with surrounding words, and 3) there are many emerging entities often unseen during training. To this end, we build a new dataset called SnapCaptionsKB, a collection of Snapchat image captions submitted to public and crowd-sourced stories, with named entity mentions fully annotated and linked to entities in an external knowledge base. We then build a deep zeroshot multimodal network for MNED that 1) extracts contexts from both text and image, and 2) predicts correct entity in the knowledge graph embeddings space, allowing for zeroshot disambiguation of entities unseen in training set as well. The proposed model significantly outperforms the state-of-the-art text-only NED models, showing efficacy and potentials of the MNED task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1187", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1187.flac", "type": "audio", "duration": 1345.536}, {"from": "Assistant", "value": "Social media user geolocation is vital to many applications such as event detection. In this paper, we propose GCN, a multiview geolocation model based on Graph Convolutional Networks, that uses both text and network context. We compare GCN to the state-of-the-art, and to two baselines we propose, and show that our model achieves or is competitive with the state-of-the-art over three benchmark geolocation datasets when sufficient supervision is available. We also evaluate GCN under a minimal supervision scenario, and show it outperforms baselines. We find that highway network gates are essential for controlling the amount of useful neighbourhood expansion in GCN.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1188", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1188.flac", "type": "audio", "duration": 1192.576}, {"from": "Assistant", "value": "Document modeling is essential to a variety of natural language understanding tasks. We propose to use external information to improve document modeling for problems that can be framed as sentence extraction. We develop a framework composed of a hierarchical document encoder and an attention-based extractor with attention over external information. We evaluate our model on extractive document summarization (where the external information is image captions and the title of the document) and answer selection (where the external information is a question). We show that our model consistently outperforms strong baselines, in terms of both informativeness and fluency (for CNN document summarization) and achieves state-of-the-art results for answer selection on WikiQA and NewsQA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1189", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1189.flac", "type": "audio", "duration": 1265.408}, {"from": "Assistant", "value": "Most real-world document collections involve various types of metadata, such as author, source, and date, and yet the most commonly-used approaches to modeling text corpora ignore this information. While specialized models have been developed for particular applications, few are widely used in practice, as customization typically requires derivation of a custom inference algorithm. In this paper, we build on recent advances in variational inference methods and propose a general neural framework, based on topic models, to enable flexible incorporation of metadata and allow for rapid exploration of alternative models. Our approach achieves strong performance, with a manageable tradeoff between perplexity, coherence, and sparsity. Finally, we demonstrate the potential of our framework through an exploration of a corpus of articles about US immigration.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1190", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1190.flac", "type": "audio", "duration": 1746.539}, {"from": "Assistant", "value": "Semantic hashing has become a powerful paradigm for fast similarity search in many information retrieval systems. While fairly successful, previous techniques generally require two-stage training, and the binary constraints are handledad-hoc. In this paper, we present anend-to-endNeural Architecture for Semantic Hashing (NASH), where the binary hashing codes are treated asBernoullilatent variables. A neural variational inference framework is proposed for training, where gradients are directly backpropagated through the discrete latent variable to optimize the hash function. We also draw the connections between proposed method andrate-distortion theory, which provides a theoretical foundation for the effectiveness of our framework. Experimental results on three public datasets demonstrate that our method significantly outperforms several state-of-the-art models on bothunsupervisedandsupervisedscenarios.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1191", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1191.flac", "type": "audio", "duration": 1398.848}, {"from": "Assistant", "value": "We present a new large-scale corpus of Question-Answer driven Semantic Role Labeling (QA-SRL) annotations, and the first high-quality QA-SRL parser. Our corpus, QA-SRL Bank 2.0, consists of over 250,000 question-answer pairs for over 64,000 sentences across 3 domains and was gathered with a new crowd-sourcing scheme that we show has high precision and good recall at modest cost. We also present neural models for two QA-SRL subtasks: detecting argument spans for a predicate and generating questions to label the semantic relationship. The best models achieve question accuracy of 82.6% and span-level accuracy of 77.6% (under human evaluation) on the full pipelined QA-SRL prediction task. They can also, as we show, be used to gather additional annotations at low cost.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1192", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1192.flac", "type": "audio", "duration": 1249.643}, {"from": "Assistant", "value": "Semantic role labeling (SRL) is dedicated to recognizing the predicate-argument structure of a sentence. Previous studies have shown syntactic information has a remarkable contribution to SRL performance. However, such perception was challenged by a few recent neural SRL models which give impressive performance without a syntactic backbone. This paper intends to quantify the importance of syntactic information to dependency SRL in deep learning framework. We propose an enhanced argument labeling model companying with an extended korder argument pruning algorithm for effectively exploiting syntactic information. Our model achieves state-of-the-art results on the CoNLL-2008, 2009 benchmarks for both English and Chinese, showing the quantitative significance of syntax to neural SRL together with a thorough empirical survey over existing models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1193", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1193.flac", "type": "audio", "duration": 1368.725}, {"from": "Assistant", "value": "We propose a learning approach for mapping context-dependent sequential instructions to actions. We address the problem of discourse and state dependencies with an attention-based model that considers both the history of the interaction and the state of the world. To train from start and goal states without access to demonstrations, we propose SESTRA, a learning algorithm that takes advantage of single-step reward observations and immediate expected reward maximization. We evaluate on the SCONE domains, and show absolute accuracy improvements of 9.8%-25.3% across the domains over approaches that use high-level logical representations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1194", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1194.flac", "type": "audio", "duration": 1389.973}, {"from": "Assistant", "value": "The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: “Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?”. In answer, we develop novel methods to exploit the rich expressiveness of REs at different levels within a NN, showing that the combination significantly enhances the learning effectiveness when a small number of training examples are available. We evaluate our approach by applying it to spoken language understanding for intent detection and slot filling. Experimental results show that our approach is highly effective in exploiting the available training data, giving a clear boost to the RE-unaware NN.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1195", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1195.flac", "type": "audio", "duration": 1356.928}, {"from": "Assistant", "value": "Despite the effectiveness of recurrent neural network language models, their maximum likelihood estimation suffers from two limitations. It treats all sentences that do not match the ground truth as equally poor, ignoring the structure of the output space. Second, it suffers from ’exposure bias’: during training tokens are predicted given ground-truth sequences, while at test time prediction is conditioned on generated output sequences. To overcome these limitations we build upon the recent reward augmented maximum likelihood approach that encourages the model to predict sentences that are close to the ground truth according to a given performance metric. We extend this approach to token-level loss smoothing, and propose improvements to the sequence-level smoothing approach. Our experiments on two different tasks, image captioning and machine translation, show that token-level and sequence-level loss smoothing are complementary, and significantly improve results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1196", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1196.flac", "type": "audio", "duration": 1438.763}, {"from": "Assistant", "value": "Numeracy is the ability to understand and work with numbers. It is a necessary skill for composing and understanding documents in clinical, scientific, and other technical domains. In this paper, we explore different strategies for modelling numerals with language models, such as memorisation and digit-by-digit composition, and propose a novel neural architecture that uses a continuous probability density function to model numerals from an open vocabulary. Our evaluation on clinical and scientific datasets shows that using hierarchical models to distinguish numerals from words improves a perplexity metric on the subset of numerals by 2 and 4 orders of magnitude, respectively, over non-hierarchical models. A combination of strategies can further improve perplexity. Our continuous probability density function model reduces mean absolute percentage errors by 18% and 54% in comparison to the second best strategy for each dataset, respectively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1197", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1197.flac", "type": "audio", "duration": 1217.813}, {"from": "Assistant", "value": "With the recent success of Recurrent Neural Networks (RNNs) in Machine Translation (MT), attention mechanisms have become increasingly popular. The purpose of this paper is two-fold; firstly, we propose a novel attention model on Tree Long Short-Term Memory Networks (Tree-LSTMs), a tree-structured generalization of standard LSTM. Secondly, we study the interaction between attention and syntactic structures, by experimenting with three LSTM variants: bidirectional-LSTMs, Constituency Tree-LSTMs, and Dependency Tree-LSTMs. Our models are evaluated on two semantic relatedness tasks: semantic relatedness scoring for sentence pairs (SemEval 2012, Task 6 and SemEval 2014, Task 1) and paraphrase detection for question pairs (Quora, 2017).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1198", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1198.flac", "type": "audio", "duration": 1582.699}, {"from": "Assistant", "value": "Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing. “Downstream” tasks, often based on sentence classification, are commonly used to evaluate the quality of sentence representations. The complexity of the tasks makes it however difficult to infer what kind of information is present in the representations. We introduce here 10 probing tasks designed to capture simple linguistic features of sentences, and we use them to study embeddings generated by three different encoders trained in eight distinct ways, uncovering intriguing properties of both encoders and training methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1199", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1199.flac", "type": "audio", "duration": 1319.083}, {"from": "Assistant", "value": "Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost—The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-the-art approaches focus on selecting one-best sentence or calculating soft attention weights over the set of the sentences of one specific entity pair. However, these methods are suboptimal, and the false positive problem is still a key stumbling bottleneck for the performance. We argue that those incorrectly-labeled candidate sentences must be treated with a hard decision, rather than being dealt with soft attention weights. To do this, our paper describes a radical solution—We explore a deep reinforcement learning strategy to generate the false-positive indicator, where we automatically recognize false positives for each relation type without any supervised information. Unlike the removal operation in the previous studies, we redistribute them into the negative examples. The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1200", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1200.flac", "type": "audio", "duration": 1288.256}, {"from": "Assistant", "value": "Embedding models for entities and relations are extremely useful for recovering missing facts in a knowledge base. Intuitively, a relation can be modeled by a matrix mapping entity vectors. However, relations reside on low dimension sub-manifolds in the parameter space of arbitrary matrices – for one reason, composition of two relations M1, M2 may match a third M3 (e.g. composition of relations currency_of_country and country_of_film usually matches currency_of_film_budget), which imposes compositional constraints to be satisfied by the parameters (i.e. M1*M2=M3). In this paper we investigate a dimension reduction technique by training relations jointly with an autoencoder, which is expected to better capture compositional constraints. We achieve state-of-the-art on Knowledge Base Completion tasks with strongly improved Mean Rank, and show that joint training with an autoencoder leads to interpretable sparse codings of relations, helps discovering compositional constraints and benefits from compositional training. Our source code is released atgithub.com/tianran/glimvec.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1201", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1201.flac", "type": "audio", "duration": 1520.171}, {"from": "Assistant", "value": "Most previous supervised event extraction methods have relied on features derived from manual annotations, and thus cannot be applied to new event types without extra annotation effort. We take a fresh look at event extraction and model it as a generic grounding problem: mapping each event mention to a specific type in a target event ontology. We design a transferable architecture of structural and compositional neural networks to jointly represent and map event mentions and types into a shared semantic space. Based on this new framework, we can select, for each event mention, the event type which is semantically closest in this space as its type. By leveraging manual annotations available for a small set of existing event types, our framework can be applied to new unseen event types without additional manual annotations. When tested on 23 unseen event types, our zero-shot framework, without manual annotations, achieved performance comparable to a supervised model trained from 3,000 sentences annotated with 500 event mentions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1202", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1202.flac", "type": "audio", "duration": 1280.896}, {"from": "Assistant", "value": "Fine-grained opinion analysis aims to extract aspect and opinion terms from each sentence for opinion summarization. Supervised learning methods have proven to be effective for this task. However, in many domains, the lack of labeled data hinders the learning of a precise extraction model. In this case, unsupervised domain adaptation methods are desired to transfer knowledge from the source domain to any unlabeled target domain. In this paper, we develop a novel recursive neural network that could reduce domain shift effectively in word level through syntactic relations. We treat these relations as invariant “pivot information” across domains to build structural correspondences and generate an auxiliary task to predict the relation between any two adjacent words in the dependency tree. In the end, we demonstrate state-of-the-art results on three benchmark datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1203", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1203.flac", "type": "audio", "duration": 1413.483}, {"from": "Assistant", "value": "Training a task-completion dialogue agent via reinforcement learning (RL) is costly because it requires many interactions with real users. One common alternative is to use a user simulator. However, a user simulator usually lacks the language complexity of human interlocutors and the biases in its design may tend to degrade the agent. To address these issues, we present Deep Dyna-Q, which to our knowledge is the first deep RL framework that integrates planning for task-completion dialogue policy learning. We incorporate into the dialogue agent a model of the environment, referred to as the world model, to mimic real user response and generate simulated experience. During dialogue policy learning, the world model is constantly updated with real user experience to approach real user behavior, and in turn, the dialogue agent is optimized using both real experience and simulated experience. The effectiveness of our approach is demonstrated on a movie-ticket booking task in both simulated and human-in-the-loop settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1204", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1204.flac", "type": "audio", "duration": 1300.8}, {"from": "Assistant", "value": "Asking good questions in open-domain conversational systems is quite significant but rather untouched. This task, substantially different from traditional question generation, requires to question not only with various patterns but also on diverse and relevant topics. We observe that a good question is a natural composition of interrogatives, topic words, and ordinary words. Interrogatives lexicalize the pattern of questioning, topic words address the key information for topic transition in dialogue, and ordinary words play syntactical and grammatical roles in making a natural sentence. We devise two typed decoders (soft typed decoder and hard typed decoder) in which a type distribution over the three types is estimated and the type distribution is used to modulate the final generation distribution. Extensive experiments show that the typed decoders outperform state-of-the-art baselines and can generate more meaningful questions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1205", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1205.flac", "type": "audio", "duration": 1631.083}, {"from": "Assistant", "value": "Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of making chit-chat more engaging by conditioning on profile information. We collect data and train models to (i)condition on their given profile information; and (ii) information about the person they are talking to, resulting in improved dialogues, as measured by next utterance prediction. Since (ii) is initially unknown our model is trained to engage its partner with personal topics, and we show the resulting dialogue can be used to predict profile information about the interlocutors.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1206", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1206.flac", "type": "audio", "duration": 1322.005}, {"from": "Assistant", "value": "In this paper, we explore the task of mapping spoken language utterances to one of thousands of natural language understanding domains in intelligent personal digital assistants (IPDAs). This scenario is observed in mainstream IPDAs in industry that allow third parties to develop thousands of new domains to augment built-in first party domains to rapidly increase domain coverage and overall IPDA capabilities. We propose a scalable neural model architecture with a shared encoder, a novel attention mechanism that incorporates personalization information and domain-specific classifiers that solves the problem efficiently. Our architecture is designed to efficiently accommodate incremental domain additions achieving two orders of magnitude speed up compared to full model retraining. We consider the practical constraints of real-time production systems, and design to minimize memory footprint and runtime latency. We demonstrate that incorporating personalization significantly improves domain classification accuracy in a setting with thousands of overlapping domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1207", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1207.flac", "type": "audio", "duration": 1452.203}, {"from": "Assistant", "value": "Multimodal affective computing, learning to recognize and interpret human affect and subjective information from multiple data sources, is still a challenge because: (i) it is hard to extract informative features to represent human affects from heterogeneous inputs; (ii) current fusion strategies only fuse different modalities at abstract levels, ignoring time-dependent interactions between modalities. Addressing such issues, we introduce a hierarchical multimodal architecture with attention and word-level fusion to classify utterance-level sentiment and emotion from text and audio data. Our introduced model outperforms state-of-the-art approaches on published datasets, and we demonstrate that our model is able to visualize and interpret synchronized attention over modalities.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1208", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1208.flac", "type": "audio", "duration": 1458.965}, {"from": "Assistant", "value": "Analyzing human multimodal language is an emerging area of research in NLP. Intrinsically this language is multimodal (heterogeneous), sequential and asynchronous; it consists of the language (words), visual (expressions) and acoustic (paralinguistic) modalities all in the form of asynchronous coordinated sequences. From a resource perspective, there is a genuine need for large scale datasets that allow for in-depth studies of this form of language. In this paper we introduce CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI), the largest dataset of sentiment analysis and emotion recognition to date. Using data from CMU-MOSEI and a novel multimodal fusion technique called the Dynamic Fusion Graph (DFG), we conduct experimentation to exploit how modalities interact with each other in human multimodal language. Unlike previously proposed fusion techniques, DFG is highly interpretable and achieves competative performance when compared to the previous state of the art.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1209", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1209.flac", "type": "audio", "duration": 1367.659}, {"from": "Assistant", "value": "Multimodal research is an emerging field of artificial intelligence, and one of the main research problems in this field is multimodal fusion. The fusion of multimodal data is the process of integrating multiple unimodal representations into one compact multimodal representation. Previous research in this field has exploited the expressiveness of tensors for multimodal representation. However, these methods often suffer from exponential increase in dimensions and in computational complexity introduced by transformation of input into tensor. In this paper, we propose the Low-rank Multimodal Fusion method, which performs multimodal fusion using low-rank tensors to improve efficiency. We evaluate our model on three different tasks: multimodal sentiment analysis, speaker trait analysis, and emotion recognition. Our model achieves competitive results on all these tasks while drastically reducing computational complexity. Additional experiments also show that our model can perform robustly for a wide range of low-rank settings, and is indeed much more efficient in both training and inference compared to other methods that utilize tensor representations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1210", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1210.flac", "type": "audio", "duration": 1421.888}, {"from": "Assistant", "value": "Theories of discourse coherence posit relations between discourse segments as a key feature of coherent text. Our prior work suggests that multiple discourse relations can be simultaneously operative between two segments for reasons not predicted by the literature. Here we test how this joint presence can lead participants to endorse seemingly divergent conjunctions (e.g., BUT and SO) to express the link they see between two segments. These apparent divergences are not symptomatic of participant naivety or bias, but arise reliably from the concurrent availability of multiple relations between segments – some available through explicit signals and some via inference. We believe that these new results can both inform future progress in theoretical work on discourse coherence and lead to higher levels of performance in discourse parsing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1211", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1211.flac", "type": "audio", "duration": 1373.248}, {"from": "Assistant", "value": "We present a generative probabilistic model of documents as sequences of sentences, and show that inference in it can lead to extraction of long-range latent discourse structure from a collection of documents. The approach is based on embedding sequences of sentences from longer texts into a 2- or 3-D spatial grids, in which one or two coordinates model smooth topic transitions, while the third captures the sequential nature of the modeled text. A significant advantage of our approach is that the learned models are naturally visualizable and interpretable, as semantic similarity and sequential structure are modeled along orthogonal directions in the grid. We show that the method is effective in capturing discourse structures in narrative text across multiple genres, including biographies, stories, and newswire reports. In particular, our method outperforms or is competitive with state-of-the-art generative approaches on tasks such as predicting the outcome of a story, and sentence ordering.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1212", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1212.flac", "type": "audio", "duration": 1243.008}, {"from": "Assistant", "value": "Understanding temporal and causal relations between events is a fundamental natural language understanding task. Because a cause must occur earlier than its effect, temporal and causal relations are closely related and one relation often dictates the value of the other. However, limited attention has been paid to studying these two relations jointly. This paper presents a joint inference framework for them using constrained conditional models (CCMs). Specifically, we formulate the joint problem as an integer linear programming (ILP) problem, enforcing constraints that are inherent in the nature of time and causality. We show that the joint inference framework results in statistically significant improvement in the extraction of both temporal and causal relations from text.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1213", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1213.flac", "type": "audio", "duration": 1546.368}, {"from": "Assistant", "value": "Understanding a narrative requires reading between the lines and reasoning about the unspoken but obvious implications about events and people’s mental states — a capability that is trivial for humans but remarkably hard for machines. To facilitate research addressing this challenge, we introduce a new annotation framework to explain naive psychology of story characters as fully-specified chains of mental states with respect to motivations and emotional reactions. Our work presents a new large-scale dataset with rich low-level annotations and establishes baseline performance on several new tasks, suggesting avenues for future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1254", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1254.flac", "type": "audio", "duration": 1411.328}, {"from": "Assistant", "value": "Recurrent neural network grammars (RNNGs) are generative models of (tree , string ) pairs that rely on neural networks to evaluate derivational choices. Parsing with them using beam search yields a variety of incremental complexity metrics such as word surprisal and parser action count. When used as regressors against human electrophysiological responses to naturalistic text, they derive two amplitude effects: an early peak and a P600-like later peak. By contrast, a non-syntactic neural language model yields no reliable effects. Model comparisons attribute the early peak to syntactic composition within the RNNG. This pattern of results recommends the RNNG+beam search combination as a mechanistic model of the syntactic processing that occurs during normal human language comprehension.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1255", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1255.flac", "type": "audio", "duration": 1327.488}, {"from": "Assistant", "value": "Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this work, we build a neural network model for the task of ranking clarification questions. Our model is inspired by the idea of expected value of perfect information: a good question is one whose expected answer will be useful. We study this problem using data from StackExchange, a plentiful online resource in which people routinely ask clarifying questions to posts so that they can better offer assistance to the original poster. We create a dataset of clarification questions consisting of 77K posts paired with a clarification question (and answer) from three domains of StackExchange: askubuntu, unix and superuser. We evaluate our model on 500 samples of this dataset against expert human judgments and demonstrate significant improvements over controlled baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-1256", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-1256.flac", "type": "audio", "duration": 1447.296}, {"from": "Assistant", "value": "We introduce the novel task of predicting adverbial presupposition triggers, which is useful for natural language generation tasks such as summarization and dialogue systems. We introduce two new corpora, derived from the Penn Treebank and the Annotated English Gigaword dataset and investigate the use of a novel attention mechanism tailored to this task. Our attention mechanism augments a baseline recurrent neural network without the need for additional trainable parameters, minimizing the added computational cost of our mechanism. We demonstrate that this model statistically outperforms our baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2055", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2055.flac", "type": "audio", "duration": 690.411}, {"from": "Assistant", "value": "Verb-noun combinations (VNCs) - e.g., blow the whistle, hit the roof, and see stars - are a common type of English idiom that are ambiguous with literal usages. In this paper we propose and evaluate models for classifying VNC usages as idiomatic or literal, based on a variety of approaches to forming distributed representations. Our results show that a model based on averaging word embeddings performs on par with, or better than, a previously-proposed approach based on skip-thoughts. Idiomatic usages of VNCs are known to exhibit lexico-syntactic fixedness. We further incorporate this information into our models, demonstrating that this rich linguistic knowledge is complementary to the information carried by distributed representations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2056", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2056.flac", "type": "audio", "duration": 899.008}, {"from": "Assistant", "value": "The methods proposed recently for specializing word embeddings according to a particular perspective generally rely on external knowledge. In this article, we propose Pseudofit, a new method for specializing word embeddings according to semantic similarity without any external knowledge. Pseudofit exploits the notion of pseudo-sense for building several representations for each word and uses these representations for making the initial embeddings more generic. We illustrate the interest of Pseudofit for acquiring synonyms and study several variants of Pseudofit according to this perspective.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2057", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2057.flac", "type": "audio", "duration": 776.128}, {"from": "Assistant", "value": "Methods for unsupervised hypernym detection may broadly be categorized according to two paradigms: pattern-based and distributional methods. In this paper, we study the performance of both approaches on several hypernymy tasks and find that simple pattern-based methods consistently outperform distributional methods on common benchmark datasets. Our results show that pattern-based models provide important contextual constraints which are not yet captured in distributional methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2058", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2058.flac", "type": "audio", "duration": 918.08}, {"from": "Assistant", "value": "Recent BIO-tagging-based neural semantic role labeling models are very high performing, but assume gold predicates as part of the input and cannot incorporate span-level features. We propose an end-to-end approach for jointly predicting all predicates, arguments spans, and the relations between them. The model makes independent decisions about what relationship, if any, holds between every possible word-span pair, and learns contextualized span representations that provide rich, shared input features for each decision. Experiments demonstrate that this approach sets a new state of the art on PropBank SRL without gold predicates.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2059", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2059.flac", "type": "audio", "duration": 730.816}, {"from": "Assistant", "value": "In neural machine translation, words are sometimes dropped from the source or generated repeatedly in the translation. We explore novel strategies to address the coverage problem that change only the attention transformation. Our approach allocates fertilities to source words, used to bound the attention each word can receive. We experiment with various sparse and constrained attention transformations and propose a new one, constrained sparsemax, shown to be differentiable and sparse. Empirical evaluation is provided in three languages pairs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2060", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2060.flac", "type": "audio", "duration": 585.408}, {"from": "Assistant", "value": "Attention-based neural machine translation (NMT) models selectively focus on specific source positions to produce a translation, which brings significant improvements over pure encoder-decoder sequence-to-sequence models. This work investigates NMT while replacing the attention component. We study a neural hidden Markov model (HMM) consisting of neural network-based alignment and lexicon models, which are trained jointly using the forward-backward algorithm. We show that the attention component can be effectively replaced by the neural network alignment model and the neural HMM approach is able to provide comparable performance with the state-of-the-art attention-based models on the WMT 2017 German↔English and Chinese→English translation tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2061", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2061.flac", "type": "audio", "duration": 912.32}, {"from": "Assistant", "value": "Gender prediction has typically focused on lexical and social network features, yielding good performance, but making systems highly language-, topic-, and platform dependent. Cross-lingual embeddings circumvent some of these limitations, but capture gender-specific style less. We propose an alternative: bleaching text, i.e., transforming lexical strings into more abstract features. This study provides evidence that such features allow for better transfer across languages. Moreover, we present a first study on the ability of humans to perform cross-lingual gender prediction. We find that human predictive power proves similar to that of our bleached models, and both perform better than lexical models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2062", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2062.flac", "type": "audio", "duration": 678.08}, {"from": "Assistant", "value": "Recent embedding-based methods in bilingual lexicon induction show good results, but do not take advantage of orthographic features, such as edit distance, which can be helpful for pairs of related languages. This work extends embedding-based methods to incorporate these features, resulting in significant accuracy gains for related languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2063", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2063.flac", "type": "audio", "duration": 844.053}, {"from": "Assistant", "value": "We propose an entity-centric neural crosslingual coreference model that builds on multi-lingual embeddings and language independent features. We perform both intrinsic and extrinsic evaluations of our model. In the intrinsic evaluation, we show that our model, when trained on English and tested on Chinese and Spanish, achieves competitive results to the models trained directly on Chinese and Spanish respectively. In the extrinsic evaluation, we show that our English model helps achieve superior entity linking accuracy on Chinese and Spanish test sets than the top 2015 TAC system without using any annotated data from Chinese or Spanish.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2064", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2064.flac", "type": "audio", "duration": 831.403}, {"from": "Assistant", "value": "Multilingual learning for Neural Named Entity Recognition (NNER) involves jointly training a neural network for multiple languages. Typically, the goal is improving the NER performance of one of the languages (the primary language) using the other assisting languages. We show that the divergence in the tag distributions of the common named entities between the primary and assisting languages can reduce the effectiveness of multilingual learning. To alleviate this problem, we propose a metric based on symmetric KL divergence to filter out the highly divergent training instances in the assisting language. We empirically show that our data selection strategy improves NER performance in many languages, including those with very limited training data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2065", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2065.flac", "type": "audio", "duration": 751.659}, {"from": "Assistant", "value": "Conventional Open Information Extraction (Open IE) systems are usually built on hand-crafted patterns from other NLP tools such as syntactic parsing, yet they face problems of error propagation. In this paper, we propose a neural Open IE approach with an encoder-decoder framework. Distinct from existing methods, the neural Open IE approach learns highly confident arguments and relation tuples bootstrapped from a state-of-the-art Open IE system. An empirical study on a large benchmark dataset shows that the neural Open IE system significantly outperforms several baselines, while maintaining comparable computational efficiency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2066", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2066.flac", "type": "audio", "duration": 598.016}, {"from": "Assistant", "value": "Document-level information is very important for event detection even at sentence level. In this paper, we propose a novel Document Embedding Enhanced Bi-RNN model, called DEEB-RNN, to detect events in sentences. This model first learns event detection oriented embeddings of documents through a hierarchical and supervised attention based RNN, which pays word-level attention to event triggers and sentence-level attention to those sentences containing events. It then uses the learned document embedding to enhance another bidirectional RNN model to identify event triggers and their types in sentences. Through experiments on the ACE-2005 dataset, we demonstrate the effectiveness and merits of the proposed DEEB-RNN model via comparison with state-of-the-art methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2067", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2067.flac", "type": "audio", "duration": 739.648}, {"from": "Assistant", "value": "We propose a method that can leverage unlabeled data to learn a matching model for response selection in retrieval-based chatbots. The method employs a sequence-to-sequence architecture (Seq2Seq) model as a weak annotator to judge the matching degree of unlabeled pairs, and then performs learning with both the weak signals and the unlabeled data. Experimental results on two public data sets indicate that matching models get significant improvements when they are learned with the proposed method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2068", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2068.flac", "type": "audio", "duration": 812.693}, {"from": "Assistant", "value": "We present a generative neural network model for slot filling based on a sequence-to-sequence (Seq2Seq) model together with a pointer network, in the situation where only sentence-level slot annotations are available in the spoken dialogue data. This model predicts slot values by jointly learning to copy a word which may be out-of-vocabulary (OOV) from an input utterance through a pointer network, or generate a word within the vocabulary through an attentional Seq2Seq model. Experimental results show the effectiveness of our slot filling model, especially at addressing the OOV problem. Additionally, we integrate the proposed model into a spoken language understanding system and achieve the state-of-the-art performance on the benchmark data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2069", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2069.flac", "type": "audio", "duration": 864.576}, {"from": "Assistant", "value": "Robust dialogue belief tracking is a key component in maintaining good quality dialogue systems. The tasks that dialogue systems are trying to solve are becoming increasingly complex, requiring scalability to multi-domain, semantically rich dialogues. However, most current approaches have difficulty scaling up with domains because of the dependency of the model parameters on the dialogue ontology. In this paper, a novel approach is introduced that fully utilizes semantic similarity between dialogue utterances and the ontology terms, allowing the information to be shared across domains. The evaluation is performed on a recently collected multi-domain dialogues dataset, one order of magnitude larger than currently available corpora. Our model demonstrates great capability in handling multi-domain dialogues, simultaneously outperforming existing state-of-the-art models in single-domain dialogue tracking tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2070", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2070.flac", "type": "audio", "duration": 828.373}, {"from": "Assistant", "value": "Identifying long-span dependencies between discourse units is crucial to improve discourse parsing performance. Most existing approaches design sophisticated features or exploit various off-the-shelf tools, but achieve little success. In this paper, we propose a new transition-based discourse parser that makes use of memory networks to take discourse cohesion into account. The automatically captured discourse cohesion benefits discourse parsing, especially for long span scenarios. Experiments on the RST discourse treebank show that our method outperforms traditional featured based methods, and the memory based discourse cohesion can improve the overall parsing performance significantly.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2071", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2071.flac", "type": "audio", "duration": 910.763}, {"from": "Assistant", "value": "Annotation corpus for discourse relations benefits NLP tasks such as machine translation and question answering. In this paper, we present SciDTB, a domain-specific discourse treebank annotated on scientific articles. Different from widely-used RST-DT and PDTB, SciDTB uses dependency trees to represent discourse structure, which is flexible and simplified to some extent but do not sacrifice structural integrity. We discuss the labeling framework, annotation workflow and some statistics about SciDTB. Furthermore, our treebank is made as a benchmark for evaluating discourse dependency parsers, on which we provide several baselines as fundamental work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2072", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2072.flac", "type": "audio", "duration": 913.28}, {"from": "Assistant", "value": "Because obtaining training data is often the most difficult part of an NLP or ML project, we develop methods for predicting how much data is required to achieve a desired test accuracy by extrapolating results from models trained on a small pilot training dataset. We model how accuracy varies as a function of training size on subsets of the pilot data, and use that model to predict how much training data would be required to achieve the desired accuracy. We introduce a new performance extrapolation task to evaluate how well different extrapolations predict accuracy on larger training sets. We show that details of hyperparameter optimisation and the extrapolation models can have dramatic effects in a document classification task. We believe this is an important first step in developing methods for estimating the resources required to meet specific engineering performance targets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2073", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2073.flac", "type": "audio", "duration": 710.059}, {"from": "Assistant", "value": "We investigate the influence that document context exerts on human acceptability judgements for English sentences, via two sets of experiments. The first compares ratings for sentences presented on their own with ratings for the same set of sentences given in their document contexts. The second assesses the accuracy with which two types of neural models — one that incorporates context during training and one that does not — predict these judgements. Our results indicate that: (1) context improves acceptability ratings for ill-formed sentences, but also reduces them for well-formed sentences; and (2) context helps unsupervised systems to model acceptability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2074", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2074.flac", "type": "audio", "duration": 922.005}, {"from": "Assistant", "value": "Feed-forward networks are widely used in cross-modal applications to bridge modalities by mapping distributed vectors of one modality to the other, or to a shared space. The predicted vectors are then used to perform e.g., retrieval or labeling. Thus, the success of the whole system relies on the ability of the mapping to make the neighborhood structure (i.e., the pairwise similarities) of the predicted vectors akin to that of the target vectors. However, whether this is achieved has not been investigated yet. Here, we propose a new similarity measure and two ad hoc experiments to shed light on this issue. In three cross-modal benchmarks we learn a large number of language-to-vision and vision-to-language neural network mappings (up to five layers) using a rich diversity of image and text features and loss functions. Our results reveal that, surprisingly, the neighborhood structure of the predicted vectors consistently resembles more that of the input vectors than that of the target vectors. In a second experiment, we further show that untrained nets do not significantly disrupt the neighborhood (i.e., semantic) structure of the input vectors.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2075", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2075.flac", "type": "audio", "duration": 760.896}, {"from": "Assistant", "value": "Dynamic oracles provide strong supervision for training constituency parsers with exploration, but must be custom defined for a given parser’s transition system. We explore using a policy gradient method as a parser-agnostic alternative. In addition to directly optimizing for a tree-level metric such as F1, policy gradient has the potential to reduce exposure bias by allowing exploration during training; moreover, it does not require a dynamic oracle for supervision. On four constituency parsers in three languages, the method substantially outperforms static oracle likelihood training in almost all settings. For parsers where a dynamic oracle is available (including a novel oracle which we define for the transition system of Dyer et al., 2016), policy gradient typically recaptures a substantial fraction of the performance gain afforded by the dynamic oracle.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2076", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2076.flac", "type": "audio", "duration": 846.208}, {"from": "Assistant", "value": "Recently, span-based constituency parsing has achieved competitive accuracies with extremely simple models by using bidirectional RNNs to model “spans”. However, the minimal span parser of Stern et al. (2017a) which holds the current state of the art accuracy is a chart parser running in cubic time,O(n3), which is too slow for longer sentences and for applications beyond sentence boundaries such as end-to-end discourse parsing and joint sentence boundary detection and parsing. We propose a linear-time constituency parser with RNNs and dynamic programming using graph-structured stack and beam search, which runs in timeO(n b2)wherebis the beam size. We further speed this up toO(n b log b)by integrating cube pruning. Compared with chart parsing baselines, this linear-time parser is substantially faster for long sentences on the Penn Treebank and orders of magnitude faster for discourse parsing, and achieves the highest F1 accuracy on the Penn Treebank among single model end-to-end systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2077", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2077.flac", "type": "audio", "duration": 818.091}, {"from": "Assistant", "value": "While syntactic dependency annotations concentrate on the surface or functional structure of a sentence, semantic dependency annotations aim to capture between-word relationships that are more closely related to the meaning of a sentence, using graph-structured representations. We extend the LSTM-based syntactic parser of Dozat and Manning (2017) to train on and generate these graph structures. The resulting system on its own achieves state-of-the-art performance, beating the previous, substantially more complex state-of-the-art system by 0.6% labeled F1. Adding linguistically richer input representations pushes the margin even higher, allowing us to beat it by 1.9% labeled F1.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2078", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2078.flac", "type": "audio", "duration": 973.653}, {"from": "Assistant", "value": "An abugida is a writing system where the consonant letters represent syllables with a default vowel and other vowels are denoted by diacritics. We investigate the feasibility of recovering the original text written in an abugida after omitting subordinate diacritics and merging consonant letters with similar phonetic values. This is crucial for developing more efficient input methods by reducing the complexity in abugidas. Four abugidas in the southern Brahmic family, i.e., Thai, Burmese, Khmer, and Lao, were studied using a newswire 20,000-sentence dataset. We compared the recovery performance of a support vector machine and an LSTM-based recurrent neural network, finding that the abugida graphemes could be recovered with 94% - 97% accuracy at the top-1 level and 98% - 99% at the top-4 level, even after omitting most diacritics (10 - 30 types) and merging the remaining 30 - 50 characters into 21 graphemes.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2100", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2100.flac", "type": "audio", "duration": 954.24}, {"from": "Assistant", "value": "Neural vector representations are ubiquitous throughout all subfields of NLP. While word vectors have been studied in much detail, thus far only little light has been shed on the properties of sentence embeddings. In this paper, we assess to what extent prominent sentence embedding methods exhibit select semantic properties. We propose a framework that generate triplets of sentences to explore how changes in the syntactic structure or semantics of a given sentence affect the similarities obtained between their sentence embeddings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2101", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2101.flac", "type": "audio", "duration": 726.891}, {"from": "Assistant", "value": "We present the Supervised Directional Similarity Network, a novel neural architecture for learning task-specific transformation functions on top of general-purpose word embeddings. Relying on only a limited amount of supervision from task-specific scores on a subset of the vocabulary, our architecture is able to generalise and transform a general-purpose distributional vector space to model the relation of lexical entailment. Experiments show excellent performance on scoring graded lexical entailment, raising the state-of-the-art on the HyperLex dataset by approximately 25%.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2102", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2102.flac", "type": "audio", "duration": 758.891}, {"from": "Assistant", "value": "Intelligent systems require common sense, but automatically extracting this knowledge from text can be difficult. We propose and assess methods for extracting one type of commonsense knowledge, object-property comparisons, from pre-trained embeddings. In experiments, we show that our approach exceeds the accuracy of previous work but requires substantially less hand-annotated knowledge. Further, we show that an active learning approach that synthesizes common-sense queries can boost accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2103", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2103.flac", "type": "audio", "duration": 872.853}, {"from": "Assistant", "value": "We create a new NLI test set that shows the deficiency of state-of-the-art models in inferences that require lexical and world knowledge. The new examples are simpler than the SNLI test set, containing sentences that differ by at most one word from sentences in the training set. Yet, the performance on the new test set is substantially worse across systems trained on SNLI, demonstrating that these systems are limited in their generalization ability, failing to capture many simple inferences.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2104", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2104.flac", "type": "audio", "duration": 631.445}, {"from": "Assistant", "value": "Neural Machine Translation (NMT) is notorious for its need for large amounts of bilingual data. An effective approach to compensate for this requirement is Multi-Task Learning (MTL) to leverage different linguistic resources as a source of inductive bias. Current MTL architectures are based on the Seq2Seq transduction, and (partially) share different components of the models among the tasks. However, this MTL approach often suffers from task interference and is not able to fully capture commonalities among subsets of tasks. We address this issue by extending the recurrent units with multiple “blocks” along with a trainable “routing network”. The routing network enables adaptive collaboration by dynamic sharing of blocks conditioned on the task at hand, input, and model state. Empirical evaluation of two low-resource translation tasks, English to Vietnamese and Farsi, show +1 BLEU score improvements compared to strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2105", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2105.flac", "type": "audio", "duration": 620.885}, {"from": "Assistant", "value": "Simultaneous interpretation, translation of the spoken word in real-time, is both highly challenging and physically demanding. Methods to predict interpreter confidence and the adequacy of the interpreted message have a number of potential applications, such as in computer-assisted interpretation interfaces or pedagogical tools. We propose the task of predicting simultaneous interpreter performance by building on existing methodology for quality estimation (QE) of machine translation output. In experiments over five settings in three language pairs, we extend a QE pipeline to estimate interpreter performance (as approximated by the METEOR evaluation metric) and propose novel features reflecting interpretation strategy and evaluation measures that further improve prediction accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2106", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2106.flac", "type": "audio", "duration": 662.251}, {"from": "Assistant", "value": "Previous approaches to multilingual semantic dependency parsing treat languages independently, without exploiting the similarities between semantic structures across languages. We experiment with a new approach where we combine resources from different languages in the CoNLL 2009 shared task to build a single polyglot semantic dependency parser. Notwithstanding the absence of parallel data, and the dissimilarity in annotations between languages, our approach results in improvement in parsing performance on several languages over a monolingual baseline. Analysis of the polyglot models’ performance provides a new understanding of the similarities and differences between languages in the shared task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2107", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2107.flac", "type": "audio", "duration": 595.456}, {"from": "Assistant", "value": "With the development of several multilingual datasets used for semantic parsing, recent research efforts have looked into the problem of learning semantic parsers in a multilingual setup. However, how to improve the performance of a monolingual semantic parser for a specific language by leveraging data annotated in different languages remains a research question that is under-explored. In this work, we present a study to show how learning distributed representations of the logical forms from data annotated in different languages can be used for improving the performance of a monolingual semantic parser. We extend two existing monolingual semantic parsers to incorporate such cross-lingual distributed logical representations as features. Experiments show that our proposed approach is able to yield improved semantic parsing results on the standard multilingual GeoQuery dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2108", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2108.flac", "type": "audio", "duration": 755.52}, {"from": "Assistant", "value": "We propose a novel neural method to extract drug-drug interactions (DDIs) from texts using external drug molecular structure information. We encode textual drug pairs with convolutional neural networks and their molecular pairs with graph convolutional networks (GCNs), and then we concatenate the outputs of these two networks. In the experiments, we show that GCNs can predict DDIs from the molecular structures of drugs in high accuracy and the molecular information can enhance text-based DDI extraction by 2.39 percent points in the F-score on the DDIExtraction 2013 shared task data set.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2109", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2109.flac", "type": "audio", "duration": 683.883}, {"from": "Assistant", "value": "Named Entity Disambiguation (NED) systems perform well on news articles and other texts covering a specific time interval. However, NED quality drops when inputs span long time periods like in archives or historic corpora. This paper presents the first time-aware method for NED that resolves ambiguities even when mention contexts give only few cues. The method is based on computing temporal signatures for entities and comparing these to the temporal contexts of input mentions. Our experiments show superior quality on a newly created diachronic corpus.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2110", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2110.flac", "type": "audio", "duration": 787.648}, {"from": "Assistant", "value": "Many corpora span broad periods of time. Language processing models trained during one time period may not work well in future time periods, and the best model may depend on specific times of year (e.g., people might describe hotels differently in reviews during the winter versus the summer). This study investigates how document classifiers trained on documents from certain time intervals perform on documents from other time intervals, considering both seasonal intervals (intervals that repeat across years, e.g., winter) and non-seasonal intervals (e.g., specific years). We show experimentally that classification performance varies over time, and that performance can be improved by using a standard domain adaptation approach to adjust for changes in time.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2111", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2111.flac", "type": "audio", "duration": 751.339}, {"from": "Assistant", "value": "Query auto-completion is a search engine feature whereby the system suggests completed queries as the user types. Recently, the use of a recurrent neural network language model was suggested as a method of generating query completions. We show how an adaptable language model can be used to generate personalized completions and how the model can use online updating to make predictions for users not seen during training. The personalized predictions are significantly better than a baseline that uses no user information.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2112", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2112.flac", "type": "audio", "duration": 667.605}, {"from": "Assistant", "value": "In this paper, we focus on the problem of building assistive systems that can help users to write reviews. We cast this problem using an encoder-decoder framework that generates personalized reviews by expanding short phrases (e.g. review summaries, product titles) provided as input to the system. We incorporate aspect-level information via an aspect encoder that learns aspect-aware user and item representations. An attention fusion layer is applied to control generation by attending on the outputs of multiple encoders. Experimental results show that our model successfully learns representations capable of generating coherent and diverse reviews. In addition, the learned aspect-aware representations discover those aspects that users are more inclined to discuss and bias the generated text toward their personalized aspect preferences.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2113", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2113.flac", "type": "audio", "duration": 985.6}, {"from": "Assistant", "value": "Text simplification (TS) is a monolingual text-to-text transformation task where an original (complex) text is transformed into a target (simpler) text. Most recent work is based on sequence-to-sequence neural models similar to those used for machine translation (MT). Different from MT, TS data comprises more elaborate transformations, such as sentence splitting. It can also contain multiple simplifications of the same original text targeting different audiences, such as school grade levels. We explore these two features of TS to build models tailored for specific grade levels. Our approach uses a standard sequence-to-sequence architecture where the original sequence is annotated with information about the target audience and/or the (predicted) type of simplification operation. We show that it outperforms state-of-the-art TS approaches (up to 3 and 12 BLEU and SARI points, respectively), including when training data for the specific complex-simple combination of grade levels is not available, i.e. zero-shot learning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2114", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2114.flac", "type": "audio", "duration": 787.499}, {"from": "Assistant", "value": "Splitting and rephrasing a complex sentence into several shorter sentences that convey the same meaning is a challenging problem in NLP. We show that while vanilla seq2seq models can reach high scores on the proposed benchmark (Narayan et al., 2017), they suffer from memorization of the training set which contains more than 89% of the unique simple sentences from the validation and test sets. To aid this, we present a new train-development-test data split and neural models augmented with a copy-mechanism, outperforming the best reported baseline by 8.68 BLEU and fostering further progress on the task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2115", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2115.flac", "type": "audio", "duration": 848.405}, {"from": "Assistant", "value": "Most of the current abstractive text summarization models are based on the sequence-to-sequence model (Seq2Seq). The source content of social media is long and noisy, so it is difficult for Seq2Seq to learn an accurate semantic representation. Compared with the source content, the annotated summary is short and well written. Moreover, it shares the same meaning as the source content. In this work, we supervise the learning of the representation of the source content with that of the summary. In implementation, we regard a summary autoencoder as an assistant supervisor of Seq2Seq. Following previous work, we evaluate our model on a popular Chinese social media dataset. Experimental results show that our model achieves the state-of-the-art performances on the benchmark dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2116", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2116.flac", "type": "audio", "duration": 918.016}, {"from": "Assistant", "value": "LSTMs were introduced to combat vanishing gradients in simple RNNs by augmenting them with gated additive recurrent connections. We present an alternative view to explain the success of LSTMs: the gates themselves are versatile recurrent models that provide more representational power than previously appreciated. We do this by decoupling the LSTM’s gates from the embedded simple RNN, producing a new class of RNNs where the recurrence computes an element-wise weighted sum of context-independent functions of the input. Ablations on a range of problems demonstrate that the gating mechanism alone performs as well as an LSTM in most settings, strongly suggesting that the gates are doing much more in practice than just alleviating vanishing gradients.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2117", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2117.flac", "type": "audio", "duration": 886.485}, {"from": "Assistant", "value": "While Recurrent Neural Networks (RNNs) are famously known to be Turing complete, this relies on infinite precision in the states and unbounded computation time. We consider the case of RNNs with finite precision whose computation time is linear in the input length. Under these limitations, we show that different RNN variants have different computational power. In particular, we show that the LSTM and the Elman-RNN with ReLU activation are strictly stronger than the RNN with a squashing activation and the GRU. This is achieved because LSTMs and ReLU-RNNs can easily implement counting behavior. We show empirically that the LSTM does indeed learn to effectively use the counting mechanism.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2118", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2118.flac", "type": "audio", "duration": 739.2}, {"from": "Assistant", "value": "Multi-choice reading comprehension is a challenging task, which involves the matching between a passage and a question-answer pair. This paper proposes a new co-matching approach to this problem, which jointly models whether a passage can match both a question and a candidate answer. Experimental results on the RACE dataset demonstrate that our approach achieves state-of-the-art performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2119", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2119.flac", "type": "audio", "duration": 805.013}, {"from": "Assistant", "value": "The Story Cloze Test (SCT) is a recent framework for evaluating story comprehension and script learning. There have been a variety of models tackling the SCT so far. Although the original goal behind the SCT was to require systems to perform deep language understanding and commonsense reasoning for successful narrative understanding, some recent models could perform significantly better than the initial baselines by leveraging human-authorship biases discovered in the SCT dataset. In order to shed some light on this issue, we have performed various data analysis and analyzed a variety of top performing models presented for this task. Given the statistics we have aggregated, we have designed a new crowdsourcing scheme that creates a new SCT dataset, which overcomes some of the biases. We benchmark a few models on the new dataset and show that the top-performing model on the original SCT dataset fails to keep up its performance. Our findings further signify the importance of benchmarking NLP systems on various evolving test sets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2120", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2120.flac", "type": "audio", "duration": 963.371}, {"from": "Assistant", "value": "Deep learning approaches for sentiment classification do not fully exploit sentiment linguistic knowledge. In this paper, we propose a Multi-sentiment-resource Enhanced Attention Network (MEAN) to alleviate the problem by integrating three kinds of sentiment linguistic knowledge (e.g., sentiment lexicon, negation words, intensity words) into the deep neural network via attention mechanisms. By using various types of sentiment resources, MEAN utilizes sentiment-relevant information from different representation sub-spaces, which makes it more effective to capture the overall semantics of the sentiment, negation and intensity words for sentiment prediction. The experimental results demonstrate that MEAN has robust superiority over strong competitors.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2121", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2121.flac", "type": "audio", "duration": 857.493}, {"from": "Assistant", "value": "The huge cost of creating labeled training data is a common problem for supervised learning tasks such as sentiment classification. Recent studies showed that pretraining with unlabeled data via a language model can improve the performance of classification models. In this paper, we take the concept a step further by using a conditional language model, instead of a language model. Specifically, we address a sentiment classification task for a tweet analysis service as a case study and propose a pretraining strategy with unlabeled dialog data (tweet-reply pairs) via an encoder-decoder model. Experimental results show that our strategy can improve the performance of sentiment classifiers and outperform several state-of-the-art strategies including language model pretraining.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2122", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2122.flac", "type": "audio", "duration": 653.525}, {"from": "Assistant", "value": "The reliability of self-labeled data is an important issue when the data are regarded as ground-truth for training and testing learning-based models. This paper addresses the issue of false-alarm hashtags in the self-labeled data for irony detection. We analyze the ambiguity of hashtag usages and propose a novel neural network-based model, which incorporates linguistic information from different aspects, to disambiguate the usage of three hashtags that are widely used to collect the training data for irony detection. Furthermore, we apply our model to prune the self-labeled training data. Experimental results show that the irony detection model trained on the less but cleaner training instances outperforms the models trained on all data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2123", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2123.flac", "type": "audio", "duration": 690.816}, {"from": "Assistant", "value": "In stance classification, the target on which the stance is made defines the boundary of the task, and a classifier is usually trained for prediction on the same target. In this work, we explore the potential for generalizing classifiers between different targets, and propose a neural model that can apply what has been learned from a source target to a destination target. We show that our model can find useful information shared between relevant targets which improves generalization in certain scenarios.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2124", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2124.flac", "type": "audio", "duration": 747.243}, {"from": "Assistant", "value": "Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for which the correct answer is not stated in the context. Existing datasets either focus exclusively on answerable questions, or use automatically generated unanswerable questions that are easy to identify. To address these weaknesses, we present SQuADRUn, a new dataset that combines the existing Stanford Question Answering Dataset (SQuAD) with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuADRUn, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. SQuADRUn is a challenging natural language understanding task for existing models: a strong neural system that gets 86% F1 on SQuAD achieves only 66% F1 on SQuADRUn. We release SQuADRUn to the community as the successor to SQuAD.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P18-2125", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P18-2125.flac", "type": "audio", "duration": 749.44}, {"from": "Assistant", "value": "We propose a novel paradigm of grounding comparative adjectives within the realm of color descriptions. Given a reference RGB color and a comparative term (e.g., lighter, darker), our model learns to ground the comparative as a direction in the RGB space such that the colors along the vector, rooted at the reference color, satisfy the comparison. Our model generates grounded representations of comparative adjectives with an average accuracy of 0.65 cosine similarity to the desired direction of change. These vectors approach colors with Delta-E scores of under 7 compared to the target colors, indicating the differences are very small with respect to human perception. Our approach makes use of a newly created dataset for this task derived from existing labeled color data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1001", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1001.flac", "type": "audio", "duration": 832.811}, {"from": "Assistant", "value": "Currently, researchers have paid great attention to retrieval-based dialogues in open-domain. In particular, people study the problem by investigating context-response matching for multi-turn response selection based on publicly recognized benchmark data sets. State-of-the-art methods require a response to interact with each utterance in a context from the beginning, but the interaction is performed in a shallow way. In this work, we let utterance-response interaction go deep by proposing an interaction-over-interaction network (IoI). The model performs matching by stacking multiple interaction blocks in which residual information from one time of interaction initiates the interaction process again. Thus, matching information within an utterance-response pair is extracted from the interaction of the pair in an iterative fashion, and the information flows along the chain of the blocks via representations. Evaluation results on three benchmark data sets indicate that IoI can significantly outperform state-of-the-art methods in terms of various matching metrics. Through further analysis, we also unveil how the depth of interaction affects the performance of IoI.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1002", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1002.flac", "type": "audio", "duration": 1111.04}, {"from": "Assistant", "value": "Document Grounded Conversations is a task to generate dialogue responses when chatting about the content of a given document. Obviously, document knowledge plays a critical role in Document Grounded Conversations, while existing dialogue models do not exploit this kind of knowledge effectively enough. In this paper, we propose a novel Transformer-based architecture for multi-turn document grounded conversations. In particular, we devise an Incremental Transformer to encode multi-turn utterances along with knowledge in related documents. Motivated by the human cognitive process, we design a two-pass decoder (Deliberation Decoder) to improve context coherence and knowledge correctness. Our empirical study on a real-world Document Grounded Dataset proves that responses generated by our model significantly outperform competitive baselines on both context coherence and knowledge relevance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1003", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1003.flac", "type": "audio", "duration": 1072.939}, {"from": "Assistant", "value": "Recent research has achieved impressive results in single-turn dialogue modelling. In the multi-turn setting, however, current models are still far from satisfactory. One major challenge is the frequently occurred coreference and information omission in our daily conversation, making it hard for machines to understand the real intention. In this paper, we propose rewriting the human utterance as a pre-process to help multi-turn dialgoue modelling. Each utterance is first rewritten to recover all coreferred and omitted information. The next processing steps are then performed based on the rewritten utterance. To properly train the utterance rewriter, we collect a new dataset with human annotations and introduce a Transformer-based utterance rewriting architecture using the pointer network. We show the proposed architecture achieves remarkably good performance on the utterance rewriting task. The trained utterance rewriter can be easily integrated into online chatbots and brings general improvement over different domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1004", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1004.flac", "type": "audio", "duration": 761.259}, {"from": "Assistant", "value": "Neural generative models have been become increasingly popular when building conversational agents. They offer flexibility, can be easily adapted to new domains, and require minimal domain engineering. A common criticism of these systems is that they seldom understand or use the available dialog history effectively. In this paper, we take an empirical approach to understanding how these models use the available dialog history by studying the sensitivity of the models to artificially introduced unnatural changes or perturbations to their context at test time. We experiment with 10 different types of perturbations on 4 multi-turn dialog datasets and find that commonly used neural dialog architectures like recurrent and transformer-based seq2seq models are rarely sensitive to most perturbations such as missing or reordering utterances, shuffling words, etc. Also, by open-sourcing our code, we believe that it will serve as a useful diagnostic tool for evaluating dialog systems in the future.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1005", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1005.flac", "type": "audio", "duration": 452.715}, {"from": "Assistant", "value": "Neural models have become one of the most important approaches to dialog response generation. However, they still tend to generate the most common and generic responses in the corpus all the time. To address this problem, we designed an iterative training process and ensemble method based on boosting. We combined our method with different training and decoding paradigms as the base model, including mutual-information-based decoding and reward-augmented maximum likelihood learning. Empirical results show that our approach can significantly improve the diversity and relevance of the responses generated by all base models, backed by objective measurements and human evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1006", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1006.flac", "type": "audio", "duration": 603.84}, {"from": "Assistant", "value": "Response selection plays an important role in fully automated dialogue systems. Given the dialogue context, the goal of response selection is to identify the best-matched next utterance (i.e., response) from multiple candidates. Despite the efforts of many previous useful models, this task remains challenging due to the huge semantic gap and also the large size of candidate set. To address these issues, we propose a Spatio-Temporal Matching network (STM) for response selection. In detail, soft alignment is first used to obtain the local relevance between the context and the response. And then, we construct spatio-temporal features by aggregating attention images in time dimension and make use of 3D convolution and pooling operations to extract matching information. Evaluation on two large-scale multi-turn response selection tasks has demonstrated that our proposed model significantly outperforms the state-of-the-art model. Particularly, visualization analysis shows that the spatio-temporal features enables matching information in segment pairs and time sequences, and have good interpretability for multi-turn text matching.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1007", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1007.flac", "type": "audio", "duration": 985.045}, {"from": "Assistant", "value": "Semantic parsing converts natural language queries into structured logical forms. The lack of training data is still one of the most serious problems in this area. In this work, we develop a semantic parsing framework with the dual learning algorithm, which enables a semantic parser to make full use of data (labeled and even unlabeled) through a dual-learning game. This game between a primal model (semantic parsing) and a dual model (logical form to query) forces them to regularize each other, and can achieve feedback signals from some prior-knowledge. By utilizing the prior-knowledge of logical form structures, we propose a novel reward signal at the surface and semantic levels which tends to generate complete and reasonable logical forms. Experimental results show that our approach achieves new state-of-the-art performance on ATIS dataset and gets competitive performance on OVERNIGHT dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1008", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1008.flac", "type": "audio", "duration": 849.728}, {"from": "Assistant", "value": "We investigate the capacity of mechanisms for compositional semantic parsing to describe relations between sentences and semantic representations. We prove that in order to represent certain relations, mechanisms which are syntactically projective must be able to remember an unbounded number of locations in the semantic representations, where nonprojective mechanisms need not. This is the first result of this kind, and has consequences both for grammar-based and for neural systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1009", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1009.flac", "type": "audio", "duration": 819.051}, {"from": "Assistant", "value": "We propose an attention-based model that treats AMR parsing as sequence-to-graph transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic resources, or data augmentation, our proposed parser is aligner-free, and it can be effectively trained with limited amounts of labeled AMR data. Our experimental results outperform all previously reported SMATCH scores, on both AMR 2.0 (76.3% on LDC2017T10) and AMR 1.0 (70.2% on LDC2014T12).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1010", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1010.flac", "type": "audio", "duration": 768.491}, {"from": "Assistant", "value": "Structured information about entities is critical for many semantic parsing tasks. We present an approach that uses a Graph Neural Network (GNN) architecture to incorporate information about relevant entities and their relations during parsing. Combined with a decoder copy mechanism, this approach provides a conceptually simple mechanism to generate logical forms with entities. We demonstrate that this approach is competitive with the state-of-the-art across several tasks without pre-training, and outperforms existing approaches when combined with BERT pre-training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1011", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1011.flac", "type": "audio", "duration": 1167.253}, {"from": "Assistant", "value": "Vector representations of sentences, trained on massive text corpora, are widely used as generic sentence embeddings across a variety of NLP problems. The learned representations are generally assumed to be continuous and real-valued, giving rise to a large memory footprint and slow retrieval speed, which hinders their applicability to low-resource (memory and computation) platforms, such as mobile devices. In this paper, we propose four different strategies to transform continuous and generic sentence embeddings into a binarized form, while preserving their rich semantic information. The introduced methods are evaluated across a wide range of downstream tasks, where the binarized sentence embeddings are demonstrated to degrade performance by only about 2% relative to their continuous counterparts, while reducing the storage requirement by over 98%. Moreover, with the learned binary representations, the semantic relatedness of two sentences can be evaluated by simply calculating their Hamming distance, which is more computational efficient compared with the inner product operation between continuous embeddings. Detailed analysis and case study further validate the effectiveness of proposed methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1012", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1012.flac", "type": "audio", "duration": 1030.379}, {"from": "Assistant", "value": "Classical non-neural dependency parsers put considerable effort on the design of feature functions. Especially, they benefit from information coming from structural features, such as features drawn from neighboring tokens in the dependency tree. In contrast, their BiLSTM-based successors achieve state-of-the-art performance without explicit information about the structural context. In this paper we aim to answer the question: How much structural context are the BiLSTM representations able to capture implicitly? We show that features drawn from partial subtrees become redundant when the BiLSTMs are used. We provide a deep insight into information flow in transition- and graph-based neural architectures to demonstrate where the implicit information comes from when the parsers make their decisions. Finally, with model ablations we demonstrate that the structural context is not only present in the models, but it significantly influences their performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1013", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1013.flac", "type": "audio", "duration": 871.659}, {"from": "Assistant", "value": "We propose a new domain adaptation method for Combinatory Categorial Grammar (CCG) parsing, based on the idea of automatic generation of CCG corpora exploiting cheaper resources of dependency trees. Our solution is conceptually simple, and not relying on a specific parser architecture, making it applicable to the current best-performing parsers. We conduct extensive parsing experiments with detailed discussion; on top of existing benchmark datasets on (1) biomedical texts and (2) question sentences, we create experimental datasets of (3) speech conversation and (4) math problems. When applied to the proposed method, an off-the-shelf CCG parser shows significant performance gains, improving from 90.7% to 96.6% on speech conversation, and from 88.5% to 96.8% on math problems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1014", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1014.flac", "type": "audio", "duration": 880.043}, {"from": "Assistant", "value": "We study a variant of domain adaptation for named-entity recognition where multiple, heterogeneously tagged training sets are available. Furthermore, the test tag-set is not identical to any individual training tag-set. Yet, the relations between all tags are provided in a tag hierarchy, covering the test tags as a combination of training tags. This setting occurs when various datasets are created using different annotation schemes. This is also the case of extending a tag-set with a new tag by annotating only the new tag in a new dataset. We propose to use the given tag hierarchy to jointly learn a neural network that shares its tagging layer among all tag-sets. We compare this model to combining independent models and to a model based on the multitasking approach. Our experiments show the benefit of the tag-hierarchy model, especially when facing non-trivial consolidation of tag-sets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1015", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1015.flac", "type": "audio", "duration": 976.811}, {"from": "Assistant", "value": "In cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language. While most prior work has used a single source model or a few carefully selected models, here we consider a “massive” setting with many such models. This setting raises the problem of poor transfer, particularly from distant languages. We propose two techniques for modulating the transfer, suitable for zero-shot or few-shot learning, respectively. Evaluating on named entity recognition, we show that our techniques are much more effective than strong baselines, including standard ensembling, and our unsupervised method rivals oracle selection of the single best individual model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1016", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1016.flac", "type": "audio", "duration": 986.731}, {"from": "Assistant", "value": "Word embeddings are widely used on a variety of tasks and can substantially improve the performance. However, their quality is not consistent throughout the vocabulary due to the long-tail distribution of word frequency. Without sufficient contexts, rare word embeddings are usually less reliable than those of common words. However, current models typically trust all word embeddings equally regardless of their reliability and thus may introduce noise and hurt the performance. Since names often contain rare and uncommon words, this problem is particularly critical for name tagging. In this paper, we propose a novel reliability-aware name tagging model to tackle this issue. We design a set of word frequency-based reliability signals to indicate the quality of each word embedding. Guided by the reliability signals, the model is able to dynamically select and compose features such as word embedding and character-level representation using gating mechanisms. For example, if an input word is rare, the model relies less on its word embedding and assigns higher weights to its character and contextual features. Experiments on OntoNotes 5.0 show that our model outperforms the baseline model by 2.7% absolute gain in F-score. In cross-genre experiments on five genres in OntoNotes, our model improves the performance for most genre pairs and obtains up to 5% absolute F-score gain.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1017", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1017.flac", "type": "audio", "duration": 746.133}, {"from": "Assistant", "value": "Unsupervised neural machine translation (NMT) has attracted a lot of attention recently. While state-of-the-art methods for unsupervised translation usually perform well between similar languages (e.g., English-German translation), they perform poorly between distant languages, because unsupervised alignment does not work well for distant languages. In this work, we introduce unsupervised pivot translation for distant languages, which translates a language to a distant language through multiple hops, and the unsupervised translation on each hop is relatively easier than the original direct translation. We propose a learning to route (LTR) method to choose the translation path between the source and target languages. LTR is trained on language pairs whose best translation path is available and is applied on the unseen language pairs for path selection. Experiments on 20 languages and 294 distant language pairs demonstrate the advantages of the unsupervised pivot translation for distant languages, as well as the effectiveness of the proposed LTR for path selection. Specifically, in the best case, LTR achieves an improvement of 5.58 BLEU points over the conventional direct unsupervised method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1018", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1018.flac", "type": "audio", "duration": 845.845}, {"from": "Assistant", "value": "Recent work on bilingual lexicon induction (BLI) has frequently depended either on aligned bilingual lexicons or on distribution matching, often with an assumption about the isometry of the two spaces. We propose a technique to quantitatively estimate this assumption of the isometry between two embedding spaces and empirically show that this assumption weakens as the languages in question become increasingly etymologically distant. We then propose Bilingual Lexicon Induction with Semi-Supervision (BLISS) — a semi-supervised approach that relaxes the isometric assumption while leveraging both limited aligned bilingual lexicons and a larger set of unaligned word embeddings, as well as a novel hubness filtering technique. Our proposed method obtains state of the art results on 15 of 18 language pairs on the MUSE dataset, and does particularly well when the embedding spaces don’t appear to be isometric. In addition, we also show that adding supervision stabilizes the learning procedure, and is effective even with minimal supervision.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1019", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1019.flac", "type": "audio", "duration": 913.408}, {"from": "Assistant", "value": "While machine translation has traditionally relied on large amounts of parallel corpora, a recent research line has managed to train both Neural Machine Translation (NMT) and Statistical Machine Translation (SMT) systems using monolingual corpora only. In this paper, we identify and address several deficiencies of existing unsupervised SMT approaches by exploiting subword information, developing a theoretically well founded unsupervised tuning method, and incorporating a joint refinement procedure. Moreover, we use our improved SMT system to initialize a dual NMT model, which is further fine-tuned through on-the-fly back-translation. Together, we obtain large improvements over the previous state-of-the-art in unsupervised machine translation. For instance, we get 22.5 BLEU points in English-to-German WMT 2014, 5.5 points more than the previous best unsupervised system, and 0.5 points more than the (supervised) shared task winner back in 2014.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1020", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1020.flac", "type": "audio", "duration": 495.488}, {"from": "Assistant", "value": "A regularization technique based on adversarial perturbation, which was initially developed in the field of image processing, has been successfully applied to text classification tasks and has yielded attractive improvements. We aim to further leverage this promising methodology into more sophisticated and critical neural models in the natural language processing field, i.e., neural machine translation (NMT) models. However, it is not trivial to apply this methodology to such models. Thus, this paper investigates the effectiveness of several possible configurations of applying the adversarial perturbation and reveals that the adversarial regularization technique can significantly and consistently improve the performance of widely used NMT models, such as LSTM-based and Transformer-based models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1021", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1021.flac", "type": "audio", "duration": 565.44}, {"from": "Assistant", "value": "It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions, underperforming phrase-based statistical machine translation (PBSMT) and requiring large amounts of auxiliary data to achieve competitive results. In this paper, we re-assess the validity of these results, arguing that they are the result of lack of system adaptation to low-resource settings. We discuss some pitfalls to be aware of when training low-resource NMT systems, and recent techniques that have shown to be especially helpful in low-resource settings, resulting in a set of best practices for low-resource NMT. In our experiments on German–English with different amounts of IWSLT14 training data, we show that, without the use of any auxiliary monolingual or multilingual data, an optimized NMT system can outperform PBSMT with far less data than previously claimed. We also apply these techniques to a low-resource Korean–English dataset, surpassing previously reported results by 4 BLEU.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1022", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1022.flac", "type": "audio", "duration": 592.939}, {"from": "Assistant", "value": "We investigate adaptive ensemble weighting for Neural Machine Translation, addressing the case of improving performance on a new and potentially unknown domain without sacrificing performance on the original domain. We adapt sequentially across two Spanish-English and three English-German tasks, comparing unregularized fine-tuning, L2 and Elastic Weight Consolidation. We then report a novel scheme for adaptive NMT ensemble decoding by extending Bayesian Interpolation with source information, and report strong improvements across test domains without access to the domain label.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1023", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1023.flac", "type": "audio", "duration": 859.051}, {"from": "Assistant", "value": "We study relation extraction for knowledge base (KB) enrichment. Specifically, we aim to extract entities and their relationships from sentences in the form of triples and map the elements of the extracted triples to an existing KB in an end-to-end manner. Previous studies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into the KB space. This way, NED errors may cause extraction errors that affect the overall precision and recall. To address this problem, we propose an end-to-end relation extraction model for KB enrichment based on a neural encoder-decoder model. We collect high-quality training data by distant supervision with co-reference resolution and paraphrase detection. We propose an n-gram based attention model that captures multi-word entity names in a sentence. Our model employs jointly learned word and entity embeddings to support named entity disambiguation. Finally, our model uses a modified beam search and a triple classifier to help generate high-quality triples. Our model outperforms state-of-the-art baselines by 15.51% and 8.38% in terms of F1 score on two real-world datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1024", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1024.flac", "type": "audio", "duration": 808.0}, {"from": "Assistant", "value": "Dependency trees convey rich structural information that is proven useful for extracting relations among entities in text. However, how to effectively make use of relevant information while ignoring irrelevant information from the dependency trees remains a challenging research question. Existing approaches employing rule based hard-pruning strategies for selecting relevant partial dependency structures may not always yield optimal results. In this work, we propose Attention Guided Graph Convolutional Networks (AGGCNs), a novel model which directly takes full dependency trees as inputs. Our model can be understood as a soft-pruning approach that automatically learns how to selectively attend to the relevant sub-structures useful for the relation extraction task. Extensive results on various tasks including cross-sentence n-ary relation extraction and large-scale sentence-level relation extraction show that our model is able to better leverage the structural information of the full dependency trees, giving significantly better results than previous approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1025", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1025.flac", "type": "audio", "duration": 918.805}, {"from": "Assistant", "value": "Spatial aggregation refers to merging of documents created at the same spatial location. We show that by spatial aggregation of a large collection of documents and applying a traditional topic discovery algorithm on the aggregated data we can efficiently discover spatially distinct topics. By looking at topic discovery through matrix factorization lenses we show that spatial aggregation allows low rank approximation of the original document-word matrix, in which spatially distinct topics are preserved and non-spatial topics are aggregated into a single topic. Our experiments on synthetic data confirm this observation. Our experiments on 4.7 million tweets collected during the Sandy Hurricane in 2012 show that spatial and temporal aggregation allows rapid discovery of relevant spatial and temporal topics during that period. Our work indicates that different forms of document aggregation might be effective in rapid discovery of various types of distinct topics from large collections of documents.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1026", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1026.flac", "type": "audio", "duration": 879.531}, {"from": "Assistant", "value": "Link prediction is critical for the application of incomplete knowledge graph (KG) in the downstream tasks. As a family of effective approaches for link predictions, embedding methods try to learn low-rank representations for both entities and relations such that the bilinear form defined therein is a well-behaved scoring function. Despite of their successful performances, existing bilinear forms overlook the modeling of relation compositions, resulting in lacks of interpretability for reasoning on KG. To fulfill this gap, we propose a new model called DihEdral, named after dihedral symmetry group. This new model learns knowledge graph embeddings that can capture relation compositions by nature. Furthermore, our approach models the relation embeddings parametrized by discrete values, thereby decrease the solution space drastically. Our experiments show that DihEdral is able to capture all desired properties such as (skew-) symmetry, inversion and (non-) Abelian composition, and outperforms existing bilinear form based approach and is comparable to or better than deep learning models such as ConvE.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1027", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1027.flac", "type": "audio", "duration": 968.896}, {"from": "Assistant", "value": "Pretrained contextual and non-contextual subword embeddings have become available in over 250 languages, allowing massively multilingual NLP. However, while there is no dearth of pretrained embeddings, the distinct lack of systematic evaluations makes it difficult for practitioners to choose between them. In this work, we conduct an extensive evaluation comparing non-contextual subword embeddings, namely FastText and BPEmb, and a contextual representation method, namely BERT, on multilingual named entity recognition and part-of-speech tagging. We find that overall, a combination of BERT, BPEmb, and character representations works best across languages and tasks. A more detailed analysis reveals different strengths and weaknesses: Multilingual BERT performs well in medium- to high-resource languages, but is outperformed by non-contextual subword embeddings in a low-resource setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1028", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1028.flac", "type": "audio", "duration": 876.096}, {"from": "Assistant", "value": "Today, the dominant paradigm for training neural networks involves minimizing task loss on a large dataset. Using world knowledge to inform a model, and yet retain the ability to perform end-to-end training remains an open question. In this paper, we present a novel framework for introducing declarative knowledge to neural network architectures in order to guide training and prediction. Our framework systematically compiles logical statements into computation graphs that augment a neural network without extra learnable parameters or manual redesign. We evaluate our modeling strategy on three tasks: machine comprehension, natural language inference, and text chunking. Our experiments show that knowledge-augmented networks can strongly improve over baselines, especially in low-data regimes.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1029", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1029.flac", "type": "audio", "duration": 1139.605}, {"from": "Assistant", "value": "Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning. We show how self-regulation strategies that decide when to ask for which kind of feedback from a teacher (or from oneself) can be cast as a learning-to-learn problem leading to improved cost-aware sequence-to-sequence learning. In experiments on interactive neural machine translation, we find that the self-regulator discovers an𝜖-greedy strategy for the optimal cost-quality trade-off by mixing different feedback types including corrections, error markups, and self-supervision. Furthermore, we demonstrate its robustness under domain shift and identify it as a promising alternative to active learning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1030", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1030.flac", "type": "audio", "duration": 747.371}, {"from": "Assistant", "value": "In recent NLP research, a topic of interest is universal sentence encoding, sentence representations that can be used in any supervised task. At the word sequence level, fully attention-based models suffer from two problems: a quadratic increase in memory consumption with respect to the sentence length and an inability to capture and use syntactic information. Recursive neural nets can extract very good syntactic information by traversing a tree structure. To this end, we propose Tree Transformer, a model that captures phrase level syntax for constituency trees as well as word-level dependencies for dependency trees by doing recursive traversal only with attention. Evaluation of this model on four tasks gets noteworthy results compared to the standard transformer and LSTM-based models as well as tree-structured LSTMs. Ablation studies to find whether positional information is inherently encoded in the trees and which type of attention is suitable for doing the recursive traversal are provided.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1031", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1031.flac", "type": "audio", "duration": 630.699}, {"from": "Assistant", "value": "Neural parsers obtain state-of-the-art results on benchmark treebanks for constituency parsing—but to what degree do they generalize to other domains? We present three results about the generalization of neural parsers in a zero-shot setting: training on trees from one corpus and evaluating on out-of-domain corpora. First, neural and non-neural parsers generalize comparably to new domains. Second, incorporating pre-trained encoder representations into neural parsers substantially improves their performance across all domains, but does not give a larger relative improvement for out-of-domain treebanks. Finally, despite the rich input representations they learn, neural parsers still benefit from structured output prediction of output trees, yielding higher exact match accuracy and stronger generalization both to larger text spans and to out-of-domain corpora. We analyze generalization on English and Chinese corpora, and in the process obtain state-of-the-art parsing results for the Brown, Genia, and English Web treebanks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1032", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1032.flac", "type": "audio", "duration": 640.576}, {"from": "Assistant", "value": "We propose a novel self-attention mechanism that can learn its optimal attention span. This allows us to extend significantly the maximum context size used in Transformer, while maintaining control over their memory footprint and computational time. We show the effectiveness of our approach on the task of character level language modeling, where we achieve state-of-the-art performances on text8 and enwiki8 by using a maximum context of 8k characters.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1078", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1078.flac", "type": "audio", "duration": 870.208}, {"from": "Assistant", "value": "Over-dependence on domain ontology and lack of sharing knowledge across domains are two practical and yet less studied problems of dialogue state tracking. Existing approaches generally fall short when tracking unknown slot values during inference and often have difficulties in adapting to new domains. In this paper, we propose a Transferable Dialogue State Generator (TRADE) that generates dialogue states from utterances using copy mechanism, facilitating transfer when predicting (domain, slot, value) triplets not encountered during training. Our model is composed of an utterance encoder, a slot gate, and a state generator, which are shared across domains. Empirical results demonstrate that TRADE achieves state-of-the-art 48.62% joint goal accuracy for the five domains of MultiWOZ, a human-human dialogue dataset. In addition, we show the transferring ability by simulating zero-shot and few-shot dialogue state tracking for unseen domains. TRADE achieves 60.58% joint goal accuracy in one of the zero-shot domains, and is able to adapt to few-shot cases without forgetting already trained domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1079", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1079.flac", "type": "audio", "duration": 896.491}, {"from": "Assistant", "value": "We present methods for multi-task learning that take advantage of natural groupings of related tasks. Task groups may be defined along known properties of the tasks, such as task domain or language. Such task groups represent supervised information at the inter-task level and can be encoded into the model. We investigate two variants of neural network architectures that accomplish this, learning different feature spaces at the levels of individual tasks, task groups, as well as the universe of all tasks: (1) parallel architectures encode each input simultaneously into feature spaces at different levels; (2) serial architectures encode each input successively into feature spaces at different levels in the task hierarchy. We demonstrate the methods on natural language understanding (NLU) tasks, where a grouping of tasks into different task domains leads to improved performance on ATIS, Snips, and a large in-house dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1080", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1080.flac", "type": "audio", "duration": 968.32}, {"from": "Assistant", "value": "Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems. Avenues like the E2E NLG Challenge have encouraged the development of neural approaches, particularly sequence-to-sequence (Seq2Seq) models for this problem. The semantic representations used, however, are often underspecified, which places a higher burden on the generation model for sentence planning, and also limits the extent to which generated responses can be controlled in a live system. In this paper, we (1) propose using tree-structured semantic representations, like those used in traditional rule-based NLG systems, for better discourse-level structuring and sentence-level planning; (2) introduce a challenging dataset using this representation for the weather domain; (3) introduce a constrained decoding approach for Seq2Seq models that leverages this representation to improve semantic correctness; and (4) demonstrate promising results on our dataset and the E2E dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1081", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1081.flac", "type": "audio", "duration": 832.811}, {"from": "Assistant", "value": "We study a conversational reasoning model that strategically traverses through a large-scale common fact knowledge graph (KG) to introduce engaging and contextually diverse entities and attributes. For this study, we collect a new Open-ended Dialog <-> KG parallel corpus called OpenDialKG, where each utterance from 15K human-to-human role-playing dialogs is manually annotated with ground-truth reference to corresponding entities and paths from a large-scale KG with 1M+ facts. We then propose the DialKG Walker model that learns the symbolic transitions of dialog contexts as structured traversals over KG, and predicts natural entities to introduce given previous dialog contexts via a novel domain-agnostic, attention-based graph path decoder. Automatic and human evaluations show that our model can retrieve more natural and human-like responses than the state-of-the-art baselines or rule-based models, in both in-domain and cross-domain tasks. The proposed model also generates a KG walk path for each entity retrieved, providing a natural way to explain conversational reasoning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1082", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1082.flac", "type": "audio", "duration": 862.336}, {"from": "Assistant", "value": "In this paper, we present an approach to incorporate retrieved datapoints as supporting evidence for context-dependent semantic parsing, such as generating source code conditioned on the class environment. Our approach naturally combines a retrieval model and a meta-learner, where the former learns to find similar datapoints from the training data, and the latter considers retrieved datapoints as a pseudo task for fast adaptation. Specifically, our retriever is a context-aware encoder-decoder model with a latent variable which takes context environment into consideration, and our meta-learner learns to utilize retrieved datapoints in a model-agnostic meta-learning paradigm for fast adaptation. We conduct experiments on CONCODE and CSQA datasets, where the context refers to class environment in JAVA codes and conversational history, respectively. We use sequence-to-action model as the base semantic parser, which performs the state-of-the-art accuracy on both datasets. Results show that both the context-aware retriever and the meta-learning strategy improve accuracy, and our approach performs better than retrieve-and-edit baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1083", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1083.flac", "type": "audio", "duration": 1116.16}, {"from": "Assistant", "value": "Resolving pronoun coreference requires knowledge support, especially for particular domains (e.g., medicine). In this paper, we explore how to leverage different types of knowledge to better resolve pronoun coreference with a neural model. To ensure the generalization ability of our model, we directly incorporate knowledge in the format of triplets, which is the most common format of modern knowledge graphs, instead of encoding it with features or rules as that in conventional approaches. Moreover, since not all knowledge is helpful in certain contexts, to selectively use them, we propose a knowledge attention module, which learns to select and use informative knowledge based on contexts, to enhance our model. Experimental results on two datasets from different domains prove the validity and effectiveness of our model, where it outperforms state-of-the-art baselines by a large margin. Moreover, since our model learns to use external knowledge rather than only fitting the training data, it also demonstrates superior performance to baselines in the cross-domain setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1084", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1084.flac", "type": "audio", "duration": 749.205}, {"from": "Assistant", "value": "Natural Language Inference (NLI) datasets often contain hypothesis-only biases—artifacts that allow models to achieve non-trivial performance without learning whether a premise entails a hypothesis. We propose two probabilistic methods to build models that are more robust to such biases and better transfer across datasets. In contrast to standard approaches to NLI, our methods predict the probability of a premise given a hypothesis and NLI label, discouraging models from ignoring the premise. We evaluate our methods on synthetic and existing NLI datasets by training on datasets containing biases and testing on datasets containing no (or different) hypothesis-only biases. Our results indicate that these methods can make NLI models more robust to dataset-specific artifacts, transferring better than a baseline architecture in 9 out of 12 NLI datasets. Additionally, we provide an extensive analysis of the interplay of our methods with known biases in NLI datasets, as well as the effects of encouraging models to ignore biases and fine-tuning on target datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1085", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1085.flac", "type": "audio", "duration": 929.728}, {"from": "Assistant", "value": "Fact verification (FV) is a challenging task which requires to retrieve relevant evidence from plain text and use the evidence to verify given claims. Many claims require to simultaneously integrate and reason over several pieces of evidence for verification. However, previous work employs simple models to extract information from evidence without letting evidence communicate with each other, e.g., merely concatenate the evidence for processing. Therefore, these methods are unable to grasp sufficient relational and logical information among the evidence. To alleviate this issue, we propose a graph-based evidence aggregating and reasoning (GEAR) framework which enables information to transfer on a fully-connected evidence graph and then utilizes different aggregators to collect multi-evidence information. We further employ BERT, an effective pre-trained language representation model, to improve the performance. Experimental results on a large-scale benchmark dataset FEVER have demonstrated that GEAR could leverage multi-evidence information for FV and thus achieves the promising result with a test FEVER score of 67.10%. Our code is available athttps://github.com/thunlp/GEAR.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1086", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1086.flac", "type": "audio", "duration": 1333.205}, {"from": "Assistant", "value": "We present SherLIiC, a testbed for lexical inference in context (LIiC), consisting of 3985 manually annotated inference rule candidates (InfCands), accompanied by (i) ~960k unlabeled InfCands, and (ii) ~190k typed textual relations between Freebase entities extracted from the large entity-linked corpus ClueWeb09. Each InfCand consists of one of these relations, expressed as a lemmatized dependency path, and two argument placeholders, each linked to one or more Freebase types. Due to our candidate selection process based on strong distributional evidence, SherLIiC is much harder than existing testbeds because distributional evidence is of little utility in the classification of InfCands. We also show that, due to its construction, many of SherLIiC’s correct InfCands are novel and missing from existing rule bases. We evaluate a large number of strong baselines on SherLIiC, ranging from semantic vector space models to state of the art neural models of natural language inference (NLI). We show that SherLIiC poses a tough challenge to existing NLI systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1087", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1087.flac", "type": "audio", "duration": 773.291}, {"from": "Assistant", "value": "This paper describes novel models tailored for a new application, that of extracting the symptoms mentioned in clinical conversations along with their status. Lack of any publicly available corpus in this privacy-sensitive domain led us to develop our own corpus, consisting of about 3K conversations annotated by professional medical scribes. We propose two novel deep learning approaches to infer the symptom names and their status: (1) a new hierarchical span-attribute tagging (SA-T) model, trained using curriculum learning, and (2) a variant of sequence-to-sequence model which decodes the symptoms and their status from a few speaker turns within a sliding window over the conversation. This task stems from a realistic application of assisting medical providers in capturing symptoms mentioned by patients from their clinical conversations. To reflect this application, we define multiple metrics. From inter-rater agreement, we find that the task is inherently difficult. We conduct comprehensive evaluations on several contrasting conditions and observe that the performance of the models range from an F-score of 0.5 to 0.8 depending on the condition. Our analysis not only reveals the inherent challenges of the task, but also provides useful directions to improve the models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1088", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1088.flac", "type": "audio", "duration": 946.965}, {"from": "Assistant", "value": "The quality of a counseling intervention relies highly on the active collaboration between clients and counselors. In this paper, we explore several linguistic aspects of the collaboration process occurring during counseling conversations. Specifically, we address the differences between high-quality and low-quality counseling. Our approach examines participants’ turn-by-turn interaction, their linguistic alignment, the sentiment expressed by speakers during the conversation, as well as the different topics being discussed. Our results suggest important language differences in low- and high-quality counseling, which we further use to derive linguistic features able to capture the differences between the two groups. These features are then used to build automatic classifiers that can predict counseling quality with accuracies of up to 88%.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1089", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1089.flac", "type": "audio", "duration": 855.253}, {"from": "Assistant", "value": "Mental health counseling is an enterprise with profound societal importance where conversations play a primary role. In order to acquire the conversational skills needed to face a challenging range of situations, mental health counselors must rely on training and on continued experience with actual clients. However, in the absence of large scale longitudinal studies, the nature and significance of this developmental process remain unclear. For example, prior literature suggests that experience might not translate into consequential changes in counselor behavior. This has led some to even argue that counseling is a profession without expertise. In this work, we develop a computational framework to quantify the extent to which individuals change their linguistic behavior with experience and to study the nature of this evolution. We use our framework to conduct a large longitudinal study of mental health counseling conversations, tracking over 3,400 counselors across their tenure. We reveal that overall, counselors do indeed change their conversational behavior to become more diverse across interactions, developing an individual voice that distinguishes them from other counselors. Furthermore, a finer-grained investigation shows that the rate and nature of this diversification vary across functionally different conversational components.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1090", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1090.flac", "type": "audio", "duration": 516.459}, {"from": "Assistant", "value": "We discuss ongoing work into automating a multilingual digital helpdesk service available via text messaging to pregnant and breastfeeding mothers in South Africa. Our anonymized dataset consists of short informal questions, often in low-resource languages, with unreliable language labels, spelling errors and code-mixing, as well as template answers with some inconsistencies. We explore cross-lingual word embeddings, and train parametric and non-parametric models on 90K samples for answer selection from a set of 126 templates. Preliminary results indicate that LSTMs trained end-to-end perform best, with a test accuracy of 62.13% and a recall@5 of 89.56%, and demonstrate that we can accelerate response time by several orders of magnitude.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1091", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1091.flac", "type": "audio", "duration": 716.011}, {"from": "Assistant", "value": "Negative medical findings are prevalent in clinical reports, yet discriminating them from positive findings remains a challenging task for in-formation extraction. Most of the existing systems treat this task as a pipeline of two separate tasks, i.e., named entity recognition (NER)and rule-based negation detection. We consider this as a multi-task problem and present a novel end-to-end neural model to jointly extract entities and negations. We extend a standard hierarchical encoder-decoder NER model and first adopt a shared encoder followed by separate decoders for the two tasks. This architecture performs considerably better than the previous rule-based and machine learning-based systems. To overcome the problem of increased parameter size especially for low-resource settings, we propose the Conditional Softmax Shared Decoder architecture which achieves state-of-art results for NER and negation detection on the 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1092", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1092.flac", "type": "audio", "duration": 651.648}, {"from": "Assistant", "value": "We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1093", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1093.flac", "type": "audio", "duration": 1096.896}, {"from": "Assistant", "value": "With the advancement in argument detection, we suggest to pay more attention to the challenging task of identifying the more convincing arguments. Machines capable of responding and interacting with humans in helpful ways have become ubiquitous. We now expect them to discuss with us the more delicate questions in our world, and they should do so armed with effective arguments. But what makes an argument more persuasive? What will convince you? In this paper, we present a new data set, IBM-EviConv, of pairs of evidence labeled for convincingness, designed to be more challenging than existing alternatives. We also propose a Siamese neural network architecture shown to outperform several baselines on both a prior convincingness data set and our own. Finally, we provide insights into our experimental results and the various kinds of argumentative value our method is capable of detecting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1094", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1094.flac", "type": "audio", "duration": 918.123}, {"from": "Assistant", "value": "When debating a controversial topic, it is often desirable to expand the boundaries of discussion. For example, we may consider the pros and cons of possible alternatives to the debate topic, make generalizations, or give specific examples. We introduce the task of Debate Topic Expansion - finding such related topics for a given debate topic, along with a novel annotated dataset for the task. We focus on relations between Wikipedia concepts, and show that they differ from well-studied lexical-semantic relations such as hypernyms, hyponyms and antonyms. We present algorithms for finding both consistent and contrastive expansions and demonstrate their effectiveness empirically. We suggest that debate topic expansion may have various use cases in argumentation mining.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1095", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1095.flac", "type": "audio", "duration": 1200.533}, {"from": "Assistant", "value": "Studies on emotion recognition (ER) show that combining lexical and acoustic information results in more robust and accurate models. The majority of the studies focus on settings where both modalities are available in training and evaluation. However, in practice, this is not always the case; getting ASR output may represent a bottleneck in a deployment pipeline due to computational complexity or privacy-related constraints. To address this challenge, we study the problem of efficiently combining acoustic and lexical modalities during training while still providing a deployable acoustic model that does not require lexical inputs. We first experiment with multimodal models and two attention mechanisms to assess the extent of the benefits that lexical information can provide. Then, we frame the task as a multi-view learning problem to induce semantic information from a multimodal model into our acoustic-only network using a contrastive loss function. Our multimodal model outperforms the previous state of the art on the USC-IEMOCAP dataset reported on lexical and acoustic information. Additionally, our multi-view-trained acoustic network significantly surpasses models that have been exclusively trained with acoustic features.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1096", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1096.flac", "type": "audio", "duration": 875.179}, {"from": "Assistant", "value": "Emotion cause extraction (ECE), the task aimed at extracting the potential causes behind certain emotions in text, has gained much attention in recent years due to its wide applications. However, it suffers from two shortcomings: 1) the emotion must be annotated before cause extraction in ECE, which greatly limits its applications in real-world scenarios; 2) the way to first annotate emotion and then extract the cause ignores the fact that they are mutually indicative. In this work, we propose a new task: emotion-cause pair extraction (ECPE), which aims to extract the potential pairs of emotions and corresponding causes in a document. We propose a 2-step approach to address this new ECPE task, which first performs individual emotion extraction and cause extraction via multi-task learning, and then conduct emotion-cause pairing and filtering. The experimental results on a benchmark emotion cause corpus prove the feasibility of the ECPE task as well as the effectiveness of our approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1097", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1097.flac", "type": "audio", "duration": 1188.48}, {"from": "Assistant", "value": "Competitive debaters often find themselves facing a challenging task – how to debate a topic they know very little about, with only minutes to prepare, and without access to books or the Internet? What they often do is rely on ”first principles”, commonplace arguments which are relevant to many topics, and which they have refined in past debates. In this work we aim to explicitly define a taxonomy of such principled recurring arguments, and, given a controversial topic, to automatically identify which of these arguments are relevant to the topic. As far as we know, this is the first time that this approach to argument invention is formalized and made explicit in the context of NLP. The main goal of this work is to show that it is possible to define such a taxonomy. While the taxonomy suggested here should be thought of as a ”first attempt” it is nonetheless coherent, covers well the relevant topics and coincides with what professional debaters actually argue in their speeches, and facilitates automatic argument invention for new topics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1098", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1098.flac", "type": "audio", "duration": 786.496}, {"from": "Assistant", "value": "The most important obstacles facing multi-document summarization include excessive redundancy in source descriptions and the looming shortage of training data. These obstacles prevent encoder-decoder models from being used directly, but optimization-based methods such as determinantal point processes (DPPs) are known to handle them well. In this paper we seek to strengthen a DPP-based method for extractive multi-document summarization by presenting a novel similarity measure inspired by capsule networks. The approach measures redundancy between a pair of sentences based on surface form and semantic information. We show that our DPP system with improved similarity measure performs competitively, outperforming strong summarization baselines on benchmark datasets. Our findings are particularly meaningful for summarizing documents created by multiple authors containing redundant yet lexically diverse expressions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1099", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1099.flac", "type": "audio", "duration": 748.928}, {"from": "Assistant", "value": "We propose a global optimization method under length constraint (GOLC) for neural text summarization models. GOLC increases the probabilities of generating summaries that have high evaluation scores, ROUGE in this paper, within a desired length. We compared GOLC with two optimization methods, a maximum log-likelihood and a minimum risk training, on CNN/Daily Mail and a Japanese single document summarization data set of The Mainichi Shimbun Newspapers. The experimental results show that a state-of-the-art neural summarization model optimized with GOLC generates fewer overlength summaries while maintaining the fastest processing speed; only 6.70% overlength summaries on CNN/Daily and 7.8% on long summary of Mainichi, compared to the approximately 20% to 50% on CNN/Daily Mail and 10% to 30% on Mainichi with the other optimization methods. We also demonstrate the importance of the generation of in-length summaries for post-editing with the dataset Mainich that is created with strict length constraints. The ex- perimental results show approximately 30% to 40% improved post-editing time by use of in-length summaries.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1100", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1100.flac", "type": "audio", "duration": 832.896}, {"from": "Assistant", "value": "The recent years have seen remarkable success in the use of deep neural networks on text summarization. However, there is no clear understanding of why they perform so well, or how they might be improved. In this paper, we seek to better understand how neural extractive summarization systems could benefit from different types of model architectures, transferable knowledge and learning schemas. Besides, we find an effective way to improve the current framework and achieve the state-of-the-art result on CNN/DailyMail by a large margin based on our observations and analysis. Hopefully, our work could provide more hints for future research on extractive summarization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1101", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1101.flac", "type": "audio", "duration": 872.533}, {"from": "Assistant", "value": "Research on summarization has mainly been driven by empirical approaches, crafting systems to perform well on standard datasets with the notion of information Importance remaining latent. We argue that establishing theoretical models of Importance will advance our understanding of the task and help to further improve summarization systems. To this end, we propose simple but rigorous definitions of several concepts that were previously used only intuitively in summarization: Redundancy, Relevance, and Informativeness. Importance arises as a single quantity naturally unifying these concepts. Additionally, we provide intuitions to interpret the proposed quantities and experiments to demonstrate the potential of the framework to inform and guide subsequent works.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1102", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1102.flac", "type": "audio", "duration": 851.605}, {"from": "Assistant", "value": "Automatic generation of summaries from multiple news articles is a valuable tool as the number of online publications grows rapidly. Single document summarization (SDS) systems have benefited from advances in neural encoder-decoder model thanks to the availability of large datasets. However, multi-document summarization (MDS) of news articles has been limited to datasets of a couple of hundred examples. In this paper, we introduce Multi-News, the first large-scale MDS news dataset. Additionally, we propose an end-to-end model which incorporates a traditional extractive summarization model with a standard SDS model and achieves competitive results on MDS datasets. We benchmark several methods on Multi-News and hope that this work will promote advances in summarization in the multi-document setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1103", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1103.flac", "type": "audio", "duration": 839.168}, {"from": "Assistant", "value": "We address the problem of adversarial attacks on text classification, which is rarely studied comparing to attacks on image classification. The challenge of this task is to generate adversarial examples that maintain lexical correctness, grammatical correctness and semantic similarity. Based on the synonyms substitution strategy, we introduce a new word replacement order determined by both the word saliency and the classification probability, and propose a greedy algorithm called probability weighted word saliency (PWWS) for text adversarial attack. Experiments on three popular datasets using convolutional as well as LSTM models show that PWWS reduces the classification accuracy to the most extent, and keeps a very low word substitution rate. A human evaluation study shows that our generated adversarial examples maintain the semantic similarity well and are hard for humans to perceive. Performing adversarial training using our perturbed datasets improves the robustness of the models. At last, our method also exhibits a good transferability on the generated adversarial examples.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1104", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1104.flac", "type": "audio", "duration": 1248.96}, {"from": "Assistant", "value": "Authorship verification is the task of determining whether two texts were written by the same author. We deal with the adversary task, called authorship obfuscation: preventing verification by altering a to-be-obfuscated text. Our new obfuscation approach (1) models writing style difference as the Jensen-Shannon distance between the character n-gram distributions of texts, and (2) manipulates an author’s subconsciously encoded writing style in a sophisticated manner using heuristic search. To obfuscate, we analyze the huge space of textual variants for a paraphrased version of the to-be-obfuscated text that has a sufficient Jensen-Shannon distance at minimal costs in terms of text quality. We analyze, quantify, and illustrate the rationale of this approach, define paraphrasing operators, derive obfuscation thresholds, and develop an effective obfuscation framework. Our authorship obfuscation approach defeats state-of-the-art verification approaches, including unmasking and compression models, while keeping text changes at a minimum.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1105", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1105.flac", "type": "audio", "duration": 1217.323}, {"from": "Assistant", "value": "Distributions of the senses of words are often highly skewed and give a strong influence of the domain of a document. This paper follows the assumption and presents a method for text categorization by leveraging the predominant sense of words depending on the domain, i.e., domain-specific senses. The key idea is that the features learned from predominant senses are possible to discriminate the domain of the document and thus improve the overall performance of text categorization. We propose multi-task learning framework based on the neural network model, transformer, which trains a model to simultaneously categorize documents and predicts a predominant sense for each word. The experimental results using four benchmark datasets show that our method is comparable to the state-of-the-art categorization approach, especially our model works well for categorization of multi-label documents.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1159", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1159.flac", "type": "audio", "duration": 928.96}, {"from": "Assistant", "value": "As Natural Language Processing (NLP) and Machine Learning (ML) tools rise in popularity, it becomes increasingly vital to recognize the role they play in shaping societal biases and stereotypes. Although NLP models have shown success in modeling various applications, they propagate and may even amplify gender bias found in text corpora. While the study of bias in artificial intelligence is not new, methods to mitigate gender bias in NLP are relatively nascent. In this paper, we review contemporary studies on recognizing and mitigating gender bias in NLP. We discuss gender bias based on four forms of representation bias and analyze methods recognizing gender bias. Furthermore, we discuss the advantages and drawbacks of existing gender debiasing methods. Finally, we discuss future studies for recognizing and mitigating gender bias in NLP.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1160", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1160.flac", "type": "audio", "duration": 1138.539}, {"from": "Assistant", "value": "Word embeddings learnt from massive text collections have demonstrated significant levels of discriminative biases such as gender, racial or ethnic biases, which in turn bias the down-stream NLP applications that use those word embeddings. Taking gender-bias as a working example, we propose a debiasing method that preserves non-discriminative gender-related information, while removing stereotypical discriminative gender biases from pre-trained word embeddings. Specifically, we consider four types of information:feminine,masculine,gender-neutralandstereotypical, which represent the relationship between gender vs. bias, and propose a debiasing method that (a) preserves the gender-related information in feminine and masculine words, (b) preserves the neutrality in gender-neutral words, and (c) removes the biases from stereotypical words. Experimental results on several previously proposed benchmark datasets show that our proposed method can debias pre-trained word embeddings better than existing SoTA methods proposed for debiasing word embeddings while preserving gender-related but non-discriminative information.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1161", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1161.flac", "type": "audio", "duration": 1104.491}, {"from": "Assistant", "value": "Gender stereotypes are manifest in most of the world’s languages and are consequently propagated or amplified by NLP systems. Although research has focused on mitigating gender stereotypes in English, the approaches that are commonly employed produce ungrammatical sentences in morphologically rich languages. We present a novel approach for converting between masculine-inflected and feminine-inflected sentences in such languages. For Spanish and Hebrew, our approach achieves F1 scores of 82% and 73% at the level of tags and accuracies of 90% and 87% at the level of forms. By evaluating our approach using four different languages, we show that, on average, it reduces gender stereotyping by a factor of 2.5 without any sacrifice to grammaticality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1162", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1162.flac", "type": "audio", "duration": 545.856}, {"from": "Assistant", "value": "Word embedding models have gained a lot of traction in the Natural Language Processing community, however, they suffer from unintended demographic biases. Most approaches to evaluate these biases rely on vector space based metrics like the Word Embedding Association Test (WEAT). While these approaches offer great geometric insights into unintended biases in the embedding vector space, they fail to offer an interpretable meaning for how the embeddings could cause discrimination in downstream NLP applications. In this work, we present a transparent framework and metric for evaluating discrimination across protected groups with respect to their word embedding bias. Our metric (Relative Negative Sentiment Bias, RNSB) measures fairness in word embeddings via the relative negative sentiment associated with demographic identity terms from various protected groups. We show that our framework and metric enable useful analysis into the bias in word embeddings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1163", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1163.flac", "type": "audio", "duration": 827.243}, {"from": "Assistant", "value": "We investigate how annotators’ insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations. We first uncover unexpected correlations between surface markers of African American English (AAE) and ratings of toxicity in several widely-used hate speech datasets. Then, we show that models trained on these corpora acquire and propagate these biases, such that AAE tweets and tweets by self-identified African Americans are up to two times more likely to be labelled as offensive compared to others. Finally, we propose *dialect* and *race priming* as ways to reduce the racial bias in annotation, showing that when annotators are made explicitly aware of an AAE tweet’s dialect they are significantly less likely to label the tweet as offensive.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1164", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1164.flac", "type": "audio", "duration": 571.413}, {"from": "Assistant", "value": "We present the first challenge set and evaluation protocol for the analysis of gender bias in machine translation (MT). Our approach uses two recent coreference resolution datasets composed of English sentences which cast participants into non-stereotypical gender roles (e.g., “The doctor asked the nurse to help her in the operation”). We devise an automatic gender bias evaluation method for eight target languages with grammatical gender, based on morphological analysis (e.g., the use of female inflection for the word “doctor”). Our analyses show that four popular industrial MT systems and two recent state-of-the-art academic MT models are significantly prone to gender-biased translation errors for all tested target languages. Our data and code are publicly available athttps://github.com/gabrielStanovsky/mt_gender.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1165", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1165.flac", "type": "audio", "duration": 694.933}, {"from": "Assistant", "value": "While word embeddings are now a de facto standard representation of words in most NLP tasks, recently the attention has been shifting towards vector representations which capture the different meanings, i.e., senses, of words. In this paper we explore the capabilities of a bidirectional LSTM model to learn representations of word senses from semantically annotated corpora. We show that the utilization of an architecture that is aware of word order, like an LSTM, enables us to create better representations. We assess our proposed model on various standard benchmarks for evaluating semantic representations, reaching state-of-the-art performance on the SemEval-2014 word-to-sense similarity task. We release the code and the resulting word and sense embeddings athttp://lcl.uniroma1.it/LSTMEmbed.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1166", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1166.flac", "type": "audio", "duration": 857.173}, {"from": "Assistant", "value": "Word embeddings are often criticized for capturing undesirable word associations such as gender stereotypes. However, methods for measuring and removing such biases remain poorly understood. We show that for any embedding model that implicitly does matrix factorization, debiasing vectors post hoc using subspace projection (Bolukbasi et al., 2016) is, under certain conditions, equivalent to training on an unbiased corpus. We also prove that WEAT, the most common association test for word embeddings, systematically overestimates bias. Given that the subspace projection method is provably effective, we use it to derive a new measure of association called the relational inner product association (RIPA). Experiments with RIPA reveal that, on average, skipgram with negative sampling (SGNS) does not make most words any more gendered than they are in the training corpus. However, for gender-stereotyped words, SGNS actually amplifies the gender association in the corpus.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1167", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1167.flac", "type": "audio", "duration": 879.573}, {"from": "Assistant", "value": "Studying the ways in which language is gendered has long been an area of interest in sociolinguistics. Studies have explored, for example, the speech of male and female characters in film and the language used to describe male and female politicians. In this paper, we aim not to merely study this phenomenon qualitatively, but instead to quantify the degree to which the language used to describe men and women is different and, moreover, different in a positive or negative way. To that end, we introduce a generative latent-variable model that jointly represents adjective (or verb) choice, with its sentiment, given the natural gender of a head (or dependent) noun. We find that there are significant differences between descriptions of male and female nouns and that these differences align with common gender stereotypes: Positive adjectives used to describe women are more often related to their bodies than adjectives used to describe men.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1168", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1168.flac", "type": "audio", "duration": 848.853}, {"from": "Assistant", "value": "Given a small corpus D_T pertaining to a limited set of focused topics, our goal is to train embeddings that accurately capture the sense of words in the topic in spite of the limited size of D_T. These embeddings may be used in various tasks involving D_T. A popular strategy in limited data settings is to adapt pretrained embeddings E trained on a large corpus. To correct for sense drift, fine-tuning, regularization, projection, and pivoting have been proposed recently. Among these, regularization informed by a word’s corpus frequency performed well, but we improve upon it using a new regularizer based on the stability of its cooccurrence with other words. However, a thorough comparison across ten topics, spanning three tasks, with standardized settings of hyper-parameters, reveals that even the best embedding adaptation strategies provide small gains beyond well-tuned baselines, which many earlier comparisons ignored. In a bold departure from adapting pretrained embeddings, we propose using D_T to probe, attend to, and borrow fragments from any large, topic-rich source corpus (such as Wikipedia), which need not be the corpus used to pretrain embeddings. This step is made scalable and practical by suitable indexing. We reach the surprising conclusion that even limited corpus augmentation is more useful than adapting embeddings, which suggests that non-dominant sense information may be irrevocably obliterated from pretrained embeddings and cannot be salvaged by adaptation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1169", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1169.flac", "type": "audio", "duration": 887.573}, {"from": "Assistant", "value": "Lexical relations describe how meanings of terms relate to each other. Typical examples include hypernymy, synonymy, meronymy, etc. Automatic distinction of lexical relations is vital for NLP applications, and also challenging due to the lack of contextual signals to discriminate between such relations. In this work, we present a neural representation learning model to distinguish lexical relations among term pairs based on Hyperspherical Relation Embeddings (SphereRE). Rather than learning embeddings for individual terms, the model learns representations of relation triples by mapping them to the hyperspherical embedding space, where relation triples of different lexical relations are well separated. Experiments over several benchmarks confirm SphereRE outperforms state-of-the-arts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1170", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1170.flac", "type": "audio", "duration": 1121.963}, {"from": "Assistant", "value": "In this work we approach the task of learning multilingual word representations in an offline manner by fitting a generative latent variable model to a multilingual dictionary. We model equivalent words in different languages as different views of the same word generated by a common latent variable representing their latent lexical meaning. We explore the task of alignment by querying the fitted model for multilingual embeddings achieving competitive results across a variety of tasks. The proposed model is robust to noise in the embedding space making it a suitable method for distributed representations learned from noisy corpora.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1171", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1171.flac", "type": "audio", "duration": 825.856}, {"from": "Assistant", "value": "A longstanding debate in semiotics centers on the relationship between linguistic signs and their corresponding semantics: is there an arbitrary relationship between a word form and its meaning, or does some systematic phenomenon pervade? For instance, does the character bigram ‘gl’ have any systematic relationship to the meaning of words like ‘glisten’, ‘gleam’ and ‘glow’? In this work, we offer a holistic quantification of the systematicity of the sign using mutual information and recurrent neural networks. We employ these in a data-driven and massively multilingual approach to the question, examining 106 languages. We find a statistically significant reduction in entropy when modeling a word form conditioned on its semantic representation. Encouragingly, we also recover well-attested English examples of systematic affixes. We conclude with the meta-point: Our approximate effect size (measured in bits) is quite small—despite some amount of systematicity between form and meaning, an arbitrary relationship and its resulting benefits dominate human language.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1172", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1172.flac", "type": "audio", "duration": 1205.44}, {"from": "Assistant", "value": "A large percentage of computational tools are concentrated in a very small subset of the planet’s languages. Compounding the issue, many languages lack the high-quality linguistic annotation necessary for the construction of such tools with current machine learning methods. In this paper, we address both issues simultaneously: leveraging the high accuracy of English taggers and parsers, we project morphological information onto translations of the Bible in 26 varied test languages. Using an iterative discovery, constraint, and training process, we build inflectional lexica in the target languages. Through a combination of iteration, ensembling, and reranking, we see double-digit relative error reductions in lemmatization and morphological analysis over a strong initial system.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1173", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1173.flac", "type": "audio", "duration": 838.251}, {"from": "Assistant", "value": "Morphological tagging is challenging for morphologically rich languages due to the large target space and the need for more training data to minimize model sparsity. Dialectal variants of morphologically rich languages suffer more as they tend to be more noisy and have less resources. In this paper we explore the use of multitask learning and adversarial training to address morphological richness and dialectal variations in the context of full morphological tagging. We use multitask learning for joint morphological modeling for the features within two dialects, and as a knowledge-transfer scheme for cross-dialectal modeling. We use adversarial training to learn dialect invariant features that can help the knowledge-transfer scheme from the high to low-resource variants. We work with two dialectal variants: Modern Standard Arabic (high-resource “dialect’”) and Egyptian Arabic (low-resource dialect) as a case study. Our models achieve state-of-the-art results for both. Furthermore, adversarial training provides more significant improvement when using smaller training datasets in particular.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1174", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1174.flac", "type": "audio", "duration": 2396.48}, {"from": "Assistant", "value": "The reordering model plays an important role in phrase-based statistical machine translation. However, there are few works that exploit the reordering information in neural machine translation. In this paper, we propose a reordering mechanism to learn the reordering embedding of a word based on its contextual information. These learned reordering embeddings are stacked together with self-attention networks to learn sentence representation for machine translation. The reordering mechanism can be easily integrated into both the encoder and the decoder in the Transformer translation system. Experimental results on WMT’14 English-to-German, NIST Chinese-to-English, and WAT Japanese-to-English translation tasks demonstrate that the proposed methods can significantly improve the performance of the Transformer.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1175", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1175.flac", "type": "audio", "duration": 1154.325}, {"from": "Assistant", "value": "We present a simple yet powerful data augmentation method for boosting Neural Machine Translation (NMT) performance by leveraging information retrieved from a Translation Memory (TM). We propose and test two methods for augmenting NMT training data with fuzzy TM matches. Tests on the DGT-TM data set for two language pairs show consistent and substantial improvements over a range of baseline systems. The results suggest that this method is promising for any translation environment in which a sizeable TM is available and a certain amount of repetition across translations is to be expected, especially considering its ease of implementation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1176", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1176.flac", "type": "audio", "duration": 1157.696}, {"from": "Assistant", "value": "Transformer is the state-of-the-art model in recent machine translation evaluations. Two strands of research are promising to improve models of this kind: the first uses wide networks (a.k.a. Transformer-Big) and has been the de facto standard for development of the Transformer system, and the other uses deeper language representation but faces the difficulty arising from learning deep networks. Here, we continue the line of research on the latter. We claim that a truly deep Transformer model can surpass the Transformer-Big counterpart by 1) proper use of layer normalization and 2) a novel way of passing the combination of previous layers to the next. On WMT’16 English-German and NIST OpenMT’12 Chinese-English tasks, our deep system (30/25-layer encoder) outperforms the shallow Transformer-Big/Base baseline (6-layer encoder) by 0.4-2.4 BLEU points. As another bonus, the deep model is 1.6X smaller in size and 3X faster in training than Transformer-Big.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1177", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1177.flac", "type": "audio", "duration": 749.099}, {"from": "Assistant", "value": "Users of machine translation systems may desire to obtain multiple candidates translated in different ways. In this work, we attempt to obtain diverse translations by using sentence codes to condition the sentence generation. We describe two methods to extract the codes, either with or without the help of syntax information. For diverse generation, we sample multiple candidates, each of which conditioned on a unique code. Experiments show that the sampled translations have much higher diversity scores when using reasonable sentence codes, where the translation quality is still on par with the baselines even under strong constraint imposed by the codes. In qualitative analysis, we show that our method is able to generate paraphrase translations with drastically different structures. The proposed approach can be easily adopted to existing translation systems as no modification to the model is required.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1178", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1178.flac", "type": "audio", "duration": 834.219}, {"from": "Assistant", "value": "We present a simple new method where an emergent NMT system is used for simultaneously selecting training data and learning internal NMT representations. This is done in a self-supervised way without parallel data, in such a way that both tasks enhance each other during training. The method is language independent, introduces no additional hyper-parameters, and achieves BLEU scores of 29.21 (en2fr) and 27.36 (fr2en) on newstest2014 using English and French Wikipedia data for training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1179", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1179.flac", "type": "audio", "duration": 778.816}, {"from": "Assistant", "value": "Previous work on end-to-end translation from speech has primarily used frame-level features as speech representations, which creates longer, sparser sequences than text. We show that a naive method to create compressed phoneme-like speech representations is far more effective and efficient for translation than traditional frame-level speech features. Specifically, we generate phoneme labels for speech frames and average consecutive frames with the same label to create shorter, higher-level source sequences for translation. We see improvements of up to 5 BLEU on both our high and low resource language pairs, with a reduction in training time of 60%. Our improvements hold across multiple data sizes and two language pairs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1180", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1180.flac", "type": "audio", "duration": 850.048}, {"from": "Assistant", "value": "We present the Visually Grounded Neural Syntax Learner (VG-NSL), an approach for learning syntactic representations and structures without any explicit supervision. The model learns by looking at natural images and reading paired captions. VG-NSL generates constituency parse trees of texts, recursively composes representations for constituents, and matches them with images. We define concreteness of constituents by their matching scores with images, and use it to guide the parsing of text. Experiments on the MSCOCO data set show that VG-NSL outperforms various unsupervised parsing approaches that do not use visual grounding, in terms of F1 scores against gold parse trees. We find that VGNSL is much more stable with respect to the choice of random initialization and the amount of training data. We also find that the concreteness acquired by VG-NSL correlates well with a similar measure defined by linguists. Finally, we also apply VG-NSL to multiple languages in the Multi30K data set, showing that our model consistently outperforms prior unsupervised approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1181", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1181.flac", "type": "audio", "duration": 1962.453}, {"from": "Assistant", "value": "Advances in learning and representations have reinvigorated work that connects language to other modalities. A particularly exciting direction is Vision-and-Language Navigation(VLN), in which agents interpret natural language instructions and visual scenes to move through environments and reach goals. Despite recent progress, current research leaves unclear how much of a role language under-standing plays in this task, especially because dominant evaluation metrics have focused on goal completion rather than the sequence of actions corresponding to the instructions. Here, we highlight shortcomings of current metrics for the Room-to-Room dataset (Anderson et al.,2018b) and propose a new metric, Coverage weighted by Length Score (CLS). We also show that the existing paths in the dataset are not ideal for evaluating instruction following because they are direct-to-goal shortest paths. We join existing short paths to form more challenging extended paths to create a new data set, Room-for-Room (R4R). Using R4R and CLS, we show that agents that receive rewards for instruction fidelity outperform agents that focus on goal completion.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1182", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1182.flac", "type": "audio", "duration": 879.531}, {"from": "Assistant", "value": "Describing images with text is a fundamental problem in vision-language research. Current studies in this domain mostly focus on single image captioning. However, in various real applications (e.g., image editing, difference interpretation, and retrieval), generating relational captions for two images, can also be very useful. This important problem has not been explored mostly due to lack of datasets and effective models. To push forward the research in this direction, we first introduce a new language-guided image editing dataset that contains a large number of real image pairs with corresponding editing instructions. We then propose a new relational speaker model based on an encoder-decoder architecture with static relational attention and sequential multi-head attention. We also extend the model with dynamic relational attention, which calculates visual alignment while decoding. Our models are evaluated on our newly collected and two public datasets consisting of image pairs annotated with relationship sentences. Experimental results, based on both automatic and human evaluation, demonstrate that our model outperforms all baselines and existing methods on all the datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1183", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1183.flac", "type": "audio", "duration": 884.096}, {"from": "Assistant", "value": "In this paper, we address a novel task, namely weakly-supervised spatio-temporally grounding natural sentence in video. Specifically, given a natural sentence and a video, we localize a spatio-temporal tube in the video that semantically corresponds to the given sentence, with no reliance on any spatio-temporal annotations during training. First, a set of spatio-temporal tubes, referred to as instances, are extracted from the video. We then encode these instances and the sentence using our newly proposed attentive interactor which can exploit their fine-grained relationships to characterize their matching behaviors. Besides a ranking loss, a novel diversity loss is introduced to train our attentive interactor to strengthen the matching behaviors of reliable instance-sentence pairs and penalize the unreliable ones. We also contribute a dataset, called VID-sentence, based on the ImageNet video object detection dataset, to serve as a benchmark for our task. Results from extensive experiments demonstrate the superiority of our model over the baseline approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1184", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1184.flac", "type": "audio", "duration": 1191.339}, {"from": "Assistant", "value": "This paper introduces the PhotoBook dataset, a large-scale collection of visually-grounded, task-oriented dialogues in English designed to investigate shared dialogue history accumulating during conversation. Taking inspiration from seminal work on dialogue analysis, we propose a data-collection task formulated as a collaborative game prompting two online participants to refer to images utilising both their visual context as well as previously established referring expressions. We provide a detailed description of the task setup and a thorough analysis of the 2,500 dialogues collected. To further illustrate the novel features of the dataset, we propose a baseline model for reference resolution which uses a simple method to take into account shared information accumulated in a reference chain. Our results show that this information is particularly important to resolve later descriptions and underline the need to develop more sophisticated models of common ground in dialogue interaction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1185", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1185.flac", "type": "audio", "duration": 1173.12}, {"from": "Assistant", "value": "Architecture search is the process of automatically learning the neural model or cell structure that best suits the given task. Recently, this approach has shown promising performance improvements (on language modeling and image classification) with reasonable training speed, using a weight sharing strategy called Efficient Neural Architecture Search (ENAS). In our work, we first introduce a novel continual architecture search (CAS) approach, so as to continually evolve the model parameters during the sequential training of several tasks, without losing performance on previously learned tasks (via block-sparsity and orthogonality constraints), thus enabling life-long learning. Next, we explore a multi-task architecture search (MAS) approach over ENAS for finding a unified, single cell structure that performs well across multiple tasks (via joint controller rewards), and hence allows more generalizable transfer of the cell structure knowledge to an unseen new task. We empirically show the effectiveness of our sequential continual learning and parallel multi-task learning based architecture search approaches on diverse sentence-pair classification tasks (GLUE) and multimodal-generation based video captioning tasks. Further, we present several ablations and analyses on the learned cell structures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1186", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1186.flac", "type": "audio", "duration": 845.568}, {"from": "Assistant", "value": "Supervised models of NLP rely on large collections of text which closely resemble the intended testing setting. Unfortunately matching text is often not available in sufficient quantity, and moreover, within any domain of text, data is often highly heterogenous. In this paper we propose a method to distill the important domain signal as part of a multi-domain learning system, using a latent variable model in which parts of a neural model are stochastically gated based on the inferred domain. We compare the use of discrete versus continuous latent variables, operating in a domain-supervised or a domain semi-supervised setting, where the domain is known only for a subset of training inputs. We show that our model leads to substantial performance improvements over competitive benchmark domain adaptation methods, including methods using adversarial learning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1187", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1187.flac", "type": "audio", "duration": 916.971}, {"from": "Assistant", "value": "Modern entity linking systems rely on large collections of documents specifically annotated for the task (e.g., AIDA CoNLL). In contrast, we propose an approach which exploits only naturally occurring information: unlabeled documents and Wikipedia. Our approach consists of two stages. First, we construct a high recall list of candidate entities for each mention in an unlabeled document. Second, we use the candidate lists as weak supervision to constrain our document-level entity linking model. The model treats entities as latent variables and, when estimated on a collection of unlabelled texts, learns to choose entities relying both on local context of each mention and on coherence with other entities in the document. The resulting approach rivals fully-supervised state-of-the-art systems on standard test sets. It also approaches their performance in the very challenging setting: when tested on a test set sampled from the data used to estimate the supervised systems. By comparing to Wikipedia-only training of our model, we demonstrate that modeling unlabeled documents is beneficial.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1188", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1188.flac", "type": "audio", "duration": 821.376}, {"from": "Assistant", "value": "We consider the problem of learning to map from natural language instructions to state transitions (actions) in a data-efficient manner. Our method takes inspiration from the idea that it should be easier to ground language to concepts that have already been formed through pre-linguistic observation. We augment a baseline instruction-following learner with an initial environment-learning phase that uses observations of language-free state transitions to induce a suitable latent representation of actions before processing the instruction-following training data. We show that mapping to pre-learned representations substantially improves performance over systems whose representations are learned from limited instructional data alone.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1253", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1253.flac", "type": "audio", "duration": 850.091}, {"from": "Assistant", "value": "Domain adaptation is an essential task in dialog system building because there are so many new dialog tasks created for different needs every day. Collecting and annotating training data for these new tasks is costly since it involves real user interactions. We propose a domain adaptive dialog generation method based on meta-learning (DAML). DAML is an end-to-end trainable dialog system model that learns from multiple rich-resource tasks and then adapts to new domains with minimal training samples. We train a dialog system model using multiple rich-resource single-domain dialog data by applying the model-agnostic meta-learning algorithm to dialog domain. The model is capable of learning a competitive dialog system on a new domain with only a few training examples in an efficient manner. The two-step gradient updates in DAML enable the model to learn general features across multiple tasks. We evaluate our method on a simulated dialog dataset and achieve state-of-the-art performance, which is generalizable to new tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1254", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1254.flac", "type": "audio", "duration": 734.208}, {"from": "Assistant", "value": "Writers often rely on plans or sketches to write long stories, but most current language models generate word by word from left to right. We explore coarse-to-fine models for creating narrative texts of several hundred words, and introduce new models which decompose stories by abstracting over actions and entities. The model first generates the predicate-argument structure of the text, where different mentions of the same entity are marked with placeholder tokens. It then generates a surface realization of the predicate-argument structure, and finally replaces the entity placeholders with context-sensitive names and references. Human judges prefer the stories from our models to a wide range of previous approaches to hierarchical text generation. Extensive analysis shows that our methods can help improve the diversity and coherence of events and entities in generated stories.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1255", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1255.flac", "type": "audio", "duration": 619.776}, {"from": "Assistant", "value": "Automatic argument generation is an appealing but challenging task. In this paper, we study the specific problem of counter-argument generation, and present a novel framework, CANDELA. It consists of a powerful retrieval system and a novel two-step generation model, where a text planning decoder first decides on the main talking points and a proper language style for each sentence, then a content realization decoder reflects the decisions and constructs an informative paragraph-level argument. Furthermore, our generation model is empowered by a retrieval system indexed with 12 million articles collected from Wikipedia and popular English news media, which provides access to high-quality content with diversity. Automatic evaluation on a large-scale dataset collected from Reddit shows that our model yields significantly higher BLEU, ROUGE, and METEOR scores than the state-of-the-art and non-trivial comparisons. Human evaluation further indicates that our system arguments are more appropriate for refutation and richer in content.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1256", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1256.flac", "type": "audio", "duration": 598.72}, {"from": "Assistant", "value": "Recent neural language generation systems oftenhallucinatecontents (i.e., producing irrelevant or contradicted facts), especially when trained on loosely corresponding pairs of the input structure and text. To mitigate this issue, we propose to integrate a language understanding module for data refinement with self-training iterations to effectively induce strong equivalence between the input data and the paired text. Experiments on the E2E challenge dataset show that our proposed framework can reduce more than 50% relative unaligned noise from the original data-text pairs. A vanilla sequence-to-sequence neural NLG model trained on the refined data has improved on content correctness compared with the current state-of-the-art ensemble generator.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1257", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1257.flac", "type": "audio", "duration": 479.125}, {"from": "Assistant", "value": "Automatic commenting of online articles can provide additional opinions and facts to the reader, which improves user experience and engagement on social media platforms. Previous work focuses on automatic commenting based solely on textual content. However, in real-scenarios, online articles usually contain multiple modal contents. For instance, graphic news contains plenty of images in addition to text. Contents other than text are also vital because they are not only more attractive to the reader but also may provide critical information. To remedy this, we propose a new task: cross-model automatic commenting (CMAC), which aims to make comments by integrating multiple modal contents. We construct a large-scale dataset for this task and explore several representative methods. Going a step further, an effective co-attention model is presented to capture the dependency between textual and visual information. Evaluation results show that our proposed model can achieve better performance than competitive baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1258", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1258.flac", "type": "audio", "duration": 587.328}, {"from": "Assistant", "value": "Recently, to incorporate external Knowledge Base (KB) information, one form of world knowledge, several end-to-end task-oriented dialog systems have been proposed. These models, however, tend to confound the dialog history with KB tuples and simply store them into one memory. Inspired by the psychological studies on working memory, we propose a working memory model (WMM2Seq) for dialog response generation. Our WMM2Seq adopts a working memory to interact with two separated long-term memories, which are the episodic memory for memorizing dialog history and the semantic memory for storing KB tuples. The working memory consists of a central executive to attend to the aforementioned memories, and a short-term storage system to store the “activated” contents from the long-term memories. Furthermore, we introduce a context-sensitive perceptual process for the token representations of dialog history, and then feed them into the episodic memory. Extensive experiments on two task-oriented dialog datasets demonstrate that our WMM2Seq significantly outperforms the state-of-the-art results in several evaluation metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1259", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1259.flac", "type": "audio", "duration": 1155.328}, {"from": "Assistant", "value": "We propose a new CogQA framework for multi-hop reading comprehension question answering in web-scale documents. Founded on the dual process theory in cognitive science, the framework gradually builds acognitive graphin an iterative process by coordinating an implicit extraction module (System 1) and an explicit reasoning module (System 2). While giving accurate answers, our framework further provides explainable reasoning paths. Specifically, our implementation based on BERT and graph neural network efficiently handles millions of documents for multi-hop reasoning questions in the HotpotQA fullwiki dataset, achieving a winning jointF1score of 34.9 on the leaderboard, compared to 23.1 of the best competitor.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1260", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1260.flac", "type": "audio", "duration": 970.923}, {"from": "Assistant", "value": "Multi-hop reading comprehension (RC) across documents poses new challenge over single-document RC because it requires reasoning over multiple documents to reach the final answer. In this paper, we propose a new model to tackle the multi-hop RC problem. We introduce a heterogeneous graph with different types of nodes and edges, which is named as Heterogeneous Document-Entity (HDE) graph. The advantage of HDE graph is that it contains different granularity levels of information including candidates, documents and entities in specific document contexts. Our proposed model can do reasoning over the HDE graph with nodes representation initialized with co-attention and self-attention based context encoders. We employ Graph Neural Networks (GNN) based message passing algorithms to accumulate evidences on the proposed HDE graph. Evaluated on the blind test set of the Qangaroo WikiHop data set, our HDE graph based single model delivers competitive result, and the ensemble model achieves the state-of-the-art performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1261", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1261.flac", "type": "audio", "duration": 980.203}, {"from": "Assistant", "value": "Multi-hop reading comprehension requires the model to explore and connect relevant information from multiple sentences/documents in order to answer the question about the context. To achieve this, we propose an interpretable 3-module system called Explore-Propose-Assemble reader (EPAr). First, the Document Explorer iteratively selects relevant documents and represents divergent reasoning chains in a tree structure so as to allow assimilating information from all chains. The Answer Proposer then proposes an answer from every root-to-leaf path in the reasoning tree. Finally, the Evidence Assembler extracts a key sentence containing the proposed answer from every path and combines them to predict the final answer. Intuitively, EPAr approximates the coarse-to-fine-grained comprehension behavior of human readers when facing multiple long documents. We jointly optimize our 3 modules by minimizing the sum of losses from each stage conditioned on the previous stage’s output. On two multi-hop reading comprehension datasets WikiHop and MedHop, our EPAr model achieves significant improvements over the baseline and competitive results compared to the state-of-the-art model. We also present multiple reasoning-chain-recovery tests and ablation studies to demonstrate our system’s ability to perform interpretable and accurate reasoning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1262", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1262.flac", "type": "audio", "duration": 961.856}, {"from": "Assistant", "value": "Multi-hop question answering requires a model to connect multiple pieces of evidence scattered in a long context to answer the question. In this paper, we show that in the multi-hop HotpotQA (Yang et al., 2018) dataset, the examples often contain reasoning shortcuts through which models can directly locate the answer by word-matching the question with a sentence in the context. We demonstrate this issue by constructing adversarial documents that create contradicting answers to the shortcut but do not affect the validity of the original answer. The performance of strong baseline models drops significantly on our adversarial test, indicating that they are indeed exploiting the shortcuts rather than performing multi-hop reasoning. After adversarial training, the baseline’s performance improves but is still limited on the adversarial test. Hence, we use a control unit that dynamically attends to the question at different reasoning hops to guide the model’s multi-hop reasoning. We show that our 2-hop model trained on the regular data is more robust to the adversaries than the baseline. After adversarial training, it not only achieves significant improvements over its counterpart trained on regular data, but also outperforms the adversarially-trained baseline significantly. Finally, we sanity-check that these improvements are not obtained by exploiting potential new shortcuts in the adversarial data, but indeed due to robust multi-hop reasoning skills of the models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1263", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1263.flac", "type": "audio", "duration": 888.619}, {"from": "Assistant", "value": "We propose a novel, path-based reasoning approach for the multi-hop reading comprehension task where a system needs to combine facts from multiple passages to answer a question. Although inspired by multi-hop reasoning over knowledge graphs, our proposed approach operates directly over unstructured text. It generates potential paths through passages and scores them without any direct path supervision. The proposed model, named PathNet, attempts to extract implicit relations from text through entity pair representations, and compose them to encode each path. To capture additional context, PathNet also composes the passage representations along each path to compute a passage-based representation. Unlike previous approaches, our model is then able to explain its reasoning via these explicit paths through the passages. We show that our approach outperforms prior models on the multi-hop Wikihop dataset, and also can be generalized to apply to the OpenBookQA dataset, matching state-of-the-art performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1264", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1264.flac", "type": "audio", "duration": 872.811}, {"from": "Assistant", "value": "For evaluating machine-generated texts, automatic methods hold the promise of avoiding collection of human judgments, which can be expensive and time-consuming. The most common automatic metrics, like BLEU and ROUGE, depend on exact word matching, an inflexible approach for measuring semantic similarity. We introduce methods based on sentence mover’s similarity; our automatic metrics evaluate text in a continuous space using word and sentence embeddings. We find that sentence-based metrics correlate with human judgments significantly better than ROUGE, both on machine-generated summaries (average length of 3.4 sentences) and human-authored essays (average length of 7.5). We also show that sentence mover’s similarity can be used as a reward when learning a generation model via reinforcement learning; we present both automatic and human evaluations of summaries learned in this way, finding that our approach outperforms ROUGE.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1265", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1265.flac", "type": "audio", "duration": 853.44}, {"from": "Assistant", "value": "Many complex discourse-level tasks can aid domain experts in their work but require costly expert annotations for data creation. To speed up and ease annotations, we investigate the viability of automatically generated annotation suggestions for such tasks. As an example, we choose a task that is particularly hard for both humans and machines: the segmentation and classification of epistemic activities in diagnostic reasoning texts. We create and publish a new dataset covering two domains and carefully analyse the suggested annotations. We find that suggestions have positive effects on annotation speed and performance, while not introducing noteworthy biases. Envisioning suggestion models that improve with newly annotated texts, we contrast methods for continuous model adjustment and suggest the most effective setup for suggestions in future expert tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1266", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1266.flac", "type": "audio", "duration": 814.379}, {"from": "Assistant", "value": "Comparing between Deep Neural Network (DNN) models based on their performance on unseen data is crucial for the progress of the NLP field. However, these models have a large number of hyper-parameters and, being non-convex, their convergence point depends on the random values chosen at initialization and during training. Proper DNN comparison hence requires a comparison between their empirical score distributions on unseen data, rather than between single evaluation scores as is standard for more simple, convex models. In this paper, we propose to adapt to this problem a recently proposed test for the Almost Stochastic Dominance relation between two distributions. We define the criteria for a high quality comparison method between DNNs, and show, both theoretically and through analysis of extensive experimental results with leading DNN models for sequence tagging tasks, that the proposed test meets all criteria while previously proposed methods fail to do so. We hope the test we propose here will set a new working practice in the NLP community.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1267", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1267.flac", "type": "audio", "duration": 648.256}, {"from": "Assistant", "value": "It is standard practice in speech & language technology to rank systems according to their performance on a test set held out for evaluation. However, few researchers apply statistical tests to determine whether differences in performance are likely to arise by chance, and few examine the stability of system ranking across multiple training-testing splits. We conduct replication and reproduction experiments with nine part-of-speech taggers published between 2000 and 2018, each of which claimed state-of-the-art performance on a widely-used “standard split”. While we replicate results on the standard split, we fail to reliably reproduce some rankings when we repeat this analysis with randomly generated training-testing splits. We argue that randomly generated splits should be used in system evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1268", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1268.flac", "type": "audio", "duration": 599.445}, {"from": "Assistant", "value": "Existing datasets for scoring text pairs in terms of semantic similarity contain instances whose resolution differs according to the degree of difficulty. This paper proposes to distinguish obvious from non-obvious text pairs based on superficial lexical overlap and ground-truth labels. We characterise existing datasets in terms of containing difficult cases and find that recently proposed models struggle to capture the non-obvious cases of semantic similarity. We describe metrics that emphasise cases of similarity which require more complex inference and propose that these are used for evaluating systems for semantic similarity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1269", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1269.flac", "type": "audio", "duration": 558.336}, {"from": "Assistant", "value": "Accurate, automatic evaluation of machine translation is critical for system tuning, and evaluating progress in the field. We proposed a simple unsupervised metric, and additional supervised metrics which rely on contextual word embeddings to encode the translation and reference sentences. We find that these models rival or surpass all existing metrics in the WMT 2017 sentence-level and system-level tracks, and our trained model has a substantially higher correlation with human judgements than all existing metrics on the WMT 2017 to-English sentence level dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1270", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1270.flac", "type": "audio", "duration": 821.291}, {"from": "Assistant", "value": "As the online world continues its exponential growth, interpersonal communication has come to play an increasingly central role in opinion formation and change. In order to help users better engage with each other online, we study a challenging problem of re-entry prediction foreseeing whether a user will come back to a conversation they once participated in. We hypothesize that both the context of the ongoing conversations and the users’ previous chatting history will affect their continued interests in future engagement. Specifically, we propose a neural framework with three main layers, each modeling context, user history, and interactions between them, to explore how the conversation context and user chatting history jointly result in their re-entry behavior. We experiment with two large-scale datasets collected from Twitter and Reddit. Results show that our proposed framework with bi-attention achieves an F1 score of 61.1 on Twitter conversations, outperforming the state-of-the-art methods from previous work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1271", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1271.flac", "type": "audio", "duration": 948.16}, {"from": "Assistant", "value": "Although there is an unprecedented effort to provide adequate responses in terms of laws and policies to hate content on social media platforms, dealing with hatred online is still a tough problem. Tackling hate speech in the standard way of content deletion or user suspension may be charged with censorship and overblocking. One alternate strategy, that has received little attention so far by the research community, is to actually oppose hate content with counter-narratives (i.e. informed textual responses). In this paper, we describe the creation of the first large-scale, multilingual, expert-based dataset of hate-speech/counter-narrative pairs. This dataset has been built with the effort of more than 100 operators from three different NGOs that applied their training and expertise to the task. Together with the collected data we also provide additional annotations about expert demographics, hate and response type, and data augmentation through translation and paraphrasing. Finally, we provide initial experiments to assess the quality of our data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1272", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1272.flac", "type": "audio", "duration": 879.339}, {"from": "Assistant", "value": "Text in social media posts is frequently accompanied by images in order to provide content, supply context, or to express feelings. This paper studies how the meaning of the entire tweet is composed through the relationship between its textual content and its image. We build and release a data set of image tweets annotated with four classes which express whether the text or the image provides additional information to the other modality. We show that by combining the text and image information, we can build a machine learning approach that accurately distinguishes between the relationship types. Further, we derive insights into how these relationships are materialized through text and image content analysis and how they are impacted by user demographic traits. These methods can be used in several downstream applications including pre-training image tagging models, collecting distantly supervised data for image captioning, and can be directly used in end-user applications to optimize screen estate.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1273", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1273.flac", "type": "audio", "duration": 806.571}, {"from": "Assistant", "value": "Understanding the structures of political debates (which actors make what claims) is essential for understanding democratic political decision making. The vision of computational construction of such discourse networks from newspaper reports brings together political science and natural language processing. This paper presents three contributions towards this goal: (a) a requirements analysis, linking the task to knowledge base population; (b) an annotated pilot corpus of migration claims based on German newspaper reports; (c) initial modeling results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1274", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1274.flac", "type": "audio", "duration": 601.92}, {"from": "Assistant", "value": "Research on social media has to date assumed that all posts from an account are authored by the same person. In this study, we challenge this assumption and study the linguistic differences between posts signed by the account owner or attributed to their staff. We introduce a novel data set of tweets posted by U.S. politicians who self-reported their tweets using a signature. We analyze the linguistic topics and style features that distinguish the two types of tweets. Predictive results show that we are able to predict owner and staff attributed tweets with good accuracy, even when not using any training data from that account.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1275", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1275.flac", "type": "audio", "duration": 586.283}, {"from": "Assistant", "value": "We investigate the impact of using author context on textual sarcasm detection. We define author context as the embedded representation of their historical posts on Twitter and suggest neural models that extract these representations. We experiment with two tweet datasets, one labelled manually for sarcasm, and the other via tag-based distant supervision. We achieve state-of-the-art performance on the second dataset, but not on the one labelled manually, indicating a difference between intended sarcasm, captured by distant supervision, and perceived sarcasm, captured by manual labelling.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1276", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1276.flac", "type": "audio", "duration": 1068.651}, {"from": "Assistant", "value": "We consider open domain event extraction, the task of extracting unconstraint types of events from news clusters. A novel latent variable neural model is constructed, which is scalable to very large corpus. A dataset is collected and manually annotated, with task-specific evaluation metrics being designed. Results show that the proposed unsupervised model gives better performance compared to the state-of-the-art method for event schema induction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1277", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1277.flac", "type": "audio", "duration": 971.136}, {"from": "Assistant", "value": "This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. Previous studies on this topic adopt prototypical networks, which calculate the embedding vector of a query instance and the prototype vector of the support set for each relation candidate independently. On the contrary, our proposed MLMAN model encodes the query instance and each support set in an interactive way by considering their matching information at both local and instance levels. The final class prototype for each support set is obtained by attentive aggregation over the representations of support instances, where the weights are calculated using the query instance. Experimental results demonstrate the effectiveness of our proposed methods, which achieve a new state-of-the-art performance on the FewRel dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1278", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1278.flac", "type": "audio", "duration": 816.299}, {"from": "Assistant", "value": "We introduce a conceptually simple and effective method to quantify the similarity between relations in knowledge bases. Specifically, our approach is based on the divergence between the conditional probability distributions over entity pairs. In this paper, these distributions are parameterized by a very simple neural network. Although computing the exact similarity is in-tractable, we provide a sampling-based method to get a good approximation. We empirically show the outputs of our approach significantly correlate with human judgments. By applying our method to various tasks, we also find that (1) our approach could effectively detect redundant relations extracted by open information extraction (Open IE) models, that (2) even the most competitive models for relational classification still make mistakes among very similar relations, and that (3) our approach could be incorporated into negative sampling and softmax classification to alleviate these mistakes.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1279", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1279.flac", "type": "audio", "duration": 990.123}, {"from": "Assistant", "value": "General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction. Efforts have been made to build general purpose extractors that represent relations with their surface forms, or which jointly embed surface forms with relations from an existing knowledge graph. However, both of these approaches are limited in their ability to generalize. In this paper, we build on extensions of Harris’ distributional hypothesis to relations, as well as recent advances in learning text representations (specifically, BERT), to build task agnostic relation representations solely from entity-linked text. We show that these representations significantly outperform previous work on exemplar based relation extraction (FewRel) even without using any of that task’s training data. We also show that models initialized with our task agnostic representations, and then tuned on supervised relation extraction datasets, significantly outperform the previous methods on SemEval 2010 Task 8, KBP37, and TACRED", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1280", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1280.flac", "type": "audio", "duration": 857.216}, {"from": "Assistant", "value": "We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales. We use this framework to construct the largest temporal relations dataset to date, covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to train models for jointly predicting fine-grained temporal relations and event durations. We report strong results on our data and show the efficacy of a transfer-learning approach for predicting categorical relations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1281", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1281.flac", "type": "audio", "duration": 1157.845}, {"from": "Assistant", "value": "We present FIESTA, a model selection approach that significantly reduces the computational resources required to reliably identify state-of-the-art performance from large collections of candidate models. Despite being known to produce unreliable comparisons, it is still common practice to compare model evaluations based on single choices of random seeds. We show that reliable model selection also requires evaluations based on multiple train-test splits (contrary to common practice in many shared tasks). Using bandit theory from the statistics literature, we are able to adaptively determine appropriate numbers of data splits and random seeds used to evaluate each model, focusing computational resources on the evaluation of promising models whilst avoiding wasting evaluations on models with lower performance. Furthermore, our user-friendly Python implementation produces confidence guarantees of correctly selecting the optimal model. We evaluate our algorithms by selecting between 8 target-dependent sentiment analysis methods using dramatically fewer model evaluations than current model selection approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1282", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1282.flac", "type": "audio", "duration": 890.965}, {"from": "Assistant", "value": "Attention mechanisms have recently boosted performance on a range of NLP tasks. Because attention layers explicitly weight input components’ representations, it is also often assumed that attention can be used to identify information that models found important (e.g., specific contextualized word tokens). We test whether that assumption holds by manipulating attention weights in already-trained text classification models and analyzing the resulting differences in their predictions. While we observe some ways in which higher attention weights correlate with greater impact on model predictions, we also find many ways in which this does not hold, i.e., where gradient-based rankings of attention weights better predict their effects than their magnitudes. We conclude that while attention noisily predicts input components’ overall importance to a model, it is by no means a fail-safe indicator.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1283", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1283.flac", "type": "audio", "duration": 1187.733}, {"from": "Assistant", "value": "Analysis methods which enable us to better understand the representations and functioning of neural models of language are increasingly needed as deep learning becomes the dominant approach in NLP. Here we present two methods based on Representational Similarity Analysis (RSA) and Tree Kernels (TK) which allow us to directly quantify how strongly the information encoded in neural activation patterns corresponds to information represented by symbolic structures such as syntax trees. We first validate our methods on the case of a simple synthetic language for arithmetic expressions with clearly defined syntax and semantics, and show that they exhibit the expected pattern of results. We then our methods to correlate neural representations of English sentences with their constituency parse trees.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1285", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1285.flac", "type": "audio", "duration": 876.523}, {"from": "Assistant", "value": "Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1330", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1330.flac", "type": "audio", "duration": 954.56}, {"from": "Assistant", "value": "There has been substantial progress in summarization research enabled by the availability of novel, often large-scale, datasets and recent advances on neural network-based approaches. However, manual evaluation of the system generated summaries is inconsistent due to the difficulty the task poses to human non-expert readers. To address this issue, we propose a novel approach for manual evaluation, Highlight-based Reference-less Evaluation of Summarization (HighRES), in which summaries are assessed by multiple annotators against the source document via manually highlighted salient content in the latter. Thus summary assessment on the source document by human judges is facilitated, while the highlights can be used for evaluating multiple systems. To validate our approach we employ crowd-workers to augment with highlights a recently proposed dataset and compare two state-of-the-art systems. We demonstrate that HighRES improves inter-annotator agreement in comparison to using the source document directly, while they help emphasize differences among systems that would be ignored under other evaluation approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1331", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1331.flac", "type": "audio", "duration": 924.736}, {"from": "Assistant", "value": "We present the first sentence simplification model that learns explicit edit operations (ADD, DELETE, and KEEP) via a neural programmer-interpreter approach. Most current neural sentence simplification systems are variants of sequence-to-sequence models adopted from machine translation. These methods learn to simplify sentences as a byproduct of the fact that they are trained on complex-simple sentence pairs. By contrast, our neural programmer-interpreter is directly trained to predict explicit edit operations on targeted parts of the input sentence, resembling the way that humans perform simplification and revision. Our model outperforms previous state-of-the-art neural sentence simplification models (without external knowledge) by large margins on three benchmark text simplification corpora in terms of SARI (+0.95 WikiLarge, +1.89 WikiSmall, +1.41 Newsela), and is judged by humans to produce overall better and simpler output sentences.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1332", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1332.flac", "type": "audio", "duration": 821.333}, {"from": "Assistant", "value": "Paraphrasing exists at different granularity levels, such as lexical level, phrasal level and sentential level. This paper presents Decomposable Neural Paraphrase Generator (DNPG), a Transformer-based model that can learn and generate paraphrases of a sentence at different levels of granularity in a disentangled way. Specifically, the model is composed of multiple encoders and decoders with different structures, each of which corresponds to a specific granularity. The empirical study shows that the decomposition mechanism of DNPG makes paraphrase generation more interpretable and controllable. Based on DNPG, we further develop an unsupervised domain adaptation method for paraphrase generation. Experimental results show that the proposed model achieves competitive in-domain performance compared to state-of-the-art neural models, and significantly better performance when adapting to a new domain.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1333", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1333.flac", "type": "audio", "duration": 908.331}, {"from": "Assistant", "value": "We present an approach for recursively splitting and rephrasing complex English sentences into a novel semantic hierarchy of simplified sentences, with each of them presenting a more regular structure that may facilitate a wide variety of artificial intelligence tasks, such as machine translation (MT) or information extraction (IE). Using a set of hand-crafted transformation rules, input sentences are recursively transformed into a two-layered hierarchical representation in the form of core sentences and accompanying contexts that are linked via rhetorical relations. In this way, the semantic relationship of the decomposed constituents is preserved in the output, maintaining its interpretability for downstream applications. Both a thorough manual analysis and automatic evaluation across three datasets from two different domains demonstrate that the proposed syntactic simplification approach outperforms the state of the art in structural text simplification. Moreover, an extrinsic evaluation shows that when applying our framework as a preprocessing step the performance of state-of-the-art Open IE systems can be improved by up to 346% in precision and 52% in recall. To enable reproducible research, all code is provided online.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1334", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1334.flac", "type": "audio", "duration": 832.896}, {"from": "Assistant", "value": "A machine learning system can score well on a given test set by relying on heuristics that are effective for frequent example types but break down in more challenging cases. We study this issue within natural language inference (NLI), the task of determining whether one sentence entails another. We hypothesize that statistical NLI models may adopt three fallible syntactic heuristics: the lexical overlap heuristic, the subsequence heuristic, and the constituent heuristic. To determine whether models have adopted these heuristics, we introduce a controlled evaluation set called HANS (Heuristic Analysis for NLI Systems), which contains many examples where the heuristics fail. We find that models trained on MNLI, including BERT, a state-of-the-art model, perform very poorly on HANS, suggesting that they have indeed adopted these heuristics. We conclude that there is substantial room for improvement in NLI systems, and that the HANS dataset can motivate and measure progress in this area.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1335", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1335.flac", "type": "audio", "duration": 943.808}, {"from": "Assistant", "value": "We present the zero-shot entity linking task, where mentions must be linked to unseen entities without in-domain labeled data. The goal is to enable robust transfer to highly specialized domains, and so no metadata or alias tables are assumed. In this setting, entities are only identified by text descriptions, and models must rely strictly on language understanding to resolve the new entities. First, we show that strong reading comprehension models pre-trained on large unlabeled data can be used to generalize to unseen entities. Second, we propose a simple and effective adaptive pre-training strategy, which we term domain-adaptive pre-training (DAP), to address the domain shift problem associated with linking unseen entities in a new domain. We present experiments on a new dataset that we construct for this task and show that DAP improves over strong pre-training baselines, including BERT. The data and code are available athttps://github.com/lajanugen/zeshel.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1336", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1336.flac", "type": "audio", "duration": 850.773}, {"from": "Assistant", "value": "We propose a new neural transfer method termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER). Specifically, two variants of DATNet, i.e., DATNet-F and DATNet-P, are investigated to explore effective feature fusion between high and low resource. To address the noisy and imbalanced training data, we propose a novel Generalized Resource-Adversarial Discriminator (GRAD). Additionally, adversarial training is adopted to boost model generalization. In experiments, we examine the effects of different components in DATNet across domains and languages and show that significant improvement can be obtained especially for low-resource data, without augmenting any additional hand-crafted features and pre-trained language model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1337", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1337.flac", "type": "audio", "duration": 892.373}, {"from": "Assistant", "value": "Prior work has shown that, on small amounts of training data, syntactic neural language models learn structurally sensitive generalisations more successfully than sequential language models. However, their computational complexity renders scaling difficult, and it remains an open question whether structural biases are still necessary when sequential models have access to ever larger amounts of training data. To answer this question, we introduce an efficient knowledge distillation (KD) technique that transfers knowledge from a syntactic language model trained on a small corpus to an LSTM language model, hence enabling the LSTM to develop a more structurally sensitive representation of the larger training data it learns from. On targeted syntactic evaluations, we find that, while sequential LSTMs perform much better than previously reported, our proposed technique substantially improves on this baseline, yielding a new state of the art. Our findings and analysis affirm the importance of structural biases, even in models that learn from large amounts of data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1338", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1338.flac", "type": "audio", "duration": 587.051}, {"from": "Assistant", "value": "Recently, there has been an increasing interest in unsupervised parsers that optimize semantically oriented objectives, typically using reinforcement learning. Unfortunately, the learned trees often do not match actual syntax trees well. Shen et al. (2018) propose a structured attention mechanism for language modeling (PRPN), which induces better syntactic structures but relies on ad hoc heuristics. Also, their model lacks interpretability as it is not grounded in parsing actions. In our work, we propose an imitation learning approach to unsupervised parsing, where we transfer the syntactic knowledge induced by PRPN to a Tree-LSTM model with discrete parsing actions. Its policy is then refined by Gumbel-Softmax training towards a semantically oriented objective. We evaluate our approach on the All Natural Language Inference dataset and show that it achieves a new state of the art in terms of parsing F-score, outperforming our base models, including PRPN.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1339", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1339.flac", "type": "audio", "duration": 706.965}, {"from": "Assistant", "value": "Several linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have still not adapted to account for these differences. To address this, we annotate the Wall Street Journal part of the Penn Treebank with the gender information of the articles’ authors, and build taggers and parsers trained on this data that show performance differences in text written by men and women. Further analyses reveal numerous part-of-speech tags and syntactic relations whose prediction performances benefit from the prevalence of a specific gender in the training data. The results underscore the importance of accounting for gendered differences in syntactic tasks, and outline future venues for developing more accurate taggers and parsers. We release our data to the research community.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1340", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1340.flac", "type": "audio", "duration": 625.771}, {"from": "Assistant", "value": "We show that constituency parsing benefits from unsupervised pre-training across a variety of languages and a range of pre-training conditions. We first compare the benefits of no pre-training, fastText, ELMo, and BERT for English and find that BERT outperforms ELMo, in large part due to increased model capacity, whereas ELMo in turn outperforms the non-contextual fastText embeddings. We also find that pre-training is beneficial across all 11 languages tested; however, large model sizes (more than 100 million parameters) make it computationally expensive to train separate models for each language. To address this shortcoming, we show that joint multilingual pre-training and fine-tuning allows sharing all but a small number of parameters between ten languages in the final model. The 10x reduction in model size compared to fine-tuning one model per language causes only a 3.2% relative error increase in aggregate. We further explore the idea of joint fine-tuning and show that it gives low-resource languages a way to benefit from the larger datasets of other languages. Finally, we demonstrate new state-of-the-art results for 11 languages, including English (95.8 F1) and Chinese (91.8 F1).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1341", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1341.flac", "type": "audio", "duration": 934.976}, {"from": "Assistant", "value": "We present a new method for sentiment lexicon induction that is designed to be applicable to the entire range of typological diversity of the world’s languages. We evaluate our method on Parallel Bible Corpus+ (PBC+), a parallel corpus of 1593 languages. The key idea is to use Byte Pair Encodings (BPEs) as basic units for multilingual embeddings. Through zero-shot transfer from English sentiment, we learn a seed lexicon for each language in the domain of PBC+. Through domain adaptation, we then generalize the domain-specific lexicon to a general one. We show – across typologically diverse languages in PBC+ – good quality of seed and general-domain sentiment lexicons by intrinsic and extrinsic and by automatic and human evaluation. We make freely available our code, seed sentiment lexicons for all 1593 languages and induced general-domain sentiment lexicons for 200 languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1342", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1342.flac", "type": "audio", "duration": 922.24}, {"from": "Assistant", "value": "Tree-LSTMs have been used for tree-based sentiment analysis over Stanford Sentiment Treebank, which allows the sentiment signals over hierarchical phrase structures to be calculated simultaneously. However, traditional tree-LSTMs capture only the bottom-up dependencies between constituents. In this paper, we propose a tree communication model using graph convolutional neural network and graph recurrent neural network, which allows rich information exchange between phrases constituent tree. Experiments show that our model outperforms existing work on bidirectional tree-LSTMs in both accuracy and efficiency, providing more consistent predictions on phrase-level sentiments.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1343", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1343.flac", "type": "audio", "duration": 819.285}, {"from": "Assistant", "value": "Multilingual writers and speakers often alternate between two languages in a single discourse. This practice is called “code-switching”. Existing sentiment detection methods are usually trained on sentiment-labeled monolingual text. Manually labeled code-switched text, especially involving minority languages, is extremely rare. Consequently, the best monolingual methods perform relatively poorly on code-switched text. We present an effective technique for synthesizing labeled code-switched text from labeled monolingual text, which is relatively readily available. The idea is to replace carefully selected subtrees of constituency parses of sentences in the resource-rich language with suitable token spans selected from automatic translations to the resource-poor language. By augmenting the scarce labeled code-switched text with plentiful synthetic labeled code-switched text, we achieve significant improvements in sentiment labeling accuracy (1.5%, 5.11% 7.20%) for three different language pairs (English-Hindi, English-Spanish and English-Bengali). The improvement is even significant in hatespeech detection whereby we achieve a 4% improvement using only synthetic code-switched data (6% with data augmentation).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1344", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1344.flac", "type": "audio", "duration": 1041.728}, {"from": "Assistant", "value": "Aspect term extraction (ATE) aims at identifying all aspect terms in a sentence and is usually modeled as a sequence labeling problem. However, sequence labeling based methods cannot make full use of the overall meaning of the whole sentence and have the limitation in processing dependencies between labels. To tackle these problems, we first explore to formalize ATE as a sequence-to-sequence (Seq2Seq) learning task where the source sequence and target sequence are composed of words and labels respectively. At the same time, to make Seq2Seq learning suit to ATE where labels correspond to words one by one, we design the gated unit networks to incorporate corresponding word representation into the decoder, and position-aware attention to pay more attention to the adjacent words of a target word. The experimental results on two datasets show that Seq2Seq learning is effective in ATE accompanied with our proposed gated unit networks and position-aware attention mechanism.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1345", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1345.flac", "type": "audio", "duration": 797.056}, {"from": "Assistant", "value": "In the literature, existing studies on aspect sentiment classification (ASC) focus on individual non-interactive reviews. This paper extends the research to interactive reviews and proposes a new research task, namely Aspect Sentiment Classification towards Question-Answering (ASC-QA), for real-world applications. This new task aims to predict sentiment polarities for specific aspects from interactive QA style reviews. In particular, a high-quality annotated corpus is constructed for ASC-QA to facilitate corresponding research. On this basis, a Reinforced Bidirectional Attention Network (RBAN) approach is proposed to address two inherent challenges in ASC-QA, i.e., semantic matching between question and answer, and data noise. Experimental results demonstrate the great advantage of the proposed approach to ASC-QA against several state-of-the-art baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1346", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1346.flac", "type": "audio", "duration": 857.365}, {"from": "Assistant", "value": "We introduce the first large-scale corpus for long form question answering, a task requiring elaborate and in-depth answers to open-ended questions. The dataset comprises 270K threads from the Reddit forum “Explain Like I’m Five” (ELI5) where an online community provides answers to questions which are comprehensible by five year olds. Compared to existing datasets, ELI5 comprises diverse questions requiring multi-sentence answers. We provide a large set of web documents to help answer the question. Automatic and human evaluations show that an abstractive model trained with a multi-task objective outperforms conventional Seq2Seq, language modeling, as well as a strong extractive baseline. However, our best model is still far from human performance since raters prefer gold responses in over 86% of cases, leaving ample opportunity for future improvement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1347", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1347.flac", "type": "audio", "duration": 892.245}, {"from": "Assistant", "value": "In this work, we introduce a novel algorithm for solving the textbook question answering (TQA) task which describes more realistic QA problems compared to other recent tasks. We mainly focus on two related issues with analysis of the TQA dataset. First, solving the TQA problems requires to comprehend multi-modal contexts in complicated input data. To tackle this issue of extracting knowledge features from long text lessons and merging them with visual features, we establish a context graph from texts and images, and propose a new module f-GCN based on graph convolutional networks (GCN). Second, scientific terms are not spread over the chapters and subjects are split in the TQA dataset. To overcome this so called ‘out-of-domain’ issue, before learning QA problems, we introduce a novel self-supervised open-set learning process without any annotations. The experimental results show that our model significantly outperforms prior state-of-the-art methods. Moreover, ablation studies validate that both methods of incorporating f-GCN for extracting knowledge from multi-modal contexts and our newly proposed self-supervised learning process are effective for TQA problems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1348", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1348.flac", "type": "audio", "duration": 871.531}, {"from": "Assistant", "value": "Visual question answering (VQA) and image captioning require a shared body of general knowledge connecting language and vision. We present a novel approach to better VQA performance that exploits this connection by jointly generating captions that are targeted to help answer a specific visual question. The model is trained using an existing caption dataset by automatically determining question-relevant captions using an online gradient-based method. Experimental results on the VQA v2 challenge demonstrates that our approach obtains state-of-the-art VQA performance (e.g. 68.4% in the Test-standard set using a single model) by simultaneously generating question-relevant captions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1349", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1349.flac", "type": "audio", "duration": 516.565}, {"from": "Assistant", "value": "Attention mechanisms are widely used in Visual Question Answering (VQA) to search for visual clues related to the question. Most approaches train attention models from a coarse-grained association between sentences and images, which tends to fail on small objects or uncommon concepts. To address this problem, this paper proposes a multi-grained attention method. It learns explicit word-object correspondence by two types of word-level attention complementary to the sentence-image association. Evaluated on the VQA benchmark, the multi-grained attention model achieves competitive performance with state-of-the-art models. And the visualized attention maps demonstrate that addition of object-level groundings leads to a better understanding of the images and locates the attended objects more precisely.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1350", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1350.flac", "type": "audio", "duration": 569.323}, {"from": "Assistant", "value": "We study the issue of catastrophic forgetting in the context of neural multimodal approaches to Visual Question Answering (VQA). Motivated by evidence from psycholinguistics, we devise a set of linguistically-informed VQA tasks, which differ by the types of questions involved (Wh-questions and polar questions). We test what impact task difficulty has on continual learning, and whether the order in which a child acquires question types facilitates computational models. Our results show that dramatic forgetting is at play and that task difficulty and order matter. Two well-known current continual learning methods mitigate the problem only to a limiting degree.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1351", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1351.flac", "type": "audio", "duration": 652.288}, {"from": "Assistant", "value": "Paragraph-style image captions describe diverse aspects of an image as opposed to the more common single-sentence captions that only provide an abstract description of the image. These paragraph captions can hence contain substantial information of the image for tasks such as visual question answering. Moreover, this textual information is complementary with visual information present in the image because it can discuss both more abstract concepts and more explicit, intermediate symbolic information about objects, events, and scenes that can directly be matched with the textual question and copied into the textual answer (i.e., via easier modality match). Hence, we propose a combined Visual and Textual Question Answering (VTQA) model which takes as input a paragraph caption as well as the corresponding image, and answers the given question based on both inputs. In our model, the inputs are fused to extract related information by cross-attention (early fusion), then fused again in the form of consensus (late fusion), and finally expected answers are given an extra score to enhance the chance of selection (later fusion). Empirical results show that paragraph captions, even when automatically generated (via an RL-based encoder-decoder model), help correctly answer more visual questions. Overall, our joint model, when trained on the Visual Genome dataset, significantly improves the VQA performance over a strong baseline model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1352", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1352.flac", "type": "audio", "duration": 801.813}, {"from": "Assistant", "value": "Word embedding is central to neural machine translation (NMT), which has attracted intensive research interest in recent years. In NMT, the source embedding plays the role of the entrance while the target embedding acts as the terminal. These layers occupy most of the model parameters for representation learning. Furthermore, they indirectly interface via a soft-attention mechanism, which makes them comparatively isolated. In this paper, we propose shared-private bilingual word embeddings, which give a closer relationship between the source and target embeddings, and which also reduce the number of model parameters. For similar source and target words, their embeddings tend to share a part of the features and they cooperatively learn these common representation units. Experiments on 5 language pairs belonging to 6 different language families and written in 5 different alphabets demonstrate that the proposed model provides a significant performance boost over the strong baselines with dramatically fewer model parameters.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1353", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1353.flac", "type": "audio", "duration": 865.259}, {"from": "Assistant", "value": "In this work we present a new dataset of literary events—events that are depicted as taking place within the imagined space of a novel. While previous work has focused on event detection in the domain of contemporary news, literature poses a number of complications for existing systems, including complex narration, the depiction of a broad array of mental states, and a strong emphasis on figurative language. We outline the annotation decisions of this new dataset and compare several models for predicting events; the best performing model, a bidirectional LSTM with BERT token representations, achieves an F1 score of 73.9. We then apply this model to a corpus of novels split across two dimensions—prestige and popularity—and demonstrate that there are statistically significant differences in the distribution of events for prestige.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1354", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1354.flac", "type": "audio", "duration": 817.856}, {"from": "Assistant", "value": "Self-attention networks (SAN) have attracted a lot of interests due to their high parallelization and strong performance on a variety of NLP tasks, e.g. machine translation. Due to the lack of recurrence structure such as recurrent neural networks (RNN), SAN is ascribed to be weak at learning positional information of words for sequence modeling. However, neither this speculation has been empirically confirmed, nor explanations for their strong performances on machine translation tasks when “lacking positional information” have been explored. To this end, we propose a novel word reordering detection task to quantify how well the word order information learned by SAN and RNN. Specifically, we randomly move one word to another position, and examine whether a trained model can detect both the original and inserted positions. Experimental results reveal that: 1) SAN trained on word reordering detection indeed has difficulty learning the positional information even with the position embedding; and 2) SAN trained on machine translation learns better positional information than its RNN counterpart, in which position embedding plays a critical role. Although recurrence structure make the model more universally-effective on learning word order, learning objectives matter more in the downstream tasks such as machine translation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1355", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1355.flac", "type": "audio", "duration": 669.312}, {"from": "Assistant", "value": "Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in accuracy across many NLP tasks. However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption. As a result these models are costly to train and develop, both financially, due to the cost of hardware and electricity or cloud compute time, and environmentally, due to the carbon footprint required to fuel modern tensor processing hardware. In this paper we bring this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training a variety of recently successful neural network models for NLP. Based on these findings, we propose actionable recommendations to reduce costs and improve equity in NLP research and practice.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1356", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1356.flac", "type": "audio", "duration": 598.165}, {"from": "Assistant", "value": "BERT is a recent language representation model that has surprisingly performed well in diverse language understanding benchmarks. This result indicates the possibility that BERT networks capture structural information about language. In this work, we provide novel support for this claim by performing a series of experiments to unpack the elements of English language structure learned by BERT. Our findings are fourfold. BERT’s phrasal representation captures the phrase-level information in the lower layers. The intermediate layers of BERT compose a rich hierarchy of linguistic information, starting with surface features at the bottom, syntactic features in the middle followed by semantic features at the top. BERT requires deeper layers while tracking subject-verb agreement to handle long-term dependency problem. Finally, the compositional scheme underlying BERT mimics classical, tree-like structures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1357", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1357.flac", "type": "audio", "duration": 646.571}, {"from": "Assistant", "value": "Online abusive behavior affects millions and the NLP community has attempted to mitigate this problem by developing technologies to detect abuse. However, current methods have largely focused on a narrow definition of abuse to detriment of victims who seek both validation and solutions. In this position paper, we argue that the community needs to make three substantive changes: (1) expanding our scope of problems to tackle both more subtle and more serious forms of abuse, (2) developing proactive technologies that counter or inhibit abuse before it harms, and (3) reframing our effort within a framework of justice to promote healthy communities.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1408", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1408.flac", "type": "audio", "duration": 807.893}, {"from": "Assistant", "value": "The common practice in coreference resolution is to identify and evaluate the maximum span of mentions. The use of maximum spans tangles coreference evaluation with the challenges of mention boundary detection like prepositional phrase attachment. To address this problem, minimum spans are manually annotated in smaller corpora. However, this additional annotation is costly and therefore, this solution does not scale to large corpora. In this paper, we propose the MINA algorithm for automatically extracting minimum spans to benefit from minimum span evaluation in all corpora. We show that the extracted minimum spans by MINA are consistent with those that are manually annotated by experts. Our experiments show that using minimum spans is in particular important in cross-dataset coreference evaluation, in which detected mention boundaries are noisier due to domain shift. We have integrated MINA intohttps://github.com/ns-moosavi/covalfor reporting standard coreference scores based on both maximum and automatically detected minimum spans.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1409", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1409.flac", "type": "audio", "duration": 912.939}, {"from": "Assistant", "value": "Recognizing coreferring events and entities across multiple texts is crucial for many NLP applications. Despite the task’s importance, research focus was given mostly to within-document entity coreference, with rather little attention to the other variants. We propose a neural architecture for cross-document coreference resolution. Inspired by Lee et al. (2012), we jointly model entity and event coreference. We represent an event (entity) mention using its lexical span, surrounding context, and relation to entity (event) mentions via predicate-arguments structures. Our model outperforms the previous state-of-the-art event coreference model on ECB+, while providing the first entity coreference results on this corpus. Our analysis confirms that all our representation elements, including the mention span itself, its context, and the relation to other mentions contribute to the model’s success.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1410", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1410.flac", "type": "audio", "duration": 856.043}, {"from": "Assistant", "value": "We propose an efficient neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory (RST). Our framework comprises a discourse segmenter to identify the elementary discourse units (EDU) in a text, and a discourse parser that constructs a discourse tree in a top-down fashion. Both the segmenter and the parser are based on Pointer Networks and operate in linear time. Our segmenter yields an F1 score of 95.4%, and our parser achieves an F1 score of 81.7% on the aggregated labeled (relation) metric, surpassing previous approaches by a good margin and approaching human agreement on both tasks (98.3 and 83.0 F1).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1411", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1411.flac", "type": "audio", "duration": 510.4}, {"from": "Assistant", "value": "It has been shown that implicit connectives can be exploited to improve the performance of the models for implicit discourse relation recognition (IDRR). An important property of the implicit connectives is that they can be accurately mapped into the discourse relations conveying their functions. In this work, we explore this property in a multi-task learning framework for IDRR in which the relations and the connectives are simultaneously predicted, and the mapping is leveraged to transfer knowledge between the two prediction tasks via the embeddings of relations and connectives. We propose several techniques to enable such knowledge transfer that yield the state-of-the-art performance for IDRR on several settings of the benchmark dataset (i.e., the Penn Discourse Treebank dataset).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1412", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1412.flac", "type": "audio", "duration": 614.805}, {"from": "Assistant", "value": "When a speaker, Mary, asks “Do you know that Florence is packed with visitors?”, we take her to believe that Florence is packed with visitors, but not if she asks “Do you think that Florence is packed with visitors?”. Inferring speaker commitment (aka event factuality) is crucial for information extraction and question answering. Here, we explore the hypothesis that linguistic deficits drive the error patterns of existing speaker commitment models by analyzing the linguistic correlates of model error on a challenging naturalistic dataset. We evaluate two state-of-the-art speaker commitment models on the CommitmentBank, an English dataset of naturally occurring discourses. The CommitmentBank is annotated with speaker commitment towards the content of the complement (“Florence is packed with visitors” in our example) of clause-embedding verbs (“know”, “think”) under four entailment-canceling environments (negation, modal, question, conditional). A breakdown of items by linguistic features reveals asymmetrical error patterns: while the models achieve good performance on some classes (e.g., negation), they fail to generalize to the diverse linguistic constructions (e.g., conditionals) in natural language, highlighting directions for improvement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1413", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1413.flac", "type": "audio", "duration": 831.765}, {"from": "Assistant", "value": "Modeling script knowledge can be useful for a wide range of NLP tasks. Current statistical script learning approaches embed the events, such that their relationships are indicated by their similarity in the embedding. While intuitive, these approaches fall short of representing nuanced relations, needed for downstream tasks. In this paper, we suggest to view learning event embedding as a multi-relational problem, which allows us to capture different aspects of event pairs. We model a rich set of event relations, such as Cause and Contrast, derived from the Penn Discourse Tree Bank. We evaluate our model on three types of tasks, the popular Mutli-Choice Narrative Cloze and its variants, several multi-relational prediction tasks, and a related downstream task—implicit discourse sense classification.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1414", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1414.flac", "type": "audio", "duration": 808.363}, {"from": "Assistant", "value": "In this paper, we propose a method for why-question answering (why-QA) that uses an adversarial learning framework. Existing why-QA methods retrieve “answer passages” that usually consist of several sentences. These multi-sentence passages contain not only the reason sought by a why-question and its connection to the why-question, but also redundant and/or unrelated parts. We use our proposed “Adversarial networks for Generating compact-answer Representation” (AGR) to generate from a passage a vector representation of the non-redundant reason sought by a why-question and exploit the representation for judging whether the passage actually answers the why-question. Through a series of experiments using Japanese why-QA datasets, we show that these representations improve the performance of our why-QA neural model as well as that of a BERT-based why-QA model. We show that they also improve a state-of-the-art distantly supervised open-domain QA (DS-QA) method on publicly available English datasets, even though the target task is not a why-QA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1415", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1415.flac", "type": "audio", "duration": 817.685}, {"from": "Assistant", "value": "Machine reading comprehension with unanswerable questions is a challenging task. In this work, we propose a data augmentation technique by automatically generating relevant unanswerable questions according to an answerable question paired with its corresponding paragraph that contains the answer. We introduce a pair-to-sequence model for unanswerable question generation, which effectively captures the interactions between the question and the paragraph. We also present a way to construct training data for our question generation models by leveraging the existing reading comprehension dataset. Experimental results show that the pair-to-sequence model performs consistently better compared with the sequence-to-sequence baseline. We further use the automatically generated unanswerable questions as a means of data augmentation on the SQuAD 2.0 dataset, yielding 1.9 absolute F1 improvement with BERT-base model and 1.7 absolute F1 improvement with BERT-large model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1416", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1416.flac", "type": "audio", "duration": 572.693}, {"from": "Assistant", "value": "Multi-hop reading comprehension (RC) questions are challenging because they require reading and reasoning over multiple paragraphs. We argue that it can be difficult to construct large multi-hop RC datasets. For example, even highly compositional questions can be answered with a single hop if they target specific entity types, or the facts needed to answer them are redundant. Our analysis is centered on HotpotQA, where we show that single-hop reasoning can solve much more of the dataset than previously thought. We introduce a single-hop BERT-based RC model that achieves 67 F1—comparable to state-of-the-art multi-hop models. We also design an evaluation setting where humans are not shown all of the necessary paragraphs for the intended multi-hop reasoning but can still answer over 80% of questions. Together with detailed error analysis, these results suggest there should be an increasing focus on the role of evidence in multi-hop reasoning and possibly even a shift towards information retrieval style evaluations with large and diverse evidence collections.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1417", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1417.flac", "type": "audio", "duration": 556.416}, {"from": "Assistant", "value": "We propose a new end-to-end question answering model, which learns to aggregate answer evidence from an incomplete knowledge base (KB) and a set of retrieved text snippets. Under the assumptions that structured data is easier to query and the acquired knowledge can help the understanding of unstructured text, our model first accumulates knowledge ofKB entities from a question-related KB sub-graph; then reformulates the question in the latent space and reads the text with the accumulated entity knowledge at hand. The evidence from KB and text are finally aggregated to predict answers. On the widely-used KBQA benchmark WebQSP, our model achieves consistent improvements across settings with different extents of KB incompleteness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1418", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1418.flac", "type": "audio", "duration": 634.048}, {"from": "Assistant", "value": "Neural semantic parsers utilize the encoder-decoder framework to learn an end-to-end model for semantic parsing that transduces a natural language sentence to the formal semantic representation. To keep the model aware of the underlying grammar in target sequences, many constrained decoders were devised in a multi-stage paradigm, which decode to the sketches or abstract syntax trees first, and then decode to target semantic tokens. We instead to propose an adaptive decoding method to avoid such intermediate representations. The decoder is guided by model uncertainty and automatically uses deeper computations when necessary. Thus it can predict tokens adaptively. Our model outperforms the state-of-the-art neural models and does not need any expertise like predefined grammar or sketches in the meantime.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1419", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1419.flac", "type": "audio", "duration": 852.096}, {"from": "Assistant", "value": "The non-indexed parts of the Internet (the Darknet) have become a haven for both legal and illegal anonymous activity. Given the magnitude of these networks, scalably monitoring their activity necessarily relies on automated tools, and notably on NLP tools. However, little is known about what characteristics texts communicated through the Darknet have, and how well do off-the-shelf NLP tools do on this domain. This paper tackles this gap and performs an in-depth investigation of the characteristics of legal and illegal text in the Darknet, comparing it to a clear net website with similar content as a control condition. Taking drugs-related websites as a test case, we find that texts for selling legal and illegal drugs have several linguistic characteristics that distinguish them from one another, as well as from the control condition, among them the distribution of POS tags, and the coverage of their named entities in Wikipedia.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1420", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1420.flac", "type": "audio", "duration": 661.76}, {"from": "Assistant", "value": "Cognitive task analysis (CTA) is a type of analysis in applied psychology aimed at eliciting and representing the knowledge and thought processes of domain experts. In CTA, often heavy human labor is involved to parse the interview transcript into structured knowledge (e.g., flowchart for different actions). To reduce human efforts and scale the process, automated CTA transcript parsing is desirable. However, this task has unique challenges as (1) it requires the understanding of long-range context information in conversational text; and (2) the amount of labeled data is limited and indirect—i.e., context-aware, noisy, and low-resource. In this paper, we propose a weakly-supervised information extraction framework for automated CTA transcript parsing. We partition the parsing process into a sequence labeling task and a text span-pair relation extraction task, with distant supervision from human-curated protocol files. To model long-range context information for extracting sentence relations, neighbor sentences are involved as a part of input. Different types of models for capturing context dependency are then applied. We manually annotate real-world CTA transcripts to facilitate the evaluation of the parsing tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1421", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1421.flac", "type": "audio", "duration": 772.331}, {"from": "Assistant", "value": "As Massive Open Online Courses (MOOCs) become increasingly popular, it is promising to automatically provide extracurricular knowledge for MOOC users. Suffering from semantic drifts and lack of knowledge guidance, existing methods can not effectively expand course concepts in complex MOOC environments. In this paper, we first build a novel boundary during searching for new concepts via external knowledge base and then utilize heterogeneous features to verify the high-quality results. In addition, to involve human efforts in our model, we design an interactive optimization mechanism based on a game. Our experiments on the four datasets from Coursera and XuetangX show that the proposed method achieves significant improvements(+0.19 by MAP) over existing methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1422", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1422.flac", "type": "audio", "duration": 505.216}, {"from": "Assistant", "value": "We show that the imperceptibility of several existing linguistic steganographic systems (Fang et al., 2017; Yang et al., 2018) relies on implicit assumptions on statistical behaviors of fluent text. We formally analyze them and empirically evaluate these assumptions. Furthermore, based on these observations, we propose an encoding algorithm called patient-Huffman with improved near-imperceptible guarantees.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1423", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1423.flac", "type": "audio", "duration": 549.163}, {"from": "Assistant", "value": "Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies. Existing methods do not fully exploit such dependencies. We present a novel inter-sentence relation extraction model that builds a labelled edge graph convolutional neural network model on a document-level graph. The graph is constructed using various inter- and intra-sentence dependencies to capture local and non-local dependency information. In order to predict the relation of an entity pair, we utilise multi-instance learning with bi-affine pairwise scoring. Experimental results show that our model achieves comparable performance to the state-of-the-art neural models on two biochemistry datasets. Our analysis shows that all the types in the graph are effective for inter-sentence relation extraction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1424", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1424.flac", "type": "audio", "duration": 580.203}, {"from": "Assistant", "value": "Legal judgment prediction is the task of automatically predicting the outcome of a court case, given a text describing the case’s facts. Previous work on using neural models for this task has focused on Chinese; only feature-based models (e.g., using bags of words and topics) have been considered in English. We release a new English legal judgment prediction dataset, containing cases from the European Court of Human Rights. We evaluate a broad variety of neural models on the new dataset, establishing strong baselines that surpass previous feature-based models in three tasks: (1) binary violation classification; (2) multi-label classification; (3) case importance prediction. We also explore if models are biased towards demographic information via data anonymization. As a side-product, we propose a hierarchical version of BERT, which bypasses BERT’s length limitation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1425", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1425.flac", "type": "audio", "duration": 977.28}, {"from": "Assistant", "value": "Neural machine translation (NMT) often suffers from the vulnerability to noisy perturbations in the input. We propose an approach to improving the robustness of NMT models, which consists of two parts: (1) attack the translation model with adversarial source examples; (2) defend the translation model with adversarial target inputs to improve its robustness against the adversarial source inputs. For the generation of adversarial inputs, we propose a gradient-based method to craft adversarial examples informed by the translation loss over the clean inputs. Experimental results on Chinese-English and English-German translation tasks demonstrate that our approach achieves significant improvements (2.8 and 1.6 BLEU points) over Transformer on standard clean benchmarks as well as exhibiting higher robustness on noisy data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1426", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1426.flac", "type": "audio", "duration": 680.64}, {"from": "Assistant", "value": "Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words. At training time, it predicts with the ground truth words as context while at inference it has to generate the entire sequence from scratch. This discrepancy of the fed context leads to error accumulation among the way. Furthermore, word-level training requires strict matching between the generated sequence and the ground truth sequence which leads to overcorrection over different but reasonable translations. In this paper, we address these issues by sampling context words not only from the ground truth sequence but also from the predicted sequence by the model during training, where the predicted sequence is selected with a sentence-level optimum. Experiment results on Chinese->English and WMT’14 English->German translation tasks demonstrate that our approach can achieve significant improvements on multiple datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1427", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1427.flac", "type": "audio", "duration": 875.968}, {"from": "Assistant", "value": "While most neural machine translation (NMT)systems are still trained using maximum likelihood estimation, recent work has demonstrated that optimizing systems to directly improve evaluation metrics such as BLEU can significantly improve final translation accuracy. However, training with BLEU has some limitations: it doesn’t assign partial credit, it has a limited range of output values, and it can penalize semantically correct hypotheses if they differ lexically from the reference. In this paper, we introduce an alternative reward function for optimizing NMT systems that is based on recent work in semantic similarity. We evaluate on four disparate languages trans-lated to English, and find that training with our proposed metric results in better translations as evaluated by BLEU, semantic similarity, and human evaluation, and also that the optimization procedure converges faster. Analysis suggests that this is because the proposed metric is more conducive to optimization, assigning partial credit and providing more diversity in scores than BLEU", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1428", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1428.flac", "type": "audio", "duration": 793.643}, {"from": "Assistant", "value": "The process of extracting knowledge from natural language text poses a complex problem that requires both a combination of machine learning techniques and proper feature selection. Recent advances in Automatic Machine Learning (AutoML) provide effective tools to explore large sets of algorithms, hyper-parameters and features to find out the most suitable combination of them. This paper proposes a novel AutoML strategy based on probabilistic grammatical evolution, which is evaluated on the health domain by facing the knowledge discovery challenge in Spanish text documents. Our approach achieves state-of-the-art results and provides interesting insights into the best combination of parameters and algorithms to use when dealing with this challenge. Source code is provided for the research community.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1429", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1429.flac", "type": "audio", "duration": 773.419}, {"from": "Assistant", "value": "Event detection systems rely on discrimination knowledge to distinguish ambiguous trigger words and generalization knowledge to detect unseen/sparse trigger words. Current neural event detection approaches focus on trigger-centric representations, which work well on distilling discrimination knowledge, but poorly on learning generalization knowledge. To address this problem, this paper proposes a Delta-learning approach to distill discrimination and generalization knowledge by effectively decoupling, incrementally learning and adaptively fusing event representation. Experiments show that our method significantly outperforms previous approaches on unseen/sparse trigger words, and achieves state-of-the-art performance on both ACE2005 and KBP2017 datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1430", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1430.flac", "type": "audio", "duration": 661.419}, {"from": "Assistant", "value": "Chinese relation extraction is conducted using neural networks with either character-based or word-based inputs, and most existing methods typically suffer from segmentation errors and ambiguity of polysemy. To address the issues, we propose a multi-grained lattice framework (MG lattice) for Chinese relation extraction to take advantage of multi-grained language information and external linguistic knowledge. In this framework, (1) we incorporate word-level information into character sequence inputs so that segmentation errors can be avoided. (2) We also model multiple senses of polysemous words with the help of external linguistic knowledge, so as to alleviate polysemy ambiguity. Experiments on three real-world datasets in distinct domains show consistent and significant superiority and robustness of our model, as compared with other baselines. We will release the source code of this paper in the future.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1431", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1431.flac", "type": "audio", "duration": 623.296}, {"from": "Assistant", "value": "State-of-the-art models for knowledge graph completion aim at learning a fixed embedding representation of entities in a multi-relational graph which can generalize to infer unseen entity relationships at test time. This can be sub-optimal as it requires memorizing and generalizing to all possible entity relationships using these fixed representations. We thus propose a novel attention-based method to learn query-dependent representation of entities which adaptively combines the relevant graph neighborhood of an entity leading to more accurate KG completion. The proposed method is evaluated on two benchmark datasets for knowledge graph completion, and experimental results show that the proposed model performs competitively or better than existing state-of-the-art, including recent methods for explicit multi-hop reasoning. Qualitative probing offers insight into how the model can reason about facts involving multiple hops in the knowledge graph, through the use of neighborhood attention.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1432", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1432.flac", "type": "audio", "duration": 548.011}, {"from": "Assistant", "value": "Event factuality prediction (EFP) is the task of assessing the degree to which an event mentioned in a sentence has happened. For this task, both syntactic and semantic information are crucial to identify the important context words. The previous work for EFP has only combined these information in a simple way that cannot fully exploit their coordination. In this work, we introduce a novel graph-based neural network for EFP that can integrate the semantic and syntactic information more effectively. Our experiments demonstrate the advantage of the proposed model for EFP.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1433", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1433.flac", "type": "audio", "duration": 620.523}, {"from": "Assistant", "value": "Data-driven models have demonstrated state-of-the-art performance in inferring the temporal ordering of events in text. However, these models often overlook explicit temporal signals, such as dates and time windows. Rule-based methods can be used to identify the temporal links between these time expressions (timexes), but they fail to capture timexes’ interactions with events and are hard to integrate with the distributed representations of neural net models. In this paper, we introduce a framework to infuse temporal awareness into such models by learning a pre-trained model to embed timexes. We generate synthetic data consisting of pairs of timexes, then train a character LSTM to learn embeddings and classify the timexes’ temporal relation. We evaluate the utility of these embeddings in the context of a strong neural model for event temporal ordering, and show a small increase in performance on the MATRES dataset and more substantial gains on an automatically collected dataset with more frequent event-timex interactions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1434", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1434.flac", "type": "audio", "duration": 886.571}, {"from": "Assistant", "value": "We consider a novel question answering (QA) task where the machine needs to read from large streaming data (long documents or videos) without knowing when the questions will be given, which is difficult to solve with existing QA methods due to their lack of scalability. To tackle this problem, we propose a novel end-to-end deep network model for reading comprehension, which we refer to as Episodic Memory Reader (EMR) that sequentially reads the input contexts into an external memory, while replacing memories that are less important for answering unseen questions. Specifically, we train an RL agent to replace a memory entry when the memory is full, in order to maximize its QA accuracy at a future timepoint, while encoding the external memory using either the GRU or the Transformer architecture to learn representations that considers relative importance between the memory entries. We validate our model on a synthetic dataset (bAbI) as well as real-world large-scale textual QA (TriviaQA) and video QA (TVQA) datasets, on which it achieves significant improvements over rule based memory scheduling policies or an RL based baseline that independently learns the query-specific importance of each memory.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1435", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1435.flac", "type": "audio", "duration": 999.296}, {"from": "Assistant", "value": "Natural Language Sentence Matching (NLSM) has gained substantial attention from both academics and the industry, and rich public datasets contribute a lot to this process. However, biased datasets can also hurt the generalization performance of trained models and give untrustworthy evaluation results. For many NLSM datasets, the providers select some pairs of sentences into the datasets, and this sampling procedure can easily bring unintended pattern, i.e., selection bias. One example is the QuoraQP dataset, where some content-independent naive features are unreasonably predictive. Such features are the reflection of the selection bias and termed as the “leakage features.” In this paper, we investigate the problem of selection bias on six NLSM datasets and find that four out of them are significantly biased. We further propose a training and evaluation framework to alleviate the bias. Experimental results on QuoraQP suggest that the proposed framework can improve the generalization ability of trained models, and give more trustworthy evaluation results for real-world adoptions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1436", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1436.flac", "type": "audio", "duration": 840.085}, {"from": "Assistant", "value": "Existing open-domain question answering (QA) models are not suitable for real-time usage because they need to process several long documents on-demand for every input query, which is computationally prohibitive. In this paper, we introduce query-agnostic indexable representations of document phrases that can drastically speed up open-domain QA. In particular, our dense-sparse phrase encoding effectively captures syntactic, semantic, and lexical information of the phrases and eliminates the pipeline filtering of context documents. Leveraging strategies for optimizing training and inference time, our model can be trained and deployed even in a single 4-GPU server. Moreover, by representing phrases as pointers to their start and end tokens, our model indexes phrases in the entire English Wikipedia (up to 60 billion phrases) using under 2TB. Our experiments on SQuAD-Open show that our model is on par with or more accurate than previous models with 6000x reduced computational cost, which translates into at least 68x faster end-to-end inference benchmark on CPUs. Code and demo are available at nlp.cs.washington.edu/denspi", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1437", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1437.flac", "type": "audio", "duration": 830.037}, {"from": "Assistant", "value": "Sequential recurrent neural networks have achieved superior performance on language modeling, but overlook the structure information in natural language. Recent works on structure-aware models have shown promising results on language modeling. However, how to incorporate structure knowledge on corpus without syntactic annotations remains an open problem. In this work, we propose neural variational language model (NVLM), which enables the sharing of grammar knowledge among different corpora. Experimental results demonstrate the effectiveness of our framework on two popular benchmark datasets. With the help of shared grammar, our language model converges significantly faster to a lower perplexity on new training corpus.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1479", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1479.flac", "type": "audio", "duration": 847.68}, {"from": "Assistant", "value": "Automatic article commenting is helpful in encouraging user engagement on online news platforms. However, the news documents are usually too long for models under traditional encoder-decoder frameworks, which often results in general and irrelevant comments. In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph. By organizing the article into graph structure, our model can better understand the internal structure of the article and the connection between topics, which makes it better able to generate coherent and informative comments. We collect and release a large scale news-comment corpus from a popular Chinese online news platform Tencent Kuaibao. Extensive experiment results show that our model can generate much more coherent and informative comments compared with several strong baseline models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1480", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1480.flac", "type": "audio", "duration": 919.68}, {"from": "Assistant", "value": "We study the problem of generating interconnected questions in question-answering style conversations. Compared with previous works which generate questions based on a single sentence (or paragraph), this setting is different in two major aspects: (1) Questions are highly conversational. Almost half of them refer back to conversation history using coreferences. (2) In a coherent conversation, questions have smooth transitions between turns. We propose an end-to-end neural model with coreference alignment and conversation flow modeling. The coreference alignment modeling explicitly aligns coreferent mentions in conversation history with corresponding pronominal references in generated questions, which makes generated questions interconnected to conversation history. The conversation flow modeling builds a coherent conversation by starting questioning on the first few sentences in a text passage and smoothly shifting the focus to later parts. Extensive experiments show that our system outperforms several baselines and can generate highly conversational questions. The code implementation is released athttps://github.com/Evan-Gao/conversaional-QG.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1481", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1481.flac", "type": "audio", "duration": 917.056}, {"from": "Assistant", "value": "Automatic question generation (QG) is a challenging problem in natural language understanding. QG systems are typically built assuming access to a large number of training instances where each instance is a question and its corresponding answer. For a new language, such training instances are hard to obtain making the QG problem even more challenging. Using this as our motivation, we study the reuse of an available large QG dataset in a secondary language (e.g. English) to learn a QG model for a primary language (e.g. Hindi) of interest. For the primary language, we assume access to a large amount of monolingual text but only a small QG dataset. We propose a cross-lingual QG model which uses the following training regime: (i) Unsupervised pretraining of language models in both primary and secondary languages and (ii) joint supervised training for QG in both languages. We demonstrate the efficacy of our proposed approach using two different primary languages, Hindi and Chinese. Our proposed framework clearly outperforms a number of baseline models, including a fully-supervised transformer-based model trained on the QG datasets in the primary language. We also create and release a new question answering dataset for Hindi consisting of 6555 sentences.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1482", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1482.flac", "type": "audio", "duration": 741.291}, {"from": "Assistant", "value": "Unsupervised text style transfer aims to alter text styles while preserving the content, without aligned data for supervision. Existing seq2seq methods face three challenges: 1) the transfer is weakly interpretable, 2) generated outputs struggle in content preservation, and 3) the trade-off between content and style is intractable. To address these challenges, we propose a hierarchical reinforced sequence operation method, named Point-Then-Operate (PTO), which consists of a high-level agent that proposes operation positions and a low-level agent that alters the sentence. We provide comprehensive training objectives to control the fluency, style, and content of the outputs and a mask-based inference algorithm that allows for multi-step revision based on the single-step trained agents. Experimental results on two text style transfer datasets show that our method significantly outperforms recent methods and effectively addresses the aforementioned challenges.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1483", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1483.flac", "type": "audio", "duration": 923.093}, {"from": "Assistant", "value": "Automatically constructed datasets for generating text from semi-structured data (tables), such as WikiBio, often contain reference texts that diverge from the information in the corresponding semi-structured data. We show that metrics which rely solely on the reference texts, such as BLEU and ROUGE, show poor correlation with human judgments when those references diverge. We propose a new metric, PARENT, which aligns n-grams from the reference and generated texts to the semi-structured data before computing their precision and recall. Through a large scale human evaluation study of table-to-text models for WikiBio, we show that PARENT correlates with human judgments better than existing text generation metrics. We also adapt and evaluate the information extraction based evaluation proposed by Wiseman et al (2017), and show that PARENT has comparable correlation to it, while being easier to use. We show that PARENT is also applicable when the reference texts are elicited from humans using the data from the WebNLG challenge.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1484", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1484.flac", "type": "audio", "duration": 860.096}, {"from": "Assistant", "value": "Obtaining training data for Question Answering (QA) is time-consuming and resource-intensive, and existing QA datasets are only available for limited domains and languages. In this work, we explore to what extent high quality training data is actually required for Extractive QA, and investigate the possibility of unsupervised Extractive QA. We approach this problem by first learning to generate context, question and answer triples in an unsupervised manner, which we then use to synthesize Extractive QA training data automatically. To generate such triples, we first sample random context paragraphs from a large corpus of documents and then random noun phrases or Named Entity mentions from these paragraphs as answers. Next we convert answers in context to “fill-in-the-blank” cloze questions and finally translate them into natural questions. We propose and compare various unsupervised ways to perform cloze-to-natural question translation, including training an unsupervised NMT model using non-aligned corpora of natural questions and cloze questions as well as a rule-based approach. We find that modern QA models can learn to answer human questions surprisingly well using only synthetic training data. We demonstrate that, without using the SQuAD training data at all, our approach achieves 56.4 F1 on SQuAD v1 (64.5 F1 when the answer is a Named Entity mention), outperforming early supervised models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1485", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1485.flac", "type": "audio", "duration": 894.763}, {"from": "Assistant", "value": "A large number of reading comprehension (RC) datasets has been created recently, but little analysis has been done on whether they generalize to one another, and the extent to which existing datasets can be leveraged for improving performance on new ones. In this paper, we conduct such an investigation over ten RC datasets, training on one or more source RC datasets, and evaluating generalization, as well as transfer to a target RC dataset. We analyze the factors that contribute to generalization, and show that training on a source RC dataset and transferring to a target dataset substantially improves performance, even in the presence of powerful contextual representations from BERT (Devlin et al., 2019). We also find that training on multiple source RC datasets leads to robust generalization and transfer, and can reduce the cost of example collection for a new RC dataset. Following our analysis, we propose MultiQA, a BERT-based model, trained on multiple RC datasets, which leads to state-of-the-art performance on five RC datasets. We share our infrastructure for the benefit of the research community.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1486", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1486.flac", "type": "audio", "duration": 893.376}, {"from": "Assistant", "value": "This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens. We propose a curriculum learning (CL) based Pointer-Generator framework for reading/sampling over large documents, enabling diverse training of the neural model based on the notion of alternating contextual difficulty. This can be interpreted as a form of domain randomization and/or generative pretraining during training. To this end, the usage of the Pointer-Generator softens the requirement of having the answer within the context, enabling us to construct diverse training samples for learning. Additionally, we propose a new Introspective Alignment Layer (IAL), which reasons over decomposed alignments using block-based self-attention. We evaluate our proposed method on the NarrativeQA reading comprehension benchmark, achieving state-of-the-art performance, improving existing baselines by 51% relative improvement on BLEU-4 and 17% relative improvement on Rouge-L. Extensive ablations confirm the effectiveness of our proposed IAL and CL components.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1487", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1487.flac", "type": "audio", "duration": 943.979}, {"from": "Assistant", "value": "Deep learning models perform poorly on tasks that require commonsense reasoning, which often necessitates some form of world-knowledge or reasoning over information not immediately present in the input. We collect human explanations for commonsense reasoning in the form of natural language sequences and highlighted annotations in a new dataset called Common Sense Explanations (CoS-E). We use CoS-E to train language models to automatically generate explanations that can be used during training and inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework. CAGE improves the state-of-the-art by 10% on the challenging CommonsenseQA task. We further study commonsense reasoning in DNNs using both human and auto-generated explanations including transfer to out-of-domain tasks. Empirical results indicate that we can effectively leverage language models for commonsense reasoning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1488", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1488.flac", "type": "audio", "duration": 811.691}, {"from": "Assistant", "value": "Interpretability of machine learning (ML) models becomes more relevant with their increasing adoption. In this work, we address the interpretability of ML based question answering (QA) models on a combination of knowledge bases (KB) and text documents. We adapt post hoc explanation methods such as LIME and input perturbation (IP) and compare them with the self-explanatory attention mechanism of the model. For this purpose, we propose an automatic evaluation paradigm for explanation methods in the context of QA. We also conduct a study with human annotators to evaluate whether explanations help them identify better QA models. Our results suggest that IP provides better explanations than LIME or attention, according to both automatic and human evaluation. We obtain the same ranking of methods in both experiments, which supports the validity of our automatic evaluation paradigm.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1489", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1489.flac", "type": "audio", "duration": 881.579}, {"from": "Assistant", "value": "Cross-lingual word embeddings encode the meaning of words from different languages into a shared low-dimensional space. An important requirement for many downstream tasks is that word similarity should be independent of language—i.e., word vectors within one language should not be more similar to each other than to words in another language. We measure this characteristic using modularity, a network measurement that measures the strength of clusters in a graph. Modularity has a moderate to strong correlation with three downstream tasks, even though modularity is based only on the structure of embeddings and does not require any external resources. We show through experiments that modularity can serve as an intrinsic validation metric to improve unsupervised cross-lingual word embeddings, particularly on distant language pairs in low-resource settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1490", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1490.flac", "type": "audio", "duration": 916.416}, {"from": "Assistant", "value": "Grounded in cognitive linguistics, graded lexical entailment (GR-LE) is concerned with fine-grained assertions regarding the directional hierarchical relationships between concepts on a continuous scale. In this paper, we present the first work on cross-lingual generalisation of GR-LE relation. Starting from HyperLex, the only available GR-LE dataset in English, we construct new monolingual GR-LE datasets for three other languages, and combine those to create a set of six cross-lingual GR-LE datasets termed CL-HYPERLEX. We next present a novel method dubbed CLEAR (Cross-Lingual Lexical Entailment Attract-Repel) for effectively capturing graded (and binary) LE, both monolingually in different languages as well as across languages (i.e., on CL-HYPERLEX). Coupled with a bilingual dictionary, CLEAR leverages taxonomic LE knowledge in a resource-rich language (e.g., English) and propagates it to other languages. Supported by cross-lingual LE transfer, CLEAR sets competitive baseline performance on three new monolingual GR-LE datasets and six cross-lingual GR-LE datasets. In addition, we show that CLEAR outperforms current state-of-the-art on binary cross-lingual LE detection by a wide margin for diverse language pairs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1491", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1491.flac", "type": "audio", "duration": 993.579}, {"from": "Assistant", "value": "How language-agnostic are current state-of-the-art NLP tools? Are there some types of language that are easier to model with current methods? In prior work (Cotterell et al., 2018) we attempted to address this question for language modeling, and observed that recurrent neural network language models do not perform equally well over all the high-resource European languages found in the Europarl corpus. We speculated that inflectional morphology may be the primary culprit for the discrepancy. In this paper, we extend these earlier experiments to cover 69 languages from 13 language families using a multilingual Bible corpus. Methodologically, we introduce a new paired-sample multiplicative mixed-effects model to obtain language difficulty coefficients from at-least-pairwise parallel corpora. In other words, the model is aware of inter-sentence variation and can handle missing data. Exploiting this model, we show that “translationese” is not any easier to model than natively written language in a fair comparison. Trying to answer the question of what features difficult languages have in common, we try and fail to reproduce our earlier (Cotterell et al., 2018) observation about morphological complexity and instead reveal far simpler statistics of the data that seem to drive complexity in a much larger sample.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1492", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1492.flac", "type": "audio", "duration": 554.005}, {"from": "Assistant", "value": "Recent research in cross-lingual word embeddings has almost exclusively focused on offline methods, which independently train word embeddings in different languages and map them to a shared space through linear transformations. While several authors have questioned the underlying isomorphism assumption, which states that word embeddings in different languages have approximately the same structure, it is not clear whether this is an inherent limitation of mapping approaches or a more general issue when learning cross-lingual embeddings. So as to answer this question, we experiment with parallel corpora, which allows us to compare offline mapping to an extension of skip-gram that jointly learns both embedding spaces. We observe that, under these ideal conditions, joint learning yields to more isomorphic embeddings, is less sensitive to hubness, and obtains stronger results in bilingual lexicon induction. We thus conclude that current mapping methods do have strong limitations, calling for further research to jointly learn cross-lingual embeddings with a weaker cross-lingual signal.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1493", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1493.flac", "type": "audio", "duration": 584.725}, {"from": "Assistant", "value": "In this paper, we show that Multilingual BERT (M-BERT), released by Devlin et al. (2018) as a single language model pre-trained from monolingual corpora in 104 languages, is surprisingly good at zero-shot cross-lingual model transfer, in which task-specific annotations in one language are used to fine-tune the model for evaluation in another language. To understand why, we present a large number of probing experiments, showing that transfer is possible even to languages in different scripts, that transfer works best between typologically similar languages, that monolingual corpora can train models for code-switching, and that the model can find translation pairs. From these results, we can conclude that M-BERT does create multilingual representations, but that these representations exhibit systematic deficiencies affecting certain language pairs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1494", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1494.flac", "type": "audio", "duration": 573.163}, {"from": "Assistant", "value": "A recent research line has obtained strong results on bilingual lexicon induction by aligning independently trained word embeddings in two languages and using the resulting cross-lingual embeddings to induce word translation pairs through nearest neighbor or related retrieval methods. In this paper, we propose an alternative approach to this problem that builds on the recent work on unsupervised machine translation. This way, instead of directly inducing a bilingual lexicon from cross-lingual embeddings, we use them to build a phrase-table, combine it with a language model, and use the resulting machine translation system to generate a synthetic parallel corpus, from which we extract the bilingual lexicon using statistical word alignment techniques. As such, our method can work with any word embedding and cross-lingual mapping technique, and it does not require any additional resource besides the monolingual corpus used to train the embeddings. When evaluated on the exact same cross-lingual embeddings, our proposed method obtains an average improvement of 6 accuracy points over nearest neighbor and 4 points over CSLS retrieval, establishing a new state-of-the-art in the standard MUSE dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1495", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1495.flac", "type": "audio", "duration": 875.371}, {"from": "Assistant", "value": "Complaining is a basic speech act regularly used in human and computer mediated communication to express a negative mismatch between reality and expectations in a particular situation. Automatically identifying complaints in social media is of utmost importance for organizations or brands to improve the customer experience or in developing dialogue systems for handling and responding to complaints. In this paper, we introduce the first systematic analysis of complaints in computational linguistics. We collect a new annotated data set of written complaints expressed on Twitter. We present an extensive linguistic analysis of complaining as a speech act in social media and train strong feature-based and neural models of complaints across nine domains achieving a predictive performance of up to 79 F1 using distant supervision.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1496", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1496.flac", "type": "audio", "duration": 960.939}, {"from": "Assistant", "value": "With social media becoming increasingly popular on which lots of news and real-time events are reported, developing automated question answering systems is critical to the effective-ness of many applications that rely on real-time knowledge. While previous datasets have concentrated on question answering (QA) for formal text like news and Wikipedia, we present the first large-scale dataset for QA over social media data. To ensure that the tweets we collected are useful, we only gather tweets used by journalists to write news articles. We then ask human annotators to write questions and answers upon these tweets. Unlike otherQA datasets like SQuAD in which the answers are extractive, we allow the answers to be abstractive. We show that two recently proposed neural models that perform well on formal texts are limited in their performance when applied to our dataset. In addition, even the fine-tuned BERT model is still lagging behind human performance with a large margin. Our results thus point to the need of improved QA systems targeting social media text.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1497", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1497.flac", "type": "audio", "duration": 841.003}, {"from": "Assistant", "value": "Teaching machines to ask questions is an important yet challenging task. Most prior work focused on generating questions with fixed answers. As contents are highly limited by given answers, these questions are often not worth discussing. In this paper, we take the first step on teaching machines to ask open-answered questions from real-world news for open discussion (openQG). To generate high-qualified questions, effective ways for question evaluation are required. We take the perspective that the more answers a question receives, the better it is for open discussion, and analyze how language use affects the number of answers. Compared with other factors, e.g. topic and post time, linguistic factors keep our evaluation from being domain-specific. We carefully perform variable control on 11.5M questions from online forums to get a dataset, OQRanD, and further perform question analysis. Based on these conclusions, several models are built for question evaluation. For openQG task, we construct OQGenD, the first dataset as far as we know, and propose a model based on conditional generative adversarial networks and our question evaluation model. Experiments show that our model can generate questions with higher quality compared with commonly-used text generation methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1498", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1498.flac", "type": "audio", "duration": 979.2}, {"from": "Assistant", "value": "Learning from social-media conversations has gained significant attention recently because of its applications in areas like rumor detection. In this research, we propose a new way to represent social-media conversations as binarized constituency trees that allows comparing features in source-posts and their replies effectively. Moreover, we propose to use convolution units in Tree LSTMs that are better at learning patterns in features obtained from the source and reply posts. Our Tree LSTM models employ multi-task (stance + rumor) learning and propagate the useful stance signal up in the tree for rumor classification at the root node. The proposed models achieve state-of-the-art performance, outperforming the current best model by 12% and 15% on F1-macro for rumor-veracity classification and stance classification tasks respectively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1499", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1499.flac", "type": "audio", "duration": 853.973}, {"from": "Assistant", "value": "Neural extractive summarization models usually employ a hierarchical encoder for document encoding and they are trained using sentence-level labels, which are created heuristically using rule-based methods. Training the hierarchical encoder with theseinaccuratelabels is challenging. Inspired by the recent work on pre-training transformer sentence encoders (Devlin et al., 2018), we propose Hibert (as shorthand forHIerachicalBidirectionalEncoderRepresentations fromTransformers) for document encoding and a method to pre-train it using unlabeled data. We apply the pre-trained Hibert to our summarization model and it outperforms its randomly initialized counterpart by 1.25 ROUGE on the CNN/Dailymail dataset and by 2.0 ROUGE on a version of New York Times dataset. We also achieve the state-of-the-art performance on these two datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1500", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1500.flac", "type": "audio", "duration": 880.405}, {"from": "Assistant", "value": "In this paper, we develop a neural summarization model which can effectively process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner. We represent cross-document relationships via an attention mechanism which allows to share information as opposed to simply concatenating text spans and processing them as a flat sequence. Our model learns latent dependencies among textual units, but can also take advantage of explicit graph representations focusing on similarity or discourse relations. Empirical results on the WikiSum dataset demonstrate that the proposed architecture brings substantial improvements over several strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1501", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1501.flac", "type": "audio", "duration": 756.736}, {"from": "Assistant", "value": "This work proposes a novel framework for enhancing abstractive text summarization based on the combination of deep learning techniques along with semantic data transformations. Initially, a theoretical model for semantic-based text generalization is introduced and used in conjunction with a deep encoder-decoder architecture in order to produce a summary in generalized form. Subsequently, a methodology is proposed which transforms the aforementioned generalized summary into human-readable form, retaining at the same time important informational aspects of the original text and addressing the problem of out-of-vocabulary or rare words. The overall approach is evaluated on two popular datasets with encouraging results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1502", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1502.flac", "type": "audio", "duration": 616.299}, {"from": "Assistant", "value": "In summarization, automatic evaluation metrics are usually compared based on their ability to correlate with human judgments. Unfortunately, the few existing human judgment datasets have been created as by-products of the manual evaluations performed during the DUC/TAC shared tasks. However, modern systems are typically better than the best systems submitted at the time of these shared tasks. We show that, surprisingly, evaluation metrics which behave similarly on these datasets (average-scoring range) strongly disagree in the higher-scoring range in which current systems now operate. It is problematic because metrics disagree yet we can’t decide which one to trust. This is a call for collecting human judgments for high-scoring summaries as this would resolve the debate over which metrics to trust. This would also be greatly beneficial to further improve summarization systems and metrics alike.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1503", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1503.flac", "type": "audio", "duration": 585.813}, {"from": "Assistant", "value": "We propose an unsupervised method for sentence summarization using only language modeling. The approach employs two language models, one that is generic (i.e. pretrained), and the other that is specific to the target domain. We show that by using a product-of-experts criteria these are enough for maintaining continuous contextual matching while maintaining output fluency. Experiments on both abstractive and extractive sentence summarization data sets show promising results of our method without being exposed to any paired data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1504", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1504.flac", "type": "audio", "duration": 571.733}, {"from": "Assistant", "value": "Existing neural generation approaches create multi-sentence text as a single sequence. In this paper we propose a structured convolutional decoder that is guided by the content structure of target summaries. We compare our model with existing sequential decoders on three data sets representing different domains. Automatic and human evaluation demonstrate that our summaries have better content coverage.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1505", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1505.flac", "type": "audio", "duration": 716.971}, {"from": "Assistant", "value": "We present a study of morphological irregularity. Following recent work, we define an information-theoretic measure of irregularity based on the predictability of forms in a language. Using a neural transduction model, we estimate this quantity for the forms in 28 languages. We first present several validatory and exploratory analyses of irregularity. We then show that our analyses provide evidence for a correlation between irregularity and frequency: higher frequency items are more likely to be irregular and irregular items are more likely be highly frequent. To our knowledge, this result is the first of its breadth and confirms longstanding proposals from the linguistics literature. The correlation is more robust when aggregated at the level of whole paradigms—providing support for models of linguistic structure in which inflected forms are unified by abstract underlying stems or lexemes.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1506", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1506.flac", "type": "audio", "duration": 907.563}, {"from": "Assistant", "value": "We examine the benefits of visual context in training neural language models to perform next-word prediction. A multi-modal neural architecture is introduced that outperform its equivalent trained on language alone with a 2% decrease in perplexity, even when no visual context is available at test. Fine-tuning the embeddings of a pre-trained state-of-the-art bidirectional language model (BERT) in the language modeling framework yields a 3.5% improvement. The advantage for training with visual context when testing without is robust across different languages (English, German and Spanish) and different models (GRU, LSTM, Delta-RNN, as well as those that use BERT embeddings). Thus, language models perform better when they learn like a baby, i.e, in a multi-modal environment. This finding is compatible with the theory of situated cognition: language is inseparable from its physical context.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1507", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1507.flac", "type": "audio", "duration": 863.957}, {"from": "Assistant", "value": "What is the relationship between sentence representations learned by deep recurrent models against those encoded by the brain? Is there any correspondence between hidden layers of these recurrent models and brain regions when processing sentences? Can these deep models be used to synthesize brain data which can then be utilized in other extrinsic tasks? We investigate these questions using sentences with simple syntax and semantics (e.g., The bone was eaten by the dog.). We consider multiple neural network architectures, including recently proposed ELMo and BERT. We use magnetoencephalography (MEG) brain recording data collected from human subjects when they were reading these simple sentences. Overall, we find that BERT’s activations correlate the best with MEG brain data. We also find that the deep network representation can be used to generate brain data from new sentences to augment existing brain data. To the best of our knowledge, this is the first work showing that the MEG brain recording when reading a word in a sentence can be used to distinguish earlier words in the sentence. Our exploration is also the first to use deep neural network representations to generate synthetic brain data and to show that it helps in improving subsequent stimuli decoding task accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1508", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1508.flac", "type": "audio", "duration": 995.008}, {"from": "Assistant", "value": "Recent work shows that distributional semantic models can be used to decode patterns of brain activity associated with individual words and sentence meanings. However, it is yet unclear to what extent such models can be used to study and decode fMRI patterns associated with specific aspects of semantic composition such as the negation function. In this paper, we apply lexical and compositional semantic models to decode fMRI patterns associated with negated and affirmative sentences containing hand-action verbs. Our results show reduced decoding (correlation) of sentences where the verb is in the negated context, as compared to the affirmative one, within brain regions implicated in action-semantic processing. This supports behavioral and brain imaging studies, suggesting that negation involves reduced access to aspects of the affirmative mental representation. The results pave the way for testing alternate semantic models of negation against human semantic processing in the brain.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1509", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1509.flac", "type": "audio", "duration": 838.805}, {"from": "Assistant", "value": "Sequence-processing neural networks led to remarkable progress on many NLP tasks. As a consequence, there has been increasing interest in understanding to what extent they process language as humans do. We aim here to uncover which biases such models display with respect to “natural” word-order constraints. We train models to communicate about paths in a simple gridworld, using miniature languages that reflect or violate various natural language trends, such as the tendency to avoid redundancy or to minimize long-distance dependencies. We study how the controlled characteristics of our miniature languages affect individual learning and their stability across multiple network generations. The results draw a mixed picture. On the one hand, neural networks show a strong tendency to avoid long-distance dependencies. On the other hand, there is no clear preference for the efficient, non-redundant encoding of information that is widely attested in natural language. We thus suggest inoculating a notion of “effort” into neural networks, as a possible way to make their linguistic behavior more human-like.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1563", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1563.flac", "type": "audio", "duration": 818.88}, {"from": "Assistant", "value": "Automatically analyzing dialogue can help understand and guide behavior in domains such as counseling, where interactions are largely mediated by conversation. In this paper, we study modeling behavioral codes used to asses a psychotherapy treatment style called Motivational Interviewing (MI), which is effective for addressing substance abuse and related problems. Specifically, we address the problem of providing real-time guidance to therapists with a dialogue observer that (1) categorizes therapist and client MI behavioral codes and, (2) forecasts codes for upcoming utterances to help guide the conversation and potentially alert the therapist. For both tasks, we define neural network models that build upon recent successes in dialogue modeling. Our experiments demonstrate that our models can outperform several baselines for both tasks. We also report the results of a careful analysis that reveals the impact of the various network design tradeoffs for modeling therapy dialogue.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1564", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1564.flac", "type": "audio", "duration": 1080.405}, {"from": "Assistant", "value": "Developing Video-Grounded Dialogue Systems (VGDS), where a dialogue is conducted based on visual and audio aspects of a given video, is significantly more challenging than traditional image or text-grounded dialogue systems because (1) feature space of videos span across multiple picture frames, making it difficult to obtain semantic information; and (2) a dialogue agent must perceive and process information from different modalities (audio, video, caption, etc.) to obtain a comprehensive understanding. Most existing work is based on RNNs and sequence-to-sequence architectures, which are not very effective for capturing complex long-term dependencies (like in videos). To overcome this, we propose Multimodal Transformer Networks (MTN) to encode videos and incorporate information from different modalities. We also propose query-aware attention through an auto-encoder to extract query-aware features from non-text modalities. We develop a training procedure to simulate token-level decoding to improve the quality of generated responses during inference. We get state of the art performance on Dialogue System Technology Challenge 7 (DSTC7). Our model also generalizes to another multimodal visual-grounded dialogue task, and obtains promising performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1565", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1565.flac", "type": "audio", "duration": 887.339}, {"from": "Assistant", "value": "Many real-world open-domain conversation applications have specific goals to achieve during open-ended chats, such as recommendation, psychotherapy, education, etc. We study the problem of imposing conversational goals on open-domain chat agents. In particular, we want a conversational system to chat naturally with human and proactively guide the conversation to a designated target subject. The problem is challenging as no public data is available for learning such a target-guided strategy. We propose a structured approach that introduces coarse-grained keywords to control the intended content of system responses. We then attain smooth conversation transition through turn-level supervised learning, and drive the conversation towards the target with discourse-level constraints. We further derive a keyword-augmented conversation dataset for the study. Quantitative and human evaluations show our system can produce meaningful and effective conversations, significantly improving over other approaches", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1566", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1566.flac", "type": "audio", "duration": 1003.371}, {"from": "Assistant", "value": "Developing intelligent persuasive conversational agents to change people’s opinions and actions for social good is the frontier in advancing the ethical development of automated dialogue systems. To do so, the first step is to understand the intricate organization of strategic disclosures and appeals employed in human persuasion conversations. We designed an online persuasion task where one participant was asked to persuade the other to donate to a specific charity. We collected a large dataset with 1,017 dialogues and annotated emerging persuasion strategies from a subset. Based on the annotation, we built a baseline classifier with context information and sentence-level features to predict the 10 persuasion strategies used in the corpus. Furthermore, to develop an understanding of personalized persuasion processes, we analyzed the relationships between individuals’ demographic and psychological backgrounds including personality, morality, value systems, and their willingness for donation. Then, we analyzed which types of persuasion strategies led to a greater amount of donation depending on the individuals’ personal backgrounds. This work lays the ground for developing a personalized persuasive dialogue system.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1567", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1567.flac", "type": "audio", "duration": 794.048}, {"from": "Assistant", "value": "Current neural network-based conversational models lack diversity and generate boring responses to open-ended utterances. Priors such as persona, emotion, or topic provide additional information to dialog models to aid response generation, but annotating a dataset with priors is expensive and such annotations are rarely available. While previous methods for improving the quality of open-domain response generation focused on either the underlying model or the training objective, we present a method of filtering dialog datasets by removing generic utterances from training data using a simple entropy-based approach that does not require human supervision. We conduct extensive experiments with different variations of our method, and compare dialog models across 17 evaluation metrics to show that training on datasets filtered this way results in better conversational quality as chatbots learn to output more diverse responses.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1568", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1568.flac", "type": "audio", "duration": 632.725}, {"from": "Assistant", "value": "Word Sense Disambiguation (WSD) is a long-standing but open problem in Natural Language Processing (NLP). WSD corpora are typically small in size, owing to an expensive annotation process. Current supervised WSD methods treat senses as discrete labels and also resort to predicting the Most-Frequent-Sense (MFS) for words unseen during training. This leads to poor performance on rare and unseen senses. To overcome this challenge, we propose Extended WSD Incorporating Sense Embeddings (EWISE), a supervised model to perform WSD by predicting over a continuous sense embedding space as opposed to a discrete label space. This allows EWISE to generalize over both seen and unseen senses, thus achieving generalized zero-shot learning. To obtain target sense embeddings, EWISE utilizes sense definitions. EWISE learns a novel sentence encoder for sense definitions by using WordNet relations and also ConvE, a recently proposed knowledge graph embedding method. We also compare EWISE against other sentence encoders pretrained on large corpora to generate definition embeddings. EWISE achieves new state-of-the-art WSD performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1569", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1569.flac", "type": "audio", "duration": 934.208}, {"from": "Assistant", "value": "Contextual embeddings represent a new generation of semantic representations learned from Neural Language Modelling (NLM) that addresses the issue of meaning conflation hampering traditional word embeddings. In this work, we show that contextual embeddings can be used to achieve unprecedented gains in Word Sense Disambiguation (WSD) tasks. Our approach focuses on creating sense-level embeddings with full-coverage of WordNet, and without recourse to explicit knowledge of sense distributions or task-specific modelling. As a result, a simple Nearest Neighbors (k-NN) method using our representations is able to consistently surpass the performance of previous systems using powerful neural sequencing models. We also analyse the robustness of our approach when ignoring part-of-speech and lemma features, requiring disambiguation against the full sense inventory, and revealing shortcomings to be improved. Finally, we explore applications of our sense embeddings for concept-level analyses of contextual embeddings and their respective NLMs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1570", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1570.flac", "type": "audio", "duration": 927.445}, {"from": "Assistant", "value": "We present an unsupervised method to generate Word2Sense word embeddings that are interpretable — each dimension of the embedding space corresponds to a fine-grained sense, and the non-negative value of the embedding along the j-th dimension represents the relevance of the j-th sense to the word. The underlying LDA-based generative model can be extended to refine the representation of a polysemous word in a short context, allowing us to use the embedings in contextual tasks. On computational NLP tasks, Word2Sense embeddings compare well with other word embeddings generated by unsupervised methods. Across tasks such as word similarity, entailment, sense induction, and contextual interpretation, Word2Sense is competitive with the state-of-the-art method for that task. Word2Sense embeddings are at least as sparse and fast to compute as prior art.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1571", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1571.flac", "type": "audio", "duration": 771.52}, {"from": "Assistant", "value": "Semantic compositionality (SC) refers to the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents. Most related works focus on using complicated compositionality functions to model SC while few works consider external knowledge in models. In this paper, we verify the effectiveness of sememes, the minimum semantic units of human languages, in modeling SC by a confirmatory experiment. Furthermore, we make the first attempt to incorporate sememe knowledge into SC models, and employ the sememe-incorporated models in learning representations of multiword expressions, a typical task of SC. In experiments, we implement our models by incorporating knowledge from a famous sememe knowledge base HowNet and perform both intrinsic and extrinsic evaluations. Experimental results show that our models achieve significant performance boost as compared to the baseline methods without considering sememe knowledge. We further conduct quantitative analysis and case studies to demonstrate the effectiveness of applying sememe knowledge in modeling SC.All the code and data of this paper can be obtained onhttps://github.com/thunlp/Sememe-SC.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1572", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1572.flac", "type": "audio", "duration": 916.416}, {"from": "Assistant", "value": "The inability to quantify key aspects of creative language is a frequent obstacle to natural language understanding. To address this, we introduce novel tasks for evaluating the creativeness of language—namely, scoring and ranking text by humorousness and metaphor novelty. To sidestep the difficulty of assigning discrete labels or numeric scores, we learn from pairwise comparisons between texts. We introduce a Bayesian approach for predicting humorousness and metaphor novelty using Gaussian process preference learning (GPPL), which achieves a Spearman’s ρ of 0.56 against gold using word embeddings and linguistic features. Our experiments show that given sparse, crowdsourced annotation data, ranking using GPPL outperforms best–worst scaling. We release a new dataset for evaluating humour containing 28,210 pairwise comparisons of 4,030 texts, and make our software freely available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1573", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1573.flac", "type": "audio", "duration": 949.291}, {"from": "Assistant", "value": "The purpose of the research is to answer the question whether linguistic information is retained in vector representations of sentences. We introduce a method of analysing the content of sentence embeddings based on universal probing tasks, along with the classification datasets for two contrasting languages. We perform a series of probing and downstream experiments with different types of sentence embeddings, followed by a thorough analysis of the experimental results. Aside from dependency parser-based embeddings, linguistic information is retained best in the recently proposed LASER sentence embeddings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1574", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1574.flac", "type": "audio", "duration": 769.365}, {"from": "Assistant", "value": "Word embeddings typically represent different meanings of a word in a single conflated vector. Empirical analysis of embeddings of ambiguous words is currently limited by the small size of manually annotated resources and by the fact that word senses are treated as unrelated individual concepts. We present a large dataset based on manual Wikipedia annotations and word senses, where word senses from different words are related by semantic classes. This is the basis for novel diagnostic tests for an embedding’s content: we probe word embeddings for semantic classes and analyze the embedding space by classifying embeddings into semantic classes. Our main findings are: (i) Information about a sense is generally represented well in a single-vector embedding – if the sense is frequent. (ii) A classifier can accurately predict whether a word is single-sense or multi-sense, based only on its embedding. (iii) Although rare senses are not well represented in single-vector embeddings, this does not have negative impact on an NLP application whose performance depends on frequent senses.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1575", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1575.flac", "type": "audio", "duration": 789.12}, {"from": "Assistant", "value": "We introduce a general method for the interpretation and comparison of neural models. The method is used to factor a complex neural model into its functional components, which are comprised of sets of co-firing neurons that cut across layers of the network architecture, and which we call neural pathways. The function of these pathways can be understood by identifying correlated task level and linguistic heuristics in such a way that this knowledge acts as a lens for approximating what the network has learned to apply to its intended task. As a case study for investigating the utility of these pathways, we present an examination of pathways identified in models trained for two standard tasks, namely Named Entity Recognition and Recognizing Textual Entailment.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1576", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1576.flac", "type": "audio", "duration": 595.008}, {"from": "Assistant", "value": "Lexical relation classification is the task of predicting whether a certain relation holds between a given pair of words. In this paper, we explore to which extent the current distributional landscape based on word embeddings provides a suitable basis for classification of collocations, i.e., pairs of words between which idiosyncratic lexical relations hold. First, we introduce a novel dataset with collocations categorized according to lexical functions. Second, we conduct experiments on a subset of this benchmark, comparing it in particular to the well known DiffVec dataset. In these experiments, in addition to simple word vector arithmetic operations, we also investigate the role of unsupervised relation vectors as a complementary input. While these relation vectors indeed help, we also show that lexical function classification poses a greater challenge than the syntactic and semantic relations that are typically used for benchmarks in the literature.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1577", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1577.flac", "type": "audio", "duration": 604.139}, {"from": "Assistant", "value": "In this paper we discuss the usefulness of applying a checking procedure to existing thesauri. The procedure is based on the analysis of discrepancies of corpus-based and thesaurus-based word similarities. We applied the procedure to more than 30 thousand words of the Russian wordnet and found some serious errors in word sense description, including inaccurate relationships and missing senses of ambiguous words.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1578", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1578.flac", "type": "audio", "duration": 637.12}, {"from": "Assistant", "value": "This paper proposes Confusionset-guided Pointer Networks for Chinese Spell Check (CSC) task. More concretely, our approach utilizes the off-the-shelf confusionset for guiding the character generation. To this end, our novel Seq2Seq model jointly learns to copy a correct character from an input sentence through a pointer network, or generate a character from the confusionset rather than the entire vocabulary. We conduct experiments on three human-annotated datasets, and results demonstrate that our proposed generative model outperforms all competitor models by a large margin of up to 20% F1 score, achieving state-of-the-art performance on three datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1579", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1579.flac", "type": "audio", "duration": 922.496}, {"from": "Assistant", "value": "Low-resource language pairs with a paucity of parallel data pose challenges for machine translation in terms of both adequacy and fluency. Data augmentation utilizing a large amount of monolingual data is regarded as an effective way to alleviate the problem. In this paper, we propose a general framework of data augmentation for low-resource machine translation not only using target-side monolingual data, but also by pivoting through a related high-resource language. Specifically, we experiment with a two-step pivoting method to convert high-resource data to the low-resource language, making best use of available resources to better approximate the true distribution of the low-resource language. First, we inject low-resource words into high-resource sentences through an induced bilingual dictionary. Second, we further edit the high-resource data injected with low-resource words using a modified unsupervised machine translation framework. Extensive experiments on four low-resource datasets show that under extreme low-resource settings, our data augmentation techniques improve translation quality by up to 1.5 to 8 BLEU points compared to supervised back-translation baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1580", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1580.flac", "type": "audio", "duration": 927.936}, {"from": "Assistant", "value": "Multi-head self-attention is a key component of the Transformer, a state-of-the-art architecture for neural machine translation. In this work we evaluate the contribution made by individual attention heads to the overall performance of the model and analyze the roles played by them in the encoder. We find that the most important and confident heads play consistent and often linguistically-interpretable roles. When pruning heads using a method based on stochastic gates and a differentiable relaxation of the L0 penalty, we observe that specialized heads are last to be pruned. Our novel pruning method removes the vast majority of heads without seriously affecting performance. For example, on the English-Russian WMT dataset, pruning 38 out of 48 encoder heads results in a drop of only 0.15 BLEU.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1581", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1581.flac", "type": "audio", "duration": 677.525}, {"from": "Assistant", "value": "Unseen words, also called out-of-vocabulary words (OOVs), are difficult for machine translation. In neural machine translation, byte-pair encoding can be used to represent OOVs, but they are still often incorrectly translated. We improve the translation of OOVs in NMT using easy-to-obtain monolingual data. We look for OOVs in the text to be translated and translate them using simple-to-construct bilingual word embeddings (BWEs). In our MT experiments we take the 5-best candidates, which is motivated by intrinsic mining experiments. Using all five of the proposed target language words as queries we mine target-language sentences. We then back-translate, forcing the back-translation of each of the five proposed target-language OOV-translation-candidates to be the original source-language OOV. We show that by using this synthetic data to fine-tune our system the translation of OOVs can be dramatically improved. In our experiments we use a system trained on Europarl and mine sentences containing medical terms from monolingual data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1582", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1582.flac", "type": "audio", "duration": 616.939}, {"from": "Assistant", "value": "Simultaneous translation is widely useful but remains one of the most difficult tasks in NLP. Previous work either uses fixed-latency policies, or train a complicated two-staged model using reinforcement learning. We propose a much simpler single model that adds a “delay” token to the target vocabulary, and design a restricted dynamic oracle to greatly simplify training. Experiments on Chinese <-> English simultaneous translation show that our work leads to flexible policies that achieve better BLEU scores and lower latencies compared to both fixed and RL-learned policies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1583", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1583.flac", "type": "audio", "duration": 655.488}, {"from": "Assistant", "value": "To improve low-resource Neural Machine Translation (NMT) with multilingual corpus, training on the most related high-resource language only is generally more effective than us- ing all data available (Neubig and Hu, 2018). However, it remains a question whether a smart data selection strategy can further improve low-resource NMT with data from other auxiliary languages. In this paper, we seek to construct a sampling distribution over all multilingual data, so that it minimizes the training loss of the low-resource language. Based on this formulation, we propose and efficient algorithm, (TCS), which first samples a target sentence, and then conditionally samples its source sentence. Experiments show TCS brings significant gains of up to 2 BLEU improvements on three of four languages we test, with minimal training overhead.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1584", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1584.flac", "type": "audio", "duration": 877.76}, {"from": "Assistant", "value": "De-identification is the task of detecting protected health information (PHI) in medical text. It is a critical step in sanitizing electronic health records (EHR) to be shared for research. Automatic de-identification classifiers can significantly speed up the sanitization process. However, obtaining a large and diverse dataset to train such a classifier that works well across many types of medical text poses a challenge as privacy laws prohibit the sharing of raw medical records. We introduce a method to create privacy-preserving shareable representations of medical text (i.e. they contain no PHI) that does not require expensive manual pseudonymization. These representations can be shared between organizations to create unified datasets for training de-identification models. Our representation allows training a simple LSTM-CRF de-identification model to an F1 score of 97.4%, which is comparable to a strong baseline that exposes private information in its representation. A robust, widely available de-identification classifier based on our representation could potentially enable studies for which de-identification would otherwise be too costly.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1585", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1585.flac", "type": "audio", "duration": 870.72}, {"from": "Assistant", "value": "Named entity recognition (NER) is one of the best studied tasks in natural language processing. However, most approaches are not capable of handling nested structures which are common in many applications. In this paper we introduce a novel neural network architecture that first merges tokens and/or entities into entities forming nested structures, and then labels each of them independently. Unlike previous work, our merge and label approach predicts real-valued instead of discrete segmentation structures, which allow it to combine word and nested entity embeddings while maintaining differentiability. We evaluate our approach using the ACE 2005 Corpus, where it achieves state-of-the-art F1 of 74.6, further improved with contextual embeddings (BERT) to 82.4, an overall improvement of close to 8 F1 points over previous approaches trained on the same data. Additionally we compare it against BiLSTM-CRFs, the dominant approach for flat NER structures, demonstrating that its ability to predict nested structures does not impact performance in simpler cases.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1586", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1586.flac", "type": "audio", "duration": 922.56}, {"from": "Assistant", "value": "Entity resolution (ER) is the task of identifying different representations of the same real-world entities across databases. It is a key step for knowledge base creation and text mining. Recent adaptation of deep learning methods for ER mitigates the need for dataset-specific feature engineering by constructing distributed representations of entity records. While these methods achieve state-of-the-art performance over benchmark data, they require large amounts of labeled data, which are typically unavailable in realistic ER applications. In this paper, we develop a deep learning-based method that targets low-resource settings for ER through a novel combination of transfer learning and active learning. We design an architecture that allows us to learn a transferable model from a high-resource setting to a low-resource one. To further adapt to the target dataset, we incorporate active learning that carefully selects a few informative examples to fine-tune the transferred model. Empirical evaluation demonstrates that our method achieves comparable, if not better, performance compared to state-of-the-art learning-based methods while using an order of magnitude fewer labels.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1587", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1587.flac", "type": "audio", "duration": 596.011}, {"from": "Assistant", "value": "Named entity recognition (NER) is the backbone of many NLP solutions. F1 score, the harmonic mean of precision and recall, is often used to select/evaluate the best models. However, when precision needs to be prioritized over recall, a state-of-the-art model might not be the best choice. There is little in literature that directly addresses training-time modifications to achieve higher precision information extraction. In this paper, we propose a neural semi-Markov structured support vector machine model that controls the precision-recall trade-off by assigning weights to different types of errors in the loss-augmented inference during training. The semi-Markov property provides more accurate phrase-level predictions, thereby improving performance. We empirically demonstrate the advantage of our model when high precision is required by comparing against strong baselines based on CRF. In our experiments with the CoNLL 2003 dataset, our model achieves a better precision-recall trade-off at various precision levels.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1588", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1588.flac", "type": "audio", "duration": 564.011}, {"from": "Assistant", "value": "This paper studies automatic keyphrase extraction on social media. Previous works have achieved promising results on it, but they neglect human reading behavior during keyphrase annotating. The human attention is a crucial element of human reading behavior. It reveals the relevance of words to the main topics of the target text. Thus, this paper aims to integrate human attention into keyphrase extraction models. First, human attention is represented by the reading duration estimated from eye-tracking corpus. Then, we merge human attention with neural network models by an attention mechanism. In addition, we also integrate human attention into unsupervised models. To the best of our knowledge, we are the first to utilize human attention on keyphrase extraction tasks. The experimental results show that our models have significant improvements on two Twitter datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1589", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1589.flac", "type": "audio", "duration": 491.328}, {"from": "Assistant", "value": "In this paper we frame the task of supervised relation classification as an instance of meta-learning. We propose a model-agnostic meta-learning protocol for training relation classifiers to achieve enhanced predictive performance in limited supervision settings. During training, we aim to not only learn good parameters for classifying relations with sufficient supervision, but also learn model parameters that can be fine-tuned to enhance predictive performance for relations with limited supervision. In experiments conducted on two relation classification datasets, we demonstrate that the proposed meta-learning approach improves the predictive performance of two state-of-the-art supervised relation classification models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1590", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1590.flac", "type": "audio", "duration": 745.088}, {"from": "Assistant", "value": "We introduce VAMPIRE, a lightweight pretraining framework for effective text classification when data and computing resources are limited. We pretrain a unigram document model as a variational autoencoder on in-domain, unlabeled data and use its internal states as features in a downstream classifier. Empirically, we show the relative strength of VAMPIRE against computationally expensive contextual embeddings and other popular semi-supervised baselines under low resource settings. We also find that fine-tuning to in-domain data is crucial to achieving decent performance from contextual embeddings when working with limited supervision. We accompany this paper with code to pretrain and use VAMPIRE embeddings in downstream tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1591", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1591.flac", "type": "audio", "duration": 841.088}, {"from": "Assistant", "value": "Pivot Based Language Modeling (PBLM) (Ziser and Reichart, 2018a), combining LSTMs with pivot-based methods, has yielded significant progress in unsupervised domain adaptation. However, this approach is still challenged by the large pivot detection problem that should be solved, and by the inherent instability of LSTMs. In this paper we propose a Task Refinement Learning (TRL) approach, in order to solve these problems. Our algorithms iteratively train the PBLM model, gradually increasing the information exposed about each pivot. TRL-PBLM achieves stateof- the-art accuracy in six domain adaptation setups for sentiment classification. Moreover, it is much more stable than plain PBLM across model configurations, making the model much better fitted for practical use.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1592", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1592.flac", "type": "audio", "duration": 863.125}, {"from": "Assistant", "value": "String similarity models are vital for record linkage, entity resolution, and search. In this work, we present STANCE–a learned model for computing the similarity of two strings. Our approach encodes the characters of each string, aligns the encodings using Sinkhorn Iteration (alignment is posed as an instance of optimal transport) and scores the alignment with a convolutional neural network. We evaluate STANCE’s ability to detect whether two strings can refer to the same entity–a task we term alias detection. We construct five new alias detection datasets (and make them publicly available). We show that STANCE (or one of its variants) outperforms both state-of-the-art and classic, parameter-free similarity models on four of the five datasets. We also demonstrate STANCE’s ability to improve downstream tasks by applying it to an instance of cross-document coreference and show that it leads to a 2.8 point improvement in Bˆ3 F1 over the previous state-of-the-art approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1593", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1593.flac", "type": "audio", "duration": 783.808}, {"from": "Assistant", "value": "We present a new architecture for storing and accessing entity mentions during online text processing. While reading the text, entity references are identified, and may be stored by either updating or overwriting a cell in a fixed-length memory. The update operation implies coreference with the other mentions that are stored in the same cell; the overwrite operation causes these mentions to be forgotten. By encoding the memory operations as differentiable gates, it is possible to train the model end-to-end, using both a supervised anaphora resolution objective as well as a supplementary language modeling objective. Evaluation on a dataset of pronoun-name anaphora demonstrates strong performance with purely incremental text processing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1594", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1594.flac", "type": "audio", "duration": 589.483}, {"from": "Assistant", "value": "Spectral models for learning weighted non-deterministic automata have nice theoretical and algorithmic properties. Despite this, it has been challenging to obtain competitive results in language modeling tasks, for two main reasons. First, in order to capture long-range dependencies of the data, the method must use statistics from long substrings, which results in very large matrices that are difficult to decompose. The second is that the loss function behind spectral learning, based on moment matching, differs from the probabilistic metrics used to evaluate language models. In this work we employ a technique for scaling up spectral learning, and use interpolated predictions that are optimized to maximize perplexity. Our experiments in character-based language modeling show that our method matches the performance of state-of-the-art ngram models, while being very fast to train.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "P19-1595", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/P19-1595.flac", "type": "audio", "duration": 627.691}, {"from": "Assistant", "value": "It can be challenging to train multi-task neural networks that outperform or even match their single-task counterparts. To help address this, we propose using knowledge distillation where single-task models teach a multi-task model. We enhance this training with teacher annealing, a novel method that gradually transitions the model from distillation to supervised learning, helping the multi-task model surpass its single-task teachers. We evaluate our approach by multi-task fine-tuning BERT on the GLUE benchmark. Our method consistently improves over standard single-task and multi-task training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_1", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_1.flac", "type": "audio", "duration": 663.046}, {"from": "Assistant", "value": "This work investigates the use of interactively updated label suggestions to improve upon the efficiency of gathering annotations on the task of opinion mining in German Covid-19 social media data. We develop guidelines to conduct a controlled annotation study with social science students and find that suggestions from a model trained on a small, expert-annotated dataset already lead to a substantial improvement – in terms of inter-annotator agreement (+.14 Fleiss’ κ) and annotation quality – compared to students that do not receive any label suggestions. We further find that label suggestions from interactively trained models do not lead to an improvement over suggestions from a static model. Nonetheless, our analysis of suggestion bias shows that annotators remain capable of reflecting upon the suggested label in general. Finally, we confirm the quality of the annotated data in transfer learning experiments between different annotator groups. To facilitate further research in opinion mining on social media data, we release our collected data consisting of 200 expert and 2,785 student annotations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_2", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_2.flac", "type": "audio", "duration": 679.04}, {"from": "Assistant", "value": "Humor is an important social phenomenon, serving complex social and psychological functions. However, despite being studied for millennia humor is computationally not well understood, often considered an AI-complete problem. In this work, we introduce a novel setting in humor mining: automatically detecting funny and unusual scientific papers. We are inspired by the Ig Nobel prize, a satirical prize awarded annually to celebrate funny scientific achievements (example past winner: “Are cows more likely to lie down the longer they stand?”). This challenging task has unique characteristics that make it particularly suitable for automatic learning. We construct a dataset containing thousands of funny papers and use it to learn classifiers, combining findings from psychology and linguistics with recent advances in NLP. We use our models to identify potentially funny papers in a large dataset of over 630,000 articles. The results demonstrate the potential of our methods, and more broadly the utility of integrating state-of-the-art NLP methods with insights from more traditional disciplines", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_3", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_3.flac", "type": "audio", "duration": 547.093}, {"from": "Assistant", "value": "This paper presents a novel task to generate poll questions for social media posts. It offers an easy way to hear the voice from the public and learn from their feelings to important social topics. While most related work tackles formal languages (e.g., exam papers), we generate poll questions for short and colloquial social media messages exhibiting severe data sparsity. To deal with that, we propose to encode user comments and discover latent topics therein as contexts. They are then incorporated into a sequence-to-sequence (S2S) architecture for question generation and its extension with dual decoders to additionally yield poll choices (answers). For experiments, we collect a large-scale Chinese dataset from Sina Weibo containing over 20K polls. The results show that our model outperforms the popular S2S models without exploiting topics from comments and the dual decoder design can further benefit the prediction of both questions and answers. Human evaluations further exhibit our superiority in yielding high-quality polls helpful to draw user engagements.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_4", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_4.flac", "type": "audio", "duration": 679.044}, {"from": "Assistant", "value": "Detecting online hate is a difficult task that even state-of-the-art models struggle with. Typically, hate speech detection models are evaluated by measuring their performance on held-out test data using metrics such as accuracy and F1 score. However, this approach makes it difficult to identify specific model weak points. It also risks overestimating generalisable model performance due to increasingly well-evidenced systematic gaps and biases in hate speech datasets. To enable more targeted diagnostic insights, we introduce HateCheck, a suite of functional tests for hate speech detection models. We specify 29 model functionalities motivated by a review of previous research and a series of interviews with civil society stakeholders. We craft test cases for each functionality and validate their quality through a structured annotation process. To illustrate HateCheck’s utility, we test near-state-of-the-art transformer models as well as two popular commercial models, revealing critical model weaknesses.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_5", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_5.flac", "type": "audio", "duration": 539.817}, {"from": "Assistant", "value": "Recent studies constructing direct interactions between the claim and each single user response (a comment or a relevant article) to capture evidence have shown remarkable success in interpretable claim verification. Owing to different single responses convey different cognition of individual users (i.e., audiences), the captured evidence belongs to the perspective of individual cognition. However, individuals’ cognition of social things is not always able to truly reflect the objective. There may be one-sided or biased semantics in their opinions on a claim. The captured evidence correspondingly contains some unobjective and biased evidence fragments, deteriorating task performance. In this paper, we propose a Dual-view model based on the views of Collective and Individual Cognition (CICD) for interpretable claim verification. From the view of the collective cognition, we not only capture the word-level semantics based on individual users, but also focus on sentence-level semantics (i.e., the overall responses) among all users and adjust the proportion between them to generate global evidence. From the view of individual cognition, we select the top-karticles with high degree of difference and interact with the claim to explore the local key evidence fragments. To weaken the bias of individual cognition-view evidence, we devise inconsistent loss to suppress the divergence between global and local evidence for strengthening the consistent shared evidence between the both. Experiments on three benchmark datasets confirm that CICD achieves state-of-the-art performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_6", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_6.flac", "type": "audio", "duration": 695.168}, {"from": "Assistant", "value": "Rap generation, which aims to produce lyrics and corresponding singing beats, needs to model both rhymes and rhythms. Previous works for rap generation focused on rhyming lyrics, but ignored rhythmic beats, which are important for rap performance. In this paper, we develop DeepRapper, a Transformer-based rap generation system that can model both rhymes and rhythms. Since there is no available rap datasets with rhythmic beats, we develop a data mining pipeline to collect a large-scale rap dataset, which includes a large number of rap songs with aligned lyrics and rhythmic beats. Second, we design a Transformer-based autoregressive language model which carefully models rhymes and rhythms. Specifically, we generate lyrics in the reverse order with rhyme representation and constraint for rhyme enhancement, and insert a beat symbol into lyrics for rhythm/beat modeling. To our knowledge, DeepRapper is the first system to generate rap with both rhymes and rhythms. Both objective and subjective evaluations demonstrate that DeepRapper generates creative and high-quality raps with rhymes and rhythms.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_7", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_7.flac", "type": "audio", "duration": 700.736}, {"from": "Assistant", "value": "In this paper, we formulate the personalized news headline generation problem whose goal is to output a user-specific title based on both a user’s reading interests and a candidate news body to be exposed to her. To build up a benchmark for this problem, we publicize a large-scale dataset named PENS (PErsonalized News headlineS). The training set is collected from user impressions logs of Microsoft News, and the test set is manually created by hundreds of native speakers to enable a fair testbed for evaluating models in an offline mode. We propose a generic framework as a preparatory solution to our problem. At its heart, user preference is learned by leveraging the user behavioral data, and three kinds of user preference injections are proposed to personalize a text generator and establish personalized headlines. We investigate our dataset by implementing several state-of-the-art user modeling methods in our framework to demonstrate a benchmark score for the proposed dataset. The dataset is available athttps://msnews.github.io/pens.html.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_8", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_8.flac", "type": "audio", "duration": 662.047}, {"from": "Assistant", "value": "Text style transfer aims to alter the style (e.g., sentiment) of a sentence while preserving its content. A common approach is to map a given sentence to content representation that is free of style, and the content representation is fed to a decoder with a target style. Previous methods in filtering style completely remove tokens with style at the token level, which incurs the loss of content information. In this paper, we propose to enhance content preservation by implicitly removing the style information of each token with reverse attention, and thereby retain the content. Furthermore, we fuse content information when building the target style representation, making it dynamic with respect to the content. Our method creates not only style-independent content representation, but also content-dependent style representation in transferring style. Empirical results show that our method outperforms the state-of-the-art baselines by a large margin in terms of content preservation. In addition, it is also competitive in terms of style transfer accuracy and fluency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_9", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_9.flac", "type": "audio", "duration": 721.049}, {"from": "Assistant", "value": "This paper focuses on Seq2Seq (S2S) constrained text generation where the text generator is constrained to mention specific words which are inputs to the encoder in the generated outputs. Pre-trained S2S models or a Copy Mechanism are trained to copy the surface tokens from encoders to decoders, but they cannot guarantee constraint satisfaction. Constrained decoding algorithms always produce hypotheses satisfying all constraints. However, they are computationally expensive and can lower the generated text quality. In this paper, we propose Mention Flags (MF), which traces whether lexical constraints are satisfied in the generated outputs in an S2S decoder. The MF models can be trained to generate tokens in a hypothesis until all constraints are satisfied, guaranteeing high constraint satisfaction. Our experiments on the Common Sense Generation task (CommonGen) (Lin et al., 2020), End2end Restaurant Dialog task (E2ENLG) (Duˇsek et al., 2020) and Novel Object Captioning task (nocaps) (Agrawal et al., 2019) show that the MF models maintain higher constraint satisfaction and text quality than the baseline models and other constrained decoding algorithms, achieving state-of-the-art performance on all three tasks. These results are achieved with a much lower run-time than constrained decoding algorithms. We also show that the MF models work well in the low-resource setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_10", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_10.flac", "type": "audio", "duration": 702.055}, {"from": "Assistant", "value": "Concept-to-text Natural Language Generation is the task of expressing an input meaning representation in natural language. Previous approaches in this task have been able to generalise to rare or unseen instances by relying on a delexicalisation of the input. However, this often requires that the input appears verbatim in the output text. This poses challenges in multilingual settings, where the task expands to generate the output text in multiple languages given the same input. In this paper, we explore the application of multilingual models in concept-to-text and propose Language Agnostic Delexicalisation, a novel delexicalisation method that uses multilingual pretrained embeddings, and employs a character-level post-editing model to inflect words in their correct form during relexicalisation. Our experiments across five datasets and five languages show that multilingual models outperform monolingual models in concept-to-text and that our framework outperforms previous approaches, especially in low resource conditions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_11", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_11.flac", "type": "audio", "duration": 624.059}, {"from": "Assistant", "value": "Nowadays, open-domain dialogue models can generate acceptable responses according to the historical context based on the large-scale pre-trained language models. However, they generally concatenate the dialogue history directly as the model input to predict the response, which we named as theflat patternand ignores the dynamic information flow across dialogue utterances. In this work, we propose theDialoFlowmodel, in which we introduce a dynamic flow mechanism to model the context flow, and design three training objectives to capture the information dynamics across dialogue utterances by addressing the semantic influence brought about by each utterance in large-scale pre-training. Experiments on the multi-reference Reddit Dataset and DailyDialog Dataset demonstrate that our DialoFlow significantly outperforms the DialoGPT on the dialogue generation task. Besides, we propose theFlow score, an effective automatic metric for evaluating interactive human-bot conversation quality based on the pre-trained DialoFlow, which presents high chatbot-level correlation (r=0.9) with human ratings among 11 chatbots. Code and pre-trained models will be public.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_12", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_12.flac", "type": "audio", "duration": 717.056}, {"from": "Assistant", "value": "The goal of dialogue state tracking (DST) is to predict the current dialogue state given all previous dialogue contexts. Existing approaches generally predict the dialogue state at every turn from scratch. However, the overwhelming majority of the slots in each turn should simply inherit the slot values from the previous turn. Therefore, the mechanism of treating slots equally in each turn not only is inefficient but also may lead to additional errors because of the redundant slot value generation. To address this problem, we devise the two-stage DSS-DST which consists of the Dual Slot Selector based on the current turn dialogue, and the Slot Value Generator based on the dialogue history. The Dual Slot Selector determines each slot whether to update slot value or to inherit the slot value from the previous turn from two aspects: (1) if there is a strong relationship between it and the current turn dialogue utterances; (2) if a slot value with high reliability can be obtained for it through the current turn dialogue. The slots selected to be updated are permitted to enter the Slot Value Generator to update values by a hybrid method, while the other slots directly inherit the values from the previous turn. Empirical results show that our method achieves 56.93%, 60.73%, and 58.04% joint accuracy on MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.2 datasets respectively and achieves a new state-of-the-art performance with significant improvements.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_13", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_13.flac", "type": "audio", "duration": 631.049}, {"from": "Assistant", "value": "One of the difficulties in training dialogue systems is the lack of training data. We explore the possibility of creating dialogue data through the interaction between a dialogue system and a user simulator. Our goal is to develop a modelling framework that can incorporate new dialogue scenarios through self-play between the two agents. In this framework, we first pre-train the two agents on a collection of source domain dialogues, which equips the agents to converse with each other via natural language. With further fine-tuning on a small amount of target domain data, the agents continue to interact with the aim of improving their behaviors using reinforcement learning with structured reward functions. In experiments on the MultiWOZ dataset, two practical transfer learning problems are investigated: 1) domain adaptation and 2) single-to-multiple domain transfer. We demonstrate that the proposed framework is highly effective in bootstrapping the performance of the two agents in transfer learning. We also show that our method leads to improvements in dialogue system performance on complete datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_14", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_14.flac", "type": "audio", "duration": 609.245}, {"from": "Assistant", "value": "Maintaining a consistent persona is essential for dialogue agents. Although tremendous advancements have been brought, the limited-scale of annotated personalized dialogue datasets is still a barrier towards training robust and consistent persona-based dialogue models. This work shows how this challenge can be addressed by disentangling persona-based dialogue generation into two sub-tasks with a novel BERT-over-BERT (BoB) model. Specifically, the model consists of a BERT-based encoder and two BERT-based decoders, where one decoder is for response generation, and another is for consistency understanding. In particular, to learn the ability of consistency understanding from large-scale non-dialogue inference data, we train the second decoder in an unlikelihood manner. Under different limited data settings, both automatic and human evaluations demonstrate that the proposed model outperforms strong baselines in response quality and persona consistency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_15", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_15.flac", "type": "audio", "duration": 533.653}, {"from": "Assistant", "value": "Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention. However, the state-of-the-art joint models heavily rely on autoregressive approaches, resulting in two issues: slow inference speed and information leakage. In this paper, we explore a non-autoregressive model for joint multiple intent detection and slot filling, achieving more fast and accurate. Specifically, we propose a Global-Locally Graph Interaction Network (GL-GIN) where a local slot-aware graph interaction layer is proposed to model slot dependency for alleviating uncoordinated slots problem while a global intent-slot graph interaction layer is introduced to model the interaction between multiple intents and all slots in the utterance. Experimental results on two public datasets show that our framework achieves state-of-the-art performance while being 11.5 times faster.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_16", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_16.flac", "type": "audio", "duration": 386.24}, {"from": "Assistant", "value": "Both performance and efficiency are crucial factors for sequence labeling tasks in many real-world scenarios. Although the pre-trained models (PTMs) have significantly improved the performance of various sequence labeling tasks, their computational cost is expensive. To alleviate this problem, we extend the recent successful early-exit mechanism to accelerate the inference of PTMs for sequence labeling tasks. However, existing early-exit mechanisms are specifically designed for sequence-level tasks, rather than sequence labeling. In this paper, we first propose a simple extension of sentence-level early-exit for sequence labeling tasks. To further reduce the computational cost, we also propose a token-level early-exit mechanism that allows partial tokens to exit early at different layers. Considering the local dependency inherent in sequence labeling, we employed a window-based criterion to decide for a token whether or not to exit. The token-level early-exit brings the gap between training and inference, so we introduce an extra self-sampling fine-tuning stage to alleviate it. The extensive experiments on three popular sequence labeling tasks show that our approach can save up to 66%∼75% inference cost with minimal performance degradation. Compared with competitive compressed models such as DistilBERT, our approach can achieve better performance under the same speed-up ratios of 2×, 3×, and 4×.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_17", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_17.flac", "type": "audio", "duration": 582.055}, {"from": "Assistant", "value": "Although the existing Named Entity Recognition (NER) models have achieved promising performance, they suffer from certain drawbacks. The sequence labeling-based NER models do not perform well in recognizing long entities as they focus only on word-level information, while the segment-based NER models which focus on processing segment instead of single word are unable to capture the word-level dependencies within the segment. Moreover, as boundary detection and type prediction may cooperate with each other for the NER task, it is also important for the two sub-tasks to mutually reinforce each other by sharing their information. In this paper, we propose a novel Modularized Interaction Network (MIN) model which utilizes both segment-level information and word-level dependencies, and incorporates an interaction mechanism to support information sharing between boundary detection and type prediction to enhance the performance for the NER task. We have conducted extensive experiments based on three NER benchmark datasets. The performance results have shown that the proposed MIN model has outperformed the current state-of-the-art models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_18", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_18.flac", "type": "audio", "duration": 707.883}, {"from": "Assistant", "value": "Capturing interactions among event arguments is an essential step towards robust event argument extraction (EAE). However, existing efforts in this direction suffer from two limitations: 1) The argument role type information of contextual entities is mainly utilized as training signals, ignoring the potential merits of directly adopting it as semantically rich input features; 2) The argument-level sequential semantics, which implies the overall distribution pattern of argument roles over an event mention, is not well characterized. To tackle the above two bottlenecks, we formalize EAE as a Seq2Seq-like learning problem for the first time, where a sentence with a specific event trigger is mapped to a sequence of event argument roles. A neural architecture with a novel Bi-directional Entity-level Recurrent Decoder (BERD) is proposed to generate argument roles by incorporating contextual entities’ argument role predictions, like a word-by-word text generation process, thereby distinguishing implicit argument distribution patterns within an event more accurately.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_19", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_19.flac", "type": "audio", "duration": 598.848}, {"from": "Assistant", "value": "Many joint entity relation extraction models setup two separated label spaces for the two sub-tasks (i.e., entity detection and relation classification). We argue that this setting may hinder the information interaction between entities and relations. In this work, we propose to eliminate the different treatment on the two sub-tasks’ label spaces. The input of our model is a table containing all word pairs from a sentence. Entities and relations are represented by squares and rectangles in the table. We apply a unified classifier to predict each cell’s label, which unifies the learning of two sub-tasks. For testing, an effective (yet fast) approximate decoder is proposed for finding squares and rectangles from tables. Experiments on three benchmarks (ACE04, ACE05, SciERC) show that, using only half the number of parameters, our model achieves competitive accuracy with the best extractor, and is faster.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_20", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_20.flac", "type": "audio", "duration": 715.662}, {"from": "Assistant", "value": "Continual learning has gained increasing attention in recent years, thanks to its biological interpretation and efficiency in many real-world applications. As a typical task of continual learning, continual relation extraction (CRE) aims to extract relations between entities from texts, where the samples of different relations are delivered into the model continuously. Some previous works have proved that storing typical samples of old relations in memory can help the model keep a stable understanding of old relations and avoid forgetting them. However, most methods heavily depend on the memory size in that they simply replay these memorized samples in subsequent tasks. To fully utilize memorized samples, in this paper, we employ relation prototype to extract useful information of each relation. Specifically, the prototype embedding for a specific relation is computed based on memorized samples of this relation, which is collected by K-means algorithm. The prototypes of all observed relations at current learning stage are used to re-initialize a memory network to refine subsequent sample embeddings, which ensures the model’s stable understanding on all observed relations when learning a new task. Compared with previous CRE models, our model utilizes the memory information sufficiently and efficiently, resulting in enhanced CRE performance. Our experiments show that the proposed model outperforms the state-of-the-art CRE models and has great advantage in avoiding catastrophic forgetting. The code and datasets are released onhttps://github.com/fd2014cl/RP-CRE.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_21", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_21.flac", "type": "audio", "duration": 715.058}, {"from": "Assistant", "value": "Existing multilingual machine translation approaches mainly focus on English-centric directions, while the non-English directions still lag behind. In this work, we aim to build a many-to-many translation system with an emphasis on the quality of non-English language directions. Our intuition is based on the hypothesis that a universal cross-language representation leads to better multilingual translation performance. To this end, we propose mRASP2, a training method to obtain a single unified multilingual translation model. mRASP2 is empowered by two techniques: a) a contrastive learning scheme to close the gap among representations of different languages, and b) data augmentation on both multiple parallel and monolingual data to further align token representations. For English-centric directions, mRASP2 achieves competitive or even better performance than a strong pre-trained model mBART on tens of WMT benchmarks. For non-English directions, mRASP2 achieves an improvement of average 10+ BLEU compared with the multilingual baseline", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_22", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_22.flac", "type": "audio", "duration": 730.059}, {"from": "Assistant", "value": "Neural Machine Translation (NMT) currently exhibits biases such as producing translations that are too short and overgenerating frequent words, and shows poor robustness to copy noise in training data or domain shift. Recent work has tied these shortcomings to beam search – the de facto standard inference algorithm in NMT – and Eikema & Aziz (2020) propose to use Minimum Bayes Risk (MBR) decoding on unbiased samples instead. In this paper, we empirically investigate the properties of MBR decoding on a number of previously reported biases and failure cases of beam search. We find that MBR still exhibits a length and token frequency bias, owing to the MT metrics used as utility functions, but that MBR also increases robustness against copy noise in the training data and domain shift.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_23", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_23.flac", "type": "audio", "duration": 338.779}, {"from": "Assistant", "value": "One of the reasons Transformer translation models are popular is that self-attention networks for context modelling can be easily parallelized at sequence level. However, the computational complexity of a self-attention network isO(n2), increasing quadratically with sequence length. By contrast, the complexity of LSTM-based approaches is only O(n). In practice, however, LSTMs are much slower to train than self-attention networks as they cannot be parallelized at sequence level: to model context, the current LSTM state relies on the full LSTM computation of the preceding state. This has to be computed n times for a sequence of length n. The linear transformations involved in the LSTM gate and state computations are the major cost factors in this. To enable sequence-level parallelization of LSTMs, we approximate full LSTM context modelling by computing hidden states and gates with the current input and a simple bag-of-words representation of the preceding tokens context. This allows us to compute each input step efficiently in parallel, avoiding the formerly costly sequential linear transformations. We then connect the outputs of each parallel step with computationally cheap element-wise computations. We call this the Highly Parallelized LSTM. To further constrain the number of LSTM parameters, we compute several small HPLSTMs in parallel like multi-head attention in the Transformer. The experiments show that our MHPLSTM decoder achieves significant BLEU improvements, while being even slightly faster than the self-attention network in training, and much faster than the standard LSTM.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_24", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_24.flac", "type": "audio", "duration": 439.019}, {"from": "Assistant", "value": "Word alignment and machine translation are two closely related tasks. Neural translation models, such as RNN-based and Transformer models, employ a target-to-source attention mechanism which can provide rough word alignments, but with a rather low accuracy. High-quality word alignment can help neural machine translation in many different ways, such as missing word detection, annotation transfer and lexicon injection. Existing methods for learning word alignment include statistical word aligners (e.g. GIZA++) and recently neural word alignment models. This paper presents a bidirectional Transformer based alignment (BTBA) model for unsupervised learning of the word alignment task. Our BTBA model predicts the current target word by attending the source context and both left-side and right-side target context to produce accurate target-to-source attention (alignment). We further fine-tune the target-to-source attention in the BTBA model to obtain better alignments using a full context based optimization method and self-supervised training. We test our method on three word alignment tasks and show that our method outperforms both previous neural word alignment approaches and the popular statistical word aligner GIZA++.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_25", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_25.flac", "type": "audio", "duration": 616.049}, {"from": "Assistant", "value": "Multilingual neural machine translation aims at learning a single translation model for multiple languages. These jointly trained models often suffer from performance degradationon rich-resource language pairs. We attribute this degeneration to parameter interference. In this paper, we propose LaSS to jointly train a single unified multilingual MT model. LaSS learns Language Specific Sub-network (LaSS) for each language pair to counter parameter interference. Comprehensive experiments on IWSLT and WMT datasets with various Transformer architectures show that LaSS obtains gains on 36 language pairs by up to 1.2 BLEU. Besides, LaSS shows its strong generalization performance at easy adaptation to new language pairs and zero-shot translation. LaSS boosts zero-shot translation with an average of 8.3 BLEU on 30 language pairs. Codes and trained models are available athttps://github.com/NLP-Playground/LaSS.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_26", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_26.flac", "type": "audio", "duration": 714.803}, {"from": "Assistant", "value": "While state-of-the-art NLP models have been achieving the excellent performance of a wide range of tasks in recent years, important questions are being raised about their robustness and their underlying sensitivity to systematic biases that may exist in their training and test data. Such issues come to be manifest in performance problems when faced with out-of-distribution data in the field. One recent solution has been to use counterfactually augmented datasets in order to reduce any reliance on spurious patterns that may exist in the original data. Producing high-quality augmented data can be costly and time-consuming as it usually needs to involve human feedback and crowdsourcing efforts. In this work, we propose an alternative by describing and evaluating an approach to automatically generating counterfactual data for the purpose of data augmentation and explanation. A comprehensive evaluation on several different datasets and using a variety of state-of-the-art benchmarks demonstrate how our approach can achieve significant improvements in model performance when compared to models training on the original data and even when compared to models trained with the benefit of human-generated augmented data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_27", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_27.flac", "type": "audio", "duration": 632.043}, {"from": "Assistant", "value": "As a fine-grained task, the annotation cost of aspect term extraction is extremely high. Recent attempts alleviate this issue using domain adaptation that transfers common knowledge across domains. Since most aspect terms are domain-specific, they cannot be transferred directly. Existing methods solve this problem by associating aspect terms with pivot words (we call this passive domain adaptation because the transfer of aspect terms relies on the links to pivots). However, all these methods need either manually labeled pivot words or expensive computing resources to build associations. In this paper, we propose a novel active domain adaptation method. Our goal is to transfer aspect terms by actively supplementing transferable knowledge. To this end, we construct syntactic bridges by recognizing syntactic roles as pivots instead of as links to pivots. We also build semantic bridges by retrieving transferable semantic prototypes. Extensive experiments show that our method significantly outperforms previous approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_28", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_28.flac", "type": "audio", "duration": 713.963}, {"from": "Assistant", "value": "With the popularity of smartphones, we have witnessed the rapid proliferation of multimodal posts on various social media platforms. We observe that the multimodal sentiment expression has specific global characteristics, such as the interdependencies of objects or scenes within the image. However, most previous studies only considered the representation of a single image-text post and failed to capture the global co-occurrence characteristics of the dataset. In this paper, we propose Multi-channel Graph Neural Networks with Sentiment-awareness (MGNNS) for image-text sentiment detection. Specifically, we first encode different modalities to capture hidden representations. Then, we introduce multi-channel graph neural networks to learn multimodal representations based on the global characteristics of the dataset. Finally, we implement multimodal in-depth fusion with the multi-head attention mechanism to predict the sentiment of image-text pairs. Extensive experiments conducted on three publicly available datasets demonstrate the effectiveness of our approach for multimodal sentiment detection.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_29", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_29.flac", "type": "audio", "duration": 726.059}, {"from": "Assistant", "value": "Product reviews contain a large number of implicit aspects and implicit opinions. However, most of the existing studies in aspect-based sentiment analysis ignored this problem. In this work, we introduce a new task, named Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction, with the goal to extract all aspect-category-opinion-sentiment quadruples in a review sentence and provide full support for aspect-based sentiment analysis with implicit aspects and opinions. We furthermore construct two new datasets, Restaurant-ACOS and Laptop-ACOS, for this new task, both of which contain the annotations of not only aspect-category-opinion-sentiment quadruples but also implicit aspects and opinions. The former is an extension of the SemEval Restaurant dataset; the latter is a newly collected and annotated Laptop dataset, twice the size of the SemEval Laptop dataset. We finally benchmark the task with four baseline systems. Experiments demonstrate the feasibility of the new task and its effectiveness in extracting and describing implicit aspects and implicit opinions. The two datasets and source code of four systems are publicly released athttps://github.com/NUSTM/ACOS.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_30", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_30.flac", "type": "audio", "duration": 703.04}, {"from": "Assistant", "value": "The product reviews summarization task aims to automatically produce a short summary for a set of reviews of a given product. Such summaries are expected to aggregate a range of different opinions in a concise, coherent and informative manner. This challenging task gives rise to two shortcomings in existing work. First, summarizers tend to favor generic content that appears in reviews for many different products, resulting in template-like, less informative summaries. Second, as reviewers often disagree on the pros and cons of a given product, summarizers sometimes yield inconsistent, self-contradicting summaries. We propose the PASS system (Perturb-and-Select Summarizer) that employs a large pre-trained Transformer-based model (T5 in our case), which follows a few-shot fine-tuning scheme. A key component of the PASS system relies on applying systematic perturbations to the model’s input during inference, which allows it to generate multiple different summaries per product. We develop a method for ranking these summaries according to desired criteria, coherence in our case, enabling our system to almost entirely avoid the problem of self-contradiction. We compare our system against strong baselines on publicly available datasets, and show that it produces summaries which are more informative, diverse and coherent.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_31", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_31.flac", "type": "audio", "duration": 634.496}, {"from": "Assistant", "value": "For sentence-level extractive summarization, there is a disproportionate ratio of selected and unselected sentences, leading to flatting the summary features when maximizing the accuracy. The imbalanced classification of summarization is inherent, which can’t be addressed by common algorithms easily. In this paper, we conceptualize the single-document extractive summarization as a rebalance problem and present a deep differential amplifier framework. Specifically, we first calculate and amplify the semantic difference between each sentence and all other sentences, and then apply the residual unit as the second item of the differential amplifier to deepen the architecture. Finally, to compensate for the imbalance, the corresponding objective loss of minority class is boosted by a weighted cross-entropy. In contrast to previous approaches, this model pays more attention to the pivotal information of one sentence, instead of all the informative context modeling by recurrent or Transformer architecture. We demonstrate experimentally on two benchmark datasets that our summarizer performs competitively against state-of-the-art methods. Our source code will be available on Github.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_32", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_32.flac", "type": "audio", "duration": 731.057}, {"from": "Assistant", "value": "In this paper, we address a novel task, Multiple TimeLine Summarization (MTLS), which extends the flexibility and versatility of Time-Line Summarization (TLS). Given any collection of time-stamped news articles, MTLS automatically discovers important yet different stories and generates a corresponding time-line for each story. To achieve this, we propose a novel unsupervised summarization framework based on two-stage affinity propagation. We also introduce a quantitative evaluation measure for MTLS based on previousTLS evaluation methods. Experimental results show that our MTLS framework demonstrates high effectiveness and MTLS task can give bet-ter results than TLS.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_33", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_33.flac", "type": "audio", "duration": 617.045}, {"from": "Assistant", "value": "Recently, opinion summarization, which is the generation of a summary from multiple reviews, has been conducted in a self-supervised manner by considering a sampled review as a pseudo summary. However, non-text data such as image and metadata related to reviews have been considered less often. To use the abundant information contained in non-text data, we propose a self-supervised multimodal opinion summarization framework called MultimodalSum. Our framework obtains a representation of each modality using a separate encoder for each modality, and the text decoder generates a summary. To resolve the inherent heterogeneity of multimodal data, we propose a multimodal training pipeline. We first pretrain the text encoder–decoder based solely on text modality data. Subsequently, we pretrain the non-text modality encoders by considering the pretrained text decoder as a pivot for the homogeneous representation of multimodal data. Finally, to fuse multimodal representations, we train the entire framework in an end-to-end manner. We demonstrate the superiority of MultimodalSum by conducting experiments on Yelp and Amazon datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_34", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_34.flac", "type": "audio", "duration": 722.048}, {"from": "Assistant", "value": "In recent years, reference-based and supervised summarization evaluation metrics have been widely explored. However, collecting human-annotated references and ratings are costly and time-consuming. To avoid these limitations, we propose a training-free and reference-free summarization evaluation metric. Our metric consists of a centrality-weighted relevance score and a self-referenced redundancy score. The relevance score is computed between the pseudo reference built from the source document and the given summary, where the pseudo reference content is weighted by the sentence centrality to provide importance guidance. Besides anF1-based relevance score, we also design anF𝛽-based variant that pays more attention to the recall score. As for the redundancy score of the summary, we compute a self-masked similarity score with the summary itself to evaluate the redundant information in the summary. Finally, we combine the relevance and redundancy scores to produce the final evaluation score of the given summary. Extensive experiments show that our methods can significantly outperform existing methods on both multi-document and single-document summarization evaluation. The source code is released athttps://github.com/Chen-Wang-CUHK/Training-Free-and-Ref-Free-Summ-Evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_35", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_35.flac", "type": "audio", "duration": 565.76}, {"from": "Assistant", "value": "Short textual descriptions of entities provide summaries of their key attributes and have been shown to be useful sources of background knowledge for tasks such as entity linking and question answering. However, generating entity descriptions, especially for new and long-tail entities, can be challenging since relevant information is often scattered across multiple sources with varied content and style. We introduce DESCGEN: given mentions spread over multiple documents, the goal is to generate an entity summary description. DESCGEN consists of 37K entity descriptions from Wikipedia and Fandom, each paired with nine evidence documents on average. The documents were collected using a combination of entity linking and hyperlinks into the entity pages, which together provide high-quality distant supervision. Compared to other multi-document summarization tasks, our task is entity-centric, more abstractive, and covers a wide range of domains. We also propose a two-stage extract-then-generate baseline and show that there exists a large gap (19.9% in ROUGE-L) between state-of-art models and human performance, suggesting that the data will support significant future work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_36", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_36.flac", "type": "audio", "duration": 601.002}, {"from": "Assistant", "value": "With the recent success of pre-trained models in NLP, a significant focus was put on interpreting their representations. One of the most prominent approaches is structural probing (Hewitt and Manning, 2019), where a linear projection of word embeddings is performed in order to approximate the topology of dependency structures. In this work, we introduce a new type of structural probing, where the linear projection is decomposed into 1. iso-morphic space rotation; 2. linear scaling that identifies and scales the most relevant dimensions. In addition to syntactic dependency, we evaluate our method on two novel tasks (lexical hypernymy and position in a sentence). We jointly train the probes for multiple tasks and experimentally show that lexical and syntactic information is separated in the representations. Moreover, the orthogonal constraint makes the Structural Probes less vulnerable to memorization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_37", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_37.flac", "type": "audio", "duration": 778.411}, {"from": "Assistant", "value": "Backdoor attacks are a kind of insidious security threat against machine learning models. After being injected with a backdoor in training, the victim model will produce adversary-specified outputs on the inputs embedded with predesigned triggers but behave properly on normal inputs during inference. As a sort of emergent attack, backdoor attacks in natural language processing (NLP) are investigated insufficiently. As far as we know, almost all existing textual backdoor attack methods insert additional contents into normal samples as triggers, which causes the trigger-embedded samples to be detected and the backdoor attacks to be blocked without much effort. In this paper, we propose to use the syntactic structure as the trigger in textual backdoor attacks. We conduct extensive experiments to demonstrate that the syntactic trigger-based attack method can achieve comparable attack performance (almost 100% success rate) to the insertion-based methods but possesses much higher invisibility and stronger resistance to defenses. These results also reveal the significant insidiousness and harmfulness of textual backdoor attacks. All the code and data of this paper can be obtained athttps://github.com/thunlp/HiddenKiller.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_38", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_38.flac", "type": "audio", "duration": 689.045}, {"from": "Assistant", "value": "Since language models are used to model a wide variety of languages, it is natural to ask whether the neural architectures used for the task have inductive biases towards modeling particular types of languages. Investigation of these biases has proved complicated due to the many variables that appear in the experimental setup. Languages vary in many typological dimensions, and it is difficult to single out one or two to investigate without the others acting as confounders. We propose a novel method for investigating the inductive biases of language models using artificial languages. These languages are constructed to allow us to create parallel corpora across languages that differ only in the typological feature being investigated, such as word order. We then use them to train and test language models. This constitutes a fully controlled causal framework, and demonstrates how grammar engineering can serve as a useful tool for analyzing neural models. Using this method, we find that commonly used neural architectures exhibit different inductive biases: LSTMs display little preference with respect to word ordering, while transformers display a clear preference for some orderings over others. Further, we find that neither the inductive bias of the LSTM nor that of the transformer appear to reflect any tendencies that we see in attested natural languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_39", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_39.flac", "type": "audio", "duration": 658.053}, {"from": "Assistant", "value": "Despite the success of contextualized language models on various NLP tasks, it is still unclear what these models really learn. In this paper, we contribute to the current efforts of explaining such models by exploring the continuum between function and content words with respect to contextualization in BERT, based on linguistically-informed insights. In particular, we utilize scoring and visual analytics techniques: we use an existing similarity-based score to measure contextualization and integrate it into a novel visual analytics technique, presenting the model’s layers simultaneously and highlighting intra-layer properties and inter-layer differences. We show that contextualization is neither driven by polysemy nor by pure context variation. We also provide insights on why BERT fails to model words in the middle of the functionality continuum.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_40", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_40.flac", "type": "audio", "duration": 640.363}, {"from": "Assistant", "value": "Neural network architectures in natural language processing often use attention mechanisms to produce probability distributions over input token representations. Attention has empirically been demonstrated to improve performance in various tasks, while its weights have been extensively used as explanations for model predictions. Recent studies (Jain and Wallace, 2019; Serrano and Smith, 2019; Wiegreffe and Pinter, 2019) have showed that it cannot generally be considered as a faithful explanation (Jacovi and Goldberg, 2020) across encoders and tasks. In this paper, we seek to improve the faithfulness of attention-based explanations for text classification. We achieve this by proposing a new family of Task-Scaling (TaSc) mechanisms that learn task-specific non-contextualised information to scale the original attention weights. Evaluation tests for explanation faithfulness, show that the three proposed variants of TaSc improve attention-based explanations across two attention mechanisms, five encoders and five text classification datasets without sacrificing predictive performance. Finally, we demonstrate that TaSc consistently provides more faithful attention-based explanations compared to three widely-used interpretability techniques.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_41", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_41.flac", "type": "audio", "duration": 665.484}, {"from": "Assistant", "value": "Car-focused navigation services are based on turns and distances of named streets, whereas navigation instructions naturally used by humans are centered around physical objects called landmarks. We present a neural model that takes OpenStreetMap representations as input and learns to generate navigation instructions that contain visible and salient landmarks from human natural language instructions. Routes on the map are encoded in a location- and rotation-invariant graph representation that is decoded into natural language instructions. Our work is based on a novel dataset of 7,672 crowd-sourced instances that have been verified by human navigation in Street View. Our evaluation shows that the navigation instructions generated by our system have similar properties as human-generated instructions, and lead to successful human navigation in Street View.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_42", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_42.flac", "type": "audio", "duration": 621.291}, {"from": "Assistant", "value": "Vision-language pre-training (VLP) on large-scale image-text pairs has achieved huge success for the cross-modal downstream tasks. The most existing pre-training methods mainly adopt a two-step training procedure, which firstly employs a pre-trained object detector to extract region-based visual features, then concatenates the image representation and text embedding as the input of Transformer to train. However, these methods face problems of using task-specific visual representation of the specific object detector for generic cross-modal understanding, and the computation inefficiency of two-stage pipeline. In this paper, we propose the first end-to-end vision-language pre-trained model for both V+L understanding and generation, namely E2E-VLP, where we build a unified Transformer framework to jointly learn visual representation, and semantic alignments between image and text. We incorporate the tasks of object detection and image captioning into pre-training with a unified Transformer encoder-decoder architecture for enhancing visual learning. An extensive set of experiments have been conducted on well-established vision-language downstream tasks to demonstrate the effectiveness of this novel VLP paradigm.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_43", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_43.flac", "type": "audio", "duration": 690.051}, {"from": "Assistant", "value": "Despite the achievements of large-scale multimodal pre-training approaches, cross-modal retrieval, e.g., image-text retrieval, remains a challenging task. To bridge the semantic gap between the two modalities, previous studies mainly focus on word-region alignment at the object level, lacking the matching between the linguistic relation among the words and the visual relation among the regions. The neglect of such relation consistency impairs the contextualized representation of image-text pairs and hinders the model performance and the interpretability. In this paper, we first propose a novel metric, Intra-modal Self-attention Distance (ISD), to quantify the relation consistency by measuring the semantic distance between linguistic and visual relations. In response, we present Inter-modal Alignment on Intra-modal Self-attentions (IAIS), a regularized training method to optimize the ISD and calibrate intra-modal self-attentions from the two modalities mutually via inter-modal alignment. The IAIS regularizer boosts the performance of prevailing models on Flickr30k and MS COCO datasets by a considerable margin, which demonstrates the superiority of our approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_44", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_44.flac", "type": "audio", "duration": 719.052}, {"from": "Assistant", "value": "We present Knowledge Enhanced Multimodal BART (KM-BART), which is a Transformer-based sequence-to-sequence model capable of reasoning about commonsense knowledge from multimodal inputs of images and texts. We adapt the generative BART architecture (Lewis et al., 2020) to a multimodal model with visual and textual inputs. We further develop novel pretraining tasks to improve the model performance on the Visual Commonsense Generation (VCG) task. In particular, our pretraining task of Knowledge-based Commonsense Generation (KCG) boosts model performance on the VCG task by leveraging commonsense knowledge from a large language model pretrained on external commonsense knowledge graphs. To the best of our knowledge, we are the first to propose a dedicated task for improving model performance on the VCG task. Experimental results show that our model reaches state-of-the-art performance on the VCG task (Park et al., 2020) by applying these novel pretraining tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_45", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_45.flac", "type": "audio", "duration": 708.046}, {"from": "Assistant", "value": "Transformers have advanced the field of natural language processing (NLP) on a variety of important tasks. At the cornerstone of the Transformer architecture is the multi-head attention (MHA) mechanism which models pairwise interactions between the elements of the sequence. Despite its massive success, the current framework ignores interactions among different heads, leading to the problem that many of the heads are redundant in practice, which greatly wastes the capacity of the model. To improve parameter efficiency, we re-formulate the MHA as a latent variable model from a probabilistic perspective. We presentcascaded head-collidingattention (CODA) which explicitly models the interactions between attention heads through a hierarchical variational distribution. We conduct extensive experiments and demonstrate that CODA outperforms the transformer baseline, by 0.6 perplexity on Wikitext-103 in language modeling, and by 0.6 BLEU on WMT14 EN-DE in machine translation, due to its improvements on the parameter efficiency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_46", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_46.flac", "type": "audio", "duration": 691.722}, {"from": "Assistant", "value": "Knowledge distillation is a critical technique to transfer knowledge between models, typically from a large model (the teacher) to a more fine-grained one (the student). The objective function of knowledge distillation is typically the cross-entropy between the teacher and the student’s output distributions. However, for structured prediction problems, the output space is exponential in size; therefore, the cross-entropy objective becomes intractable to compute and optimize directly. In this paper, we derive a factorized form of the knowledge distillation objective for structured prediction, which is tractable for many typical choices of the teacher and student models. In particular, we show the tractability and empirical effectiveness of structural knowledge distillation between sequence labeling and dependency parsing models under four different scenarios: 1) the teacher and student share the same factorization form of the output structure scoring function; 2) the student factorization produces more fine-grained substructures than the teacher factorization; 3) the teacher factorization produces more fine-grained substructures than the student factorization; 4) the factorization forms from the teacher and the student are incompatible.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_47", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_47.flac", "type": "audio", "duration": 625.003}, {"from": "Assistant", "value": "State-of-the-art parameter-efficient fine-tuning methods rely on introducing adapter modules between the layers of a pretrained language model. However, such modules are trained separately for each task and thus do not enable sharing information across tasks. In this paper, we show that we can learn adapter parameters for all layers and tasks by generating them using shared hypernetworks, which condition on task, adapter position, and layer id in a transformer model. This parameter-efficient multi-task learning framework allows us to achieve the best of both worlds by sharing knowledge across tasks via hypernetworks while enabling the model to adapt to each individual task through task-specific adapters. Experiments on the well-known GLUE benchmark show improved performance in multi-task learning while adding only 0.29% parameters per task. We additionally demonstrate substantial performance improvements in few-shot domain generalization across a variety of tasks. Our code is publicly available inhttps://github.com/rabeehk/hyperformer.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_48", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_48.flac", "type": "audio", "duration": 689.052}, {"from": "Assistant", "value": "Pre-trained multilingual language models, e.g., multilingual-BERT, are widely used in cross-lingual tasks, yielding the state-of-the-art performance. However, such models suffer from a large performance gap between source and target languages, especially in the zero-shot setting, where the models are fine-tuned only on English but tested on other languages for the same task. We tackle this issue by incorporating language-agnostic information, specifically, universal syntax such as dependency relations and POS tags, into language models, based on the observation that universal syntax is transferable across different languages. Our approach, called COunterfactual SYntax (COSY), includes the design of SYntax-aware networks as well as a COunterfactual training method to implicitly force the networks to learn not only the semantics but also the syntax. To evaluate COSY, we conduct cross-lingual experiments on natural language inference and question answering using mBERT and XLM-R as network backbones. Our results show that COSY achieves the state-of-the-art performance for both tasks, without using auxiliary training data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_49", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_49.flac", "type": "audio", "duration": 675.051}, {"from": "Assistant", "value": "Recent studies on neural networks with pre-trained weights (i.e., BERT) have mainly focused on a low-dimensional subspace, where the embedding vectors computed from input words (or their contexts) are located. In this work, we propose a new approach, called OoMMix, to finding and regularizing the remainder of the space, referred to as out-of-manifold, which cannot be accessed through the words. Specifically, we synthesize the out-of-manifold embeddings based on two embeddings obtained from actually-observed words, to utilize them for fine-tuning the network. A discriminator is trained to detect whether an input embedding is located inside the manifold or not, and simultaneously, a generator is optimized to produce new embeddings that can be easily identified as out-of-manifold by the discriminator. These two modules successfully collaborate in a unified and end-to-end manner for regularizing the out-of-manifold. Our extensive evaluation on various text classification benchmarks demonstrates the effectiveness of our approach, as well as its good compatibility with existing data augmentation techniques which aim to enhance the manifold.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_50", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_50.flac", "type": "audio", "duration": 720.043}, {"from": "Assistant", "value": "Stereotypical language expresses widely-held beliefs about different social categories. Many stereotypes are overtly negative, while others may appear positive on the surface, but still lead to negative consequences. In this work, we present a computational approach to interpreting stereotypes in text through the Stereotype Content Model (SCM), a comprehensive causal theory from social psychology. The SCM proposes that stereotypes can be understood along two primary dimensions: warmth and competence. We present a method for defining warmth and competence axes in semantic embedding space, and show that the four quadrants defined by this subspace accurately represent the warmth and competence concepts, according to annotated lexicons. We then apply our computational SCM model to textual stereotype data and show that it compares favourably with survey-based studies in the psychological literature. Furthermore, we explore various strategies to counter stereotypical beliefs with anti-stereotypes. It is known that countering stereotypes with anti-stereotypical examples is one of the most effective ways to reduce biased thinking, yet the problem of generating anti-stereotypes has not been previously studied. Thus, a better understanding of how to generate realistic and effective anti-stereotypes can contribute to addressing pressing societal concerns of stereotyping, prejudice, and discrimination.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_51", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_51.flac", "type": "audio", "duration": 719.052}, {"from": "Assistant", "value": "Misinformation has recently become a well-documented matter of public concern. Existing studies on this topic have hitherto adopted a coarse concept of misinformation, which incorporates a broad spectrum of story types ranging from political conspiracies to misinterpreted pranks. This paper aims to structurize these misinformation stories by leveraging fact-check articles. Our intuition is that key phrases in a fact-check article that identify the misinformation type(s) (e.g., doctored images, urban legends) also act as rationales that determine the verdict of the fact-check (e.g., false). We experiment on rationalized models with domain knowledge as weak supervision to extract these phrases as rationales, and then cluster semantically similar rationales to summarize prevalent misinformation types. Using archived fact-checks from Snopes.com, we identify ten types of misinformation stories. We discuss how these types have evolved over the last ten years and compare their prevalence between the 2016/2020 US presidential elections and the H1N1/COVID-19 pandemics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_52", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_52.flac", "type": "audio", "duration": 635.634}, {"from": "Assistant", "value": "While there is an abundance of advice to podcast creators on how to speak in ways that engage their listeners, there has been little data-driven analysis of podcasts that relates linguistic style with engagement. In this paper, we investigate how various factors – vocabulary diversity, distinctiveness, emotion, and syntax, among others – correlate with engagement, based on analysis of the creators’ written descriptions and transcripts of the audio. We build models with different textual representations, and show that the identified features are highly predictive of engagement. Our analysis tests popular wisdom about stylistic elements in high-engagement podcasts, corroborating some pieces of advice and adding new perspectives on others.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_53", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_53.flac", "type": "audio", "duration": 702.055}, {"from": "Assistant", "value": "People debate on a variety of topics on online platforms such as Reddit, or Facebook. Debates can be lengthy, with users exchanging a wealth of information and opinions. However, conversations do not always go smoothly, and users sometimes engage in unsound argumentation techniques to prove a claim. These techniques are called fallacies. Fallacies are persuasive arguments that provide insufficient or incorrect evidence to support the claim. In this paper, we study the most frequent fallacies on Reddit, and we present them using the pragma-dialectical theory of argumentation. We construct a new annotated dataset of fallacies, using user comments containing fallacy mentions as noisy labels, and cleaning the data via crowdsourcing. Finally, we study the task of classifying fallacies using neural models. We find that generally the models perform better in the presence of conversational context. We have released the data and the code at github.com/sahaisaumya/informal_fallacies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_54", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_54.flac", "type": "audio", "duration": 452.053}, {"from": "Assistant", "value": "Inferring social relations from dialogues is vital for building emotionally intelligent robots to interpret human language better and act accordingly. We model the social network as an And-or Graph, named SocAoG, for the consistency of relations among a group and leveraging attributes as inference cues. Moreover, we formulate a sequential structure prediction task, and propose an𝛼-𝛽-𝛾strategy to incrementally parse SocAoG for the dynamic inference upon any incoming utterance: (i) an𝛼process predicting attributes and relations conditioned on the semantics of dialogues, (ii) a𝛽process updating the social relations based on related attributes, and (iii) a𝛾process updating individual’s attributes based on interpersonal social relations. Empirical results on DialogRE and MovieGraph show that our model infers social relations more accurately than the state-of-the-art methods. Moreover, the ablation study shows the three processes complement each other, and the case study demonstrates the dynamic relational inference.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_55", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_55.flac", "type": "audio", "duration": 734.848}, {"from": "Assistant", "value": "We present a data-driven, end-to-end approach to transaction-based dialog systems that performs at near-human levels in terms of verbal response quality and factual grounding accuracy. We show that two essential components of the system produce these results: a sufficiently large and diverse, in-domain labeled dataset, and a neural network-based, pre-trained model that generates both verbal responses and API call predictions. In terms of data, we introduce TicketTalk, a movie ticketing dialog dataset with 23,789 annotated conversations. The conversations range from completely open-ended and unrestricted to more structured, both in terms of their knowledge base, discourse features, and number of turns. In qualitative human evaluations, model-generated responses trained on just 10,000 TicketTalk dialogs were rated to “make sense” 86.5% of the time, almost the same as human responses in the same contexts. Our simple, API-focused annotation schema results in a much easier labeling task making it faster and more cost effective. It is also the key component for being able to predict API calls accurately. We handle factual grounding by incorporating API calls in the training data, allowing our model to learn which actions to take and when. Trained on the same 10,000-dialog set, the model’s API call predictions were rated to be correct 93.9% of the time in our evaluations, surpassing the ratings for the corresponding human labels. We show how API prediction and response generation scores improve as the dataset size incrementally increases from 5000 to 21,000 dialogs. Our analysis also clearly illustrates the benefits of pre-training. To facilitate future work on transaction-based dialog systems, we are publicly releasing the TicketTalk dataset athttps://git.io/JL8an.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_56", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_56.flac", "type": "audio", "duration": 680.171}, {"from": "Assistant", "value": "In this paper, we explore the ability to model and infer personality types of opponents, predict their responses, and use this information to adapt a dialog agent’s high-level strategy in negotiation tasks. Inspired by the idea of incorporating a theory of mind (ToM) into machines, we introduce a probabilistic formulation to encapsulate the opponent’s personality type during both learning and inference. We test our approach on the CraigslistBargain dataset (He et al. 2018) and show that our method using ToM inference achieves a 20% higher dialog agreement rate compared to baselines on a mixed population of opponents. We also demonstrate that our model displays diverse negotiation behavior with different types of opponents.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_57", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_57.flac", "type": "audio", "duration": 717.056}, {"from": "Assistant", "value": "In this paper, we propose Inverse Adversarial Training (IAT) algorithm for training neural dialogue systems to avoid generic responses and model dialogue history better. In contrast to standard adversarial training algorithms, IAT encourages the model to be sensitive to the perturbation in the dialogue history and therefore learning from perturbations. By giving higher rewards for responses whose output probability reduces more significantly when dialogue history is perturbed, the model is encouraged to generate more diverse and consistent responses. By penalizing the model when generating the same response given perturbed dialogue history, the model is forced to better capture dialogue history and generate more informative responses. Experimental results on two benchmark datasets show that our approach can better model dialogue history and generate more diverse and consistent responses. In addition, we point out a problem of the widely used maximum mutual information (MMI) based methods for improving the diversity of dialogue response generation models and demonstrate it empirically.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_58", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_58.flac", "type": "audio", "duration": 673.773}, {"from": "Assistant", "value": "Knowledge-grounded dialogue systems are intended to convey information that is based on evidence provided in a given source text. We discuss the challenges of training a generative neural dialogue model for such systems that is controlled to stay faithful to the evidence. Existing datasets contain a mix of conversational responses that are faithful to selected evidence as well as more subjective or chit-chat style responses. We propose different evaluation measures to disentangle these different styles of responses by quantifying the informativeness and objectivity. At training time, additional inputs based on these evaluation measures are given to the dialogue model. At generation time, these additional inputs act as stylistic controls that encourage the model to generate responses that are faithful to the provided evidence. We also investigate the usage of additional controls at decoding time using resampling techniques. In addition to automatic metrics, we perform a human evaluation study where raters judge the output of these controlled generation models to be generally more objective and faithful to the evidence compared to baseline dialogue systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_59", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_59.flac", "type": "audio", "duration": 718.059}, {"from": "Assistant", "value": "Automatically extracting key information from scientific documents has the potential to help scientists work more efficiently and accelerate the pace of scientific progress. Prior work has considered extracting document-level entity clusters and relations end-to-end from raw scientific text, which can improve literature search and help identify methods and materials for a given problem. Despite the importance of this task, most existing works on scientific information extraction (SciIE) consider extraction solely based on the content of an individual paper, without considering the paper’s place in the broader literature. In contrast to prior work, we augment our text representations by leveraging a complementary source of document context: the citation graph of referential links between citing and cited papers. On a test set of English-language scientific documents, we show that simple ways of utilizing the structure and content of the citation graph can each lead to significant gains in different scientific information extraction tasks. When these tasks are combined, we observe a sizable improvement in end-to-end information extraction over the state-of-the-art, suggesting the potential for future work along this direction. We release software tools to facilitate citation-aware SciIE development.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_60", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_60.flac", "type": "audio", "duration": 728.062}, {"from": "Assistant", "value": "Current event-centric knowledge graphs highly rely on explicit connectives to mine relations between events. Unfortunately, due to the sparsity of connectives, these methods severely undermine the coverage of EventKGs. The lack of high-quality labelled corpora further exacerbates that problem. In this paper, we propose a knowledge projection paradigm for event relation extraction: projecting discourse knowledge to narratives by exploiting the commonalities between them. Specifically, we propose Multi-tier Knowledge Projection Network (MKPNet), which can leverage multi-tier discourse knowledge effectively for event relation extraction. In this way, the labelled data requirement is significantly reduced, and implicit event relations can be effectively extracted. Intrinsic experimental results show that MKPNet achieves the new state-of-the-art performance and extrinsic experimental results verify the value of the extracted event relations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_61", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_61.flac", "type": "audio", "duration": 650.005}, {"from": "Assistant", "value": "Neural methods have been shown to achieve high performance in Named Entity Recognition (NER), but rely on costly high-quality labeled data for training, which is not always available across languages. While previous works have shown that unlabeled data in a target language can be used to improve cross-lingual model performance, we propose a novel adversarial approach (AdvPicker) to better leverage such data and further improve results. We design an adversarial learning framework in which an encoder learns entity domain knowledge from labeled source-language data and better shared features are captured via adversarial training - where a discriminator selects less language-dependent target-language data via similarity to the source language. Experimental results on standard benchmark datasets well demonstrate that the proposed method benefits strongly from this data selection process and outperforms existing state-of-the-art methods; without requiring any additional external resources (e.g., gazetteers or via machine translation).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_62", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_62.flac", "type": "audio", "duration": 687.488}, {"from": "Assistant", "value": "Nowadays, fake news detection, which aims to verify whether a news document is trusted or fake, has become urgent and important. Most existing methods rely heavily on linguistic and semantic features from the news content, and fail to effectively exploit external knowledge which could help determine whether the news document is trusted. In this paper, we propose a novel end-to-end graph neural model called CompareNet, which compares the news to the knowledge base (KB) through entities for fake news detection. Considering that fake news detection is correlated with topics, we also incorporate topics to enrich the news representation. Specifically, we first construct adirected heterogeneous document graphfor each news incorporating topics and entities. Based on the graph, we develop aheterogeneous graph attention networkfor learning the topic-enriched news representation as well as the contextual entity representations that encode the semantics of the news content. The contextual entity representations are then compared to the corresponding KB-based entity representations through a carefully designedentity comparison network, to capture the consistency between the news content and KB. Finally, the topic-enriched news representation combining the entity comparison features is fed into a fake news classifier. Experimental results on two benchmark datasets demonstrate that CompareNet significantly outperforms state-of-the-art methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_63", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_63.flac", "type": "audio", "duration": 726.528}, {"from": "Assistant", "value": "Named entity recognition (NER) remains challenging when entity mentions can be discontinuous. Existing methods break the recognition process into several sequential steps. In training, they predict conditioned on the golden intermediate results, while at inference relying on the model output of the previous steps, which introduces exposure bias. To solve this problem, we first construct a segment graph for each sentence, in which each node denotes a segment (a continuous entity on its own, or a part of discontinuous entities), and an edge links two nodes that belong to the same entity. The nodes and edges can be generated respectively in one stage with a grid tagging scheme and learned jointly using a novel architecture named Mac. Then discontinuous NER can be reformulated as a non-parametric process of discovering maximal cliques in the graph and concatenating the spans in each clique. Experiments on three benchmarks show that our method outperforms the state-of-the-art (SOTA) results, with up to 3.5 percentage points improvement on F1, and achieves 5x speedup over the SOTA model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_64", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_64.flac", "type": "audio", "duration": 670.221}, {"from": "Assistant", "value": "Entity linking (EL) is the task of disambiguating mentions appearing in text by linking them to entities in a knowledge graph, a crucial task for text understanding, question answering or conversational systems. In the special case of short-text EL, which poses additional challenges due to limited context, prior approaches have reached good performance by employing heuristics-based methods or purely neural approaches. Here, we take a different, neuro-symbolic approach that combines the advantages of using interpretable rules based on first-order logic with the performance of neural learning. Even though constrained to use rules, we show that we reach competitive or better performance with SoTA black-box neural approaches. Furthermore, our framework has the benefits of extensibility and transferability. We show that we can easily blend existing rule templates given by a human expert, with multiple types of features (priors, BERT encodings, box embeddings, etc), and even with scores resulting from previous EL methods, thus improving on such methods. As an example of improvement, on the LC-QuAD-1.0 dataset, we show more than 3% increase in F1 score relative to previous SoTA. Finally, we show that the inductive bias offered by using logic results in a set of learned rules that transfers from one dataset to another, sometimes without finetuning, while still having high accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_65", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_65.flac", "type": "audio", "duration": 703.89}, {"from": "Assistant", "value": "Context-aware machine translation models are designed to leverage contextual information, but often fail to do so. As a result, they inaccurately disambiguate pronouns and polysemous words that require context for resolution. In this paper, we ask several questions: What contexts do human translators use to resolve ambiguous words? Are models paying large amounts of attention to the same context? What if we explicitly train them to do so? To answer these questions, we introduce SCAT (Supporting Context for Ambiguous Translations), a new English-French dataset comprising supporting context words for 14K translations that professional translators found useful for pronoun disambiguation. Using SCAT, we perform an in-depth analysis of the context used to disambiguate, examining positional and lexical characteristics of the supporting words. Furthermore, we measure the degree of alignment between the model’s attention scores and the supporting context from SCAT, and apply a guided attention strategy to encourage agreement between the two.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_66", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_66.flac", "type": "audio", "duration": 690.048}, {"from": "Assistant", "value": "The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages. Fortunately, some low-resource languages are linguistically related or similar to high-resource languages; these related languages may share many lexical or syntactic structures. In this work, we exploit this linguistic overlap to facilitate translating to and from a low-resource language with only monolingual data, in addition to any parallel data in the related high-resource language. Our method, NMT-Adapt, combines denoising autoencoding, back-translation and adversarial objectives to utilize monolingual data for low-resource adaptation. We experiment on 7 languages from three different language families and show that our technique significantly improves translation into low-resource language compared to other translation baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_67", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_67.flac", "type": "audio", "duration": 712.063}, {"from": "Assistant", "value": "Bilingual lexicons map words in one language to their translations in another, and are typically induced by learning linear projections to align monolingual word embedding spaces. In this paper, we show it is possible to produce much higher quality lexicons with methods that combine (1) unsupervised bitext mining and (2) unsupervised word alignment. Directly applying a pipeline that uses recent algorithms for both subproblems significantly improves induced lexicon quality and further gains are possible by learning to filter the resulting lexical entries, with both unsupervised and semi-supervised schemes. Our final model outperforms the state of the art on the BUCC 2020 shared task by 14 F1 points averaged over 12 language pairs, while also providing a more interpretable approach that allows for rich reasoning of word meaning in context. Further analysis of our output and the standard reference lexicons suggests they are of comparable quality, and new benchmarks may be needed to measure further progress on this task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_68", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_68.flac", "type": "audio", "duration": 508.053}, {"from": "Assistant", "value": "We present a simple yet effective approach to build multilingual speech-to-text (ST) translation through efficient transfer learning from a pretrained speech encoder and text decoder. Our key finding is that a minimalistic LNA (LayerNorm and Attention) finetuning can achieve zero-shot crosslingual and cross-modality transfer ability by only finetuning 10 50% of the pretrained parameters. This effectively leverages large pretrained models at low training cost such as wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation. This sets a new state-of-the-art for 36 translation directions (and surpassing cascaded ST for 26 of them) on the large-scale multilingual ST benchmark CoVoST 2 (+6.4 BLEU on average for En-X directions and +6.7 BLEU for X-En directions). Our approach demonstrates strong zero-shot performance in a many-to-many multilingual model (+5.6 BLEU on average across 28 non-English directions), making it an appealing approach for attaining high-quality speech translation with improved parameter and data efficiency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_69", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_69.flac", "type": "audio", "duration": 690.283}, {"from": "Assistant", "value": "Learning contextual text embeddings that represent causal graphs has been useful in improving the performance of downstream tasks like causal treatment effect estimation. However, existing causal embeddings which are trained to predict direct causal links, fail to capture other indirect causal links of the graph, thus leading to spurious correlations in downstream tasks. In this paper, we define the faithfulness property of contextual embeddings to capture geometric distance-based properties of directed acyclic causal graphs. By incorporating these faithfulness properties, we learn text embeddings that are 31.3% more faithful to human validated causal graphs with about 800K and 200K causal links and achieve 21.1% better Precision-Recall AUC in a link prediction fine-tuning task. Further, in a crowdsourced causal question-answering task on Yahoo! Answers with questions of the form “What causes X?”, our faithful embeddings achieved a precision of the first ranked answer (P@1) of 41.07%, outperforming the existing baseline by 10.2%.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_70", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_70.flac", "type": "audio", "duration": 716.22}, {"from": "Assistant", "value": "Transformer-based language models benefit from conditioning on contexts of hundreds to thousands of previous tokens. What aspects of these contexts contribute to accurate model prediction? We describe a series of experiments that measure usable information by selectively ablating lexical and structural information in transformer language models trained on English Wikipedia. In both mid- and long-range contexts, we find that several extremely destructive context manipulations—including shuffling word order within sentences and deleting all words other than nouns—remove less than 15% of the usable information. Our results suggest that long contexts, but not their detailed syntactic and propositional content, are important for the low perplexity of current transformer language models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_71", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_71.flac", "type": "audio", "duration": 629.696}, {"from": "Assistant", "value": "In this paper, we introduce Integrated Directional Gradients (IDG), a method for attributing importance scores to groups of features, indicating their relevance to the output of a neural network model for a given input. The success of Deep Neural Networks has been attributed to their ability to capture higher level feature interactions. Hence, in the last few years capturing the importance of these feature interactions has received increased prominence in ML interpretability literature. In this paper, we formally define the feature group attribution problem and outline a set of axioms that any intuitive feature group attribution method should satisfy. Earlier, cooperative game theory inspired axiomatic methods only borrowed axioms from solution concepts (such as Shapley value) for individual feature attributions and introduced their own extensions to model interactions. In contrast, our formulation is inspired by axioms satisfied by characteristic functions as well as solution concepts in cooperative game theory literature. We believe that characteristic functions are much better suited to model importance of groups compared to just solution concepts. We demonstrate that our proposed method, IDG, satisfies all the axioms. Using IDG we analyze two state-of-the-art text classifiers on three benchmark datasets for sentiment analysis. Our experiments show that IDG is able to effectively capture semantic interactions in linguistic models via negations and conjunctions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_72", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_72.flac", "type": "audio", "duration": 722.453}, {"from": "Assistant", "value": "Sentence embeddings are an important component of many natural language processing (NLP) systems. Like word embeddings, sentence embeddings are typically learned on large text corpora and then transferred to various downstream tasks, such as clustering and retrieval. Unlike word embeddings, the highest performing solutions for learning sentence embeddings require labelled data, limiting their usefulness to languages and domains where labelled data is abundant. In this paper, we present DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations. Inspired by recent advances in deep metric learning (DML), we carefully design a self-supervised objective for learning universal sentence embeddings that does not require labelled training data. When used to extend the pretraining of transformer-based language models, our approach closes the performance gap between unsupervised and supervised pretraining for universal sentence encoders. Importantly, our experiments suggest that the quality of the learned embeddings scale with both the number of trainable parameters and the amount of unlabelled training data. Our code and pretrained models are publicly available and can be easily adapted to new domains or used to embed unseen text.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_73", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_73.flac", "type": "audio", "duration": 713.062}, {"from": "Assistant", "value": "Due to the scarcity of annotated data, Abstract Meaning Representation (AMR) research is relatively limited and challenging for languages other than English. Upon the availability of English AMR dataset and English-to- X parallel datasets, in this paper we propose a novel cross-lingual pre-training approach via multi-task learning (MTL) for both zeroshot AMR parsing and AMR-to-text generation. Specifically, we consider three types of relevant tasks, including AMR parsing, AMR-to-text generation, and machine translation. We hope that knowledge gained while learning for English AMR parsing and text generation can be transferred to the counterparts of other languages. With properly pretrained models, we explore four different finetuning methods, i.e., vanilla fine-tuning with a single task, one-for-all MTL fine-tuning, targeted MTL fine-tuning, and teacher-studentbased MTL fine-tuning. Experimental results on AMR parsing and text generation of multiple non-English languages demonstrate that our approach significantly outperforms a strong baseline of pre-training approach, and greatly advances the state of the art. In detail, on LDC2020T07 we have achieved 70.45%, 71.76%, and 70.80% in Smatch F1 for AMR parsing of German, Spanish, and Italian, respectively, while for AMR-to-text generation of the languages, we have obtained 25.69, 31.36, and 28.42 in BLEU respectively. We make our code available on githubhttps://github.com/xdqkid/XLPT-AMR.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_74", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_74.flac", "type": "audio", "duration": 712.043}, {"from": "Assistant", "value": "Despite the success of sequence-to-sequence (seq2seq) models in semantic parsing, recent work has shown that they fail in compositional generalization, i.e., the ability to generalize to new structures built of components observed during training. In this work, we posit that a span-based parser should lead to better compositional generalization. we propose SpanBasedSP, a parser that predicts a span tree over an input utterance, explicitly encoding how partial programs compose over spans in the input. SpanBasedSP extends Pasupat et al. (2019) to be comparable to seq2seq models by (i) training from programs, without access to gold trees, treating trees as latent variables, (ii) parsing a class of non-projective trees through an extension to standard CKY. On GeoQuery, SCAN and CLOSURE datasets, SpanBasedSP performs similarly to strong seq2seq baselines on random splits, but dramatically improves performance compared to baselines on splits that require compositional generalization: from 61.0 → 88.9 average accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_75", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_75.flac", "type": "audio", "duration": 588.092}, {"from": "Assistant", "value": "Sequence-to-sequence models excel at handling natural language variation, but have been shown to struggle with out-of-distribution compositional generalization. This has motivated new specialized architectures with stronger compositional biases, but most of these approaches have only been evaluated on synthetically-generated datasets, which are not representative of natural language variation. In this work we ask: can we develop a semantic parsing approach that handles both natural language variation and compositional generalization? To better assess this capability, we propose new train and test splits of non-synthetic datasets. We demonstrate that strong existing approaches do not perform well across a broad set of evaluations. We also propose NQG-T5, a hybrid model that combines a high-precision grammar-based approach with a pre-trained sequence-to-sequence model. It outperforms existing approaches across several compositional generalization challenges on non-synthetic data, while also being competitive with the state-of-the-art on standard evaluations. While still far from solving this problem, our study highlights the importance of diverse evaluations and the open challenge of handling both compositional generalization and natural language variation in semantic parsing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_76", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_76.flac", "type": "audio", "duration": 673.936}, {"from": "Assistant", "value": "We present a targeted, scaled-up comparison of incremental processing in humans and neural language models by collecting by-word reaction time data for sixteen different syntactic test suites across a range of structural phenomena. Human reaction time data comes from a novel online experimental paradigm called the Interpolated Maze task. We compare human reaction times to by-word probabilities for four contemporary language models, with different architectures and trained on a range of data set sizes. We find that across many phenomena, both humans and language models show increased processing difficulty in ungrammatical sentence regions with human and model ‘accuracy’ scores a la Marvin and Linzen (2018) about equal. However, although language model outputs match humans in direction, we show that models systematically under-predict the difference in magnitude of incremental processing difficulty between grammatical and ungrammatical sentences. Specifically, when models encounter syntactic violations they fail to accurately predict the longer reading times observed in the human data. These results call into question whether contemporary language models are approaching human-like performance for sensitivity to syntactic violations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_77", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_77.flac", "type": "audio", "duration": 745.802}, {"from": "Assistant", "value": "Modality is the linguistic ability to describe vents with added information such as how desirable, plausible, or feasible they are. Modality is important for many NLP downstream tasks such as the detection of hedging, uncertainty, speculation, and more. Previous studies that address modality detection in NLP often restrict modal expressions to a closed syntactic class, and the modal sense labels are vastly different across different studies, lacking an accepted standard. Furthermore, these senses are often analyzed independently of the events that they modify. This work builds on the theoretical foundations of the Georgetown Gradable Modal Expressions (GME) work by Rubinstein et al. (2013) to propose an event-based modality detection task where modal expressions can be words of any syntactic class and sense labels are drawn from a comprehensive taxonomy which harmonizes the modal concepts contributed by the different studies. We present experiments on the GME corpus aiming to detect and classify fine-grained modal concepts and associate them with their modified events. We show that detecting and classifying modal expressions is not only feasible, it also improves the detection of modal events in their own right.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_78", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_78.flac", "type": "audio", "duration": 725.02}, {"from": "Assistant", "value": "Part-of-Speech (POS) tags are routinely included as features in many NLP tasks. However, the importance and usefulness of POS tags needs to be examined as NLP expands to low-resource languages because linguists who provide many annotated resources do not place priority on early identification and tagging of POS. This paper describes an empirical study about the effect that POS tags have on two computational morphological tasks with the Transformer architecture. Each task is tested twice on identical data except for the presence/absence of POS tags, using published data in ten high- to low-resource languages or unpublished linguistic field data in five low-resource languages. We find that the presence or absence of POS tags does not have a significant bearing on performance. In joint segmentation and glossing, the largest average difference is an .09 improvement in F1-scores by removing POS tags. In reinflection, the greatest average difference is 1.2% in accuracy for published data and 5% for unpublished and noisy field data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_79", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_79.flac", "type": "audio", "duration": 687.125}, {"from": "Assistant", "value": "Parsing spoken dialogue poses unique difficulties, including disfluencies and unmarked boundaries between sentence-like units. Previous work has shown that prosody can help with parsing disfluent speech (Tran et al. 2018), but has assumed that the input to the parser is already segmented into sentence-like units (SUs), which isn’t true in existing speech applications. We investigate how prosody affects a parser that receives an entire dialogue turn as input (a turn-based model), instead of gold standard pre-segmented SUs (an SU-based model). In experiments on the English Switchboard corpus, we find that when using transcripts alone, the turn-based model has trouble segmenting SUs, leading to worse parse performance than the SU-based model. However, prosody can effectively replace gold standard SU boundaries: with prosody, the turn-based model performs as well as the SU-based model (91.38 vs. 91.06 F1 score, respectively), despite performing two tasks (SU segmentation and parsing) rather than one (parsing alone). Analysis shows that pitch and intensity features are the most important for this corpus, since they allow the model to correctly distinguish an SU boundary from a speech disfluency – a distinction that the model otherwise struggles to make.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_80", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_80.flac", "type": "audio", "duration": 563.061}, {"from": "Assistant", "value": "We introduce VoxPopuli, a large-scale multilingual corpus providing 400K hours of unlabeled speech data in 23 languages. It is the largest open data to date for unsupervised representation learning as well as semi-supervised learning. VoxPopuli also contains 1.8K hours of transcribed speeches in 15 languages and their aligned oral interpretations into 15 target languages totaling 17.3K hours. We provide speech recognition (ASR) baselines and validate the versatility of VoxPopuli unlabeled data in semi-supervised ASR and speech-to-text translation under challenging out-of-domain settings. The corpus is available athttps://github.com/facebookresearch/voxpopuli.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_81", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_81.flac", "type": "audio", "duration": 663.046}, {"from": "Assistant", "value": "Auditing NLP systems for computational harms like surfacing stereotypes is an elusive goal. Several recent efforts have focused on benchmark datasets consisting of pairs of contrastive sentences, which are often accompanied by metrics that aggregate an NLP system’s behavior on these pairs into measurements of harms. We examine four such benchmarks constructed for two NLP tasks: language modeling and coreference resolution. We apply a measurement modeling lens—originating from the social sciences—to inventory a range of pitfalls that threaten these benchmarks’ validity as measurement models for stereotyping. We find that these benchmarks frequently lack clear articulations of what is being measured, and we highlight a range of ambiguities and unstated assumptions that affect how these benchmarks conceptualize and operationalize stereotyping.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_82", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_82.flac", "type": "audio", "duration": 650.043}, {"from": "Assistant", "value": "Knowledge Graph (KG) completion research usually focuses on densely connected benchmark datasets that are not representative of real KGs. We curate two KG datasets that include biomedical and encyclopedic knowledge and use an existing commonsense KG dataset to explore KG completion in the more realistic setting where dense connectivity is not guaranteed. We develop a deep convolutional network that utilizes textual entity representations and demonstrate that our model outperforms recent KG completion methods in this challenging setting. We find that our model’s performance improvements stem primarily from its robustness to sparsity. We then distill the knowledge from the convolutional network into a student network that re-ranks promising candidate entities. This re-ranking stage leads to further improvements in performance and demonstrates the effectiveness of entity re-ranking for KG completion.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_83", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_83.flac", "type": "audio", "duration": 710.484}, {"from": "Assistant", "value": "Computing precise evidences, namely minimal sets of sentences that support or refute a given claim, rather than larger evidences is crucial in fact verification (FV), since larger evidences may contain conflicting pieces some of which support the claim while the other refute, thereby misleading FV. Despite being important, precise evidences are rarely studied by existing methods for FV. It is challenging to find precise evidences due to a large search space with lots of local optimums. Inspired by the strong exploration ability of the deep Q-learning network (DQN), we propose a DQN-based approach to retrieval of precise evidences. In addition, to tackle the label bias on Q-values computed by DQN, we design a post-processing strategy which seeks best thresholds for determining the true labels of computed evidences. Experimental results confirm the effectiveness of DQN in computing precise evidences and demonstrate improvements in achieving accurate claim verification.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_84", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_84.flac", "type": "audio", "duration": 680.136}, {"from": "Assistant", "value": "In selective prediction, a classifier is allowed to abstain from making predictions on low-confidence examples. Though this setting is interesting and important, selective prediction has rarely been examined in natural language processing (NLP) tasks. To fill this void in the literature, we study in this paper selective prediction for NLP, comparing different models and confidence estimators. We further propose a simple error regularization trick that improves confidence estimation without substantially increasing the computation budget. We show that recent pre-trained transformer models simultaneously improve both model accuracy and confidence estimation effectiveness. We also find that our proposed regularization improves confidence estimation and can be applied to other relevant scenarios, such as using classifier cascades for accuracy–efficiency trade-offs. Source code for this paper can be found athttps://github.com/castorini/transformers-selective.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_85", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_85.flac", "type": "audio", "duration": 714.06}, {"from": "Assistant", "value": "Deployed real-world machine learning applications are often subject to uncontrolled and even potentially malicious inputs. Those out-of-domain inputs can lead to unpredictable outputs and sometimes catastrophic safety issues. Prior studies on out-of-domain detection require in-domain task labels and are limited to supervised classification scenarios. Our work tackles the problem of detecting out-of-domain samples with only unsupervised in-domain data. We utilize the latent representations of pre-trained transformers and propose a simple yet effective method to transform features across all layers to construct out-of-domain detectors efficiently. Two domain-specific fine-tuning approaches are further proposed to boost detection accuracy. Our empirical evaluations of related methods on two datasets validate that our method greatly improves out-of-domain detection ability in a more general scenario.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_86", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_86.flac", "type": "audio", "duration": 841.05}, {"from": "Assistant", "value": "The advent of large pre-trained language models has given rise to rapid progress in the field of Natural Language Processing (NLP). While the performance of these models on standard benchmarks has scaled with size, compression techniques such as knowledge distillation have been key in making them practical. We present MATE-KD, a novel text-based adversarial training algorithm which improves the performance of knowledge distillation. MATE-KD first trains a masked language model-based generator to perturb text by maximizing the divergence between teacher and student logits. Then using knowledge distillation a student is trained on both the original and the perturbed training samples. We evaluate our algorithm, using BERT-based models, on the GLUE benchmark and demonstrate that MATE-KD outperforms competitive adversarial learning and data augmentation baselines. On the GLUE test set our 6 layer RoBERTa based model outperforms BERT-large.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_87", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_87.flac", "type": "audio", "duration": 485.645}, {"from": "Assistant", "value": "Language model fine-tuning is essential for modern natural language processing, but is computationally expensive and time-consuming. Further, the effectiveness of fine-tuning is limited by the inclusion of training examples that negatively affect performance. Here we present a general fine-tuning method that we call information gain filtration for improving the overall training efficiency and final performance of language model fine-tuning. We define the information gain of an example as the improvement on a validation metric after training on that example. A secondary learner is then trained to approximate this quantity. During fine-tuning, this learner selects informative examples and skips uninformative ones. We show that our method has consistent improvement across datasets, fine-tuning tasks, and language model architectures. For example, we achieve a median perplexity of 54.0 on a books dataset compared to 57.3 for standard fine-tuning. We present statistical evidence that offers insight into the improvements of our method over standard fine-tuning. The generality of our method leads us to propose a new paradigm for language model fine-tuning — we encourage researchers to release pretrained secondary learners on common corpora to promote efficient and effective fine-tuning, thereby improving the performance and reducing the overall energy footprint of language model fine-tuning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_88", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_88.flac", "type": "audio", "duration": 758.735}, {"from": "Assistant", "value": "Text simplification reduces the language complexity of professional content for accessibility purposes. End-to-end neural network models have been widely adopted to directly generate the simplified version of input text, usually functioning as a blackbox. We show that text simplification can be decomposed into a compact pipeline of tasks to ensure the transparency and explainability of the process. The first two steps in this pipeline are often neglected: 1) to predict whether a given piece of text needs to be simplified, and 2) if yes, to identify complex parts of the text. The two tasks can be solved separately using either lexical or deep learning methods, or solved jointly. Simply applying explainable complexity prediction as a preliminary step, the out-of-sample text simplification performance of the state-of-the-art, black-box simplification models can be improved by a large margin.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_89", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_89.flac", "type": "audio", "duration": 684.06}, {"from": "Assistant", "value": "Retrieving relevant contexts from a large corpus is a crucial step for tasks such as open-domain question answering and fact checking. Although neural retrieval outperforms traditional methods like tf-idf and BM25, its performance degrades considerably when applied to out-of-domain data. Driven by the question of whether a neural retrieval model can be _universal_ and perform robustly on a wide variety of problems, we propose a multi-task trained model. Our approach not only outperforms previous methods in the few-shot setting, but also rivals specialised neural retrievers, even when in-domain training data is abundant. With the help of our retriever, we improve existing models for downstream tasks and closely match or improve the state of the art on multiple benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_90", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_90.flac", "type": "audio", "duration": 612.775}, {"from": "Assistant", "value": "NLP is currently dominated by language models like RoBERTa which are pretrained on billions of words. But what exact knowledge or skills do Transformer LMs learn from large-scale pretraining that they cannot learn from less data? To explore this question, we adopt five styles of evaluation: classifier probing, information-theoretic probing, unsupervised relative acceptability judgments, unsupervised language model knowledge probing, and fine-tuning on NLU tasks. We then draw learning curves that track the growth of these different measures of model ability with respect to pretraining data volume using the MiniBERTas, a group of RoBERTa models pretrained on 1M, 10M, 100M and 1B words. We find that these LMs require only about 10M to 100M words to learn to reliably encode most syntactic and semantic features we test. They need a much larger quantity of data in order to acquire enough commonsense knowledge and other skills required to master typical downstream NLU tasks. The results suggest that, while the ability to encode linguistic features is almost certainly necessary for language understanding, it is likely that other, unidentified, forms of knowledge are the major drivers of recent improvements in language understanding among large pretrained models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_91", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_91.flac", "type": "audio", "duration": 791.057}, {"from": "Assistant", "value": "In Neural Machine Translation (and, more generally, conditional language modeling), the generation of a target token is influenced by two types of context: the source and the prefix of the target sequence. While many attempts to understand the internal workings of NMT models have been made, none of them explicitly evaluates relative source and target contributions to a generation decision. We argue that this relative contribution can be evaluated by adopting a variant of Layerwise Relevance Propagation (LRP). Its underlying ‘conservation principle’ makes relevance propagation unique: differently from other methods, it evaluates not an abstract quantity reflecting token importance, but the proportion of each token’s influence. We extend LRP to the Transformer and conduct an analysis of NMT models which explicitly evaluates the source and target relative contributions to the generation process. We analyze changes in these contributions when conditioning on different types of prefixes, when varying the training objective or the amount of training data, and during the training process. We find that models trained with more data tend to rely on source information more and to have more sharp token contributions; the training process is non-monotonic with several stages of different nature.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_92", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_92.flac", "type": "audio", "duration": 863.062}, {"from": "Assistant", "value": "Recent years have seen numerous NLP datasets introduced to evaluate the performance of fine-tuned models on natural language understanding tasks. Recent results from large pretrained models, though, show that many of these datasets are largely saturated and unlikely to be able to detect further progress. What kind of datasets are still effective at discriminating among strong models, and what kind of datasets should we expect to be able to detect future improvements? To measure this uniformly across datasets, we draw on Item Response Theory and evaluate 29 datasets using predictions from 18 pretrained Transformer models on individual test examples. We find that Quoref, HellaSwag, and MC-TACO are best suited for distinguishing among state-of-the-art models, while SNLI, MNLI, and CommitmentBank seem to be saturated for current strong models. We also observe span selection task format, which is used for QA datasets like QAMR or SQuAD2.0, is effective in differentiating between strong and weak models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_93", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_93.flac", "type": "audio", "duration": 774.06}, {"from": "Assistant", "value": "A growing body of literature has focused on detailing the linguistic knowledge embedded in large, pretrained language models. Existing work has shown that non-linguistic biases in models can drive model behavior away from linguistic generalizations. We hypothesized that competing linguistic processes within a language, rather than just non-linguistic model biases, could obscure underlying linguistic knowledge. We tested this claim by exploring a single phenomenon in four languages: English, Chinese, Spanish, and Italian. While human behavior has been found to be similar across languages, we find cross-linguistic variation in model behavior. We show that competing processes in a language act as constraints on model behavior and demonstrate that targeted fine-tuning can re-weight the learned constraints, uncovering otherwise dormant linguistic knowledge in models. Our results suggest that models need to learn both the linguistic constraints in a language and their relative ranking, with mismatches in either producing non-human-like behavior.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_94", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_94.flac", "type": "audio", "duration": 521.056}, {"from": "Assistant", "value": "Interpretability is an important aspect of the trustworthiness of a model’s predictions. Transformer’s predictions are widely explained by the attention weights, i.e., a probability distribution generated at its self-attention unit (head). Current empirical studies provide shreds of evidence that attention weights are not explanations by proving that they are not unique. A recent study showed theoretical justifications to this observation by proving the non-identifiability of attention weights. For a given input to a head and its output, if the attention weights generated in it are unique, we call the weights identifiable. In this work, we provide deeper theoretical analysis and empirical observations on the identifiability of attention weights. Ignored in the previous works, we find the attention weights are more identifiable than we currently perceive by uncovering the hidden role of the key vector. However, the weights are still prone to be non-unique attentions that make them unfit for interpretation. To tackle this issue, we provide a variant of the encoder layer that decouples the relationship between key and value vector and provides identifiable weights up to the desired length of the input. We prove the applicability of such variations by providing empirical justifications on varied text classification tasks. The implementations are available athttps://github.com/declare-lab/identifiable-transformers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_95", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_95.flac", "type": "audio", "duration": 609.454}, {"from": "Assistant", "value": "Natural Language Generation (NLG) is a key component in a task-oriented dialogue system, which converts the structured meaning representation (MR) to the natural language. For large-scale conversational systems, where it is common to have over hundreds of intents and thousands of slots, neither template-based approaches nor model-based approaches are scalable. Recently, neural NLGs started leveraging transfer learning and showed promising results in few-shot settings. This paper proposes AugNLG, a novel data augmentation approach that combines a self-trained neural retrieval model with a few-shot learned NLU model, to automatically create MR-to-Text data from open-domain texts. The proposed system mostly outperforms the state-of-the-art methods on the FewshotWOZ data in both BLEU and Slot Error Rate. We further confirm improved results on the FewshotSGD data and provide comprehensive analysis results on key components of our system. Our code and data are available athttps://github.com/XinnuoXu/AugNLG.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_96", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_96.flac", "type": "audio", "duration": 709.045}, {"from": "Assistant", "value": "In this paper we implement and compare 7 different data augmentation strategies for the task of automatic scoring of children’s ability to understand others’ thoughts, feelings, and desires (or “mindreading”). We recruit in-domain experts to re-annotate augmented samples and determine to what extent each strategy preserves the original rating. We also carry out multiple experiments to measure how much each augmentation strategy improves the performance of automatic scoring systems. To determine the capabilities of automatic systems to generalize to unseen data, we create UK-MIND-20 - a new corpus of children’s performance on tests of mindreading, consisting of 10,320 question-answer pairs. We obtain a new state-of-the-art performance on the MIND-CA corpus, improving macro-F1-score by 6 points. Results indicate that both the number of training examples and the quality of the augmentation strategies affect the performance of the systems. The task-specific augmentations generally outperform task-agnostic augmentations. Automatic augmentations based on vectors (GloVe, FastText) perform the worst. We find that systems trained on MIND-CA generalize well to UK-MIND-20. We demonstrate that data augmentation strategies also improve the performance on unseen data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_97", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_97.flac", "type": "audio", "duration": 622.782}, {"from": "Assistant", "value": "Reply suggestion models help users process emails and chats faster. Previous work only studies English reply suggestion. Instead, we present MRS, a multilingual reply suggestion dataset with ten languages. MRS can be used to compare two families of models: 1) retrieval models that select the reply from a fixed set and 2) generation models that produce the reply from scratch. Therefore, MRS complements existing cross-lingual generalization benchmarks that focus on classification and sequence labeling tasks. We build a generation model and a retrieval model as baselines for MRS. The two models have different strengths in the monolingual setting, and they require different strategies to generalize across languages. MRS is publicly available athttps://github.com/zhangmozhi/mrs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_98", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_98.flac", "type": "audio", "duration": 714.687}, {"from": "Assistant", "value": "Crowdsourcing is widely used to create data for common natural language understanding tasks. Despite the importance of these datasets for measuring and refining model understanding of language, there has been little focus on the crowdsourcing methods used for collecting the datasets. In this paper, we compare the efficacy of interventions that have been proposed in prior work as ways of improving data quality. We use multiple-choice question answering as a testbed and run a randomized trial by assigning crowdworkers to write questions under one of four different data collection protocols. We find that asking workers to write explanations for their examples is an ineffective stand-alone strategy for boosting NLU example difficulty. However, we find that training crowdworkers, and then using an iterative process of collecting data, sending feedback, and qualifying workers based on expert judgments is an effective means of collecting challenging data. But using crowdsourced, instead of expert judgments, to qualify workers and send feedback does not prove to be effective. We observe that the data from the iterative protocol with expert assessments is more challenging by several measures. Notably, the human–model gap on the unanimous agreement portion of this data is, on average, twice as large as the gap for the baseline protocol data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_99", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_99.flac", "type": "audio", "duration": 674.052}, {"from": "Assistant", "value": "Ideology of legislators is typically estimated by ideal point models from historical records of votes. It represents legislators and legislation as points in a latent space and shows promising results for modeling voting behavior. However, it fails to capture more specific attitudes of legislators toward emerging issues and is unable to model newly-elected legislators without voting histories. In order to mitigate these two problems, we explore to incorporate both voting behavior and public statements on Twitter to jointly model legislators. In addition, we propose a novel task, namely hashtag usage prediction to model the ideology of legislators on Twitter. In practice, we construct a heterogeneous graph for the legislative context and use relational graph neural networks to learn the representation of legislators with the guidance of historical records of their voting and hashtag usage. Experiment results indicate that our model yields significant improvements for the task of roll call vote prediction. Further analysis further demonstrates that legislator representation we learned captures nuances in statements.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_100", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_100.flac", "type": "audio", "duration": 807.979}, {"from": "Assistant", "value": "Languages are dynamic systems: word usage may change over time, reflecting various societal factors. However, all languages do not evolve identically: the impact of an event, the influence of a trend or thinking, can differ between communities. In this paper, we propose to track these divergences by comparing the evolution of a word and its translation across two languages. We investigate several methods of building time-varying and bilingual word embeddings, using contextualised and non-contextualised embeddings. We propose a set of scenarios to characterize semantic divergence across two languages, along with a setup to differentiate them in a bilingual corpus. We evaluate the different methods by generating a corpus of synthetic semantic change across two languages, English and French, before applying them to newspaper corpora to detect bilingual semantic divergence and provide qualitative insight for the task. We conclude that BERT embeddings coupled with a clustering step lead to the best performance on synthetic corpora; however, the performance of CBOW embeddings is very competitive and more adapted to an exploratory analysis on a large corpus.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_506", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_506.flac", "type": "audio", "duration": 658.053}, {"from": "Assistant", "value": "Recent research on cross-lingual word embeddings has been dominated by unsupervised mapping approaches that align monolingual embeddings. Such methods critically rely on those embeddings having a similar structure, but it was recently shown that the separate training in different languages causes departures from this assumption. In this paper, we propose an alternative approach that does not have this limitation, while requiring a weak seed dictionary (e.g., a list of identical words) as the only form of supervision. Rather than aligning two fixed embedding spaces, our method works by fixing the target language embeddings, and learning a new set of embeddings for the source language that are aligned with them. To that end, we use an extension of skip-gram that leverages translated context words as anchor points, and incorporates self-learning and iterative restarts to reduce the dependency on the initial dictionary. Our approach outperforms conventional mapping methods on bilingual lexicon induction, and obtains competitive results in the downstream XNLI task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_507", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_507.flac", "type": "audio", "duration": 720.051}, {"from": "Assistant", "value": "We show that margin-based bitext mining in a multilingual sentence space can be successfully scaled to operate on monolingual corpora of billions of sentences. We use 32 snapshots of a curated common crawl corpus (Wenzel et al, 2019) totaling 71 billion unique sentences. Using one unified approach for 90 languages, we were able to mine 10.8 billion parallel sentences, out of which only 2.9 billions are aligned with English. We illustrate the capability of our scalable mining system to create high quality training sets from one language to any other by training hundreds of different machine translation models and evaluating them on the many-to-many TED benchmark. Further, we evaluate on competitive translation benchmarks such as WMT and WAT. Using only mined bitext, we set a new state of the art for a single system on the WMT’19 test set for English-German/Russian/Chinese. In particular, our English/German and English/Russian systems outperform the best single ones by over 4 BLEU points and are on par with best WMT’19 systems, which train on the WMT training data and augment it with backtranslation. We also achieve excellent results for distant languages pairs like Russian/Japanese, outperforming the best submission at the 2020 WAT workshop. All of the mined bitext will be freely available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_508", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_508.flac", "type": "audio", "duration": 695.04}, {"from": "Assistant", "value": "Despite transformers’ impressive accuracy, their computational cost is often prohibitive to use with limited computational resources. Most previous approaches to improve inference efficiency require a separate model for each possible computational budget. In this paper, we extend PoWER-BERT (Goyal et al., 2020) and propose Length-Adaptive Transformer that can be used for various inference scenarios after one-shot training. We train a transformer with LengthDrop, a structural variant of dropout, which stochastically determines a sequence length at each layer. We then conduct a multi-objective evolutionary search to find a length configuration that maximizes the accuracy and minimizes the efficiency metric under any given computational budget. Additionally, we significantly extend the applicability of PoWER-BERT beyond sequence-level classification into token-level classification with Drop-and-Restore process that drops word-vectors temporarily in intermediate layers and restores at the last layer if necessary. We empirically verify the utility of the proposed approach by demonstrating the superior accuracy-efficiency trade-off under various setups, including span-based question answering and text classification. Code is available athttps://github.com/clovaai/lengthadaptive-transformer.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_509", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_509.flac", "type": "audio", "duration": 686.057}, {"from": "Assistant", "value": "Transformer-based pre-trained language models like BERT, though powerful in many tasks, are expensive in both memory and computation, due to their large number of parameters. Previous works show that some parameters in these models can be pruned away without severe accuracy drop. However, these redundant features contribute to a comprehensive understanding of the training data and removing them weakens the model’s representation ability. In this paper, we propose GhostBERT, which generates more features with very cheap operations from the remaining features. In this way, GhostBERT has similar memory and computational cost as the pruned model, but enjoys much larger representation power. The proposed ghost module can also be applied to unpruned BERT models to enhance their performance with negligible additional parameters and computation. Empirical results on the GLUE benchmark on three backbone models (i.e., BERT, RoBERTa and ELECTRA) verify the efficacy of our proposed method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_510", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_510.flac", "type": "audio", "duration": 716.057}, {"from": "Assistant", "value": "The Lottery Ticket Hypothesis suggests that an over-parametrized network consists of ”lottery tickets”, and training a certain collection of them (i.e., a subnetwork) can match the performance of the full model. In this paper, we study such a collection of tickets, which is referred to as ”winning tickets”, in extremely over-parametrized models, e.g., pre-trained language models. We observe that at certain compression ratios, the generalization performance of the winning tickets can not only match but also exceed that of the full model. In particular, we observe a phase transition phenomenon: As the compression ratio increases, generalization performance of the winning tickets first improves then deteriorates after a certain threshold. We refer to the tickets on the threshold as ”super tickets”. We further show that the phase transition is task and model dependent — as the model size becomes larger and the training data set becomes smaller, the transition becomes more pronounced. Our experiments on the GLUE benchmark show that the super tickets improve single task fine-tuning by 0.9 points on BERT-base and 1.0 points on BERT-large, in terms of task-average score. We also demonstrate that adaptively sharing the super tickets across tasks benefits multi-task learning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_511", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_511.flac", "type": "audio", "duration": 720.051}, {"from": "Assistant", "value": "Learning disentangled representations of textual data is essential for many natural language tasks such as fair classification, style transfer and sentence generation, among others. The existent dominant approaches in the context of text data either rely on training an adversary (discriminator) that aims at making attribute values difficult to be inferred from the latent code or rely on minimising variational bounds of the mutual information between latent code and the value attribute. However, the available methods suffer of the impossibility to provide a fine-grained control of the degree (or force) of disentanglement. In contrast to adversarial methods, which are remarkably simple, although the adversary seems to be performing perfectly well during the training phase, after it is completed a fair amount of information about the undesired attribute still remains. This paper introduces a novel variational upper bound to the mutual information between an attribute and the latent code of an encoder. Our bound aims at controlling the approximation error via the Renyi’s divergence, leading to both better disentangled representations and in particular, a precise control of the desirable degree of disentanglement than state-of-the-art methods proposed for textual data. Furthermore, it does not suffer from the degeneracy of other losses in multi-class scenarios. We show the superiority of this method on fair classification and on textual style transfer tasks. Additionally, we provide new insights illustrating various trade-offs in style transfer when attempting to learn disentangled representations and quality of the generated sentence.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_512", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_512.flac", "type": "audio", "duration": 712.063}, {"from": "Assistant", "value": "Beam search is a go-to strategy for decoding neural sequence models. The algorithm can naturally be viewed as a subset optimization problem, albeit one where the corresponding set function does not reflect interactions between candidates. Empirically, this leads to sets often exhibiting high overlap, e.g., strings may differ by only a single word. Yet in use-cases that call for multiple solutions, a diverse or representative set is often desired. To address this issue, we propose a reformulation of beam search, which we call determinantal beam search. Determinantal beam search has a natural relationship to determinantal point processes (DPPs), models over sets that inherently encode intra-set interactions. By posing iterations in beam search as a series of subdeterminant maximization problems, we can turn the algorithm into a diverse subset selection process. In a case study, we use the string subsequence kernel to explicitly encourage n-gram coverage in text generated from a sequence model. We observe that our algorithm offers competitive performance against other diverse set generation strategies in the context of language generation, while providing a more general approach to optimizing for diversity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_513", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_513.flac", "type": "audio", "duration": 720.051}, {"from": "Assistant", "value": "Graph convolutional network (GCN) has become popular in various natural language processing (NLP) tasks with its superiority in long-term and non-consecutive word interactions. However, existing single-hop graph reasoning in GCN may miss some important non-consecutive dependencies. In this study, we define the spectral graph convolutional network with the high-order dynamic Chebyshev approximation (HDGCN), which augments the multi-hop graph reasoning by fusing messages aggregated from direct and long-term dependencies into one convolutional layer. To alleviate the over-smoothing in high-order Chebyshev approximation, a multi-vote-based cross-attention (MVCAttn) with linear computation complexity is also proposed. The empirical results on four transductive and inductive NLP tasks and the ablation study verify the efficacy of the proposed model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_514", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_514.flac", "type": "audio", "duration": 721.003}, {"from": "Assistant", "value": "Typing every character in a text message may require more time or effort than strictly necessary. Skipping spaces or other characters may be able to speed input and reduce a user’s physical input effort. This can be particularly important for people with motor impairments. In a large crowdsourced study, we found workers frequently abbreviated text by omitting mid-word vowels. We designed a recognizer optimized for expanding noisy abbreviated input where users often omit spaces and mid-word vowels. We show using neural language models for selecting conversational-style training text and for rescoring the recognizer’s n-best sentences improved accuracy. On noisy touchscreen data collected from hundreds of users, we found accurate abbreviated input was possible even if a third of characters was omitted. Finally, in a study where users had to dwell for a second on each key, sentence abbreviated input was competitive with a conventional keyboard with word predictions. After practice, users wrote abbreviated sentences at 9.6 words-per-minute versus word input at 9.9 words-per-minute.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_515", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_515.flac", "type": "audio", "duration": 629.052}, {"from": "Assistant", "value": "Behavior of deep neural networks can be inconsistent between different versions. Regressions during model update are a common cause of concern that often over-weigh the benefits in accuracy or efficiency gain. This work focuses on quantifying, reducing and analyzing regression errors in the NLP model updates. Using negative flip rate as regression measure, we show that regression has a prevalent presence across tasks in the GLUE benchmark. We formulate the regression-free model updates into a constrained optimization problem, and further reduce it into a relaxed form which can be approximately optimized through knowledge distillation training method. We empirically analyze how model ensemble reduces regression. Finally, we conduct CheckList behavioral testing to understand the distribution of regressions across linguistic phenomena, and the efficacy of ensemble and distillation methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_516", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_516.flac", "type": "audio", "duration": 682.048}, {"from": "Assistant", "value": "Propaganda can be defined as a form of communication that aims to influence the opinions or the actions of people towards a specific goal; this is achieved by means of well-defined rhetorical and psychological devices. Propaganda, in the form we know it today, can be dated back to the beginning of the 17th century. However, it is with the advent of the Internet and the social media that propaganda has started to spread on a much larger scale than before, thus becoming major societal and political issue. Nowadays, a large fraction of propaganda in social media is multimodal, mixing textual with visual content. With this in mind, here we propose a new multi-label multimodal task: detecting the type of propaganda techniques used in memes. We further create and release a new corpus of 950 memes, carefully annotated with 22 propaganda techniques, which can appear in the text, in the image, or in both. Our analysis of the corpus shows that understanding both modalities together is essential for detecting these techniques. This is further confirmed in our experiments with several state-of-the-art multimodal models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_517", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_517.flac", "type": "audio", "duration": 488.043}, {"from": "Assistant", "value": "In adversarial data collection (ADC), a human workforce interacts with a model in real time, attempting to produce examples that elicit incorrect predictions. Researchers hope that models trained on these more challenging datasets will rely less on superficial patterns, and thus be less brittle. However, despite ADC’s intuitive appeal, it remains unclear when training on adversarial datasets produces more robust models. In this paper, we conduct a large-scale controlled study focused on question answering, assigning workers at random to compose questions either (i) adversarially (with a model in the loop); or (ii) in the standard fashion (without a model). Across a variety of models and datasets, we find that models trained on adversarial data usually perform better on other adversarial datasets but worse on a diverse collection of out-of-domain evaluation sets. Finally, we provide a qualitative analysis of adversarial (vs standard) data, identifying key differences and offering guidance for future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_518", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_518.flac", "type": "audio", "duration": 719.052}, {"from": "Assistant", "value": "Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019). However, current phrase retrieval models heavily depend on sparse representations and still underperform retriever-reader approaches. In this work, we show for the first time that we can learn dense representations of phrases alone that achieve much stronger performance in open-domain QA. We present an effective method to learn phrase representations from the supervision of reading comprehension tasks, coupled with novel negative sampling methods. We also propose a query-side fine-tuning strategy, which can support transfer learning and reduce the discrepancy between training and inference. On five popular open-domain QA datasets, our model DensePhrases improves over previous phrase retrieval models by 15%-25% absolute accuracy and matches the performance of state-of-the-art retriever-reader models. Our model is easy to parallelize due to pure dense representations and processes more than 10 questions per second on CPUs. Finally, we directly use our pre-indexed dense phrase representations for two slot filling tasks, showing the promise of utilizing DensePhrases as a dense knowledge base for downstream tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_519", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_519.flac", "type": "audio", "duration": 695.043}, {"from": "Assistant", "value": "Recent work on training neural retrievers for open-domain question answering (OpenQA) has employed both supervised and unsupervised approaches. However, it remains unclear how unsupervised and supervised methods can be used most effectively for neural retrievers. In this work, we systematically study retriever pre-training. We first propose an approach of unsupervised pre-training with the Inverse Cloze Task and masked salient spans, followed by supervised finetuning using question-context pairs. This approach leads to absolute gains of 2+ points over the previous best result in the top-20 retrieval accuracy on Natural Questions and TriviaQA datasets. We next explore two approaches for end-to-end training of the reader and retriever components in OpenQA models, which differ in the manner the reader ingests the retrieved documents. Our experiments demonstrate the effectiveness of these approaches as we obtain state-of-the-art results. On the Natural Questions dataset, we obtain a top-20 retrieval accuracy of 84%, an improvement of 5 points over the recent DPR model. We also achieve good results on answer extraction, outperforming recent models like REALM and RAG by 3+ points.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_520", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_520.flac", "type": "audio", "duration": 708.046}, {"from": "Assistant", "value": "Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by providing temporal scopes (start and end times) on each edge in the KG. While Question Answering over KG (KGQA) has received some attention from the research community, QA over Temporal KGs (Temporal KGQA) is a relatively unexplored area. Lack of broad coverage datasets has been another factor limiting progress in this area. We address this challenge by presenting CRONQUESTIONS, the largest known Temporal KGQA dataset, clearly stratified into buckets of structural complexity. CRONQUESTIONS expands the only known previous dataset by a factor of 340x. We find that various state-of-the-art KGQA methods fall far short of the desired performance on this new dataset. In response, we also propose CRONKGQA, a transformer-based solution that exploits recent advances in Temporal KG embeddings, and achieves performance superior to all baselines, with an increase of 120% in accuracy over the next best performing method. Through extensive experiments, we give detailed insights into the workings of CRONKGQA, as well as situations where significant further improvements appear possible. In addition to the dataset, we have released our code as well.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_521", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_521.flac", "type": "audio", "duration": 621.056}, {"from": "Assistant", "value": "Although automated metrics are commonly used to evaluate NLG systems, they often correlate poorly with human judgements. Newer metrics such as BERTScore have addressed many weaknesses in prior metrics such as BLEU and ROUGE, which rely on n-gram matching. These newer methods, however, are still limited in that they do not consider the generation context, so they cannot properly reward generated text that is correct but deviates from the given reference. In this paper, we propose Language Model Augmented Relevance Score (MARS), a new context-aware metric for NLG evaluation. MARS leverages off-the-shelf language models, guided by reinforcement learning, to create augmented references that consider both the generation context and available human references, which are then used as additional references to score generated text. Compared with seven existing metrics in three common NLG tasks, MARS not only achieves higher correlation with human reference judgements, but also differentiates well-formed candidates from adversarial samples to a larger degree.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_522", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_522.flac", "type": "audio", "duration": 570.453}, {"from": "Assistant", "value": "Despite recent advances in natural language generation, it remains challenging to control attributes of generated text. We propose DExperts: Decoding-time Experts, a decoding-time method for controlled text generation that combines a pretrained language model with “expert” LMs and/or “anti-expert” LMs in a product of experts. Intuitively, under the ensemble, tokens only get high probability if they are considered likely by the experts, and unlikely by the anti-experts. We apply DExperts to language detoxification and sentiment-controlled generation, where we outperform existing controllable generation methods on both automatic and human evaluations. Moreover, because DExperts operates only on the output of the pretrained LM, it is effective with (anti-)experts of smaller size, including when operating on GPT-3. Our work highlights the promise of tuning small LMs on text with (un)desirable attributes for efficient decoding-time steering.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_523", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_523.flac", "type": "audio", "duration": 740.043}, {"from": "Assistant", "value": "While counterfactual examples are useful for analysis and training of NLP models, current generation methods either rely on manual labor to create very few counterfactuals, or only instantiate limited types of perturbations such as paraphrases or word substitutions. We present Polyjuice, a general-purpose counterfactual generator that allows for control over perturbation types and locations, trained by finetuning GPT-2 on multiple datasets of paired sentences. We show that Polyjuice produces diverse sets of realistic counterfactuals, which in turn are useful in various distinct applications: improving training and evaluation on three different tasks (with around 70% less annotation effort than manual generation), augmenting state-of-the-art explanation techniques, and supporting systematic counterfactual error analysis by revealing behaviors easily missed by human experts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_524", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_524.flac", "type": "audio", "duration": 646.374}, {"from": "Assistant", "value": "Generating metaphors is a difficult task as it requires understanding nuanced relationships between abstract concepts. In this paper, we aim to generate a metaphoric sentence given a literal expression by replacing relevant verbs. Guided by conceptual metaphor theory, we propose to control the generation process by encoding conceptual mappings between cognitive domains to generate meaningful metaphoric expressions. To achieve this, we develop two methods: 1) using FrameNet-based embeddings to learn mappings between domains and applying them at the lexical level (CM-Lex), and 2) deriving source/target pairs to train a controlled seq-to-seq generation model (CM-BART). We assess our methods through automatic and human evaluation for basic metaphoricity and conceptual metaphor presence. We show that the unsupervised CM-Lex model is competitive with recent deep learning metaphor generation systems, and CM-BART outperforms all other models both in automatic and human evaluations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_525", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_525.flac", "type": "audio", "duration": 713.062}, {"from": "Assistant", "value": "Wet laboratory protocols (WLPs) are critical for conveying reproducible procedures in biological research. They are composed of instructions written in natural language describing the step-wise processing of materials by specific actions. This process flow description for reagents and materials synthesis in WLPs can be captured by material state transfer graphs (MSTGs), which encode global temporal and causal relationships between actions. Here, we propose methods to automatically generate a MSTG for a given protocol by extracting all action relationships across multiple sentences. We also note that previous corpora and methods focused primarily on local intra-sentence relationships between actions and entities and did not address two critical issues: (i) resolution of implicit arguments and (ii) establishing long-range dependencies across sentences. We propose a new model that incrementally learns latent structures and is better suited to resolving inter-sentence relations and implicit arguments. This model draws upon a new corpus WLP-MSTG which was created by extending annotations in the WLP corpora for inter-sentence relations and implicit arguments. Our model achieves an F1 score of 54.53% for temporal and causal relations in protocols from our corpus, which is a significant improvement over previous models - DyGIE++:28.17%; spERT:27.81%. We make our annotated WLP-MSTG corpus available to the research community.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_526", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_526.flac", "type": "audio", "duration": 668.373}, {"from": "Assistant", "value": "Risk prediction is an essential task in financial markets. Merger and Acquisition (M&A) calls provide key insights into the claims made by company executives about the restructuring of the financial firms. Extracting vocal and textual cues from M&A calls can help model the risk associated with such financial activities. To aid the analysis of M&A calls, we curate a dataset of conference call transcripts and their corresponding audio recordings for the time period ranging from 2016 to 2020. We introduce M3ANet, a baseline architecture that takes advantage of the multimodal multi-speaker input to forecast the financial risk associated with the M&A calls. Empirical results prove that the task is challenging, with the pro-posed architecture performing marginally better than strong BERT-based baselines. We release the M3A dataset and benchmark models to motivate future research on this challenging problem domain.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_527", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_527.flac", "type": "audio", "duration": 659.168}, {"from": "Assistant", "value": "To translate large volumes of text in a globally connected world, more and more translators are integrating machine translation (MT) and post-editing (PE) into their translation workflows to generate publishable quality translations. While this process has been shown to save time and reduce errors, the task of translation is changing from mostly text production from scratch to fixing errors within useful but partly incorrect MT output. This is affecting the interface design of translation tools, where better support for text editing tasks is required. Here, we present the first study that investigates the usefulness of mid-air hand gestures in combination with the keyboard (GK) for text editing in PE of MT. Guided by a gesture elicitation study with 14 freelance translators, we develop a prototype supporting mid-air hand gestures for cursor placement, text selection, deletion, and reordering. These gestures combined with the keyboard facilitate all editing types required for PE. An evaluation of the prototype shows that the average editing duration of GK is only slightly slower than the standard mouse and keyboard (MK), even though participants are very familiar with the latter, and relative novices to the former. Furthermore, the qualitative analysis shows positive attitudes towards hand gestures for PE, especially when manipulating single words.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_528", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_528.flac", "type": "audio", "duration": 719.702}, {"from": "Assistant", "value": "Geometry problem solving has attracted much attention in the NLP community recently. The task is challenging as it requires abstract problem understanding and symbolic reasoning with axiomatic knowledge. However, current datasets are either small in scale or not publicly available. Thus, we construct a new large-scale benchmark, Geometry3K, consisting of 3,002 geometry problems with dense annotation in formal language. We further propose a novel geometry solving approach with formal language and symbolic reasoning, called Interpretable Geometry Problem Solver (Inter-GPS). Inter-GPS first parses the problem text and diagram into formal language automatically via rule-based text parsing and neural object detecting, respectively. Unlike implicit learning in existing methods, Inter-GPS incorporates theorem knowledge as conditional rules and performs symbolic reasoning step by step. Also, a theorem predictor is designed to infer the theorem application sequence fed to the symbolic solver for the more efficient and reasonable searching path. Extensive experiments on the Geometry3K and GEOS datasets demonstrate that Inter-GPS achieves significant improvements over existing methods. The project with code and data is available athttps://lupantech.github.io/inter-gps.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_529", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_529.flac", "type": "audio", "duration": 625.045}, {"from": "Assistant", "value": "Structured information is an important knowledge source for automatic verification of factual claims. Nevertheless, the majority of existing research into this task has focused on textual data, and the few recent inquiries into structured data have been for the closed-domain setting where appropriate evidence for each claim is assumed to have already been retrieved. In this paper, we investigate verification over structured data in the open-domain setting, introducing a joint reranking-and-verification model which fuses evidence documents in the verification component. Our open-domain model achieves performance comparable to the closed-domain state-of-the-art on the TabFact dataset, and demonstrates performance gains from the inclusion of multiple tables as well as a significant improvement over a heuristic retrieval baseline.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_530", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_530.flac", "type": "audio", "duration": 652.053}, {"from": "Assistant", "value": "Collecting together microblogs representing opinions about the same topics within the same timeframe is useful to a number of different tasks and practitioners. A major question is how to evaluate the quality of such thematic clusters. Here we create a corpus of microblog clusters from three different domains and time windows and define the task of evaluating thematic coherence. We provide annotation guidelines and human annotations of thematic coherence by journalist experts. We subsequently investigate the efficacy of different automated evaluation metrics for the task. We consider a range of metrics including surface level metrics, ones for topic model coherence and text generation metrics (TGMs). While surface level metrics perform well, outperforming topic coherence metrics, they are not as consistent as TGMs. TGMs are more reliable than all other metrics considered for capturing thematic coherence in microblog clusters due to being less sensitive to the effect of time windows.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_531", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_531.flac", "type": "audio", "duration": 642.055}, {"from": "Assistant", "value": "Monolingual word alignment is important for studying fine-grained editing operations (i.e., deletion, addition, and substitution) in text-to-text generation tasks, such as paraphrase generation, text simplification, neutralizing biased language, etc. In this paper, we present a novel neural semi-Markov CRF alignment model, which unifies word and phrase alignments through variable-length spans. We also create a new benchmark with human annotations that cover four different text genres to evaluate monolingual word alignment models in more realistic settings. Experimental results show that our proposed model outperforms all previous approaches for monolingual word alignment as well as a competitive QA-based baseline, which was previously only applied to bilingual data. Our model demonstrates good generalizability to three out-of-domain datasets and shows great utility in two downstream applications: automatic text simplification and sentence pair classification tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_532", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_532.flac", "type": "audio", "duration": 731.925}, {"from": "Assistant", "value": "Organisations disclose their privacy practices by posting privacy policies on their websites. Even though internet users often care about their digital privacy, they usually do not read privacy policies, since understanding them requires a significant investment of time and effort. Natural language processing has been used to create experimental tools to interpret privacy policies, but there has been a lack of large privacy policy corpora to facilitate the creation of large-scale semi-supervised and unsupervised models to interpret and simplify privacy policies. Thus, we present the PrivaSeer Corpus of 1,005,380 English language website privacy policies collected from the web. The number of unique websites represented in PrivaSeer is about ten times larger than the next largest public collection of web privacy policies, and it surpasses the aggregate of unique websites represented in all other publicly available privacy policy corpora combined. We describe a corpus creation pipeline with stages that include a web crawler, language detection, document classification, duplicate and near-duplicate removal, and content extraction. We employ an unsupervised topic modelling approach to investigate the contents of policy documents in the corpus and discuss the distribution of topics in privacy policies at web scale. We further investigate the relationship between privacy policy domain PageRanks and text features of the privacy policies. Finally, we use the corpus to pretrain PrivBERT, a transformer-based privacy policy language model, and obtain state of the art results on the data practice classification and question answering tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_533", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_533.flac", "type": "audio", "duration": 664.021}, {"from": "Assistant", "value": "Estimating the expected output quality of generation systems is central to NLG. This paper qualifies the notion that automatic metrics are not as good as humans in estimating system-level quality. Statistically, humans are unbiased, high variance estimators, while metrics are biased, low variance estimators. We compare these estimators by their error in pairwise prediction (which generation system is better?) using the bootstrap. Measuring this error is complicated: predictions are evaluated against noisy, human predicted labels instead of the ground truth, and metric predictions fluctuate based on the test sets they were calculated on. By applying a bias-variance-noise decomposition, we adjust this error to a noise-free, infinite test set setting. Our analysis compares the adjusted error of metrics to humans and a derived, perfect segment-level annotator, both of which are unbiased estimators dependent on the number of judgments collected. In MT, we identify two settings where metrics outperform humans due to a statistical advantage in variance: when the number of human judgments used is small, and when the quality difference between compared systems is small.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_534", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_534.flac", "type": "audio", "duration": 706.325}, {"from": "Assistant", "value": "We present InferWiki, a Knowledge Graph Completion (KGC) dataset that improves upon existing benchmarks in inferential ability, assumptions, and patterns. First, each testing sample is predictable with supportive data in the training set. To ensure it, we propose to utilize rule-guided train/test generation, instead of conventional random split. Second, InferWiki initiates the evaluation following the open-world assumption and improves the inferential difficulty of the closed-world assumption, by providing manually annotated negative and unknown triples. Third, we include various inference patterns (e.g., reasoning path length and types) for comprehensive evaluation. In experiments, we curate two settings of InferWiki varying in sizes and structures, and apply the construction process on CoDEx as comparative datasets. The results and empirical analyses demonstrate the necessity and high-quality of InferWiki. Nevertheless, the performance gap among various inferential assumptions and patterns presents the difficulty and inspires future research direction. Our datasets can be found inhttps://github.com/TaoMiner/inferwiki.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_535", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_535.flac", "type": "audio", "duration": 676.053}, {"from": "Assistant", "value": "While online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles. This research gap is due, in part, to the lack of standardized datasets for summarizing online discussions. To address this gap, we design annotation protocols motivated by an issues–viewpoints–assertions framework to crowdsource four new datasets on diverse online conversation forms of news comments, discussion forums, community question answering forums, and email threads. We benchmark state-of-the-art models on our datasets and analyze characteristics associated with the data. To create a comprehensive benchmark, we also evaluate these models on widely-used conversation summarization datasets to establish strong baselines in this domain. Furthermore, we incorporate argument mining through graph construction to directly model the issues, viewpoints, and assertions present in a conversation and filter noisy input, showing comparable or improved results according to automatic and human evaluations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_536", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_536.flac", "type": "audio", "duration": 697.045}, {"from": "Assistant", "value": "A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents. The fact that automatic summarization may produce plausible-sounding yet inaccurate summaries is a major concern that limits its wide application. In this paper we present an approach to address factual consistency in summarization. We first propose an efficient automatic evaluation metric to measure factual consistency; next, we propose a novel learning algorithm that maximizes the proposed metric during model training. Through extensive experiments, we confirm that our method is effective in improving factual consistency and even overall quality of the summaries, as judged by both automatic metrics and human evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_537", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_537.flac", "type": "audio", "duration": 791.04}, {"from": "Assistant", "value": "Recent years have brought about an interest in the challenging task of summarizing conversation threads (meetings, online discussions, etc.). Such summaries help analysis of the long text to quickly catch up with the decisions made and thus improve our work or communication efficiency. To spur research in thread summarization, we have developed an abstractive Email Thread Summarization (EmailSum) dataset, which contains human-annotated short (<30 words) and long (<100 words) summaries of 2,549 email threads (each containing 3 to 10 emails) over a wide variety of topics. We perform a comprehensive empirical study to explore different summarization techniques (including extractive and abstractive methods, single-document and hierarchical models, as well as transfer and semisupervised learning) and conduct human evaluations on both short and long summary generation tasks. Our results reveal the key challenges of current abstractive summarization models in this task, such as understanding the sender’s intent and identifying the roles of sender and receiver. Furthermore, we find that widely used automatic evaluation metrics (ROUGE, BERTScore) are weakly correlated with human judgments on this email thread summarization task. Hence, we emphasize the importance of human evaluation and the development of better metrics by the community.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_538", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_538.flac", "type": "audio", "duration": 719.052}, {"from": "Assistant", "value": "Parallel cross-lingual summarization data is scarce, requiring models to better use the limited available cross-lingual resources. Existing methods to do so often adopt sequence-to-sequence networks with multi-task frameworks. Such approaches apply multiple decoders, each of which is utilized for a specific task. However, these independent decoders share no parameters, hence fail to capture the relationships between the discrete phrases of summaries in different languages, breaking the connections in order to transfer the knowledge of the high-resource languages to low-resource languages. To bridge these connections, we propose a novel Multi-Task framework for Cross-Lingual Abstractive Summarization (MCLAS) in a low-resource setting. Employing one unified decoder to generate the sequential concatenation of monolingual and cross-lingual summaries, MCLAS makes the monolingual summarization task a prerequisite of the CLS task. In this way, the shared decoder learns interactions involving alignments and summary patterns across languages, which encourages attaining knowledge transfer. Experiments on two CLS datasets demonstrate that our model significantly outperforms three baseline models in both low-resource and full-dataset scenarios. Moreover, in-depth analysis on the generated summaries and attention heads verifies that interactions are learned well using MCLAS, which benefits the CLS task under limited parallel resources.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_539", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_539.flac", "type": "audio", "duration": 711.851}, {"from": "Assistant", "value": "Despite the prominence of neural abstractive summarization models, we know little about how they actually form summaries and how to understand where their decisions come from. We propose a two-step method to interpret summarization model decisions. We first analyze the model’s behavior by ablating the full model to categorize each decoder decision into one of several generation modes: roughly, is the model behaving like a language model, is it relying heavily on the input, or is it somewhere in between? After isolating decisions that do depend on the input, we explore interpreting these decisions using several different attribution methods. We compare these techniques based on their ability to select content and reconstruct the model’s predicted token from perturbations of the input, thus revealing whether highlighted attributions are truly important for the generation of the next token. While this machinery can be broadly useful even beyond summarization, we specifically demonstrate its capability to identify phrases the summarization model has memorized and determine where in the training pipeline this memorization happened, as well as study complex generation phenomena like sentence fusion on a per-instance basis.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_540", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_540.flac", "type": "audio", "duration": 712.32}, {"from": "Assistant", "value": "Humans create things for a reason. Ancient people created spears for hunting, knives for cutting meat, pots for preparing food, etc. The prototypical function of a physical artifact is a kind of commonsense knowledge that we rely on to understand natural language. For example, if someone says “She borrowed the book” then you would assume that she intends to read the book, or if someone asks “Can I use your knife?” then you would assume that they need to cut something. In this paper, we introduce a new NLP task of learning the prototypical uses for human-made physical objects. We use frames from FrameNet to represent a set of common functions for objects, and describe a manually annotated data set of physical objects labeled with their prototypical function. We also present experimental results for this task, including BERT-based models that use predictions from masked patterns as well as artifact sense definitions from WordNet and frame definitions from FrameNet.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_541", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_541.flac", "type": "audio", "duration": 613.053}, {"from": "Assistant", "value": "Linguistic probing of pretrained Transformer-based language models (LMs) revealed that they encode a range of syntactic and semantic properties of a language. However, they are still prone to fall back on superficial cues and simple heuristics to solve downstream tasks, rather than leverage deeper linguistic information. In this paper, we target a specific facet of linguistic knowledge, the interplay between verb meaning and argument structure. We investigate whether injecting explicit information on verbs’ semantic-syntactic behaviour improves the performance of pretrained LMs in event extraction tasks, where accurate verb processing is paramount. Concretely, we impart the verb knowledge from curated lexical resources into dedicated adapter modules (verb adapters), allowing it to complement, in downstream tasks, the language knowledge obtained during LM-pretraining. We first demonstrate that injecting verb knowledge leads to performance gains in English event extraction. We then explore the utility of verb adapters for event extraction in other languages: we investigate 1) zero-shot language transfer with multilingual Transformers and 2) transfer via (noisy automatic) translation of English verb-based lexical knowledge. Our results show that the benefits of verb knowledge injection indeed extend to other languages, even when relying on noisily translated lexical knowledge.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_542", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_542.flac", "type": "audio", "duration": 707.048}, {"from": "Assistant", "value": "Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts. Building on prior work on contextualized and dynamic word embeddings, we introduce dynamic contextualized word embeddings that represent words as a function of both linguistic and extralinguistic context. Based on a pretrained language model (PLM), dynamic contextualized word embeddings model time and social space jointly, which makes them attractive for a range of NLP tasks involving semantic variability. We highlight potential application scenarios by means of qualitative and quantitative analyses on four English datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_543", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_543.flac", "type": "audio", "duration": 691.008}, {"from": "Assistant", "value": "While there is a large amount of research in the field of Lexical Semantic Change Detection, only few approaches go beyond a standard benchmark evaluation of existing models. In this paper, we propose a shift of focus from change detection to change discovery, i.e., discovering novel word senses over time from the full corpus vocabulary. By heavily fine-tuning a type-based and a token-based approach on recently published German data, we demonstrate that both models can successfully be applied to discover new words undergoing meaning change. Furthermore, we provide an almost fully automated framework for both evaluation and discovery.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_545", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_545.flac", "type": "audio", "duration": 680.896}, {"from": "Assistant", "value": "In this paper we explore the improvement of intent recognition in conversational systems by the use of meta-knowledge embedded in intent identifiers. Developers often include such knowledge, structure as taxonomies, in the documentation of chatbots. By using neuro-symbolic algorithms to incorporate those taxonomies into embeddings of the output space, we were able to improve accuracy in intent recognition. In datasets with intents and example utterances from 200 professional chatbots, we saw decreases in the equal error rate (EER) in more than 40% of the chatbots in comparison to the baseline of the same algorithm without the meta-knowledge. The meta-knowledge proved also to be effective in detecting out-of-scope utterances, improving the false acceptance rate (FAR) in two thirds of the chatbots, with decreases of 0.05 or more in FAR in almost 40% of the chatbots. When considering only the well-developed workspaces with a high level use of taxonomies, FAR decreased more than 0.05 in 77% of them, and more than 0.1 in 39% of the chatbots.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_546", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_546.flac", "type": "audio", "duration": 683.294}, {"from": "Assistant", "value": "To improve the coherence and knowledge retrieval capabilities of non-task-oriented dialogue systems, recent Transformer-based models aim to integrate fixed background context. This often comes in the form of knowledge graphs, and the integration is done by creating pseudo utterances through paraphrasing knowledge triples, added into the accumulated dialogue context. However, the context length is fixed in these architectures, which restricts how much background or dialogue context can be kept. In this work, we propose a more concise encoding for background context structured in the form of knowledge graphs, by expressing the graph connections through restrictions on the attention weights. The results of our human evaluation show that this encoding reduces space requirements without negative effects on the precision of reproduction of knowledge and perceived consistency. Further, models trained with our proposed context encoding generate dialogues that are judged to be more comprehensive and interesting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_547", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_547.flac", "type": "audio", "duration": 682.62}, {"from": "Assistant", "value": "Emotion Recognition in Conversations (ERC) has gained increasing attention for developing empathetic machines. Recently, many approaches have been devoted to perceiving conversational context by deep learning models. However, these approaches are insufficient in understanding the context due to lacking the ability to extract and integrate emotional clues. In this work, we propose novel Contextual Reasoning Networks (DialogueCRN) to fully understand the conversational context from a cognitive perspective. Inspired by the Cognitive Theory of Emotion, we design multi-turn reasoning modules to extract and integrate emotional clues. The reasoning module iteratively performs an intuitive retrieving process and a conscious reasoning process, which imitates human unique cognitive thinking. Extensive experiments on three public benchmark datasets demonstrate the effectiveness and superiority of the proposed model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_548", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_548.flac", "type": "audio", "duration": 729.076}, {"from": "Assistant", "value": "When collecting annotations and labeled data from humans, a standard practice is to use inter-rater reliability (IRR) as a measure of data goodness (Hallgren, 2012). Metrics such as Krippendorff’s alpha or Cohen’s kappa are typically required to be above a threshold of 0.6 (Landis and Koch, 1977). These absolute thresholds are unreasonable for crowdsourced data from annotators with high cultural and training variances, especially on subjective topics. We present a new alternative to interpreting IRR that is more empirical and contextualized. It is based upon benchmarking IRR against baseline measures in a replication, one of which is a novel cross-replication reliability (xRR) measure based on Cohen’s (1960) kappa. We call this approach the xRR framework. We opensource a replication dataset of 4 million human judgements of facial expressions and analyze it with the proposed framework. We argue this framework can be used to measure the quality of crowdsourced datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_549", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_549.flac", "type": "audio", "duration": 662.047}, {"from": "Assistant", "value": "Everyday conversations require understanding everyday events, which in turn, requires understanding temporal commonsense concepts interwoven with those events. Despite recent progress with massive pre-trained language models (LMs) such as T5 and GPT-3, their capability of temporal reasoning in dialogs remains largely under-explored. In this paper, we present the first study to investigate pre-trained LMs for their temporal reasoning capabilities in dialogs by introducing a new task and a crowd-sourced English challenge set, TimeDial. We formulate TimeDial as a multiple choice cloze task with over 1.1K carefully curated dialogs. Empirical results demonstrate that even the best performing models struggle on this task compared to humans, with 23 absolute points of gap in accuracy. Furthermore, our analysis reveals that the models fail to reason about dialog context correctly; instead, they rely on shallow cues based on existing temporal patterns in context, motivating future research for modeling temporal concepts in text and robust contextual reasoning about them. The dataset is publicly available athttps://github.com/google-research-datasets/timedial.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_550", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_550.flac", "type": "audio", "duration": 686.72}, {"from": "Assistant", "value": "Most words are ambiguous—-i.e., they convey distinct meanings in different contexts—-and even the meanings of unambiguous words are context-dependent. Both phenomena present a challenge for NLP. Recently, the advent of contextualized word embeddings has led to success on tasks involving lexical ambiguity, such as Word Sense Disambiguation. However, there are few tasks that directly evaluate how well these contextualized embeddings accommodate the more continuous, dynamic nature of word meaning—-particularly in a way that matches human intuitions. We introduce RAW-C, a dataset of graded, human relatedness judgments for 112 ambiguous words in context (with 672 sentence pairs total), as well as human estimates of sense dominance. The average inter-annotator agreement (assessed using a leave-one-annotator-out method) was 0.79. We then show that a measure of cosine distance, computed using contextualized embeddings from BERT and ELMo, correlates with human judgments, but that cosine distance also systematically underestimates how similar humans find uses of the same sense of a word to be, and systematically overestimates how similar humans find uses of different-sense homonyms. Finally, we propose a synthesis between psycholinguistic theories of the mental lexicon and computational models of lexical semantics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_551", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_551.flac", "type": "audio", "duration": 711.04}, {"from": "Assistant", "value": "Pre-trained language models (LMs) are currently integral to many natural language processing systems. Although multilingual LMs were also introduced to serve many languages, these have limitations such as being costly at inference time and the size and diversity of non-English data involved in their pre-training. We remedy these issues for a collection of diverse Arabic varieties by introducing two powerful deep bidirectional transformer-based models, ARBERT and MARBERT. To evaluate our models, we also introduce ARLUE, a new benchmark for multi-dialectal Arabic language understanding evaluation. ARLUE is built using 42 datasets targeting six different task clusters, allowing us to offer a series of standardized experiments under rich conditions. When fine-tuned on ARLUE, our models collectively achieve new state-of-the-art results across the majority of tasks (37 out of 48 classification tasks, on the 42 datasets). Our best model acquires the highest ARLUE score (77.40) across all six task clusters, outperforming all other models including XLM-R Large ( 3.4x larger size). Our models are publicly available athttps://github.com/UBC-NLP/marbertand ARLUE will be released through the same repository.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_552", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_552.flac", "type": "audio", "duration": 719.563}, {"from": "Assistant", "value": "If two sentences have the same meaning, it should follow that they are equivalent in their inferential properties, i.e., each sentence should textually entail the other. However, many paraphrase datasets currently in widespread use rely on a sense of paraphrase based on word overlap and syntax. Can we teach them instead to identify paraphrases in a way that draws on the inferential properties of the sentences, and is not over-reliant on lexical and syntactic similarities of a sentence pair? We apply the adversarial paradigm to this question, and introduce a new adversarial method of dataset creation for paraphrase identification: the Adversarial Paraphrasing Task (APT), which asks participants to generate semantically equivalent (in the sense of mutually implicative) but lexically and syntactically disparate paraphrases. These sentence pairs can then be used both to test paraphrase identification models (which get barely random accuracy) and then improve their performance. To accelerate dataset generation, we explore automation of APT using T5, and show that the resulting dataset also improves accuracy. We discuss implications for paraphrase detection and release our dataset in the hope of making paraphrase detection models better able to detect sentence-level meaning equivalence.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_553", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_553.flac", "type": "audio", "duration": 749.215}, {"from": "Assistant", "value": "A false contract is more likely to be rejected than a contract is, yet a false key is less likely than a key to open doors. While correctly interpreting and assessing the effects of such adjective-noun pairs (e.g., false key) on the plausibility of given events (e.g., opening doors) underpins many natural language understanding tasks, doing so often requires a significant degree of world knowledge and common-sense reasoning. We introduce ADEPT – a large-scale semantic plausibility task consisting of over 16 thousand sentences that are paired with slightly modified versions obtained by adding an adjective to a noun. Overall, we find that while the task appears easier for human judges (85% accuracy), it proves more difficult for transformer-based models like RoBERTa (71% accuracy). Our experiments also show that neither the adjective itself nor its taxonomic class suffice in determining the correct plausibility judgement, emphasizing the importance of endowing automatic natural language understanding systems with more context sensitivity and common-sense reasoning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_554", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_554.flac", "type": "audio", "duration": 711.738}, {"from": "Assistant", "value": "We present ReadOnce Transformers, an approach to convert a transformer-based model into one that can build an information-capturing, task-independent, and compressed representation of text. The resulting representation is reusable across different examples and tasks, thereby requiring a document shared across many examples or tasks to only be read once. This leads to faster training and evaluation of models. Additionally, we extend standard text-to-text transformer models to Representation+Text-to-text models, and evaluate on multiple downstream tasks: multi-hop QA, abstractive QA, and long-document summarization. Our one-time computed representation results in a 2x-5x speedup compared to standard text-to-text models, while the compression also allows existing language models to handle longer documents without the need for designing new pre-trained models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_555", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_555.flac", "type": "audio", "duration": 710.165}, {"from": "Assistant", "value": "Models of narrative schema knowledge have proven useful for a range of event-related tasks, but they typically do not capture the temporal relationships between events. We propose a single model that addresses both temporal ordering, sorting given events into the order they occurred, and event infilling, predicting new events which fit into an existing temporally-ordered sequence. We use a BART-based conditional generation model that can capture both temporality and common event co-occurrence, meaning it can be flexibly applied to different tasks in this space. Our model is trained as a denoising autoencoder: we take temporally-ordered event sequences, shuffle them, delete some events, and then attempt to recover the original event sequence. This task teaches the model to make inferences given incomplete knowledge about the events in an underlying scenario. On the temporal ordering task, we show that our model is able to unscramble event sequences from existing datasets without access to explicitly labeled temporal training data, outperforming both a BERT-based pairwise model and a BERT-based pointer network. On event infilling, human evaluation shows that our model is able to generate events that fit better temporally into the input events when compared to GPT-2 story completion models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_556", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_556.flac", "type": "audio", "duration": 542.302}, {"from": "Assistant", "value": "The wanton spread of hate speech on the internet brings great harm to society and families. It is urgent to establish and improve automatic detection and active avoidance mechanisms for hate speech. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. In other words, getting more affective features from other affective resources will significantly affect the performance of hate speech detection. In this paper, we propose a hate speech detection framework based on sentiment knowledge sharing. While extracting the affective features of the target sentence itself, we make better use of the sentiment features from external resources, and finally fuse features from different feature extraction units to detect hate speech. Experimental results on two public datasets demonstrate the effectiveness of our model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_557", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_557.flac", "type": "audio", "duration": 720.12}, {"from": "Assistant", "value": "We propose a transition-based bubble parser to perform coordination structure identification and dependency-based syntactic analysis simultaneously. Bubble representations were proposed in the formal linguistics literature decades ago; they enhance dependency trees by encoding coordination boundaries and internal relationships within coordination structures explicitly. In this paper, we introduce a transition system and neural models for parsing these bubble-enhanced structures. Experimental results on the English Penn Treebank and the English GENIA corpus show that our parsers beat previous state-of-the-art approaches on the task of coordination structure prediction, especially for the subset of sentences with complex coordination structures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_558", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_558.flac", "type": "audio", "duration": 659.052}, {"from": "Assistant", "value": "Recent years have seen the paradigm shift of Named Entity Recognition (NER) systems from sequence labeling to span prediction. Despite its preliminary effectiveness, the span prediction model’s architectural bias has not been fully understood. In this paper, we first investigate the strengths and weaknesses when the span prediction model is used for named entity recognition compared with the sequence labeling framework and how to further improve it, which motivates us to make complementary advantages of systems based on different paradigms. We then reveal that span prediction, simultaneously, can serve as a system combiner to re-recognize named entities from different systems’ outputs. We experimentally implement 154 systems on 11 datasets, covering three languages, comprehensive results show the effectiveness of span prediction models that both serve as base NER systems and system combiners. We make all codes and datasets available:https://github.com/neulab/spanner, as well as an online system demo:http://spanner.sh. Our model also has been deployed into the ExplainaBoard platform, which allows users to flexibly perform a system combination of top-scoring systems in an interactive way:http://explainaboard.nlpedia.ai/leaderboard/task-ner/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_559", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_559.flac", "type": "audio", "duration": 765.051}, {"from": "Assistant", "value": "There are two major classes of natural language grammars — the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the assembly of one or several corresponded words. While previous unsupervised parsing methods mostly focus on only inducing one class of grammars, we introduce a novel model, StructFormer, that can induce dependency and constituency structure at the same time. To achieve this, we propose a new parsing framework that can jointly generate a constituency tree and dependency graph. Then we integrate the induced dependency relations into the transformer, in a differentiable manner, through a novel dependency-constrained self-attention mechanism. Experimental results show that our model can achieve strong results on unsupervised constituency parsing, unsupervised dependency parsing, and masked language modeling at the same time.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_560", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_560.flac", "type": "audio", "duration": 615.04}, {"from": "Assistant", "value": "Cross-lingual language tasks typically require a substantial amount of annotated data or parallel translation data. We explore whether language representations that capture relationships among languages can be learned and subsequently leveraged in cross-lingual tasks without the use of parallel data. We generate dense embeddings for 29 languages using a denoising autoencoder, and evaluate the embeddings using the World Atlas of Language Structures (WALS) and two extrinsic tasks in a zero-shot setting: cross-lingual dependency parsing and cross-lingual natural language inference.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_561", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_561.flac", "type": "audio", "duration": 424.043}, {"from": "Assistant", "value": "Decipherment of historical ciphers is a challenging problem. The language of the target plaintext might be unknown, and ciphertext can have a lot of noise. State-of-the-art decipherment methods use beam search and a neural language model to score candidate plaintext hypotheses for a given cipher, assuming the plaintext language is known. We propose an end-to-end multilingual model for solving simple substitution ciphers. We test our model on synthetic and real historical ciphers and show that our proposed method can decipher text without explicit language identification while still being robust to noise.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_562", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_562.flac", "type": "audio", "duration": 668.061}, {"from": "Assistant", "value": "While it has been shown that Neural Machine Translation (NMT) is highly sensitive to noisy parallel training samples, prior work treats all types of mismatches between source and target as noise. As a result, it remains unclear how samples that are mostly equivalent but contain a small number of semantically divergent tokens impact NMT training. To close this gap, we analyze the impact of different types of fine-grained semantic divergences on Transformer models. We show that models trained on synthetic divergences output degenerated text more frequently and are less confident in their predictions. Based on these findings, we introduce a divergent-aware NMT framework that uses factors to help NMT recover from the degradation caused by naturally occurring divergences, improving both translation quality and model calibration on EN-FR tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_563", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_563.flac", "type": "audio", "duration": 717.055}, {"from": "Assistant", "value": "Reranking models enable the integration of rich features to select a better output hypothesis within an n-best list or lattice. These models have a long history in NLP, and we revisit discriminative reranking for modern neural machine translation models by training a large transformer architecture. This takes as input both the source sentence as well as a list of hypotheses to output a ranked list. The reranker is trained to predict the observed distribution of a desired metric, e.g. BLEU, over the n-best list. Since such a discriminator contains hundreds of millions of parameters, we improve its generalization using pre-training and data augmentation techniques. Experiments on four WMT directions show that our discriminative reranking approach is effective and complementary to existing generative reranking approaches, yielding improvements of up to 4 BLEU over the beam search output.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_564", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_564.flac", "type": "audio", "duration": 740.245}, {"from": "Assistant", "value": "Active learning promises to alleviate the massive data needs of supervised machine learning: it has successfully improved sample efficiency by an order of magnitude on traditional tasks like topic classification and object recognition. However, we uncover a striking contrast to this promise: across 5 models and 4 datasets on the task of visual question answering, a wide variety of active learning approaches fail to outperform random selection. To understand this discrepancy, we profile 8 active learning methods on a per-example basis, and identify the problem as collective outliers – groups of examples that active learning methods prefer to acquire but models fail to learn (e.g., questions that ask about text in images or require external knowledge). Through systematic ablation experiments and qualitative visualizations, we verify that collective outliers are a general phenomenon responsible for degrading pool-based active learning. Notably, we show that active learning sample efficiency increases significantly as the number of collective outliers in the active learning pool decreases. We conclude with a discussion and prescriptive recommendations for mitigating the effects of these outliers in future work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_565", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_565.flac", "type": "audio", "duration": 716.22}, {"from": "Assistant", "value": "Human evaluations are typically considered the gold standard in natural language generation, but as models’ fluency improves, how well can evaluators detect and judge machine-generated text? We run a study assessing non-experts’ ability to distinguish between human- and machine-authored text (GPT2 and GPT3) in three domains (stories, news articles, and recipes). We find that, without training, evaluators distinguished between GPT3- and human-authored text at random chance level. We explore three approaches for quickly training evaluators to better identify GPT3-authored text (detailed instructions, annotated examples, and paired examples) and find that while evaluators’ accuracy improved up to 55%, it did not significantly improve across the three domains. Given the inconsistent results across text domains and the often contradictory reasons evaluators gave for their judgments, we examine the role untrained human evaluations play in NLG evaluation and provide recommendations to NLG researchers for improving human evaluations of text generated from state-of-the-art models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_566", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_566.flac", "type": "audio", "duration": 637.063}, {"from": "Assistant", "value": "This paper presents the first large-scale meta-evaluation of machine translation (MT). We annotated MT evaluations conducted in 769 research papers published from 2010 to 2020. Our study shows that practices for automatic MT evaluation have dramatically changed during the past decade and follow concerning trends. An increasing number of MT evaluations exclusively rely on differences between BLEU scores to draw conclusions, without performing any kind of statistical significance testing nor human evaluation, while at least 108 metrics claiming to be better than BLEU have been proposed. MT evaluations in recent papers tend to copy and compare automatic metric scores from previous work to claim the superiority of a method or an algorithm without confirming neither exactly the same training, validating, and testing data have been used nor the metric scores are comparable. Furthermore, tools for reporting standardized metric scores are still far from being widely adopted by the MT community. After showing how the accumulation of these pitfalls leads to dubious evaluation, we propose a guideline to encourage better automatic MT evaluation along with a simple meta-evaluation scoring method to assess its credibility.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_569", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_569.flac", "type": "audio", "duration": 755.043}, {"from": "Assistant", "value": "Recent investigations into the inner-workings of state-of-the-art large-scale pre-trained Transformer-based Natural Language Understanding (NLU) models indicate that they appear to understand human-like syntax, at least to some extent. We provide novel evidence that complicates this claim: we find that state-of-the-art Natural Language Inference (NLI) models assign the same labels to permuted examples as they do to the original, i.e. they are invariant to random word-order permutations. This behavior notably differs from that of humans; we struggle to understand the meaning of ungrammatical sentences. To measure the severity of this issue, we propose a suite of metrics and investigate which properties of particular permutations lead models to be word order invariant. For example, in MNLI dataset we find almost all (98.7%) examples contain at least one permutation which elicits the gold label. Models are even able to assign gold labels to permutations that they originally failed to predict correctly. We provide a comprehensive empirical evaluation of this phenomenon, and further show that this issue exists in pre-Transformer RNN / ConvNet based encoders, as well as across multiple languages (English and Chinese). Our code and data are available athttps://github.com/facebookresearch/unlu.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_570", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_570.flac", "type": "audio", "duration": 702.443}, {"from": "Assistant", "value": "Signed languages are the primary means of communication for many deaf and hard of hearing individuals. Since signed languages exhibit all the fundamental linguistic properties of natural language, we believe that tools and theories of Natural Language Processing (NLP) are crucial towards its modeling. However, existing research in Sign Language Processing (SLP) seldom attempt to explore and leverage the linguistic organization of signed languages. This position paper calls on the NLP community to include signed languages as a research area with high social and scientific impact. We first discuss the linguistic properties of signed languages to consider during their modeling. Then, we review the limitations of current SLP models and identify the open challenges to extend NLP to signed languages. Finally, we urge (1) the adoption of an efficient tokenization method; (2) the development of linguistically-informed models; (3) the collection of real-world signed language data; (4) the inclusion of local signed language communities as an active and leading voice in the direction of research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_571", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_571.flac", "type": "audio", "duration": 802.017}, {"from": "Assistant", "value": "The choice of token vocabulary affects the performance of machine translation. This paper aims to figure out what is a good vocabulary and whether we can find the optimal vocabulary without trial training. To answer these questions, we first provide an alternative understanding of vocabulary from the perspective of information theory. It motivates us to formulate the quest of vocabularization – finding the best token dictionary with a proper size – as an optimal transport (OT) problem. We propose VOLT, a simple and efficient solution without trial training. Empirical results show that VOLT beats widely-used vocabularies in diverse scenarios, including WMT-14 English-German translation, TED bilingual translation, and TED multilingual translation. For example, VOLT achieves 70% vocabulary size reduction and 0.5 BLEU gain on English-German translation. Also, compared to BPE-search, VOLT reduces the search time from 384 GPU hours to 30 GPU hours on English-German translation. Codes are available athttps://github.com/Jingjing-NLP/VOLT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_1", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_1.flac", "type": "audio", "duration": 420.049}, {"from": "Assistant", "value": "A snowclone is a customizable phrasal template that can be realized in multiple, instantly recognized variants. For example, “* is the new *\" (Orange is the new black, 40 is the new 30). Snowclones are extensively used in social media. In this paper, we study snowclones originating from pop-culture quotes; our goal is to automatically detect cultural references in text. We introduce a new, publicly available data set of pop-culture quotes and their corresponding snowclone usages and train models on them. We publish code for Catchphrase, an internet browser plugin to automatically detect and mark references in real-time, and examine its performance via a user study. Aside from assisting people to better comprehend cultural references, we hope that detecting snowclones can complement work on paraphrasing and help tackling long-standing questions in social science about the dynamics of information propagation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_2", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_2.flac", "type": "audio", "duration": 532.062}, {"from": "Assistant", "value": "Large-scale pretrained language models have led to dramatic improvements in text generation. Impressive performance can be achieved by finetuning only on a small number of instances (few-shot setting). Nonetheless, almost all previous work simply applies random sampling to select the few-shot training instances. Little to no attention has been paid to the selection strategies and how they would affect model performance. In this work, we present a study on training instance selection in few-shot neural text generation. The selection decision is made based only on the unlabeled data so as to identify the most worthwhile data points that should be annotated under some budget of labeling cost. Based on the intuition that the few-shot training instances should be diverse and representative of the entire data distribution, we propose a simple selection strategy with K-means clustering. We show that even with the naive clustering-based approach, the generation models consistently outperform random sampling on three text generation tasks: data-to-text generation, document summarization and question generation. The code and training data are made available. We hope that this work will call for more attention on this largely unexplored area.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_3", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_3.flac", "type": "audio", "duration": 459.058}, {"from": "Assistant", "value": "The introduction of pretrained language models has reduced many complex task-specific NLP models to simple lightweight layers. An exception to this trend is coreference resolution, where a sophisticated task-specific model is appended to a pretrained transformer encoder. While highly effective, the model has a very large memory footprint – primarily due to dynamically-constructed span and span-pair representations – which hinders the processing of complete documents and the ability to train on multiple instances in a single batch. We introduce a lightweight end-to-end coreference model that removes the dependency on span representations, handcrafted features, and heuristics. Our model performs competitively with the current standard model, while being simpler and more efficient.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_4", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_4.flac", "type": "audio", "duration": 289.493}, {"from": "Assistant", "value": "In comparison with English, due to the lack of explicit word boundary and tenses information, Chinese Named Entity Recognition (NER) is much more challenging. In this paper, we propose a boundary enhanced approach for better Chinese NER. In particular, our approach enhances the boundary information from two perspectives. On one hand, we enhance the representation of the internal dependency of phrases by an additional Graph Attention Network(GAT) layer. On the other hand, taking the entity head-tail prediction (i.e., boundaries) as an auxiliary task, we propose an unified framework to learn the boundary information and recognize the NE jointly. Experiments on both the OntoNotes and the Weibo corpora show the effectiveness of our approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_5", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_5.flac", "type": "audio", "duration": 376.043}, {"from": "Assistant", "value": "The high-quality translation results produced by machine translation (MT) systems still pose a huge challenge for automatic evaluation. Current MT evaluation pays the same attention to each sentence component, while the questions of real-world examinations (e.g., university examinations) have different difficulties and weightings. In this paper, we propose a novel difficulty-aware MT evaluation metric, expanding the evaluation dimension by taking translation difficulty into consideration. A translation that fails to be predicted by most MT systems will be treated as a difficult one and assigned a large weight in the final score function, and conversely. Experimental results on the WMT19 English-German Metrics shared tasks show that our proposed method outperforms commonly used MT metrics in terms of human correlation. In particular, our proposed method performs well even when all the MT systems are very competitive, which is when most existing metrics fail to distinguish between them. The source code is freely available athttps://github.com/NLP2CT/Difficulty-Aware-MT-Evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_6", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_6.flac", "type": "audio", "duration": 406.047}, {"from": "Assistant", "value": "Humor recognition has been widely studied as a text classification problem using data-driven approaches. However, most existing work does not examine the actual joke mechanism to understand humor. We break down any joke into two distinct components: the set-up and the punchline, and further explore the special relationship between them. Inspired by the incongruity theory of humor, we model the set-up as the part developing semantic uncertainty, and the punchline disrupting audience expectations. With increasingly powerful language models, we were able to feed the set-up along with the punchline into the GPT-2 language model, and calculate the uncertainty and surprisal values of the jokes. By conducting experiments on the SemEval 2021 Task 7 dataset, we found that these two features have better capabilities of telling jokes from non-jokes, compared with existing baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_7", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_7.flac", "type": "audio", "duration": 429.056}, {"from": "Assistant", "value": "Disentanglement of latent representations into content and style spaces has been a commonly employed method for unsupervised text style transfer. These techniques aim to learn the disentangled representations and tweak them to modify the style of a sentence. In this paper, we propose a counterfactual-based method to modify the latent representation, by posing a ‘what-if’ scenario. This simple and disciplined approach also enables a fine-grained control on the transfer strength. We conduct experiments with the proposed methodology on multiple attribute transfer tasks like Sentiment, Formality and Excitement to support our hypothesis.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_8", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_8.flac", "type": "audio", "duration": 411.328}, {"from": "Assistant", "value": "Shapley Values, a solution to the credit assignment problem in cooperative game theory, are a popular type of explanation in machine learning, having been used to explain the importance of features, embeddings, and even neurons. In NLP, however, leave-one-out and attention-based explanations still predominate. Can we draw a connection between these different methods? We formally prove that — save for the degenerate case — attention weights and leave-one-out values cannot be Shapley Values. Attention flow is a post-processed variant of attention weights obtained by running the max-flow algorithm on the attention graph. Perhaps surprisingly, we prove that attention flows are indeed Shapley Values, at least at the layerwise level. Given the many desirable theoretical qualities of Shapley Values — which has driven their adoption among the ML community — we argue that NLP practitioners should, when possible, adopt attention flow explanations alongside more traditional ones.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_9", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_9.flac", "type": "audio", "duration": 386.055}, {"from": "Assistant", "value": "Video paragraph captioning aims to generate a set of coherent sentences to describe a video that contains several events. Most previous methods simplify this task by using ground-truth event segments. In this work, we propose a novel framework by taking this task as a text summarization task. We first generate lots of sentence-level captions focusing on different video clips and then summarize these captions to obtain the final paragraph caption. Our method does not depend on ground-truth event segments. Experiments on two popular datasets ActivityNet Captions and YouCookII demonstrate the advantages of our new framework. On the ActivityNet dataset, our method even outperforms some previous methods using ground-truth event segment labels.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_10", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_10.flac", "type": "audio", "duration": 390.208}, {"from": "Assistant", "value": "Deep learning algorithms have shown promising results in visual question answering (VQA) tasks, but a more careful look reveals that they often do not understand the rich signal they are being fed with. To understand and better measure the generalization capabilities of VQA systems, we look at their robustness to counterfactually augmented data. Our proposed augmentations are designed to make a focused intervention on a specific property of the question such that the answer changes. Using these augmentations, we propose a new robustness measure, Robustness to Augmented Data (RAD), which measures the consistency of model predictions between original and augmented examples. Through extensive experimentation, we show that RAD, unlike classical accuracy measures, can quantify when state-of-the-art systems are not robust to counterfactuals. We find substantial failure cases which reveal that current VQA systems are still brittle. Finally, we connect between robustness and generalization, demonstrating the predictive power of RAD for performance on unseen augmentations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_11", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_11.flac", "type": "audio", "duration": 389.05}, {"from": "Assistant", "value": "Existing approaches for the Table-to-Text task suffer from issues such as missing information, hallucination and repetition. Many approaches to this problem use Reinforcement Learning (RL), which maximizes a single manually defined reward, such as BLEU. In this work, we instead pose the Table-to-Text task as Inverse Reinforcement Learning (IRL) problem. We explore using multiple interpretable unsupervised reward components that are combined linearly to form a composite reward function. The composite reward function and the description generator are learned jointly. We find that IRL outperforms strong RL baselines marginally. We further study the generalization of learned IRL rewards in scenarios involving domain adaptation. Our experiments reveal significant challenges in using IRL for this task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_12", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_12.flac", "type": "audio", "duration": 417.17}, {"from": "Assistant", "value": "Most fact checking models for automatic fake news detection are based on reasoning: given a claim with associated evidence, the models aim to estimate the claim veracity based on the supporting or refuting content within the evidence. When these models perform well, it is generally assumed to be due to the models having learned to reason over the evidence with regards to the claim. In this paper, we investigate this assumption of reasoning, by exploring the relationship and importance of both claim and evidence. Surprisingly, we find on political fact checking datasets that most often the highest effectiveness is obtained by utilizing only the evidence, as the impact of including the claim is either negligible or harmful to the effectiveness. This highlights an important problem in what constitutes evidence in existing approaches for automatic fake news detection.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_13", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_13.flac", "type": "audio", "duration": 429.056}, {"from": "Assistant", "value": "Despite end-to-end neural systems making significant progress in the last decade for task-oriented as well as chit-chat based dialogue systems, most dialogue systems rely on hybrid approaches which use a combination of rule-based, retrieval and generative approaches for generating a set of ranked responses. Such dialogue systems need to rely on a fallback mechanism to respond to out-of-domain or novel user queries which are not answerable within the scope of the dialogue system. While, dialogue systems today rely on static and unnatural responses like “I don’t know the answer to that question” or “I’m not sure about that”, we design a neural approach which generates responses which are contextually aware with the user query as well as say no to the user. Such customized responses provide paraphrasing ability and contextualization as well as improve the interaction with the user and reduce dialogue monotonicity. Our simple approach makes use of rules over dependency parses and a text-to-text transformer fine-tuned on synthetic data of question-response pairs generating highly relevant, grammatical as well as diverse questions. We perform automatic and manual evaluations to demonstrate the efficacy of the system.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_14", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_14.flac", "type": "audio", "duration": 371.883}, {"from": "Assistant", "value": "Spoken Language Understanding (SLU) systems parse speech into semantic structures like dialog acts and slots. This involves the use of an Automatic Speech Recognizer (ASR) to transcribe speech into multiple text alternatives (hypotheses). Transcription errors, ordinary in ASRs, impact downstream SLU performance negatively. Common approaches to mitigate such errors involve using richer information from the ASR, either in form of N-best hypotheses or word-lattices. We hypothesize that transformer models will learn better with a simpler utterance representation using the concatenation of the N-best ASR alternatives, where each alternative is separated by a special delimiter [SEP]. In our work, we test our hypothesis by using the concatenated N-best ASR alternatives as the input to the transformer encoder models, namely BERT and XLM-RoBERTa, and achieve equivalent performance to the prior state-of-the-art model on DSTC2 dataset. We also show that our approach significantly outperforms the prior state-of-the-art when subjected to the low data regime. Additionally, this methodology is accessible to users of third-party ASR APIs which do not provide word-lattice information.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_15", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_15.flac", "type": "audio", "duration": 507.285}, {"from": "Assistant", "value": "Is bias amplified when neural machine translation (NMT) models are optimized for speed and evaluated on generic test sets using BLEU? We investigate architectures and techniques commonly used to speed up decoding in Transformer-based models, such as greedy search, quantization, average attention networks (AANs) and shallow decoder models and show their effect on gendered noun translation. We construct a new gender bias test set, SimpleGEN, based on gendered noun phrases in which there is a single, unambiguous, correct answer. While we find minimal overall BLEU degradation as we apply speed optimizations, we observe that gendered noun translation performance degrades at a much faster rate.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_16", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_16.flac", "type": "audio", "duration": 420.053}, {"from": "Assistant", "value": "State-of-the-art machine translation (MT) systems are typically trained to generate “standard” target language; however, many languages have multiple varieties (regional varieties, dialects, sociolects, non-native varieties) that are different from the standard language. Such varieties are often low-resource, and hence do not benefit from contemporary NLP solutions, MT included. We propose a general framework to rapidly adapt MT systems to generate language varieties that are close to, but different from, the standard target language, using no parallel (source–variety) data. This also includes adaptation of MT systems to low-resource typologically-related target languages. We experiment with adapting an English–Russian MT system to generate Ukrainian and Belarusian, an English–Norwegian Bokmål system to generate Nynorsk, and an English–Arabic system to generate four Arabic dialects, obtaining significant improvements over competitive baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_17", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_17.flac", "type": "audio", "duration": 405.056}, {"from": "Assistant", "value": "Sparse attention has been claimed to increase model interpretability under the assumption that it highlights influential inputs. Yet the attention distribution is typically over representations internal to the model rather than the inputs themselves, suggesting this assumption may not have merit. We build on the recent work exploring the interpretability of attention; we design a set of experiments to help us understand how sparsity affects our ability to use attention as an explainability tool. On three text classification tasks, we verify that only a weak relationship between inputs and co-indexed intermediate representations exists—under sparse attention and otherwise. Further, we do not find any plausible mappings from sparse attention distributions to a sparse set of influential inputs through other avenues. Rather, we observe in this setting that inducing sparsity may make it less plausible that attention can be used as a tool for understanding model behavior.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_18", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_18.flac", "type": "audio", "duration": 419.817}, {"from": "Assistant", "value": "Mechanisms for encoding positional information are central for transformer-based language models. In this paper, we analyze the position embeddings of existing language models, finding strong evidence of translation invariance, both for the embeddings themselves and for their effect on self-attention. The degree of translation invariance increases during training and correlates positively with model performance. Our findings lead us to propose translation-invariant self-attention (TISA), which accounts for the relative position between tokens in an interpretable fashion without needing conventional position embeddings. Our proposal has several theoretical advantages over existing position-representation approaches. Proof-of-concept experiments show that it improves on regular ALBERT on GLUE tasks, while only adding orders of magnitude less positional parameters.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_19", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_19.flac", "type": "audio", "duration": 413.06}, {"from": "Assistant", "value": "Determining the relative importance of the elements in a sentence is a key factor for effortless natural language understanding. For human language processing, we can approximate patterns of relative importance by measuring reading fixations using eye-tracking technology. In neural language models, gradient-based saliency methods indicate the relative importance of a token for the target objective. In this work, we compare patterns of relative importance in English language processing by humans and models and analyze the underlying linguistic patterns. We find that human processing patterns in English correlate strongly with saliency-based importance in language models and not with attention-based importance. Our results indicate that saliency could be a cognitively more plausible metric for interpreting neural language models. The code is available on github:https://github.com/beinborn/relative_importance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_20", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_20.flac", "type": "audio", "duration": 417.17}, {"from": "Assistant", "value": "Pretrained language models (PLM) achieve surprising performance on the Choice of Plausible Alternatives (COPA) task. However, whether PLMs have truly acquired the ability of causal reasoning remains a question. In this paper, we investigate the problem of semantic similarity bias and reveal the vulnerability of current COPA models by certain attacks. Previous solutions that tackle the superficial cues of unbalanced token distribution still encounter the same problem of semantic bias, even more seriously due to the utilization of more training data. We mitigate this problem by simply adding a regularization loss and experimental results show that this solution not only improves the model’s generalization ability, but also assists the models to perform more robustly on a challenging dataset, BCOPA-CE, which has unbiased token distribution and is more difficult for models to distinguish cause and effect.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_21", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_21.flac", "type": "audio", "duration": 510.885}, {"from": "Assistant", "value": "A current open question in natural language processing is to what extent language models, which are trained with access only to the form of language, are able to capture the meaning of language. This question is challenging to answer in general, as there is no clear line between meaning and form, but rather meaning constrains form in consistent ways. The goal of this study is to offer insights into a narrower but critical subquestion: Under what conditions should we expect that meaning and form covary sufficiently, such that a language model with access only to form might nonetheless succeed in emulating meaning? Focusing on several formal languages (propositional logic and a set of programming languages), we generate training corpora using a variety of motivated constraints, and measure a distributional language model’s ability to differentiate logical symbols (AND, OR, and NOT). Our findings are largely negative: none of our simulated training corpora result in models which definitively differentiate meaningfully different symbols (e.g., AND vs. OR), suggesting a limitation to the types of semantic signals that current models are able to exploit.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_22", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_22.flac", "type": "audio", "duration": 429.058}, {"from": "Assistant", "value": "The predominant challenge in weakly supervised semantic parsing is that of spurious programs that evaluate to correct answers for the wrong reasons. Prior work uses elaborate search strategies to mitigate the prevalence of spurious programs; however, they typically consider only one input at a time. In this work we explore the use of consistency between the output programs for related inputs to reduce the impact of spurious programs. We bias the program search (and thus the model’s training signal) towards programs that map the same phrase in related inputs to the same sub-parts in their respective programs. Additionally, we study the importance of designing logical formalisms that facilitate this kind of consistency-based training. We find that a more consistent formalism leads to improved model performance even without consistency-based training. When combined together, these two insights lead to a 10% absolute improvement over the best prior result on the Natural Language Visual Reasoning dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_23", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_23.flac", "type": "audio", "duration": 385.057}, {"from": "Assistant", "value": "In this paper, we present an improved model for voicing silent speech, where audio is synthesized from facial electromyography (EMG) signals. To give our model greater flexibility to learn its own input features, we directly use EMG signals as input in the place of hand-designed features used by prior work. Our model uses convolutional layers to extract features from the signals and Transformer layers to propagate information across longer distances. To provide better signal for learning, we also introduce an auxiliary task of predicting phoneme labels in addition to predicting speech audio features. On an open vocabulary intelligibility evaluation, our model improves the state of the art for this task by an absolute 25.8%.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_24", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_24.flac", "type": "audio", "duration": 406.805}, {"from": "Assistant", "value": "Whereas much of the success of the current generation of neural language models has been driven by increasingly large training corpora, relatively little research has been dedicated to analyzing these massive sources of textual data. In this exploratory analysis, we delve deeper into the Common Crawl, a colossal web corpus that is extensively used for training language models. We find that it contains a significant amount of undesirable content, including hate speech and sexually explicit content, even after filtering procedures. We discuss the potential impacts of this content on language models and conclude with future research directions and a more mindful approach to corpus collection and analysis.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_25", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_25.flac", "type": "audio", "duration": 342.332}, {"from": "Assistant", "value": "Most current quality estimation (QE) models for machine translation are trained and evaluated in a static setting where training and test data are assumed to be from a fixed distribution. However, in real-life settings, the test data that a deployed QE model would be exposed to may differ from its training data. In particular, training samples are often labelled by one or a small set of annotators, whose perceptions of translation quality and needs may differ substantially from those of end-users, who will employ predictions in practice. To address this challenge, we propose an online Bayesian meta-learning framework for the continuous training of QE models that is able to adapt them to the needs of different users, while being robust to distributional shifts in training and test data. Experiments on data with varying number of users and language characteristics validate the effectiveness of the proposed approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_26", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_26.flac", "type": "audio", "duration": 347.092}, {"from": "Assistant", "value": "Sequential sentence classification aims to classify each sentence in the document based on the context in which sentences appear. Most existing work addresses this problem using a hierarchical sequence labeling network. However, they ignore considering the latent segment structure of the document, in which contiguous sentences often have coherent semantics. In this paper, we proposed a span-based dynamic local attention model that could explicitly capture the structural information by the proposed supervised dynamic local attention. We further introduce an auxiliary task called span-based classification to explore the span-level representations. Extensive experiments show that our model achieves better or competitive performance against state-of-the-art baselines on two benchmark datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_27", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_27.flac", "type": "audio", "duration": 426.295}, {"from": "Assistant", "value": "Ordered word sequences contain the rich structures that define language. However, it’s often not clear if or how modern pretrained language models utilize these structures. We show that the token representations and self-attention activations within BERT are surprisingly resilient to shuffling the order of input tokens, and that for several GLUE language understanding tasks, shuffling only minimally degrades performance, e.g., by 4% for QNLI. While bleak from the perspective of language understanding, our results have positive implications for cases where copyright or ethics necessitates the consideration of bag-of-words data (vs. full documents). We simulate such a scenario for three sensitive classification tasks, demonstrating minimal performance degradation vs. releasing full language sequences.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_28", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_28.flac", "type": "audio", "duration": 376.491}, {"from": "Assistant", "value": "Recent works made significant advances on summarization tasks, facilitated by summarization datasets. Several existing datasets have the form of coherent-paragraph summaries. However, these datasets were curated from academic documents that were written for experts, thus making the essential step of assessing the summarization output through human-evaluation very demanding. To overcome these limitations, we present a dataset based on article summaries appearing on the WikiHow website, composed of how-to articles and coherent-paragraph summaries written in plain language. We compare our dataset attributes to existing ones, including readability and world-knowledge, showing our dataset makes human evaluation significantly easier and thus, more effective. A human evaluation conducted on PubMed and the proposed dataset reinforces our findings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_29", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_29.flac", "type": "audio", "duration": 417.1}, {"from": "Assistant", "value": "Despite the success of various text generation metrics such as BERTScore, it is still difficult to evaluate the image captions without enough reference captions due to the diversity of the descriptions. In this paper, we introduce a new metric UMIC, an Unreferenced Metric for Image Captioning which does not require reference captions to evaluate image captions. Based on Vision-and-Language BERT, we train UMIC to discriminate negative captions via contrastive learning. Also, we observe critical problems of the previous benchmark dataset (i.e., human annotations) on image captioning metric, and introduce a new collection of human annotations on the generated captions. We validate UMIC on four datasets, including our new dataset, and show that UMIC has a higher correlation than all previous metrics that require multiple references.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_30", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_30.flac", "type": "audio", "duration": 380.041}, {"from": "Assistant", "value": "Good quality monolingual word embeddings (MWEs) can be built for languages which have large amounts of unlabeled text. MWEs can be aligned to bilingual spaces using only a few thousand word translation pairs. For low resource languages training MWEs monolingually results in MWEs of poor quality, and thus poor bilingual word embeddings (BWEs) as well. This paper proposes a new approach for building BWEs in which the vector space of the high resource source language is used as a starting point for training an embedding space for the low resource target language. By using the source vectors as anchors the vector spaces are automatically aligned during training. We experiment on English-German, English-Hiligaynon and English-Macedonian. We show that our approach results not only in improved BWEs and bilingual lexicon induction performance, but also in improved target language MWE quality as measured using monolingual word similarity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_31", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_31.flac", "type": "audio", "duration": 387.051}, {"from": "Assistant", "value": "Although multilingual neural machine translation (MNMT) enables multiple language translations, the training process is based on independent multilingual objectives. Most multilingual models can not explicitly exploit different language pairs to assist each other, ignoring the relationships among them. In this work, we propose a novel agreement-based method to encourage multilingual agreement among different translation directions, which minimizes the differences among them. We combine the multilingual training objectives with the agreement term by randomly substituting some fragments of the source language with their counterpart translations of auxiliary languages. To examine the effectiveness of our method, we conduct experiments on the multilingual translation task of 10 language pairs. Experimental results show that our method achieves significant improvements over the previous multilingual baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_32", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_32.flac", "type": "audio", "duration": 408.811}, {"from": "Assistant", "value": "Weighted finite-state machines are a fundamental building block of NLP systems. They have withstood the test of time—from their early use in noisy channel models in the 1990s up to modern-day neurally parameterized conditional random fields. This work examines the computation of higher-order derivatives with respect to the normalization constant for weighted finite-state machines. We provide a general algorithm for evaluating derivatives of all orders, which has not been previously described in the literature. In the case of second-order derivatives, our scheme runs in the optimal O(Aˆ2 Nˆ4) time where A is the alphabet size and N is the number of states. Our algorithm is significantly faster than prior algorithms. Additionally, our approach leads to a significantly faster algorithm for computing second-order expectations, such as covariance matrices and gradients of first-order expectations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_33", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_33.flac", "type": "audio", "duration": 404.004}, {"from": "Assistant", "value": "The growth of online consumer health questions has led to the necessity for reliable and accurate question answering systems. A recent study showed that manual summarization of consumer health questions brings significant improvement in retrieving relevant answers. However, the automatic summarization of long questions is a challenging task due to the lack of training data and the complexity of the related subtasks, such as the question focus and type recognition. In this paper, we introduce a reinforcement learning-based framework for abstractive question summarization. We propose two novel rewards obtained from the downstream tasks of (i) question-type identification and (ii) question-focus recognition to regularize the question generation model. These rewards ensure the generation of semantically valid questions and encourage the inclusion of key medical entities/foci in the question summary. We evaluated our proposed method on two benchmark datasets and achieved higher performance over state-of-the-art models. The manual evaluation of the summaries reveals that the generated questions are more diverse and have fewer factual inconsistencies than the baseline summaries. The source code is available here:https://github.com/shwetanlp/CHQ-Summ.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_34", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_34.flac", "type": "audio", "duration": 478.052}, {"from": "Assistant", "value": "Relation linking is a crucial component of Knowledge Base Question Answering systems. Existing systems use a wide variety of heuristics, or ensembles of multiple systems, heavily relying on the surface question text. However, the explicit semantic parse of the question is a rich source of relation information that is not taken advantage of. We propose a simple transformer-based neural model for relation linking that leverages the AMR semantic parse of a sentence. Our system significantly outperforms the state-of-the-art on 4 popular benchmark datasets. These are based on either DBpedia or Wikidata, demonstrating that our approach is effective across KGs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_35", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_35.flac", "type": "audio", "duration": 397.41}, {"from": "Assistant", "value": "Early fusion models with cross-attention have shown better-than-human performance on some question answer benchmarks, while it is a poor fit for retrieval since it prevents pre-computation of the answer representations. We present a supervised data mining method using an accurate early fusion model to improve the training of an efficient late fusion retrieval model. We first train an accurate classification model with cross-attention between questions and answers. The cross-attention model is then used to annotate additional passages in order to generate weighted training examples for a neural retrieval model. The resulting retrieval model with additional data significantly outperforms retrieval models directly trained with gold annotations on Precision at N (P@N) and Mean Reciprocal Rank (MRR).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_36", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_36.flac", "type": "audio", "duration": 392.046}, {"from": "Assistant", "value": "Generatingdescriptivesentences that convey non-trivial, detailed, and salient information about images is an important goal of image captioning. In this paper we propose a novel approach to encourage captioning models to produce more detailed captions using natural language inference, based on the motivation that, among different captions of an image, descriptive captions are more likely to entail less descriptive captions. Specifically, we construct directed inference graphs for reference captions based on natural language inference. A PageRank algorithm is then employed to estimate the descriptiveness score of each node. Built on that, we use reference sampling and weighted designated rewards to guide captioning to generate descriptive captions. The results on MSCOCO show that the proposed method outperforms the baselines significantly on a wide range of conventional and descriptiveness-related evaluation metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_37", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_37.flac", "type": "audio", "duration": 422.441}, {"from": "Assistant", "value": "We present an instance-based nearest neighbor approach to entity linking. In contrast to most prior entity retrieval systems which represent each entity with a single vector, we build a contextualized mention-encoder that learns to place similar mentions of the same entity closer in vector space than mentions of different entities. This approach allows all mentions of an entity to serve as “class prototypes” as inference involves retrieving from the full set of labeled entity mentions in the training set and applying the nearest mention neighbor’s entity label. Our model is trained on a large multilingual corpus of mention pairs derived from Wikipedia hyperlinks, and performs nearest neighbor inference on an index of 700 million mentions. It is simpler to train, gives more interpretable predictions, and outperforms all other systems on two multilingual entity linking benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_38", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_38.flac", "type": "audio", "duration": 407.046}, {"from": "Assistant", "value": "BERT has been shown to be extremely effective on a wide variety of natural language processing tasks, including sentiment analysis and emotion detection. However, the proposed pretraining objectives of BERT do not induce any sentiment or emotion-specific biases into the model. In this paper, we present Emotion Masked Language Modelling, a variation of Masked Language Modelling aimed at improving the BERT language representation model for emotion detection and sentiment analysis tasks. Using the same pre-training corpora as the original model, Wikipedia and BookCorpus, our BERT variation manages to improve the downstream performance on 4 tasks from emotion detection and sentiment analysis by an average of 1.2% F-1. Moreover, our approach shows an increased performance in our task-specific robustness tests.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_39", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_39.flac", "type": "audio", "duration": 387.262}, {"from": "Assistant", "value": "Prior work has revealed that positive words occur more frequently than negative words in human expressions, which is typically attributed to positivity bias, a tendency for people to report positive views of reality. But what about the language used in negative reviews? Consistent with prior work, we show that English negative reviews tend to contain more positive words than negative words, using a variety of datasets. We reconcile this observation with prior findings on the pragmatics of negation, and show that negations are commonly associated with positive words in negative reviews. Furthermore, in negative reviews, the majority of sentences with positive words express negative opinions based on sentiment classifiers, indicating some form of negation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_40", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_40.flac", "type": "audio", "duration": 419.051}, {"from": "Assistant", "value": "Large pre-trained language generation models such as GPT-2 have demonstrated their effectiveness as language priors by reaching state-of-the-art results in various language generation tasks. However, the performance of pre-trained models on task-oriented dialog tasks is still under-explored. We propose a Pre-trainedRole Alternating Language model (PRAL), explicitly designed for task-oriented conversational systems. We design several techniques: start position randomization, knowledge distillation, and history discount to improve pre-training performance. In addition, we introduce a high-quality large-scale task-oriented dialog pre-training dataset by post-prossessing13 dialog datasets. We effectively adapt PRALon three downstream tasks. The results show that PRAL outperforms or is on par with state-of-the-art models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_41", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_41.flac", "type": "audio", "duration": 420.053}, {"from": "Assistant", "value": "Natural reading orders of words are crucial for information extraction from form-like documents. Despite recent advances in Graph Convolutional Networks (GCNs) on modeling spatial layout patterns of documents, they have limited ability to capture reading orders of given word-level node representations in a graph. We propose Reading Order Equivariant Positional Encoding (ROPE), a new positional encoding technique designed to apprehend the sequential presentation of words in documents. ROPE generates unique reading order codes for neighboring words relative to the target word given a word-level graph connectivity. We study two fundamental document entity extraction tasks including word labeling and word grouping on the public FUNSD dataset and a large-scale payment dataset. We show that ROPE consistently improves existing GCNs with a margin up to 8.4% F1-score.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_42", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_42.flac", "type": "audio", "duration": 419.051}, {"from": "Assistant", "value": "Event extraction has long been a challenging task, addressed mostly with supervised methods that require expensive annotation and are not extensible to new event ontologies. In this work, we explore the possibility of zero-shot event extraction by formulating it as a set of Textual Entailment (TE) and/or Question Answering (QA) queries (e.g. “A city was attacked” entails “There is an attack”), exploiting pretrained TE/QA models for direct transfer. On ACE-2005 and ERE, our system achieves acceptable results, yet there is still a large gap from supervised approaches, showing that current QA and TE technologies fail in transferring to a different domain. To investigate the reasons behind the gap, we analyze the remaining key challenges, their respective impact, and possible improvement directions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_43", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_43.flac", "type": "audio", "duration": 399.808}, {"from": "Assistant", "value": "Pre-trained language models have achieved human-level performance on many Machine Reading Comprehension (MRC) tasks, but it remains unclear whether these models truly understand language or answer questions by exploiting statistical biases in datasets. Here, we demonstrate a simple yet effective method to attack MRC models and reveal the statistical biases in these models. We apply the method to the RACE dataset, for which the answer to each MRC question is selected from 4 options. It is found that several pre-trained language models, including BERT, ALBERT, and RoBERTa, show consistent preference to some options, even when these options are irrelevant to the question. When interfered by these irrelevant options, the performance of MRC models can be reduced from human-level performance to the chance-level performance. Human readers, however, are not clearly affected by these irrelevant options. Finally, we propose an augmented training method that can greatly reduce models’ statistical biases.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_44", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_44.flac", "type": "audio", "duration": 442.688}, {"from": "Assistant", "value": "Extensive work has argued in favour of paying crowd workers a wage that is at least equivalent to the U.S. federal minimum wage. Meanwhile, research on collecting high quality annotations suggests using a qualification that requires workers to have previously completed a certain number of tasks. If most requesters who pay fairly require workers to have completed a large number of tasks already then workers need to complete a substantial amount of poorly paid work before they can earn a fair wage. Through analysis of worker discussions and guidance for researchers, we estimate that workers spend approximately 2.25 months of full time effort on poorly paid tasks in order to get the qualifications needed for better paid tasks. We discuss alternatives to this qualification and conduct a study of the correlation between qualifications and work quality on two NLP tasks. We find that it is possible to reduce the burden on workers while still collecting high quality data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_45", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_45.flac", "type": "audio", "duration": 425.041}, {"from": "Assistant", "value": "Human activities can be seen as sequences of events, which are crucial to understanding societies. Disproportional event distribution for different demographic groups can manifest and amplify social stereotypes, and potentially jeopardize the ability of members in some groups to pursue certain goals. In this paper, we present the first event-centric study of gender biases in a Wikipedia corpus. To facilitate the study, we curate a corpus of career and personal life descriptions with demographic information consisting of 7,854 fragments from 10,412 celebrities. Then we detect events with a state-of-the-art event detection model, calibrate the results using strategically generated templates, and extract events that have asymmetric associations with genders. Our study discovers that the Wikipedia pages tend to intermingle personal life events with professional events for females but not for males, which calls for the awareness of the Wikipedia community to formalize guidelines and train the editors to mind the implicit biases that contributors carry. Our work also lays the foundation for future works on quantifying and discovering event biases at the corpus level.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_46", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_46.flac", "type": "audio", "duration": 213.653}, {"from": "Assistant", "value": "Neural machine translation has achieved great success in bilingual settings, as well as in multilingual settings. With the increase of the number of languages, multilingual systems tend to underperform their bilingual counterparts. Model capacity has been found crucial for massively multilingual NMT to support language pairs with varying typological characteristics. Previous work increases the modeling capacity by deepening or widening the Transformer. However, modeling cardinality based on aggregating a set of transformations with the same topology has been proven more effective than going deeper or wider when increasing capacity. In this paper, we propose to efficiently increase the capacity for multilingual NMT by increasing the cardinality. Unlike previous work which feeds the same input to several transformations and merges their outputs into one, we present a Multi-Input-Multi-Output (MIMO) architecture that allows each transformation of the block to have its own input. We also present a task-aware attention mechanism to learn to selectively utilize individual transformations from a set of transformations for different translation directions. Our model surpasses previous work and establishes a new state-of-the-art on the large scale OPUS-100 corpus while being 1.31 times as fast.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_47", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_47.flac", "type": "audio", "duration": 382.061}, {"from": "Assistant", "value": "kNN-MT, recently proposed by Khandelwal et al. (2020a), successfully combines pre-trained neural machine translation (NMT) model with token-level k-nearest-neighbor (kNN) retrieval to improve the translation accuracy. However, the traditional kNN algorithm used in kNN-MT simply retrieves a same number of nearest neighbors for each target token, which may cause prediction errors when the retrieved neighbors include noises. In this paper, we propose Adaptive kNN-MT to dynamically determine the number of k for each target token. We achieve this by introducing a light-weight Meta-k Network, which can be efficiently trained with only a few training samples. On four benchmark machine translation datasets, we demonstrate that the proposed method is able to effectively filter out the noises in retrieval results and significantly outperforms the vanilla kNN-MT model. Even more noteworthy is that the Meta-k Network learned on one domain could be directly applied to other domains and obtain consistent improvements, illustrating the generality of our method. Our implementation is open-sourced athttps://github.com/zhengxxn/adaptive-knn-mt.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_48", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_48.flac", "type": "audio", "duration": 412.053}, {"from": "Assistant", "value": "Orthogonality constraints encourage matrices to be orthogonal for numerical stability. These plug-and-play constraints, which can be conveniently incorporated into model training, have been studied for popular architectures in natural language processing, such as convolutional neural networks and recurrent neural networks. However, a dedicated study on such constraints for transformers has been absent. To fill this gap, this paper studies orthogonality constraints for transformers, showing the effectiveness with empirical evidence from ten machine translation tasks and two dialogue generation tasks. For example, on the large-scale WMT’16 En→De benchmark, simply plugging-and-playing orthogonality constraints on the original transformer model (Vaswani et al., 2017) increases the BLEU from 28.4 to 29.6, coming close to the 29.7 BLEU achieved by the very competitive dynamic convolution (Wu et al., 2019).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_49", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_49.flac", "type": "audio", "duration": 303.763}, {"from": "Assistant", "value": "Imagine you are in a supermarket. You have two bananas in your basket and want to buy four apples. How many fruits do you have in total? This seemingly straightforward question can be challenging for data-driven language models, even if trained at scale. However, we would expect such generic language models to possess some mathematical abilities in addition to typical linguistic competence. Towards this goal, we investigate if a commonly used language model, BERT, possesses such mathematical abilities and, if so, to what degree. For that, we fine-tune BERT on a popular dataset for word math problems, AQuA-RAT, and conduct several tests to understand learned representations better. Since we teach models trained on natural language to do formal mathematics, we hypothesize that such models would benefit from training on semi-formal steps that explain how math results are derived. To better accommodate such training, we also propose new pretext tasks for learning mathematical rules. We call them (Neighbor) Reasoning Order Prediction (ROP or NROP). With this new model, we achieve significantly better outcomes than data-driven baselines and even on-par with more tailored models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_50", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_50.flac", "type": "audio", "duration": 416.729}, {"from": "Assistant", "value": "Datasets with induced emotion labels are scarce but of utmost importance for many NLP tasks. We present a new, automated method for collecting texts along with their induced reaction labels. The method exploits the online use of reaction GIFs, which capture complex affective states. We show how to augment the data with induced emotion and induced sentiment labels. We use our method to create and publish ReactionGIF, a first-of-its-kind affective dataset of 30K tweets. We provide baselines for three new tasks, including induced sentiment prediction and multilabel classification of induced emotions. Our method and dataset open new research opportunities in emotion detection and affective computing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_51", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_51.flac", "type": "audio", "duration": 398.059}, {"from": "Assistant", "value": "This work explores a framework for fact verification that leverages pretrained sequence-to-sequence transformer models for sentence selection and label prediction, two key sub-tasks in fact verification. Most notably, improving on previous pointwise aggregation approaches for label prediction, we take advantage of T5 using a listwise approach coupled with data augmentation. With this enhancement, we observe that our label prediction stage is more robust to noise and capable of verifying complex claims by jointly reasoning over multiple pieces of evidence. Experimental results on the FEVER task show that our system attains a FEVER score of 75.87% on the blind test set. This puts our approach atop the competitive FEVER leaderboard at the time of our work, scoring higher than the second place submission by almost two points in label accuracy and over one point in FEVER score.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_52", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_52.flac", "type": "audio", "duration": 418.052}, {"from": "Assistant", "value": "Sentence embedding methods using natural language inference (NLI) datasets have been successfully applied to various tasks. However, these methods are only available for limited languages due to relying heavily on the large NLI datasets. In this paper, we propose DefSent, a sentence embedding method that uses definition sentences from a word dictionary, which performs comparably on unsupervised semantics textual similarity (STS) tasks and slightly better on SentEval tasks than conventional methods. Since dictionaries are available for many languages, DefSent is more broadly applicable than methods using NLI datasets without constructing additional datasets. We demonstrate that DefSent performs comparably on unsupervised semantics textual similarity (STS) tasks and slightly better on SentEval tasks to the methods using large NLI datasets. Our code is publicly available athttps://github.com/hpprc/defsent.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_53", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_53.flac", "type": "audio", "duration": 387.053}, {"from": "Assistant", "value": "Modern sentence encoders are used to generate dense vector representations that capture the underlying linguistic characteristics for a sequence of words, including phrases, sentences, or paragraphs. These kinds of representations are ideal for training a classifier for an end task such as sentiment analysis, question answering and text classification. Different models have been proposed to efficiently generate general purpose sentence representations to be used in pretraining protocols. While averaging is the most commonly used efficient sentence encoder, Discrete Cosine Transform (DCT) was recently proposed as an alternative that captures the underlying syntactic characteristics of a given text without compromising practical efficiency compared to averaging. However, as with most other sentence encoders, the DCT sentence encoder was only evaluated in English. To this end, we utilize DCT encoder to generate universal sentence representation for different languages such as German, French, Spanish and Russian. The experimental results clearly show the superior effectiveness of DCT encoding in which consistent performance improvements are achieved over strong baselines on multiple standardized datasets", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_54", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_54.flac", "type": "audio", "duration": 393.045}, {"from": "Assistant", "value": "High-quality alignment between movie scripts and plot summaries is an asset for learning to summarize stories and to generate dialogues. The alignment task is challenging as scripts and summaries substantially differ in details and abstraction levels as well as in linguistic register. This paper addresses the alignment problem by devising a fully unsupervised approach based on a global optimization model. Experimental results on ten movies show the viability of our method with 76% F1-score and its superiority over a previous baseline. We publish alignments for 914 movies to foster research in this new topic.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_55", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_55.flac", "type": "audio", "duration": 478.052}, {"from": "Assistant", "value": "Most studies on word-level Quality Estimation (QE) of machine translation focus on language-specific models. The obvious disadvantages of these approaches are the need for labelled data for each language pair and the high cost required to maintain several language-specific models. To overcome these problems, we explore different approaches to multilingual, word-level QE. We show that multilingual QE models perform on par with the current language-specific models. In the cases of zero-shot and few-shot QE, we demonstrate that it is possible to accurately predict word-level quality for any given new language pair from models trained on other language pairs. Our findings suggest that the word-level QE models based on powerful pre-trained transformers that we propose in this paper generalise well across languages, making them more useful in real-world scenarios.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_56", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_56.flac", "type": "audio", "duration": 403.051}, {"from": "Assistant", "value": "A sequence-to-sequence learning with neural networks has empirically proven to be an effective framework for Chinese Spelling Correction (CSC), which takes a sentence with some spelling errors as input and outputs the corrected one. However, CSC models may fail to correct spelling errors covered by the confusion sets, and also will encounter unseen ones. We propose a method, which continually identifies the weak spots of a model to generate more valuable training instances, and apply a task-specific pre-training strategy to enhance the model. The generated adversarial examples are gradually added to the training set. Experimental results show that such an adversarial training method combined with the pre-training strategy can improve both the generalization and robustness of multiple CSC models across three different datasets, achieving state-of-the-art performance for CSC task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_57", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_57.flac", "type": "audio", "duration": 375.049}, {"from": "Assistant", "value": "Adaptive Computation (AC) has been shown to be effective in improving the efficiency of Open-Domain Question Answering (ODQA) systems. However, the current AC approaches require tuning of all model parameters, and training state-of-the-art ODQA models requires significant computational resources that may not be available for most researchers. We propose Adaptive Passage Encoder, an AC method that can be applied to an existing ODQA model and can be trained efficiently on a single GPU. It keeps the parameters of the base ODQA model fixed, but it overrides the default layer-by-layer computation of the encoder with an AC policy that is trained to optimise the computational efficiency of the model. Our experimental results show that our method improves upon a state-of-the-art model on two datasets, and is also more accurate than previous AC methods due to the stronger base ODQA model. All source code and datasets are available athttps://github.com/uclnlp/APE.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_58", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_58.flac", "type": "audio", "duration": 423.88}, {"from": "Assistant", "value": "In this paper, we empirically investigate adversarial attack on NMT from two aspects: languages (the source vs. the target language) and positions (front vs. rear). For autoregressive NMT models that generate target words from left to right, we observe that adversarial attack on the source language is more effective than on the target language, and that attacking front positions of target sentences or positions of source sentences aligned to the front positions of corresponding target sentences is more effective than attacking other positions. We further exploit the attention distribution of the victim model to attack source sentences at positions that have a strong association with front target words. Experiment results demonstrate that our attention-based adversarial attack is more effective than adversarial attacks by sampling positions randomly or according to gradients.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_59", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_59.flac", "type": "audio", "duration": 394.043}, {"from": "Assistant", "value": "SOTA coreference resolution produces increasingly impressive scores on the OntoNotes benchmark. However lack of comparable data following the same scheme for more genres makes it difficult to evaluate generalizability to open domain data. This paper provides a dataset and comprehensive evaluation showing that the latest neural LM based end-to-end systems degrade very substantially out of domain. We make an OntoNotes-like coreference dataset called OntoGUM publicly available, converted from GUM, an English corpus covering 12 genres, using deterministic rules, which we evaluate. Thanks to the rich syntactic and discourse annotations in GUM, we are able to create the largest human-annotated coreference corpus following the OntoNotes guidelines, and the first to be evaluated for consistency with the OntoNotes scheme. Out-of-domain evaluation across 12 genres shows nearly 15-20% degradation for both deterministic and deep learning systems, indicating a lack of generalizability or covert overfitting in existing coreference resolution models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_60", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_60.flac", "type": "audio", "duration": 347.051}, {"from": "Assistant", "value": "Visual Question Answering (VQA) methods aim at leveraging visual input to answer questions that may require complex reasoning over entities. Current models are trained on labelled data that may be insufficient to learn complex knowledge representations. In this paper, we propose a new method to enhance the reasoning capabilities of a multi-modal pretrained model (Vision+Language BERT) by integrating facts extracted from an external knowledge base. Evaluation on the KVQA dataset benchmark demonstrates that our method outperforms competitive baselines by 19%, achieving new state-of-the-art results. We also perform an extensive analysis highlighting the limitations of our best performing model through an ablation study.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_61", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_61.flac", "type": "audio", "duration": 495.445}, {"from": "Assistant", "value": "Neural models for automated fact verification have achieved promising results thanks to the availability of large, human-annotated datasets. However, for each new domain that requires fact verification, creating a dataset by manually writing claims and linking them to their supporting evidence is expensive. We develop QACG, a framework for training a robust fact verification model by using automatically generated claims that can be supported, refuted, or unverifiable from evidence from Wikipedia. QACG generates question-answer pairs from the evidence and then converts them into different types of claims. Experiments on the FEVER dataset show that our QACG framework significantly reduces the demand for human-annotated training data. In a zero-shot scenario, QACG improves a RoBERTa model’s F1 from 50% to 77%, equivalent in performance to 2K+ manually-curated examples. Our QACG code is publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_62", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_62.flac", "type": "audio", "duration": 381.056}, {"from": "Assistant", "value": "Scarcity of parallel data causes formality style transfer models to have scarce success in preserving content. We show that fine-tuning pre-trained language (GPT-2) and sequence-to-sequence (BART) models boosts content preservation, and that this is possible even with limited amounts of parallel data. Augmenting these models with rewards that target style and content –the two core aspects of the task– we achieve a new state-of-the-art.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_63", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_63.flac", "type": "audio", "duration": 401.612}, {"from": "Assistant", "value": "Existing works for aspect-based sentiment analysis (ABSA) have adopted a unified approach, which allows the interactive relations among subtasks. However, we observe that these methods tend to predict polarities based on the literal meaning of aspect and opinion terms and mainly consider relations implicitly among subtasks at the word level. In addition, identifying multiple aspect–opinion pairs with their polarities is much more challenging. Therefore, a comprehensive understanding of contextual information w.r.t. the aspect and opinion are further required in ABSA. In this paper, we propose Deep Contextualized Relation-Aware Network (DCRAN), which allows interactive relations among subtasks with deep contextual information based on two modules (i.e., Aspect and Opinion Propagation and Explicit Self-Supervised Strategies). Especially, we design novel self-supervised strategies for ABSA, which have strengths in dealing with multiple aspects. Experimental results show that DCRAN significantly outperforms previous state-of-the-art methods by large margins on three widely used benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_64", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_64.flac", "type": "audio", "duration": 411.063}, {"from": "Assistant", "value": "Aspect-based sentiment analysis (ABSA) has received increasing attention recently. Most existing work tackles ABSA in a discriminative manner, designing various task-specific classification networks for the prediction. Despite their effectiveness, these methods ignore the rich label semantics in ABSA problems and require extensive task-specific designs. In this paper, we propose to tackle various ABSA tasks in a unified generative framework. Two types of paradigms, namely annotation-style and extraction-style modeling, are designed to enable the training process by formulating each ABSA task as a text generation problem. We conduct experiments on four ABSA tasks across multiple benchmark datasets where our proposed generative approach achieves new state-of-the-art results in almost all cases. This also validates the strong generality of the proposed framework which can be easily adapted to arbitrary ABSA task without additional task-specific model design.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_65", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_65.flac", "type": "audio", "duration": 393.044}, {"from": "Assistant", "value": "Recently, token-level adaptive training has achieved promising improvement in machine translation, where the cross-entropy loss function is adjusted by assigning different training weights to different tokens, in order to alleviate the token imbalance problem. However, previous approaches only use static word frequency information in the target language without considering the source language, which is insufficient for bilingual tasks like machine translation. In this paper, we propose a novel bilingual mutual information (BMI) based adaptive objective, which measures the learning difficulty for each target token from the perspective of bilingualism, and assigns an adaptive weight accordingly to improve token-level adaptive training. This method assigns larger training weights to tokens with higher BMI, so that easy tokens are updated with coarse granularity while difficult tokens are updated with fine granularity. Experimental results on WMT14 English-to-German and WMT19 Chinese-to-English demonstrate the superiority of our approach compared with the Transformer baseline and previous token-level adaptive training approaches. Further analyses confirm that our method can improve the lexical diversity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_66", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_66.flac", "type": "audio", "duration": 220.055}, {"from": "Assistant", "value": "This ability to learn consecutive tasks without forgetting how to perform previously trained problems is essential for developing an online dialogue system. This paper proposes an effective continual learning method for the task-oriented dialogue system with iterative network pruning, expanding, and masking (TPEM), which preserves performance on previously encountered tasks while accelerating learning progress on subsequent tasks. Specifically, TPEM (i) leverages network pruning to keep the knowledge for old tasks, (ii) adopts network expanding to create free weights for new tasks, and (iii) introduces task-specific network masking to alleviate the negative impact of fixed weights of old tasks on new tasks. We conduct extensive experiments on seven different tasks from three benchmark datasets and show empirically that TPEM leads to significantly improved results over the strong competitors.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_67", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_67.flac", "type": "audio", "duration": 381.12}, {"from": "Assistant", "value": "We present TIMERS - a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language. Our proposed method leverages rhetorical discourse features and temporal arguments from semantic role labels, in addition to traditional local syntactic features, trained through a Gated Relational-GCN. Extensive experiments show that the proposed model outperforms previous methods by 5-18% on the TDDiscourse, TimeBank-Dense, and MATRES datasets due to our discourse-level modeling.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_68", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_68.flac", "type": "audio", "duration": 416.043}, {"from": "Assistant", "value": "Arabic diacritization is a fundamental task for Arabic language processing. Previous studies have demonstrated that automatically generated knowledge can be helpful to this task. However, these studies regard the auto-generated knowledge instances as gold references, which limits their effectiveness since such knowledge is not always accurate and inferior instances can lead to incorrect predictions. In this paper, we propose to use regularized decoding and adversarial training to appropriately learn from such noisy knowledge for diacritization. Experimental results on two benchmark datasets show that, even with quite flawed auto-generated knowledge, our model can still learn adequate diacritics and outperform all previous studies, on both datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_69", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_69.flac", "type": "audio", "duration": 368.043}, {"from": "Assistant", "value": "Subword segmentation algorithms have been ade factochoice when building neural machine translation systems. However, most of them need to learn a segmentation model based on some heuristics, which may produce sub-optimal segmentation. This can be problematic in some scenarios when the target language has rich morphological changes or there is not enough data for learning compact composition rules. Translating at fully character level has the potential to alleviate the issue, but empirical performances of character-based models has not been fully explored. In this paper, we present an in-depth comparison between character-based and subword-based NMT systems under three settings: translating to typologically diverse languages, training with low resource, and adapting to unseen domains. Experiment results show strong competitiveness of character-based models. Further analyses show that compared to subword-based models, character-based models are better at handling morphological phenomena, generating rare and unknown words, and more suitable for transferring to unseen domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_70", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_70.flac", "type": "audio", "duration": 417.6}, {"from": "Assistant", "value": "Chinese word segmentation (CWS) is undoubtedly an important basic task in natural language processing. Previous works only focus on the textual modality, but there are often audio and video utterances (such as news broadcast and face-to-face dialogues), where textual, acoustic and visual modalities normally exist. To this end, we attempt to combine the multi-modality (mainly the converted text and actual voice information) to perform CWS. In this paper, we annotate a new dataset for CWS containing text and audio. Moreover, we propose a time-dependent multi-modal interactive model based on Transformer framework to integrate multi-modal information for word sequence labeling. The experimental results on three different training sets show the effectiveness of our approach with fusing text and audio.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_71", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_71.flac", "type": "audio", "duration": 447.054}, {"from": "Assistant", "value": "Discrimination between antonyms and synonyms is an important and challenging NLP task. Antonyms and synonyms often share the same or similar contexts and thus are hard to make a distinction. This paper proposes two underlying hypotheses and employs the mixture-of-experts framework as a solution. It works on the basis of a divide-and-conquer strategy, where a number of localized experts focus on their own domains (or subspaces) to learn their specialties, and a gating mechanism determines the space partitioning and the expert mixture. Experimental results have shown that our method achieves the state-of-the-art performance on the task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_72", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_72.flac", "type": "audio", "duration": 478.052}, {"from": "Assistant", "value": "Injecting external domain-specific knowledge (e.g., UMLS) into pretrained language models (LMs) advances their capability to handle specialised in-domain tasks such as biomedical entity linking (BEL). However, such abundant expert knowledge is available only for a handful of languages (e.g., English). In this work, by proposing a novel cross-lingual biomedical entity linking task (XL-BEL) and establishing a new XL-BEL benchmark spanning 10 typologically diverse languages, we first investigate the ability of standard knowledge-agnostic as well as knowledge-enhanced monolingual and multilingual LMs beyond the standard monolingual English BEL task. The scores indicate large gaps to English performance. We then address the challenge of transferring domain-specific knowledge in resource-rich languages to resource-poor ones. To this end, we propose and evaluate a series of cross-lingual transfer methods for the XL-BEL task, and demonstrate that general-domain bitext helps propagate the available English knowledge to languages with little to no in-domain data. Remarkably, we show that our proposed domain-specific transfer methods yield consistent gains across all target languages, sometimes up to 20 Precision@1 points, without any in-domain knowledge in the target language, and without any in-domain parallel data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_73", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_73.flac", "type": "audio", "duration": 415.01}, {"from": "Assistant", "value": "The representation degeneration problem in Contextual Word Representations (CWRs) hurts the expressiveness of the embedding space by forming an anisotropic cone where even unrelated words have excessively positive correlations. Existing techniques for tackling this issue require a learning process to re-train models with additional objectives and mostly employ a global assessment to study isotropy. Our quantitative analysis over isotropy shows that a local assessment could be more accurate due to the clustered structure of CWRs. Based on this observation, we propose a local cluster-based method to address the degeneration issue in contextual embedding spaces. We show that in clusters including punctuations and stop words, local dominant directions encode structural information, removing which can improve CWRs performance on semantic tasks. Moreover, we find that tense information in verb representations dominates sense semantics. We show that removing dominant directions of verb representations can transform the space to better suit semantic applications. Our experiments demonstrate that the proposed cluster-based method can mitigate the degeneration problem on multiple tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_74", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_74.flac", "type": "audio", "duration": 417.054}, {"from": "Assistant", "value": "Humans often refer to personal narratives, life experiences, and events to make a conversation more engaging and rich. While persona-grounded dialog models are able to generate responses that follow a given persona, they often miss out on stating detailed experiences or events related to a persona, often leaving conversations shallow and dull. In this work, we equip dialog models with ‘background stories’ related to a persona by leveraging fictional narratives from existing story datasets (e.g. ROCStories). Since current dialog datasets do not contain such narratives as responses, we perform an unsupervised adaptation of a retrieved story for generating a dialog response using a gradient-based rewriting technique. Our proposed method encourages the generated response to be fluent (i.e., highly likely) with the dialog history, minimally different from the retrieved story to preserve event ordering and consistent with the original persona. We demonstrate that our method can generate responses that are more diverse, and are rated more engaging and human-like by human evaluators, compared to outputs from existing dialog models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_75", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_75.flac", "type": "audio", "duration": 390.656}, {"from": "Assistant", "value": "The famous “laurel/yanny” phenomenon references an audio clip that elicits dramatically different responses from different listeners. For the original clip, roughly half the population hears the word “laurel,” while the other half hears “yanny.” How common are such “polyperceivable” audio clips? In this paper we apply ML techniques to study the prevalence of polyperceivability in spoken language. We devise a metric that correlates with polyperceivability of audio clips, use it to efficiently find new “laurel/yanny”-type examples, and validate these results with human experiments. Our results suggest that polyperceivable examples are surprisingly prevalent in natural language, existing for >2% of English words.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_76", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_76.flac", "type": "audio", "duration": 431.851}, {"from": "Assistant", "value": "Event language models represent plausible sequences of events. Most existing approaches train autoregressive models on text, which successfully capture event co-occurrence but unfortunately constrain the model to follow the discourse order in which events are presented. Other domains may employ different discourse orders, and for many applications, we may care about different notions of ordering (e.g., temporal) or not care about ordering at all (e.g., when predicting related events in a schema). We propose a simple yet surprisingly effective strategy for improving event language models by perturbing event sequences so we can relax model dependence on text order. Despite generating completely synthetic event orderings, we show that this technique improves the performance of the event language models on both applications and out-of-domain events data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_77", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_77.flac", "type": "audio", "duration": 423.578}, {"from": "Assistant", "value": "Information Retrieval using dense low-dimensional representations recently became popular and showed out-performance to traditional sparse-representations like BM25. However, no previous work investigated how dense representations perform with large index sizes. We show theoretically and empirically that the performance for dense representations decreases quicker than sparse representations for increasing index sizes. In extreme cases, this can even lead to a tipping point where at a certain index size sparse representations outperform dense representations. We show that this behavior is tightly connected to the number of dimensions of the representations: The lower the dimension, the higher the chance for false positives, i.e. returning irrelevant documents", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_78", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_78.flac", "type": "audio", "duration": 409.043}, {"from": "Assistant", "value": "Cross-lingual text classification aims at training a classifier on the source language and transferring the knowledge to target languages, which is very useful for low-resource languages. Recent multilingual pretrained language models (mPLM) achieve impressive results in cross-lingual classification tasks, but rarely consider factors beyond semantic similarity, causing performance degradation between some language pairs. In this paper we propose a simple yet effective method to incorporate heterogeneous information within and across languages for cross-lingual text classification using graph convolutional networks (GCN). In particular, we construct a heterogeneous graph by treating documents and words as nodes, and linking nodes with different relations, which include part-of-speech roles, semantic similarity, and document translations. Extensive experiments show that our graph-based method significantly outperforms state-of-the-art models on all tasks, and also achieves consistent performance gain over baselines in low-resource settings where external tools like translators are unavailable.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_79", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_79.flac", "type": "audio", "duration": 403.818}, {"from": "Assistant", "value": "Question answering (QA) in English has been widely explored, but multilingual datasets are relatively new, with several methods attempting to bridge the gap between high- and low-resourced languages using data augmentation through translation and cross-lingual transfer. In this project we take a step back and study which approaches allow us to take the most advantage of existing resources in order to produce QA systems in many languages. Specifically, we perform extensive analysis to measure the efficacy of few-shot approaches augmented with automatic translations and permutations of context-question-answer pairs. In addition, we make suggestions for future dataset development efforts that make better use of a fixed annotation budget, with a goal of increasing the language coverage of QA datasets and systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_80", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_80.flac", "type": "audio", "duration": 447.332}, {"from": "Assistant", "value": "We propose an effective context-sensitive neural model for time to event (TTE) prediction task, which aims to predict the amount of time to/from the occurrence of given events in streaming content. We investigate this problem in the context of a multi-task learning framework, which we enrich with time difference embeddings. In addition, we develop a multi-genre dataset of English events about soccer competitions and academy awards ceremonies, and their relevant tweets obtained from Twitter. Our model is 1.4 and 3.3 hours more accurate than the current state-of-the-art model in estimating TTE on English and Dutch tweets respectively. We examine different aspects of our model to illustrate its source of improvement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_81", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_81.flac", "type": "audio", "duration": 388.052}, {"from": "Assistant", "value": "Compositional generalization is the ability to generalize systematically to a new data distribution by combining known components. Although humans seem to have a great ability to generalize compositionally, state-of-the-art neural models struggle to do so. In this work, we study compositional generalization in classification tasks and present two main contributions. First, we study ways to convert a natural language sequence-to-sequence dataset to a classification dataset that also requires compositional generalization. Second, we show that providing structural hints (specifically, providing parse trees and entity links as attention masks for a Transformer model) helps compositional generalization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_82", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_82.flac", "type": "audio", "duration": 417.17}, {"from": "Assistant", "value": "Pre-trained text-to-text transformers such as BART have achieved impressive performance across a range of NLP tasks. Recent study further shows that they can learn to generalize to novel tasks, by including task descriptions as part of the source sequence and training the model with (source, target) examples. At test time, these fine-tuned models can make inferences on new tasks using the new task descriptions as part of the input. However, this approach has potential limitations, as the model learns to solve individual (source, target) examples (i.e., at the instance level), instead of learning to solve tasks by taking all examples within a task as a whole (i.e., at the task level). To this end, we introduce Hypter, a framework that improves text-to-text transformer’s generalization ability to unseen tasks by training a hypernetwork to generate task-specific, light-weight adapters from task descriptions. Experiments on ZEST dataset and a synthetic SQuAD dataset demonstrate that Hypter improves upon fine-tuning baselines. Notably, when using BART-Large as the main network, Hypter brings 11.3% comparative improvement on ZEST dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_83", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_83.flac", "type": "audio", "duration": 401.055}, {"from": "Assistant", "value": "Slot-filling is an essential component for building task-oriented dialog systems. In this work, we focus on the zero-shot slot-filling problem, where the model needs to predict slots and their values, given utterances from new domains without training on the target domain. Prior methods directly encode slot descriptions to generalize to unseen slot types. However, raw slot descriptions are often ambiguous and do not encode enough semantic information, limiting the models’ zero-shot capability. To address this problem, we introduce QA-driven slot filling (QASF), which extracts slot-filler spans from utterances with a span-based QA model. We use a linguistically motivated questioning strategy to turn descriptions into questions, allowing the model to generalize to unseen slot types. Moreover, our QASF model can benefit from weak supervision signals from QA pairs synthetically generated from unlabeled conversations. Our full system substantially outperforms baselines by over 5% on the SNIPS benchmark.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_84", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_84.flac", "type": "audio", "duration": 309.383}, {"from": "Assistant", "value": "Language models like BERT and SpanBERT pretrained on open-domain data have obtained impressive gains on various NLP tasks. In this paper, we probe the effectiveness of domain-adaptive pretraining objectives on downstream tasks. In particular, three objectives, including a novel objective focusing on modeling predicate-argument relations, are evaluated on two challenging dialogue understanding tasks. Experimental results demonstrate that domain-adaptive pretraining with proper objectives can significantly improve the performance of a strong baseline on these tasks, achieving the new state-of-the-art performances.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_85", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_85.flac", "type": "audio", "duration": 418.006}, {"from": "Assistant", "value": "It has become a common pattern in our field: One group introduces a language task, exemplified by a dataset, which they argue is challenging enough to serve as a benchmark. They also provide a baseline model for it, which then soon is improved upon by other groups. Often, research efforts then move on, and the pattern repeats itself. What is typically left implicit is the argumentation for why this constitutes progress, and progress towards what. In this paper, we try to step back for a moment from this pattern and work out possible argumentations and their parts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_86", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_86.flac", "type": "audio", "duration": 422.059}, {"from": "Assistant", "value": "In this work, we introduce : the largest publicly available multilingual dataset for factual verification of naturally existing real-world claims. The dataset contains short statements in 25 languages and is labeled for veracity by expert fact-checkers. The dataset includes a multilingual evaluation benchmark that measures both out-of-domain generalization, and zero-shot capabilities of the multilingual models. Using state-of-the-art multilingual transformer-based models, we develop several automated fact-checking models that, along with textual claims, make use of additional metadata and evidence from news stories retrieved using a search engine. Empirically, our best model attains an F-score of around 40%, suggesting that our dataset is a challenging benchmark for the evaluation of multilingual fact-checking models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_87", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_87.flac", "type": "audio", "duration": 406.721}, {"from": "Assistant", "value": "Recently, mT5 - a massively multilingual version of T5 - leveraged a unified text-to-text format to attain state-of-the-art results on a wide variety of multilingual NLP tasks. In this paper, we investigate the impact of incorporating parallel data into mT5 pre-training. We find that multi-tasking language modeling with objectives such as machine translation during pre-training is a straightforward way to improve performance on downstream multilingual and cross-lingual tasks. However, the gains start to diminish as the model capacity increases, suggesting that parallel data might not be as essential for larger models. At the same time, even at larger model sizes, we find that pre-training with parallel data still provides benefits in the limited labelled data regime", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_88", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_88.flac", "type": "audio", "duration": 411.063}, {"from": "Assistant", "value": "Intelligent and adaptive online education systems aim to make high-quality education available for a diverse range of students. However, existing systems usually depend on a pool of hand-made questions, limiting how fine-grained and open-ended they can be in adapting to individual students. We explore targeted question generation as a controllable sequence generation task. We first show how to fine-tune pre-trained language models for deep knowledge tracing (LM-KT). This model accurately predicts the probability of a student answering a question correctly, and generalizes to questions not seen in training. We then use LM-KT to specify the objective and data for training a model to generate questions conditioned on the student and target difficulty. Our results show we succeed at generating novel, well-calibrated language translation questions for second language learners from a real online education platform.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_90", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_90.flac", "type": "audio", "duration": 242.859}, {"from": "Assistant", "value": "Pathology imaging is broadly used for identifying the causes and effects of diseases or injuries. Given a pathology image, being able to answer questions about the clinical findings contained in the image is very important for medical decision making. In this paper, we aim to develop a pathological visual question answering framework to analyze pathology images and answer medical questions related to these images. To build such a framework, we create PathVQA, a VQA dataset with 32,795 questions asked from 4,998 pathology images. We also propose a three-level optimization framework which performs self-supervised pretraining and VQA finetuning end-to-end to learn powerful visual and textual representations jointly and automatically identifies and excludes noisy self-supervised examples from pretraining. We perform experiments on our created PathVQA dataset and the results demonstrate the effectiveness of our proposed methods. The datasets and code are available athttps://github.com/UCSD-AI4H/PathVQA", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_91", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_91.flac", "type": "audio", "duration": 408.044}, {"from": "Assistant", "value": "Text-based games (TBGs) have emerged as useful benchmarks for evaluating progress at the intersection of grounded language understanding and reinforcement learning (RL). Recent work has proposed the use of external knowledge to improve the efficiency of RL agents for TBGs. In this paper, we posit that to act efficiently in TBGs, an agent must be able to track the state of the game while retrieving and using relevant commonsense knowledge. Thus, we propose an agent for TBGs that induces a graph representation of the game state and jointly grounds it with a graph of commonsense knowledge from ConceptNet. This combination is achieved through bidirectional knowledge graph attention between the two symbolic representations. We show that agents that incorporate commonsense into the game state graph outperform baseline agents.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_92", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_92.flac", "type": "audio", "duration": 419.84}, {"from": "Assistant", "value": "We introduce mTVR, a large-scale multilingual video moment retrieval dataset, containing 218K English and Chinese queries from 21.8K TV show video clips. The dataset is collected by extending the popular TVR dataset (in English) with paired Chinese queries and subtitles. Compared to existing moment retrieval datasets, mTVR is multilingual, larger, and comes with diverse annotations. We further propose mXML, a multilingual moment retrieval model that learns and operates on data from both languages, via encoder parameter sharing and language neighborhood constraints. We demonstrate the effectiveness of mXML on the newly collected mTVR dataset, where mXML outperforms strong monolingual baselines while using fewer parameters. In addition, we also provide detailed dataset analyses and model ablations. Data and code are publicly available athttps://github.com/jayleicn/mTVRetrieval", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_93", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_93.flac", "type": "audio", "duration": 421.047}, {"from": "Assistant", "value": "Named entity recognition (NER) is well studied for the general domain, and recent systems have achieved human-level performance for identifying common entity types. However, the NER performance is still moderate for specialized domains that tend to feature complicated contexts and jargonistic entity types. To address these challenges, we propose explicitly connecting entity mentions based on both global coreference relations and local dependency relations for building better entity mention representations. In our experiments, we incorporate entity mention relations by Graph Neural Networks and show that our system noticeably improves the NER performance on two datasets from different domains. We further show that the proposed lightweight system can effectively elevate the NER performance to a higher level even when only a tiny amount of labeled data is available, which is desirable for domain-specific NER.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_94", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_94.flac", "type": "audio", "duration": 388.052}, {"from": "Assistant", "value": "Accurate terminology translation is crucial for ensuring the practicality and reliability of neural machine translation (NMT) systems. To address this, lexically constrained NMT explores various methods to ensure pre-specified words and phrases appear in the translation output. However, in many cases, those methods are studied on general domain corpora, where the terms are mostly uni- and bi-grams (>98%). In this paper, we instead tackle a more challenging setup consisting of domain-specific corpora with much longer n-gram and highly specialized terms. Inspired by the recent success of masked span prediction models, we propose a simple and effective training strategy that achieves consistent improvements on both terminology and sentence-level translation for three domain-specific corpora in two language pairs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_95", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_95.flac", "type": "audio", "duration": 387.051}, {"from": "Assistant", "value": "To help individuals express themselves better, quotation recommendation is receiving growing attention. Nevertheless, most prior efforts focus on modeling quotations and queries separately and ignore the relationship between the quotations and the queries. In this work, we introduce a transformation matrix that directly maps the query representations to quotation representations. To better learn the mapping relationship, we employ a mapping loss that minimizes the distance of two semantic spaces (one for quotation and another for mapped-query). Furthermore, we explore using the words in history queries to interpret the figurative language of quotations, where quotation-aware attention is applied on top of history queries to highlight the indicator words. Experiments on two datasets in English and Chinese show that our model outperforms previous state-of-the-art models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_96", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_96.flac", "type": "audio", "duration": 376.047}, {"from": "Assistant", "value": "Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the data. However, the resulting word groups are often not coherent, making them harder to interpret. Recently, neural topic models have shown improvements in overall coherence. Concurrently, contextual embeddings have advanced the state of the art of neural models in general. In this paper, we combine contextualized representations with neural topic models. We find that our approach produces more meaningful and coherent topics than traditional bag-of-words topic models and recent neural models. Our results indicate that future improvements in language models will translate into better topic models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_97", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_97.flac", "type": "audio", "duration": 294.243}, {"from": "Assistant", "value": "Neural semantic parsers have obtained acceptable results in the context of parsing DRSs (Discourse Representation Structures). In particular models with character sequences as input showed remarkable performance for English. But how does this approach perform on languages with a different writing system, like Chinese, a language with a large vocabulary of characters? Does rule-based tokenisation of the input help, and which granularity is preferred: characters, or words? The results are promising. Even with DRSs based on English, good results for Chinese are obtained. Tokenisation offers a small advantage for English, but not for Chinese. Overall, characters are preferred as input, both for English and Chinese.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_98", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_98.flac", "type": "audio", "duration": 428.06}, {"from": "Assistant", "value": "Training datasets for semantic parsing are typically small due to the higher expertise required for annotation than most other NLP tasks. As a result, models for this application usually need additional prior knowledge to be built into the architecture or algorithm. The increased dependency on human experts hinders automation and raises the development and maintenance costs in practice. This work investigates whether a generic transformer-based seq2seq model can achieve competitive performance with minimal code-generation-specific inductive bias design. By exploiting a relatively sizeable monolingual corpus of the target programming language, which is cheap to mine from the web, we achieved 81.03% exact match accuracy on Django and 32.57 BLEU score on CoNaLa. Both are SOTA to the best of our knowledge. This positive evidence highlights a potentially easier path toward building accurate semantic parsers in practice.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_99", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_99.flac", "type": "audio", "duration": 326.821}, {"from": "Assistant", "value": "The general format of natural language inference (NLI) makes it tempting to be used for zero-shot text classification by casting any target label into a sentence of hypothesis and verifying whether or not it could be entailed by the input, aiming at generic classification applicable on any specified label space. In this opinion piece, we point out a few overlooked issues that are yet to be discussed in this line of work. We observe huge variance across different classification datasets amongst standard BERT-based NLI models and surprisingly find that pre-trained BERT without any fine-tuning can yield competitive performance against BERT fine-tuned for NLI. With the concern that these models heavily rely on spurious lexical patterns for prediction, we also experiment with preliminary approaches for more robust NLI, but the results are in general negative. Our observations reveal implicit but challenging difficulties in entailment-based zero-shot text classification.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_100", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_100.flac", "type": "audio", "duration": 411.605}, {"from": "Assistant", "value": "Commonsense reasoning aims to incorporate sets of commonsense facts, retrieved from Commonsense Knowledge Graphs (CKG), to draw conclusion about ordinary situations. The dynamic nature of commonsense knowledge postulates models capable of performing multi-hop reasoning over new situations. This feature also results in having large-scale sparse Knowledge Graphs, where such reasoning process is needed to predict relations between new events. However, existing approaches in this area are limited by considering CKGs as a limited set of facts, thus rendering them unfit for reasoning over new unseen situations and events. In this paper, we present a neural-symbolic reasoner, which is capable of reasoning over large-scale dynamic CKGs. The logic rules for reasoning over CKGs are learned during training by our model. In addition to providing interpretable explanation, the learned logic rules help to generalise prediction to newly introduced events. Experimental results on the task of link prediction on CKGs prove the effectiveness of our model by outperforming the state-of-the-art models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_101", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_101.flac", "type": "audio", "duration": 399.979}, {"from": "Assistant", "value": "According to the self-determination theory, the levels of satisfaction of three basic needs (competence, autonomy and relatedness) have implications on people’s everyday life and career. We benchmark the novel task of automatically detecting those needs on short posts in English, by modelling it as a ternary classification task, and as three binary classification tasks. A detailed manual analysis shows that the latter has advantages in the real-world scenario, and that our best models achieve similar performances as a trained human annotator.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_102", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_102.flac", "type": "audio", "duration": 398.06}, {"from": "Assistant", "value": "Recent studies on semantic frame induction show that relatively high performance has been achieved by using clustering-based methods with contextualized word embeddings. However, there are two potential drawbacks to these methods: one is that they focus too much on the superficial information of the frame-evoking verb and the other is that they tend to divide the instances of the same verb into too many different frame clusters. To overcome these drawbacks, we propose a semantic frame induction method using masked word embeddings and two-step clustering. Through experiments on the English FrameNet data, we demonstrate that using the masked word embeddings is effective for avoiding too much reliance on the surface information of frame-evoking verbs and that two-step clustering can improve the number of resulting frame clusters for the instances of the same verb.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_103", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_103.flac", "type": "audio", "duration": 401.403}, {"from": "Assistant", "value": "Adapter modules were recently introduced as an efficient alternative to fine-tuning in NLP. Adapter tuning consists in freezing pre-trained parameters of a model and injecting lightweight modules between layers, resulting in the addition of only a small number of task-specific trainable parameters. While adapter tuning was investigated for multilingual neural machine translation, this paper proposes a comprehensive analysis of adapters for multilingual speech translation (ST). Starting from different pre-trained models (a multilingual ST trained on parallel data or a multilingual BART (mBART) trained on non parallel multilingual data), we show that adapters can be used to: (a) efficiently specialize ST to specific language pairs with a low extra cost in terms of parameters, and (b) transfer from an automatic speech recognition (ASR) task and an mBART pre-trained model to a multilingual ST task. Experiments show that adapter tuning offer competitive results to full fine-tuning, while being much more parameter-efficient.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_104", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_104.flac", "type": "audio", "duration": 375.04}, {"from": "Assistant", "value": "The importance of parameter selection in supervised learning is well known. However, due to the many parameter combinations, an incomplete or an insufficient procedure is often applied. This situation may cause misleading or confusing conclusions. In this opinion paper, through an intriguing example we point out that the seriousness goes beyond what is generally recognized. In the topic of multilabel classification for medical code prediction, one influential paper conducted a proper parameter selection on a set, but when moving to a subset of frequently occurring labels, the authors used the same parameters without a separate tuning. The set of frequent labels became a popular benchmark in subsequent studies, which kept pushing the state of the art. However, we discovered that most of the results in these studies cannot surpass the approach in the original paper if a parameter tuning had been conducted at the time. Thus it is unclear how much progress the subsequent developments have actually brought. The lesson clearly indicates that without enough attention on parameter selection, the research progress in our field can be uncertain or even illusive.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_105", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_105.flac", "type": "audio", "duration": 391.047}, {"from": "Assistant", "value": "Few-shot text classification aims to classify inputs whose label has only a few examples. Previous studies overlooked the semantic relevance between label representations. Therefore, they are easily confused by labels that are relevant. To address this problem, we propose a method that generates distinct label representations that embed information specific to each label. Our method is applicable to conventional few-shot classification models. Experimental results show that our method significantly improved the performance of few-shot text classification across models and datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_106", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_106.flac", "type": "audio", "duration": 410.048}, {"from": "Assistant", "value": "In real scenarios, a multilingual model trained to solve NLP tasks on a set of languages can be required to support new languages over time. Unfortunately, the straightforward retraining on a dataset containing annotated examples for all the languages is both expensive and time-consuming, especially when the number of target languages grows. Moreover, the original annotated material may no longer be available due to storage or business constraints. Re-training only with the new language data will inevitably result in Catastrophic Forgetting of previously acquired knowledge. We propose a Continual Learning strategy that updates a model to support new languages over time, while maintaining consistent results on previously learned languages. We define a Teacher-Student framework where the existing model “teaches” to a student model its knowledge about the languages it supports, while the student is also trained on a new language. We report an experimental evaluation in several tasks including Sentence Classification, Relational Learning and Sequence Labeling.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_107", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_107.flac", "type": "audio", "duration": 321.088}, {"from": "Assistant", "value": "Transformer is important for text modeling. However, it has difficulty in handling long documents due to the quadratic complexity with input text length. In order to handle this problem, we propose a hierarchical interactive Transformer (Hi-Transformer) for efficient and effective long document modeling. Hi-Transformer models documents in a hierarchical way, i.e., first learns sentence representations and then learns document representations. It can effectively reduce the complexity and meanwhile capture global document context in the modeling of each sentence. More specifically, we first use a sentence Transformer to learn the representations of each sentence. Then we use a document Transformer to model the global document context from these sentence representations. Next, we use another sentence Transformer to enhance sentence modeling using the global document context. Finally, we use hierarchical pooling method to obtain document embedding. Extensive experiments on three benchmark datasets validate the efficiency and effectiveness of Hi-Transformer in long document modeling.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_108", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_108.flac", "type": "audio", "duration": 424.902}, {"from": "Assistant", "value": "Transfer learning with large pretrained transformer-based language models like BERT has become a dominating approach for most NLP tasks. Simply fine-tuning those large language models on downstream tasks or combining it with task-specific pretraining is often not robust. In particular, the performance considerably varies as the random seed changes or the number of pretraining and/or fine-tuning iterations varies, and the fine-tuned model is vulnerable to adversarial attack. We propose a simple yet effective adapter-based approach to mitigate these issues. Specifically, we insert small bottleneck layers (i.e., adapter) within each layer of a pretrained model, then fix the pretrained layers and train the adapter layers on the downstream task data, with (1) task-specific unsupervised pretraining and then (2) task-specific supervised training (e.g., classification, sequence labeling). Our experiments demonstrate that such a training scheme leads to improved stability and adversarial robustness in transfer learning to various downstream tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_109", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_109.flac", "type": "audio", "duration": 382.061}, {"from": "Assistant", "value": "Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels. While many research works do not pay much attention to this fact, several recent efforts have been made to acknowledge and embrace the existence of ambiguity, such as UNLI and ChaosNLI. In this paper, we explore the option of training directly on the estimated label distribution of the annotators in the NLI task, using a learning loss based on this ambiguity distribution instead of the gold-labels. We prepare AmbiNLI, a trial dataset obtained from readily available sources, and show it is possible to reduce ChaosNLI divergence scores when finetuning on this data, a promising first step towards learning how to capture linguistic ambiguity. Additionally, we show that training on the same amount of data but targeting the ambiguity distribution instead of gold-labels can result in models that achieve higher performance and learn better representations for downstream tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_110", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_110.flac", "type": "audio", "duration": 421.248}, {"from": "Assistant", "value": "Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system. A key challenge of OOD detection is to learn discriminative semantic features. Traditional cross-entropy loss only focuses on whether a sample is correctly classified, and does not explicitly distinguish the margins between categories. In this paper, we propose a supervised contrastive learning objective to minimize intra-class variance by pulling together in-domain intents belonging to the same class and maximize inter-class variance by pushing apart samples from different classes. Besides, we employ an adversarial augmentation mechanism to obtain pseudo diverse views of a sample in the latent space. Experiments on two public datasets prove the effectiveness of our method capturing discriminative representations for OOD detection.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_111", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_111.flac", "type": "audio", "duration": 405.845}, {"from": "Assistant", "value": "Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset. In this paper, we propose to use curriculum learning (CL) to better leverage both the curriculum structure and schema structure for task-oriented dialogs. Specifically, we propose a model-agnostic framework called Schema-aware Curriculum Learning for Dialog State Tracking (SaCLog), which consists of a preview module that pre-trains a DST model with schema information, a curriculum module that optimizes the model with CL, and a review module that augments mispredicted data to reinforce the CL training. We show that our proposed approach improves DST performance over both a transformer-based and RNN-based DST model (TripPy and TRADE) and achieves new state-of-the-art results on WOZ2.0 and MultiWOZ2.1.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_112", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_112.flac", "type": "audio", "duration": 393.044}, {"from": "Assistant", "value": "Under the pandemic of COVID-19, people experiencing COVID19-related symptoms have a pressing need to consult doctors. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialog system that can provide COVID19-related consultations. We collected two dialog datasets – CovidDialog – (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19. While the largest of their kind, these two datasets are still relatively small compared with general-domain dialog datasets. Training complex dialog generation models on small datasets bears high risk of overfitting. To alleviate overfitting, we develop a multi-task learning approach, which regularizes the data-deficient dialog generation task with a masked token prediction task. Experiments on the CovidDialog datasets demonstrate the effectiveness of our approach. We perform both human evaluation and automatic evaluation of dialogs generated by our method. Results show that the generated responses are promising in being doctor-like, relevant to conversation history, clinically informative and correct. The code and the data are available athttps://github.com/UCSD-AI4H/COVID-Dialogue.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_113", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_113.flac", "type": "audio", "duration": 420.003}, {"from": "Assistant", "value": "In multi-modal dialogue systems, it is important to allow the use of images as part of a multi-turn conversation. Training such dialogue systems generally requires a large-scale dataset consisting of multi-turn dialogues that involve images, but such datasets rarely exist. In response, this paper proposes a 45k multi-modal dialogue dataset created with minimal human intervention. Our method to create such a dataset consists of (1) preparing and pre-processing text dialogue datasets, (2) creating image-mixed dialogues by using a text-to-image replacement technique, and (3) employing a contextual-similarity-based filtering step to ensure the contextual coherence of the dataset. To evaluate the validity of our dataset, we devise a simple retrieval model for dialogue sentence prediction tasks. Automatic metrics and human evaluation results on such tasks show that our dataset can be effectively used as training data for multi-modal dialogue systems which require an understanding of images and text in a context-aware manner. Our dataset and generation code is available athttps://github.com/shh1574/multi-modal-dialogue-dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_114", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_114.flac", "type": "audio", "duration": 417.054}, {"from": "Assistant", "value": "Reducing and counter-acting hate speech on Social Media is a significant concern. Most of the proposed automatic methods are conducted exclusively on English and very few consistently labeled, non-English resources have been proposed. Learning to detect hate speech on English and transferring to unseen languages seems an immediate solution. This work is the first to shed light on the limits of this zero-shot, cross-lingual transfer learning framework for hate speech detection. We use benchmark data sets in English, Italian, and Spanish to detect hate speech towards immigrants and women. Investigating post-hoc explanations of the model, we discover that non-hateful, language-specific taboo interjections are misinterpreted as signals of hate speech. Our findings demonstrate that zero-shot, cross-lingual models cannot be used as they are, but need to be carefully designed.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_115", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_115.flac", "type": "audio", "duration": 419.051}, {"from": "Assistant", "value": "Neural machine translation models are often biased toward the limited translation references seen during training. To amend this form of overfitting, in this paper we propose fine-tuning the models with a novel training objective based on the recently-proposed BERTScore evaluation metric. BERTScore is a scoring function based on contextual embeddings that overcomes the typical limitations of n-gram-based metrics (e.g. synonyms, paraphrases), allowing translations that are different from the references, yet close in the contextual embedding space, to be treated as substantially correct. To be able to use BERTScore as a training objective, we propose three approaches for generating soft predictions, allowing the network to remain completely differentiable end-to-end. Experiments carried out over four, diverse language pairs show improvements of up to 0.58 pp (3.28%) in BLEU score and up to 0.76 pp (0.98%) in BERTScore (F_BERT) when fine-tuning a strong baseline.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_116", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_116.flac", "type": "audio", "duration": 420.49}, {"from": "Assistant", "value": "Implicit discourse relation classification is a challenging task, in particular when the text domain is different from the standard Penn Discourse Treebank (PDTB; Prasad et al., 2008) training corpus domain (Wall Street Journal in 1990s). We here tackle the task of implicit discourse relation classification on the biomedical domain, for which the Biomedical Discourse Relation Bank (BioDRB; Prasad et al., 2011) is available. We show that entity information can be used to improve discourse relational argument representation. In a first step, we show that explicitly marked instances that are content-wise similar to the target relations can be used to achieve good performance in the cross-domain setting using a simple unsupervised voting pipeline. As a further step, we show that with the linked entity information from the first step, a transformer which is augmented with entity-related information (KBERT; Liu et al., 2020) sets the new state of the art performance on the dataset, outperforming the large pre-trained BioBERT (Lee et al., 2020) model by 2% points.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_117", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_117.flac", "type": "audio", "duration": 403.456}, {"from": "Assistant", "value": "In this work, we propose Masked Noun-Phrase Prediction (MNPP), a pre-training strategy to tackle pronoun resolution in a fully unsupervised setting. Firstly, We evaluate our pre-trained model on various pronoun resolution datasets without any finetuning. Our method outperforms all previous unsupervised methods on all datasets by large margins. Secondly, we proceed to a few-shot setting where we finetune our pre-trained model on WinoGrande-S and XS separately. Our method outperforms RoBERTa-large baseline with large margins, meanwhile, achieving a higher AUC score after further finetuning on the remaining three official splits of WinoGrande.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_118", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_118.flac", "type": "audio", "duration": 299.05}, {"from": "Assistant", "value": "Recently, question answering (QA) based on machine reading comprehension has become popular. This work focuses on generative QA which aims to generate an abstractive answer to a given question instead of extracting an answer span from a provided passage. Generative QA often suffers from two critical problems: (1) summarizing content irrelevant to a given question, (2) drifting away from a correct answer during generation. In this paper, we address these problems by a novel Rationale-Enriched Answer Generator (REAG), which incorporates an extractive mechanism into a generative model. Specifically, we add an extraction task on the encoder to obtain the rationale for an answer, which is the most relevant piece of text in an input document to a given question. Based on the extracted rationale and original input, the decoder is expected to generate an answer with high confidence. We jointly train REAG on the MS MARCO QA+NLG task and the experimental results show that REAG improves the quality and semantic accuracy of answers over baseline models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_119", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_119.flac", "type": "audio", "duration": 411.063}, {"from": "Assistant", "value": "In news articles the lead bias is a common phenomenon that usually dominates the learning signals for neural extractive summarizers, severely limiting their performance on data with different or even no bias. In this paper, we introduce a novel technique to demote lead bias and make the summarizer focus more on the content semantics. Experiments on two news corpora with different degrees of lead bias show that our method can effectively demote the model’s learned lead bias and improve its generality on out-of-distribution data, with little to no performance loss on in-distribution data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_120", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_120.flac", "type": "audio", "duration": 410.041}, {"from": "Assistant", "value": "Machine reading comprehension (MRC) is a crucial task in natural language processing and has achieved remarkable advancements. However, most of the neural MRC models are still far from robust and fail to generalize well in real-world applications. In order to comprehensively verify the robustness and generalization of MRC models, we introduce a real-world Chinese dataset – DuReader_robust . It is designed to evaluate the MRC models from three aspects: over-sensitivity, over-stability and generalization. Comparing to previous work, the instances in DuReader_robust are natural texts, rather than the altered unnatural texts. It presents the challenges when applying MRC models to real-world applications. The experimental results show that MRC models do not perform well on the challenge test set. Moreover, we analyze the behavior of existing models on the challenge test set, which may provide suggestions for future model development. The dataset and codes are publicly available athttps://github.com/baidu/DuReader.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_121", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_121.flac", "type": "audio", "duration": 418.816}, {"from": "Assistant", "value": "With the recent advancements in deep learning, neural solvers have gained promising results in solving math word problems. However, these SOTA solvers only generate binary expression trees that contain basic arithmetic operators and do not explicitly use the math formulas. As a result, the expression trees they produce are lengthy and uninterpretable because they need to use multiple operators and constants to represent one single formula. In this paper, we propose sequence-to-general tree (S2G) that learns to generate interpretable and executable operation trees where the nodes can be formulas with an arbitrary number of arguments. With nodes now allowed to be formulas, S2G can learn to incorporate mathematical domain knowledge into problem-solving, making the results more interpretable. Experiments show that S2G can achieve a better performance against strong baselines on problems that require domain knowledge.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_122", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_122.flac", "type": "audio", "duration": 420.699}, {"from": "Assistant", "value": "Understanding the multi-scale visual information in a video is essential for Video Question Answering (VideoQA). Therefore, we propose a novel Multi-Scale Progressive Attention Network (MSPAN) to achieve relational reasoning between cross-scale video information. We construct clips of different lengths to represent different scales of the video. Then, the clip-level features are aggregated into node features by using max-pool, and a graph is generated for each scale of clips. For cross-scale feature interaction, we design a message passing strategy between adjacent scale graphs, i.e., top-down scale interaction and bottom-up scale interaction. Under the question’s guidance of progressive attention, we realize the fusion of all-scale video features. Experimental evaluations on three benchmarks: TGIF-QA, MSVD-QA and MSRVTT-QA show our method has achieved state-of-the-art performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_123", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_123.flac", "type": "audio", "duration": 364.042}, {"from": "Assistant", "value": "Most state-of-the-art open-domain question answering systems use a neural retrieval model to encode passages into continuous vectors and extract them from a knowledge source. However, such retrieval models often require large memory to run because of the massive size of their passage index. In this paper, we introduce Binary Passage Retriever (BPR), a memory-efficient neural retrieval model that integrates a learning-to-hash technique into the state-of-the-art Dense Passage Retriever (DPR) to represent the passage index using compact binary codes rather than continuous vectors. BPR is trained with a multi-task objective over two tasks: efficient candidate generation based on binary codes and accurate reranking based on continuous vectors. Compared with DPR, BPR substantially reduces the memory cost from 65GB to 2GB without a loss of accuracy on two standard open-domain question answering benchmarks: Natural Questions and TriviaQA. Our code and trained models are available athttps://github.com/studio-ousia/bpr.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_124", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_124.flac", "type": "audio", "duration": 373.052}, {"from": "Assistant", "value": "Few-shot relation extraction (FSRE) is of great importance in long-tail distribution problem, especially in special domain with low-resource data. Most existing FSRE algorithms fail to accurately classify the relations merely based on the information of the sentences together with the recognized entity pairs, due to limited samples and lack of knowledge. To address this problem, in this paper, we proposed a novel entity CONCEPT-enhanced FEw-shot Relation Extraction scheme (ConceptFERE), which introduces the inherent concepts of entities to provide clues for relation prediction and boost the relations classification performance. Firstly, a concept-sentence attention module is developed to select the most appropriate concept from multiple concepts of each entity by calculating the semantic similarity between sentences and concepts. Secondly, a self-attention based fusion module is presented to bridge the gap of concept embedding and sentence embedding from different semantic spaces. Extensive experiments on the FSRE benchmark dataset FewRel have demonstrated the effectiveness and the superiority of the proposed ConceptFERE scheme as compared to the state-of-the-art baselines. Code is available athttps://github.com/LittleGuoKe/ConceptFERE.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_125", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_125.flac", "type": "audio", "duration": 390.049}, {"from": "Assistant", "value": "Generalization is an important ability that helps to ensure that a machine learning model can perform well on unseen data. In this paper, we study the effect of data bias on model generalization, using Chinese Named Entity Recognition (NER) as a case study. Specifically, we analyzed five benchmarking datasets for Chinese NER, and observed the following two types of data bias that can compromise model generalization ability. Firstly, the test sets of all the five datasets contain a significant proportion of entities that have been seen in the training sets. Such test data would therefore not be able to reflect the true generalization ability of a model. Secondly, all datasets are dominated by a few fat-head entities, i.e., entities appearing with particularly high frequency. As a result, a model might be able to produce high prediction accuracy simply by keyword memorization without leveraging context knowledge. To address these data biases, we first refine each test set by excluding seen entities from it, so as to better evaluate a model’s generalization ability. Then, we propose a simple yet effective entity resampling method to make entities within the same category distributed equally, encouraging a model to leverage both name and context knowledge in the training process. Experimental results demonstrate that the proposed entity resampling method significantly improves a model’s ability in detecting unseen entities, especially for company, organization and position categories.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_126", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_126.flac", "type": "audio", "duration": 420.281}, {"from": "Assistant", "value": "Document-level Relation Extraction (RE) is a more challenging task than sentence RE as it often requires reasoning over multiple sentences. Yet, human annotators usually use a small number of sentences to identify the relationship between a given entity pair. In this paper, we present an embarrassingly simple but effective method to heuristically select evidence sentences for document-level RE, which can be easily combined with BiLSTM to achieve good performance on benchmark datasets, even better than fancy graph neural network based methods. We have released our code athttps://github.com/AndrewZhe/Three-Sentences-Are-All-You-Need.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_127", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_127.flac", "type": "audio", "duration": 470.059}, {"from": "Assistant", "value": "Learning prerequisite chains is an important task for one to pick up knowledge efficiently in both known and unknown domains. For example, one may be an expert in the natural language processing (NLP) domain, but want to determine the best order in which to learn new concepts in an unfamiliar Computer Vision domain (CV). Both domains share some common concepts, such as machine learning basics and deep learning models. In this paper, we solve the task of unsupervised cross-domain concept prerequisite chain learning, using an optimized variational graph autoencoder. Our model learns to transfer concept prerequisite relations from an information-rich domain (source domain) to an information-poor domain (target domain), substantially surpassing other baseline models. In addition, we expand an existing dataset by introducing two new domains—-CV and Bioinformatics (BIO). The annotated data and resources as well as the code will be made publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_128", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_128.flac", "type": "audio", "duration": 421.047}, {"from": "Assistant", "value": "We present a text representation approach that can combine different views (representations) of the same input through effective data fusion and attention strategies for ranking purposes. We apply our model to the problem of differential diagnosis, which aims to find the most probable diseases that match with clinical descriptions of patients, using data from the Undiagnosed Diseases Network. Our model outperforms several ranking approaches (including a commercially-supported system) by effectively prioritizing and combining representations obtained from traditional and recent text representation techniques. We elaborate on several aspects of our model and shed light on its improved performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_129", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_129.flac", "type": "audio", "duration": 421.056}, {"from": "Assistant", "value": "Crowdworker-constructed natural language inference (NLI) datasets have been found to contain statistical artifacts associated with the annotation process that allow hypothesis-only classifiers to achieve better-than-random performance (CITATION). We investigate whether MedNLI, a physician-annotated dataset with premises extracted from clinical notes, contains such artifacts (CITATION). We find that entailed hypotheses contain generic versions of specific concepts in the premise, as well as modifiers related to responsiveness, duration, and probability. Neutral hypotheses feature conditions and behaviors that co-occur with, or cause, the condition(s) in the premise. Contradiction hypotheses feature explicit negation of the premise and implicit negation via assertion of good health. Adversarial filtering demonstrates that performance degrades when evaluated on thedifficultsubset. We provide partition information and recommendations for alternative dataset construction strategies for knowledge-intensive domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_130", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_130.flac", "type": "audio", "duration": 384.94}, {"from": "Assistant", "value": "With the explosion of chatbot applications, Conversational Question Answering (CQA) has generated a lot of interest in recent years. Among proposals, reading comprehension models which take advantage of the conversation history (previous QA) seem to answer better than those which only consider the current question. Nevertheless, we note that the CQA evaluation protocol has a major limitation. In particular, models are allowed, at each turn of the conversation, to access the ground truth answers of the previous turns. Not only does this severely prevent their applications in fully autonomous chatbots, it also leads to unsuspected biases in their behavior. In this paper, we highlight this effect and propose new tools for evaluation and training in order to guard against the noted issues. The new results that we bring come to reinforce methods of the current state of the art.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_131", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_131.flac", "type": "audio", "duration": 299.932}, {"from": "Assistant", "value": "Existing models on Machine Reading Comprehension (MRC) require complex model architecture for effectively modeling long texts with paragraph representation and classification, thereby making inference computationally inefficient for production use. In this work, we propose VAULT: a light-weight and parallel-efficient paragraph representation for MRC based on contextualized representation from long document input, trained using a new Gaussian distribution-based objective that pays close attention to the partially correct instances that are close to the ground-truth. We validate our VAULT architecture showing experimental results on two benchmark MRC datasets that require long context modeling; one Wikipedia-based (Natural Questions (NQ)) and the other on TechNotes (TechQA). VAULT can achieve comparable performance on NQ with a state-of-the-art (SOTA) complex document modeling approach while being 16 times faster, demonstrating the efficiency of our proposed model. We also demonstrate that our model can also be effectively adapted to a completely different domain – TechQA – with large improvement over a model fine-tuned on a previously published large PLM.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_132", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_132.flac", "type": "audio", "duration": 335.459}, {"from": "Assistant", "value": "Leveraging additional unlabeled data to boost model performance is common practice in machine learning and natural language processing. For generation tasks, if there is overlap between the additional data and the target text evaluation data, then training on the additional data is training on answers of the test set. This leads to overly-inflated scores with the additional data compared to real-world testing scenarios and problems when comparing models. We study the AMR dataset and Gigaword, which is popularly used for improving AMR-to-text generators, and find significant overlap between Gigaword and a subset of the AMR dataset. We propose methods for excluding parts of Gigaword to remove this overlap, and show that our approach leads to a more realistic evaluation of the task of AMR-to-text generation. Going forward, we give simple best-practice recommendations for leveraging additional data in AMR-to-text generation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_133", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_133.flac", "type": "audio", "duration": 369.045}, {"from": "Assistant", "value": "Social media has become a valuable resource for the study of suicidal ideation and the assessment of suicide risk. Among social media platforms, Reddit has emerged as the most promising one due to its anonymity and its focus on topic-based communities (subreddits) that can be indicative of someone’s state of mind or interest regarding mental health disorders such as r/SuicideWatch, r/Anxiety, r/depression. A challenge for previous work on suicide risk assessment has been the small amount of labeled data. We propose an empirical investigation into several classes of weakly-supervised approaches, and show that using pseudo-labeling based on related issues around mental health (e.g., anxiety, depression) helps improve model performance for suicide risk assessment.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_134", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_134.flac", "type": "audio", "duration": 415.808}, {"from": "Assistant", "value": "The Shuffle Test is the most common task to evaluate whether NLP models can measure coherence in text. Most recent work uses direct supervision on the task; we show that by simply finetuning a RoBERTa model, we can achieve a near perfect accuracy of 97.8%, a state-of-the-art. We argue that this outstanding performance is unlikely to lead to a good model of text coherence, and suggest that the Shuffle Test should be approached in a Zero-Shot setting: models should be evaluated without being trained on the task itself. We evaluate common models in this setting, such as Generative and Bi-directional Transformers, and find that larger architectures achieve high-performance out-of-the-box. Finally, we suggest the k-Block Shuffle Test, a modification of the original by increasing the size of blocks shuffled. Even though human reader performance remains high (around 95% accuracy), model performance drops from 94% to 78% as block size increases, creating a conceptually simple challenge to benchmark NLP models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_135", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_135.flac", "type": "audio", "duration": 418.048}, {"from": "Assistant", "value": "In this paper, we present a conceptually simple while empirically powerful framework for abstractive summarization, SimCLS, which can bridge the gap between the learning objective and evaluation metrics resulting from the currently dominated sequence-to-sequence learning framework by formulating text generation as a reference-free evaluation problem (i.e., quality estimation) assisted by contrastive learning. Experimental results show that, with minor modification over existing top-scoring systems, SimCLS can improve the performance of existing top-performing models by a large margin. Particularly, 2.51 absolute improvement against BART and 2.50 over PEGASUS w.r.t ROUGE-1 on the CNN/DailyMail dataset, driving the state-of-the-art performance to a new level. We have open-sourced our codes and results:https://github.com/yixinL7/SimCLS. Results of our proposed models have been deployed into ExplainaBoard platform, which allows researchers to understand our systems in a more fine-grained way.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_136", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_136.flac", "type": "audio", "duration": 388.053}, {"from": "Assistant", "value": "In this work, we introduce a corpus for satire detection in Romanian news. We gathered 55,608 public news articles from multiple real and satirical news sources, composing one of the largest corpora for satire detection regardless of language and the only one for the Romanian language. We provide an official split of the text samples, such that training news articles belong to different sources than test news articles, thus ensuring that models do not achieve high performance simply due to overfitting. We conduct experiments with two state-of-the-art deep neural models, resulting in a set of strong baselines for our novel corpus. Our results show that the machine-level accuracy for satire detection in Romanian is quite low (under 73% on the test set) compared to the human-level accuracy (87%), leaving enough room for improvement in future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_137", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_137.flac", "type": "audio", "duration": 457.34}, {"from": "Assistant", "value": "Faceted summarization provides briefings of a document from different perspectives. Readers can quickly comprehend the main points of a long document with the help of a structured outline. However, little research has been conducted on this subject, partially due to the lack of large-scale faceted summarization datasets. In this study, we present FacetSum, a faceted summarization benchmark built on Emerald journal articles, covering a diverse range of domains. Different from traditional document-summary pairs, FacetSum provides multiple summaries, each targeted at specific sections of a long document, including the purpose, method, findings, and value. Analyses and empirical results on our dataset reveal the importance of bringing structure into summaries. We believe FacetSum will spur further advances in summarization research and foster the development of NLP systems that can leverage the structured information in both long texts and summaries.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_138", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_138.flac", "type": "audio", "duration": 521.643}, {"from": "Assistant", "value": "Søgaard (2020) obtained results suggesting the fraction of trees occurring in the test data isomorphic to trees in the training set accounts for a non-trivial variation in parser performance. Similar to other statistical analyses in NLP, the results were based on evaluating linear regressions. However, the study had methodological issues and was undertaken using a small sample size leading to unreliable results. We present a replication study in which we also bin sentences by length and find that only a small subset of sentences vary in performance with respect to graph isomorphism. Further, the correlation observed between parser performance and graph isomorphism in the wild disappears when controlling for covariants. However, in a controlled experiment, where covariants are kept fixed, we do observe a correlation. We suggest that conclusions drawn from statistical analyses like this need to be tempered and that controlled experiments can complement them by more readily teasing factors apart.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-short_139", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-short_139.flac", "type": "audio", "duration": 408.043}, {"from": "Assistant", "value": "High-performing machine translation (MT) systems can help overcome language barriers while making it possible for everyone to communicate and use language technologies in the language of their choice. However, such systems require large amounts of parallel sentences for training, and translators can be difficult to find and expensive. Here, we present a data collection strategy for MT which, in contrast, is cheap and simple, as it does not require bilingual speakers. Based on the insight that humans pay specific attention to movements, we use graphics interchange formats (GIFs) as a pivot to collect parallel sentences from monolingual annotators. We use our strategy to collect data in Hindi, Tamil and English. As a baseline, we also collect data using images as a pivot. We perform an intrinsic evaluation by manually evaluating a subset of the sentence pairs and an extrinsic evaluation by finetuning mBART (Liu et al., 2020) on the collected data. We find that sentences collected via GIFs are indeed of higher quality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_1", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_1.flac", "type": "audio", "duration": 653.015}, {"from": "Assistant", "value": "Data processing is an important step in various natural language processing tasks. As the commonly used datasets in named entity recognition contain only a limited number of samples, it is important to obtain additional labeled data in an efficient and reliable manner. A common practice is to utilize large monolingual unlabeled corpora. Another popular technique is to create synthetic data from the original labeled data (data augmentation). In this work, we investigate the impact of these two methods on the performance of three different named entity recognition tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_2", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_2.flac", "type": "audio", "duration": 404.564}, {"from": "Assistant", "value": "Graph-to-text generation has benefited from pre-trained language models (PLMs) in achieving better performance than structured graph encoders. However, they fail to fully utilize the structure information of the input graph. In this paper, we aim to further improve the performance of the pre-trained language model by proposing a structured graph-to-text model with a two-step fine-tuning mechanism which first fine-tunes model on Wikipedia before adapting to the graph-to-text generation. In addition to using the traditional token and position embeddings to encode the knowledge graph (KG), we propose a novel tree-level embedding method to capture the inter-dependency structures of the input graph. This new approach has significantly improved the performance of all text generation metrics for the English WebNLG 2017 dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_3", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_3.flac", "type": "audio", "duration": 419.328}, {"from": "Assistant", "value": "The neural hidden Markov model has been proposed as an alternative to attention mechanism in machine translation with recurrent neural networks. However, since the introduction of the transformer models, its performance has been surpassed. This work proposes to introduce the concept of the hidden Markov model to the transformer architecture, which outperforms the transformer baseline. Interestingly, we find that the zero-order model already provides promising performance, giving it an edge compared to a model with first-order dependency, which performs similarly but is significantly slower in training and decoding.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_4", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_4.flac", "type": "audio", "duration": 600.932}, {"from": "Assistant", "value": "Although BERT based relation classification (RC) models have achieved significant improvements over the traditional deep learning models, it seems that no consensus can be reached on what is the optimal architecture, since there are many design choices available. In this work, we design a comprehensive search space for BERT based RC models and employ a modified version of efficient neural architecture search (ENAS) method to automatically discover the design choices mentioned above. Experiments on eight benchmark RC tasks show that our method is efficient and effective in finding better architectures than the baseline BERT based RC models. Ablation study demonstrates the necessity of our search space design and the effectiveness of our search method. We also show that our framework can also apply to other entity related tasks like coreference resolution and span based named entity recognition (NER).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_5", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_5.flac", "type": "audio", "duration": 986.128}, {"from": "Assistant", "value": "Despite the recent advancements of attention-based deep learning architectures across a majority of Natural Language Processing tasks, their application remains limited in a low-resource setting because of a lack of pre-trained models for such languages. In this study, we make the first attempt to investigate the challenges of adapting these techniques to an extremely low-resource language – Sumerian cuneiform – one of the world’s oldest written languages attested from at least the beginning of the 3rd millennium BC. Specifically, we introduce the first cross-lingual information extraction pipeline for Sumerian, which includes part-of-speech tagging, named entity recognition, and machine translation. We introduce InterpretLR, an interpretability toolkit for low-resource NLP and use it alongside human evaluations to gauge the trained models. Notably, all our techniques and most components of our pipeline can be generalised to any low-resource language. We publicly release all our implementations including a novel data set with domain-specific pre-processing to promote further research in this domain.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_6", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_6.flac", "type": "audio", "duration": 700.012}, {"from": "Assistant", "value": "This paper studies whether emergent languages in a signaling game follow Zipf’s law of abbreviation (ZLA), especially when the communication ability of agents is limited because of interfering noises. ZLA is a well-known tendency in human languages where the more frequently a word is used, the shorter it will be. Surprisingly, previous work demonstrated that emergent languages do not obey ZLA at all when neural agents play a signaling game. It also reported that a ZLA-like tendency appeared by adding an explicit penalty on word lengths, which can be considered some external factors in reality such as articulatory effort. We hypothesize, on the other hand, that there might be not only such external factors but also some internal factors related to cognitive abilities. We assume that it could be simulated by modeling the effect of noises on the agents’ environment. In our experimental setup, the hidden states of the LSTM-based speaker and listener were added with Gaussian noise, while the channel was subject to discrete random replacement. Our results suggest that noise on a speaker is one of the factors for ZLA or at least causes emergent languages to approach ZLA, while noise on a listener and a channel is not.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_7", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_7.flac", "type": "audio", "duration": 464.16}, {"from": "Assistant", "value": "Abstractive summarization is the task of compressing a long document into a coherent short document while retaining salient information. Modern abstractive summarization methods are based on deep neural networks which often require large training datasets. Since collecting summarization datasets is an expensive and time-consuming task, practical industrial settings are usually low-resource. In this paper, we study a challenging low-resource setting of summarizing long legal briefs with an average source document length of 4268 words and only 120 available (document, summary) pairs. To account for data scarcity, we used a modern pre-trained abstractive summarizer BART, which only achieves 17.9 ROUGE-L as it struggles with long documents. We thus attempt to compress these long documents by identifying salient sentences in the source which best ground the summary, using a novel algorithm based on GPT-2 language model perplexity scores, that operates within the low resource regime. On feeding the compressed documents to BART, we observe a 6.0 ROUGE-L improvement. Our method also beats several competitive salience detection baselines. Furthermore, the identified salient sentences tend to agree with independent human labeling by domain experts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_8", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_8.flac", "type": "audio", "duration": 399.012}, {"from": "Assistant", "value": "The impressive performances of pre-trained visually grounded language models have motivated a growing body of research investigating what has been learned during the pre-training. As a lot of these models are based on Transformers, several studies on the attention mechanisms used by the models to learn to associate phrases with their visual grounding in the image have been conducted. In this work, we investigate how supervising attention directly to learn visual grounding can affect the behavior of such models. We compare three different methods on attention supervision and their impact on the performances of a state-of-the-art visually grounded language model on two popular vision-and-language tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_11", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_11.flac", "type": "audio", "duration": 720.004}, {"from": "Assistant", "value": "Natural language generation systems have witnessed important progress in the last years, but they are shown to generate tokens that are unrelated to the source input. This problem affects computational models in many NLP tasks, and it is particularly unpleasant in multimodal systems. In this work, we assess the rate of object hallucination in multimodal conversational agents playing the GuessWhat?! referential game. Better visual processing has been shown to mitigate this issue in image captioning; hence, we adapt to the GuessWhat?! task the best visual processing models at disposal, and propose two new models to play the Questioner agent. We show that the new models generate few hallucinations compared to other renowned models available in the literature. Moreover, their hallucinations are less severe (affect task-accuracy less) and are more human-like. We also analyse where hallucinations tend to occur more often through the dialogue: hallucinations are less frequent in earlier turns, cause a cascade hallucination effect, and are often preceded by negative answers, which have been shown to be harder to ground.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_12", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_12.flac", "type": "audio", "duration": 758.906}, {"from": "Assistant", "value": "India is one of the most linguistically diverse nations of the world and is culturally very rich. Most of these languages are somewhat similar to each other on account of sharing a common ancestry or being in contact for a long period of time. Nowadays, researchers are constantly putting efforts in utilizing the language relatedness to improve the performance of various NLP systems such as cross lingual semantic search, machine translation, sentiment analysis systems, etc. So in this paper, we performed an extensive case study on similarity involving languages of the Indian subcontinent. Language similarity prediction is defined as the task of measuring how similar the two languages are on the basis of their lexical, morphological and syntactic features. In this study, we concentrate only on the approach to calculate lexical similarity between Indian languages by looking at various factors such as size and type of corpus, similarity algorithms, subword segmentation, etc. The main takeaways from our work are: (i) Relative order of the language similarities largely remain the same, regardless of the factors mentioned above, (ii) Similarity within the same language family is higher, (iii) Languages share more lexical features at the subword level.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_13", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_13.flac", "type": "audio", "duration": 693.184}, {"from": "Assistant", "value": "Social media is often used by individuals and organisations as a platform to spread misinformation. With the recent coronavirus pandemic we have seen a surge of misinformation on Twitter, posing a danger to public health. In this paper, we compile a large COVID-19 Twitter misinformation corpus and perform an analysis to discover patterns with respect to vocabulary usage. Among others, our analysis reveals that the variety of topics and vocabulary usage are considerably more limited and negative in tweets related to misinformation than in randomly extracted tweets. In addition to our qualitative analysis, our experimental results show that a simple linear model based only on lexical features is effective in identifying misinformation-related tweets (with accuracy over 80%), providing evidence to the fact that the vocabulary used in misinformation largely differs from generic tweets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_14", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_14.flac", "type": "audio", "duration": 557.0}, {"from": "Assistant", "value": "Currently, text chatting is one of the primary means of communication. However, modern text chat still in general does not offer any navigation or even full-featured search, although the high volumes of messages demand it. In order to mitigate these inconveniences, we formulate the problem of situation-based summarization and propose a special data annotation tool intended for developing training and gold-standard data. A situation is a subset of messages revolving around a single event in both temporal and contextual senses: e.g, a group of friends arranging a meeting in chat, agreeing on date, time, and place. Situations can be extracted via information retrieval, natural language processing, and machine learning techniques. Since the task is novel, neither training nor gold-standard datasets for it have been created yet. In this paper, we present the formulation of the situation-based summarization problem. Next, we describe Chat Corpora Annotator (CCA): the first annotation system designed specifically for exploring and annotating chat log data. We also introduce a custom query language for semi-automatic situation extraction. Finally, we present the first gold-standard dataset for situation-based summarization. The software source code and the dataset are publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_16", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_16.flac", "type": "audio", "duration": 390.002}, {"from": "Assistant", "value": "The semantics of a text is manifested not only by what is read but also by what is not read. In this article, we will study how those implicit “not read” information such as end-of-paragraph () and end-of-sequence () affect the quality of text generation. Specifically, we find that the pre-trained language model GPT2 can generate better continuations by learning to generate the in the fine-tuning stage. Experimental results on English story generation show that can lead to higher BLEU scores and lower perplexity. We also conduct experiments on a self-collected Chinese essay dataset with Chinese-GPT2, a character level LM without and during pre-training. Experimental results show that the Chinese GPT2 can generate better essay endings with .", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_17", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_17.flac", "type": "audio", "duration": 668.015}, {"from": "Assistant", "value": "Unsupervised cross-lingual word embedding(CLWE) methods learn a linear transformation matrix that maps two monolingual embedding spaces that are separately trained with monolingual corpora. This method relies on the assumption that the two embedding spaces are structurally similar, which does not necessarily hold true in general. In this paper, we argue that using a pseudo-parallel corpus generated by an unsupervised machine translation model facilitates the structural similarity of the two embedding spaces and improves the quality of CLWEs in the unsupervised mapping method. We show that our approach outperforms other alternative approaches given the same amount of data, and, through detailed analysis, we show that data augmentation with the pseudo data from unsupervised machine translation is especially effective for mapping-based CLWEs because (1) the pseudo data makes the source and target corpora (partially) parallel; (2) the pseudo data contains information on the original language that helps to learn similar embedding spaces between the source and target languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_18", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_18.flac", "type": "audio", "duration": 400.01}, {"from": "Assistant", "value": "Constructing knowledge graphs from unstructured text is an important task that is relevant to many domains. Most previous work focuses on extracting information from sentences or paragraphs, due to the difficulty of analyzing longer contexts. In this paper we propose a new jointly trained model that can be used for various information extraction tasks at the document level. The tasks performed by this system are entity and event identification, typing, and coreference resolution. In order to improve entity and event typing, we utilize context-aware representations aggregated from the detected mentions of the corresponding entities and events across the entire document. By extending our system to document-level, we can improve our results by incorporating cross-sentence dependencies and additional contextual information that might not be available at the sentence level, which allows for more globally optimized predictions. We evaluate our system on documents from the ACE05-E+ dataset and find significant improvement over the sentence-level SOTA on entity and event trigger identification and classification.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_19", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_19.flac", "type": "audio", "duration": 422.325}, {"from": "Assistant", "value": "Television shows play an important role inpropagating societal norms. Owing to the popularity of the situational comedy (sitcom) genre, it contributes significantly to the over-all development of society. In an effort to analyze the content of television shows belong-ing to this genre, we present a dataset of dialogue turns from popular sitcoms annotated for the presence of sexist remarks. We train a text classification model to detect sexism using domain adaptive learning. We apply the model to our dataset to analyze the evolution of sexist content over the years. We propose a domain-specific semi-supervised architecture for the aforementioned detection of sexism. Through extensive experiments, we show that our model often yields better classification performance over generic deep learn-ing based sentence classification that does not employ domain-specific training. We find that while sexism decreases over time on average,the proportion of sexist dialogue for the most sexist sitcom actually increases. A quantitative analysis along with a detailed error analysis presents the case for our proposed methodology", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_20", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_20.flac", "type": "audio", "duration": 403.051}, {"from": "Assistant", "value": "In this paper we present our observations and evaluations by observing the linguistic performance of the system on several steps on the training process of various English-to-German Neural Machine Translation models. The linguistic performance is measured through a semi-automatic process using a test suite. Among several linguistic observations, we find that the translation quality of some linguistic categories decreased within the recorded iterations. Additionally, we notice some drops of the translation quality of certain categories when using a larger corpus.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_21", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_21.flac", "type": "audio", "duration": 639.013}, {"from": "Assistant", "value": "Question answering (QA) models for reading comprehension have achieved human-level accuracy on in-distribution test sets. However, they have been demonstrated to lack robustness to challenge sets, whose distribution is different from that of training sets. Existing data augmentation methods mitigate this problem by simply augmenting training sets with synthetic examples sampled from the same distribution as the challenge sets. However, these methods assume that the distribution of a challenge set is known a priori, making them less applicable to unseen challenge sets. In this study, we focus on question-answer pair generation (QAG) to mitigate this problem. While most existing QAG methods aim to improve the quality of synthetic examples, we conjecture that diversity-promoting QAG can mitigate the sparsity of training sets and lead to better robustness. We present a variational QAG model that generates multiple diverse QA pairs from a paragraph. Our experiments show that our method can improve the accuracy of 12 challenge sets, as well as the in-distribution accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_22", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_22.flac", "type": "audio", "duration": 416.009}, {"from": "Assistant", "value": "The quality of the annotated data directly influences in the success of supervised NLP models. However, creating annotated datasets is often time-consuming and expensive. Although the annotation tool takes an important role, we know little about how it influences annotation quality. We compare the quality of annotations for the task of chat-untangling made by non-experts annotators using two different tools. The first is SLATE, an existing command-line based tool, and the second is Parlay, a new tool we developed that integrates mouse interaction and visual links. Our experimental results indicate that, while both tools perform similarly in terms of annotation quality, Parlay offers a significantly better user experience.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_25", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_25.flac", "type": "audio", "duration": 664.704}, {"from": "Assistant", "value": "The application of transformer-based contextual representations has became a de facto solution for solving complex NLP tasks. Despite their successes, such representations are arguably opaque as their latent dimensions are not directly interpretable. To alleviate this limitation of contextual representations, we devise such an algorithm where the output representation expresses human-interpretable information of each dimension. We achieve this by constructing a transformation matrix based on the semantic content of the embedding space and predefined semantic categories using Hellinger distance. We evaluate our inferred representations on supersense prediction task. Our experiments reveal that the interpretable nature of transformed contextual representations makes it possible to accurately predict the supersense category of a word by simply looking for its transformed coordinate with the largest coefficient. We quantify the effects of our proposed transformation when applied over traditional dense contextual embeddings. We additionally investigate and report consistent improvements for the integration of sparse contextual word representations into our proposed algorithm.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_27", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_27.flac", "type": "audio", "duration": 677.976}, {"from": "Assistant", "value": "Despite the development of pre-trained language models (PLMs) significantly raise the performances of various Chinese natural language processing (NLP) tasks, the vocabulary (vocab) for these Chinese PLMs remains to be the one provided by Google Chinese BERT (CITATION), which is based on Chinese characters (chars). Second, the masked language model pre-training is based on a single vocab, limiting its downstream task performances. In this work, we first experimentally demonstrate that building a vocab via Chinese word segmentation (CWS) guided sub-word tokenization (SGT) can improve the performances of Chinese PLMs. Then we propose two versions of multi-vocab pre-training (MVP), Hi-MVP and AL-MVP, to improve the models’ expressiveness. Experiments show that: (a) MVP training strategies improve PLMs’ downstream performances, especially it can improve the PLM’s performances on span-level tasks; (b) our AL-MVP outperforms the recent AMBERT (CITATION) after large-scale pre-training, and it is more robust against adversarial attacks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_28", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_28.flac", "type": "audio", "duration": 606.018}, {"from": "Assistant", "value": "The internet has actually come to be an essential resource of health knowledge for individuals around the world in the present situation of the coronavirus condition pandemic(COVID-19). During pandemic situations, myths, sensationalism, rumours and misinformation, generated intentionally or unintentionally, spread rapidly through social networks. Twitter is one of these popular social networks people use to share COVID-19 related news, information, and thoughts that reflect their perception and opinion about the pandemic. Evaluation of tweets for recognizing misinformation can create beneficial understanding to review the top quality and also the readability of online information concerning the COVID-19. This paper presents a multilingual COVID-19 related tweet analysis method, CMTA, that uses BERT, a deep learning model for multilingual tweet misinformation detection and classification. CMTA extracts features from multilingual textual data, which is then categorized into specific information classes. Classification is done by a Dense-CNN model trained on tweets manually annotated into information classes (i.e., ‘false’, ‘partly false’, ‘misleading’). The paper presents an analysis of multilingual tweets from February to June, showing the distribution type of information spread across different languages. To access the performance of the CMTA multilingual model, we performed a comparative analysis of 8 monolingual model and CMTA for the misinformation detection task. The results show that our proposed CMTA model has surpassed various monolingual models which consolidated the fact that through transfer learning a multilingual framework could be developed.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_29", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_29.flac", "type": "audio", "duration": 793.855}, {"from": "Assistant", "value": "Individuals with autism spectrum disorder (ASD) experience difficulties in social aspects of communication, but the linguistic characteristics associated with deficits in discourse and pragmatic expression are often difficult to precisely identify and quantify. We are currently collecting a corpus of transcribed natural conversations produced in an experimental setting in which participants with and without ASD complete a number of collaborative tasks with their neurotypical peers. Using this dyadic conversational data, we investigate three pragmatic features – politeness, uncertainty, and informativeness – and present a dataset of utterances annotated for each of these features on a three-point scale. We then introduce ongoing work in developing and training neural models to automatically predict these features, with the goal of identifying the same between-groups differences that are observed using manual annotations. We find the best performing model for all three features is a feed-forward neural network trained with BERT embeddings. Our models yield higher accuracy than ones used in previous approaches for deriving these features, with F1 exceeding 0.82 for all three pragmatic features.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_30", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_30.flac", "type": "audio", "duration": 558.022}, {"from": "Assistant", "value": "Most earlier work on text summarization is carried out on news article datasets. The summary in these datasets is naturally located at the beginning of the text. Hence, a model can spuriously utilize this correlation for summary generation instead of truly learning to summarize. To address this issue, we constructed a new dataset, SumPubMed , using scientific articles from the PubMed archive. We conducted a human analysis of summary coverage, redundancy, readability, coherence, and informativeness on SumPubMed . SumPubMed is challenging because (a) the summary is distributed throughout the text (not-localized on top), and (b) it contains rare domain-specific scientific terms. We observe that seq2seq models that adequately summarize news articles struggle to summarize SumPubMed . Thus, SumPubMed opens new avenues for the future improvement of models as well as the development of new evaluation metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_31", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_31.flac", "type": "audio", "duration": 342.688}, {"from": "Assistant", "value": "The events that took place at the Unite the Right rally held in Charlottesville, Virginia on August 11-12, 2017 caused intense reaction on social media from users across the political spectrum. We present a novel application of psycholinguistics - specifically, construal level theory - to analyze the language on social media around this event of social import through topic models. We find that including psycholinguistic measures of concreteness as covariates in topic models can lead to informed analysis of the language surrounding an event of political import.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_32", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_32.flac", "type": "audio", "duration": 509.024}, {"from": "Assistant", "value": "Misleading information spreads on the Internet at an incredible speed, which can lead to irreparable consequences in some cases. Therefore, it is becoming essential to develop fake news detection technologies. While substantial work has been done in this direction, one of the limitations of the current approaches is that these models are focused only on one language and do not use multilingual information. In this work, we propose a new technique based on cross-lingual evidence (CE) that can be used for fake news detection and improve existing approaches. The hypothesis of the usage of cross-lingual evidence as a feature for fake news detection is confirmed, firstly, by manual experiment based on a set of known true and fake news. Besides, we compared our fake news classification system based on the proposed feature with several strong baselines on two multi-domain datasets of general-topic news and one newly fake COVID-19 news dataset showing that combining cross-lingual evidence with strong baselines such as RoBERTa yields significant improvements in fake news detection.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_33", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_33.flac", "type": "audio", "duration": 669.821}, {"from": "Assistant", "value": "It is reported that grammatical information is useful for machine translation (MT) task. However, the annotation of grammatical information requires the highly human resources. Furthermore, it is not trivial to adapt grammatical information to MT since grammatical annotation usually adapts tokenization standards which might not be suitable to capture the relation of two languages, and the use of sub-word tokenization, e.g., Byte-Pair-Encoding, to alleviate out-of-vocabulary problem might not be compatible with those annotations. In this work, we propose two methods to explicitly incorporate grammatical information without supervising annotation; first, latent phrase structure is induced in an unsupervised fashion from a multi-head attention mechanism; second, the induced phrase structures in encoder and decoder are synchronized so that they are compatible with each other using constraints during training. We demonstrate that our approach produces better performance and explainability in two tasks, translation and alignment tasks without extra resources. Although we could not obtain the high quality phrase structure in constituency parsing when evaluated monolingually, we find that the induced phrase structures enhance the explainability of translation through the synchronization constraint.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_34", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_34.flac", "type": "audio", "duration": 415.611}, {"from": "Assistant", "value": "The presence of zero-pronoun (ZP) greatly affects the downstream tasks of NLP in pro-drop languages such as Japanese and Chinese. To tackle the problem, the previous works identified ZPs as sequence labeling on the word sequence or the linearlized tree nodes of the input. We propose a novel approach to ZP identification by casting it as a query-based argument span prediction task. Given a predicate as a query, our model predicts the omission with ZP. In the experiments, our model surpassed the sequence labeling baseline.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-srw_35", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-srw_35.flac", "type": "audio", "duration": 803.016}, {"from": "Assistant", "value": "Various studies show that pretrained language models such as BERT cannot straightforwardly replace encoders in neural machine translation despite their enormous success in other tasks. This is even more astonishing considering the similarities between the architectures. This paper sheds some light on the embedding spaces they create, using average cosine similarity, contextuality metrics and measures for representational similarity for comparison, revealing that BERT and NMT encoder representations look significantly different from one another. In order to address this issue, we propose a supervised transformation from one into the other using explicit alignment and fine-tuning. Our results demonstrate the need for such a transformation to improve the applicability of BERT in MT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_2", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_2.flac", "type": "audio", "duration": 292.971}, {"from": "Assistant", "value": "We present IntelliCAT, an interactive translation interface with neural models that streamline the post-editing process on machine translation output. We leverage two quality estimation (QE) models at different granularities: sentence-level QE, to predict the quality of each machine-translated sentence, and word-level QE, to locate the parts of the machine-translated sentence that need correction. Additionally, we introduce a novel translation suggestion model conditioned on both the left and right contexts, providing alternatives for specific words or phrases for correction. Finally, with word alignments, IntelliCAT automatically preserves the original document’s styles in the translated document. The experimental results show that post-editing based on the proposed QE and translation suggestions can significantly improve translation quality. Furthermore, a user study reveals that three features provided in IntelliCAT significantly accelerate the post-editing task, achieving a 52.9% speedup in translation time compared to translating from scratch. The interface is publicly available athttps://intellicat.beringlab.com/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_3", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_3.flac", "type": "audio", "duration": 286.059}, {"from": "Assistant", "value": "This paper announces version 1.0 of the Classical Language Toolkit (CLTK), an NLP framework for pre-modern languages. The vast majority of NLP, its algorithms and software, is created with assumptions particular to living languages, thus neglecting certain important characteristics of largely non-spoken historical languages. Further, scholars of pre-modern languages often have different goals than those of living-language researchers. To fill this void, the CLTK adapts ideas from several leading NLP frameworks to create a novel software architecture that satisfies the unique needs of pre-modern languages and their researchers. Its centerpiece is a modular processing pipeline that balances the competing demands of algorithmic diversity with pre-configured defaults. The CLTK currently provides pipelines, including models, for almost 20 languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_4", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_4.flac", "type": "audio", "duration": 276.971}, {"from": "Assistant", "value": "In this paper, we release an open-source library, called TextBox, to provide a unified, modularized, and extensible text generation framework. TextBox aims to support a broad set of text generation tasks and models. In our library, we implement 21 text generation models on 9 benchmark datasets, covering the categories of VAE, GAN, and pretrained language models. Meanwhile, our library maintains sufficient modularity and extensibility by properly decomposing the model architecture, inference, and learning process into highly reusable modules, which allows users to easily incorporate new models into our framework. The above features make TextBox especially suitable for researchers and practitioners to quickly reproduce baseline models and develop new models. TextBox is implemented based on PyTorch, and released under Apache License 2.0 at the linkhttps://github.com/RUCAIBox/TextBox.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_5", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_5.flac", "type": "audio", "duration": 299.413}, {"from": "Assistant", "value": "ASCENT is a fully automated methodology for extracting and consolidating commonsense assertions from web contents (Nguyen et al., 2021). It advances traditional triple-based commonsense knowledge representation by capturing semantic facets like locations and purposes, and composite concepts, i.e., subgroups and related aspects of subjects. In this demo, we present a web portal that allows users to understand its construction process, explore its content, and observe its impact in the use case of question answering. The demo website (https://ascent.mpi-inf.mpg.de) and an introductory video (https://youtu.be/qMkJXqu_Yd4) are both available online.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_6", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_6.flac", "type": "audio", "duration": 125.888}, {"from": "Assistant", "value": "Scientific knowledge is evolving at an unprecedented rate of speed, with new concepts constantly being introduced from millions of academic articles published every month. In this paper, we introduce a self-supervised end-to-end system, SciConceptMiner, for the automatic capture of emerging scientific concepts from both independent knowledge sources (semi-structured data) and academic publications (unstructured documents). First, we adopt a BERT-based sequence labeling model to predict candidate concept phrases with self-supervision data. Then, we incorporate rich Web content for synonym detection and concept selection via a web search API. This two-stage approach achieves highly accurate (94.7%) concept identification with more than 740K scientific concepts. These concepts are deployed in the Microsoft Academic production system and are the backbone for its semantic search capability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_8", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_8.flac", "type": "audio", "duration": 292.459}, {"from": "Assistant", "value": "With more than 7000 languages worldwide, multilingual natural language processing (NLP) is essential both from an academic and commercial perspective. Researching typological properties of languages is fundamental for progress in multilingual NLP. Examples include assessing language similarity for effective transfer learning, injecting inductive biases into machine learning models or creating resources such as dictionaries and inflection tables. We provide ParCourE, an online tool that allows to browse a word-aligned parallel corpus, covering 1334 languages. We give evidence that this is useful for typological research. ParCourE can be set up for any parallel corpus and can thus be used for typological research on other corpora as well as for exploring their quality and properties.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_9", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_9.flac", "type": "audio", "duration": 283.2}, {"from": "Assistant", "value": "We present MT-Telescope, a visualization platform designed to facilitate comparative analysis of the output quality of two Machine Translation (MT) systems. While automated MT evaluation metrics are commonly used to evaluate MT systems at a corpus-level, our platform supports fine-grained segment-level analysis and interactive visualisations that expose the fundamental differences in the performance of the compared systems. MT-Telescope also supports dynamic corpus filtering to enable focused analysis on specific phenomena such as; translation of named entities, handling of terminology, and the impact of input segment length on translation quality. Furthermore, the platform provides a bootstrapped t-test for statistical significance as a means of evaluating the rigor of the resulting system ranking. MT-Telescope is open source, written in Python, and is built around a user friendly and dynamic web interface. Complementing other existing tools, our platform is designed to facilitate and promote the broader adoption of more rigorous analysis practices in the evaluation of MT quality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_10", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_10.flac", "type": "audio", "duration": 299.413}, {"from": "Assistant", "value": "Health professional regulators aim to protect the health and well-being of patients and the public by setting standards for scrutinising and overseeing the training and conduct of health and care professionals. A major task of such regulators is the investigation of complaints against practitioners. However, processing a complaint often lasts several months and is particularly costly. Hence, we worked with international regulators from different countries (the UK, US and Australia), to develop the first decision support tool that aims to help such regulators process complaints more efficiently. Our system uses state-of-the-art machine learning and natural language processing techniques to process complaints and predict their risk level. Our tool also provides additional useful information including explanations, to help the regulatory staff interpret the prediction results, and similar past cases as well as non-compliance to regulations, to support the decision making.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_12", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_12.flac", "type": "audio", "duration": 294.165}, {"from": "Assistant", "value": "We present fastHan, an open-source toolkit for four basic tasks in Chinese natural language processing: Chinese word segmentation (CWS), Part-of-Speech (POS) tagging, named entity recognition (NER), and dependency parsing. The backbone of fastHan is a multi-task model based on a pruned BERT, which uses the first 8 layers in BERT. We also provide a 4-layer base model compressed from the 8-layer model. The joint-model is trained and evaluated on 13 corpora of four tasks, yielding near state-of-the-art (SOTA) performance in dependency parsing and NER, achieving SOTA performance in CWS and POS. Besides, fastHan’s transferability is also strong, performing much better than popular segmentation tools on a non-training corpus. To better meet the need of practical application, we allow users to use their own labeled data to further fine-tune fastHan. In addition to its small size and excellent performance, fastHan is user-friendly. Implemented as a python package, fastHan isolates users from the internal technical details and is convenient to use. The project is released on Github.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_13", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_13.flac", "type": "audio", "duration": 252.459}, {"from": "Assistant", "value": "In this paper, we present Tintful, an NLP annotation software that can be used both to manually annotate texts and to fix mistakes in NLP pipelines, such as Stanford CoreNLP. Using a paradigm similar to wiki-like systems, a user who notices some wrong annotation can easily fix it and submit the resulting (and right) entry back to the tool developers. Moreover, Tintful can be used to easily annotate data from scratch. The input documents do not need to be in a particular format: starting from the plain text, the sentences are first annotated with CoreNLP, then the user can edit the annotations and submit everything back through a user-friendly interface.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_14", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_14.flac", "type": "audio", "duration": 297.643}, {"from": "Assistant", "value": "We introduce Explainable Scientific Research Assistant (ESRA), a literature discovery platform that augments search results with relevant details and explanations, aiding users in understanding more about their queries and the returned papers beyond existing literature search systems. Enabled by a knowledge graph we extracted from abstracts of 23k papers on the arXiv’s cs.CL category, ESRA provides three main features: explanation (for why a paper is returned to the user), list of facts (that are relevant to the query), and graph visualization (drawing connections between the query and each paper with surrounding related entities). The experimental results with humans involved show that ESRA can accelerate the users’ search process with paper explanations and helps them better explore the landscape of the topics of interest by exploiting the underlying knowledge graph. We provide the ESRA web application athttp://esra.cp.eng.chula.ac.th/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_16", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_16.flac", "type": "audio", "duration": 297.579}, {"from": "Assistant", "value": "Why do large pre-trained transformer-based models perform so well across a wide variety of NLP tasks? Recent research suggests the key may lie in multi-headed attention mechanism’s ability to learn and represent linguistic information. Understanding how these models represent both syntactic and semantic knowledge is vital to investigate why they succeed and fail, what they have learned, and how they can improve. We present Dodrio, an open-source interactive visualization tool to help NLP researchers and practitioners analyze attention mechanisms in transformer-based models with linguistic knowledge. Dodrio tightly integrates an overview that summarizes the roles of different attention heads, and detailed views that help users compare attention weights with the syntactic structure and semantic information in the input text. To facilitate the visual comparison of attention weights and linguistic knowledge, Dodrio applies different graph visualization techniques to represent attention weights scalable to longer input text. Case studies highlight how Dodrio provides insights into understanding the attention mechanism in transformer-based models. Dodrio is available athttps://poloclub.github.io/dodrio/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_17", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_17.flac", "type": "audio", "duration": 525.003}, {"from": "Assistant", "value": "This paper presents REM, a novel tool for the semi-automated real-time moderation of large scale online forums. The growing demand for online participation and the increasing number of user comments raise challenges in filtering out harmful and undesirable content from public debates in online forums. Since a manual moderation does not scale well and pure automated approaches often lack the required level of accuracy, we suggest a semi-automated moderation approach. Our approach maximizes the efficiency of manual efforts by targeting only those comments for which human intervention is needed, e.g. due to high classification uncertainty. Our tool offers a rich visual interactive environment enabling the exploration of online debates. We conduct a preliminary evaluation experiment to demonstrate the suitability of our approach and publicly release the source code of REM.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_18", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_18.flac", "type": "audio", "duration": 134.848}, {"from": "Assistant", "value": "Novel neural architectures, training strategies, and the availability of large-scale corpora haven been the driving force behind recent progress in abstractive text summarization. However, due to the black-box nature of neural models, uninformative evaluation metrics, and scarce tooling for model and data analysis the true performance and failure modes of summarization models remain largely unknown. To address this limitation, we introduce SummVis, an open-source tool for visualizing abstractive summaries that enables fine-grained analysis of the models, data, and evaluation metrics associated with text summarization. Through its lexical and semantic visualizations, the tools offers an easy entry point for in-depth model prediction exploration across important dimensions such as factual consistency or abstractiveness. The tool together with several pre-computed model outputs is available athttps://summvis.com.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_19", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_19.flac", "type": "audio", "duration": 274.773}, {"from": "Assistant", "value": "Much past work has focused on extracting information like events, entities, and relations from documents. Very little work has focused on analyzing these results for better model understanding. In this paper, we introduce a curation interface that takes an Information Extraction (IE) system’s output in a pre-defined format and generates a graphical representation of its elements. The interface supports editing while curating schemas for complex events like Improvised Explosive Device (IED) based scenarios. We identify various schemas that either have linear event chains or contain parallel events with complicated temporal ordering. We iteratively update an induced schema to uniquely identify events specific to it, add optional events around them, and prune unnecessary events. The resulting schemas are improved and enriched versions of the machine-induced versions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_22", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_22.flac", "type": "audio", "duration": 290.24}, {"from": "Assistant", "value": "In recent years, conversational recommender systems (CRSs) have drawn a wide attention in the research community, which focus on providing high-quality recommendations to users via natural language conversations. However, due to diverse scenarios and data formats, existing studies on CRSs lack unified and standardized implementation or comparison. To tackle this challenge, we release an open-source toolkit CRSLab, which provides a unified and extensible framework with highly-decoupled modules to develop CRSs. Based on this framework, we collect 6 commonly used human-annotated CRS datasets and implement 19 models that include advanced techniques such as graph neural networks and pre-training models. Besides, our toolkit provides a series of automatic evaluation protocols and a human-machine interaction interface to evaluate and compare different CRS methods. The project and documents are released athttps://github.com/RUCAIBox/CRSLab.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_24", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_24.flac", "type": "audio", "duration": 275.017}, {"from": "Assistant", "value": "We present the first end-to-end, transformer-based table question answering (QA) system that takes natural language questions and massive table corpora as inputs to retrieve the most relevant tables and locate the correct table cells to answer the question. Our system, CLTR, extends the current state-of-the-art QA over tables model to build an end-to-end table QA architecture. This system has successfully tackled many real-world table QA problems with a simple, unified pipeline. Our proposed system can also generate a heatmap of candidate columns and rows over complex tables and allow users to quickly identify the correct cells to answer questions. In addition, we introduce two new open domain benchmarks, E2E_WTQ and E2E_GNQ, consisting of 2,005 natural language questions over 76,242 tables. The benchmarks are designed to validate CLTR as well as accommodate future table retrieval and end-to-end table QA research and experiments. Our experiments demonstrate that our system is the current state-of-the-art model on the table retrieval task and produces promising results for end-to-end table QA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_25", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_25.flac", "type": "audio", "duration": 314.091}, {"from": "Assistant", "value": "Domain experts often need to extract structured information from large corpora. We advocate for a search paradigm called “extractive search”, in which a search query is enriched with capture-slots, to allow for such rapid extraction. Such an extractive search system can be built around syntactic structures, resulting in high-precision, low-recall results. We show how the recall can be improved using neural retrieval and alignment. The goals of this paper are to concisely introduce the extractive-search paradigm; and to demonstrate a prototype neural retrieval system for extractive search and its benefits and potential. Our prototype is available athttps://spike.neural-sim.apps.allenai.org/and a video demonstration is available athttps://vimeo.com/559586687.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_30", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_30.flac", "type": "audio", "duration": 123.285}, {"from": "Assistant", "value": "Our understanding of why Transformer-based NLP models have been achieving their recent success lags behind our ability to continue scaling these models. To increase the transparency of Transformer-based language models, we present Ecco – an open-source library for the explainability of Transformer-based NLP models. Ecco provides a set of tools to capture, analyze, visualize, and interactively explore the inner mechanics of these models. This includes (1) gradient-based feature attribution for natural language generation (2) hidden states and their evolution between model layers (3) convenient access and examination tools for neuron activations in the under-explored Feed-Forward Neural Network sublayer of Transformer layers. (4) convenient examination of activation vectors via canonical correlation analysis (CCA), non-negative matrix factorization (NMF), and probing classifiers. We find that syntactic information can be retrieved from BERT’s FFNN representations in levels comparable to those in hidden state representations. More curiously, we find that the model builds up syntactic information in its hidden states even when intermediate FFNNs indicate diminished levels of syntactic information. Ecco is available athttps://www.eccox.io/", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_31", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_31.flac", "type": "audio", "duration": 134.059}, {"from": "Assistant", "value": "Adobe’s Portable Document Format (PDF) is a popular way of distributing view-only documents with a rich visual markup. This presents a challenge to NLP practitioners who wish to use the information contained within PDF documents for training models or data analysis, because annotating these documents is difficult. In this paper, we present PDF Annotation with Labels and Structure (PAWLS), a new annotation tool designed specifically for the PDF document format. PAWLS is particularly suited for mixed-mode annotation and scenarios in which annotators require extended context to annotate accurately. PAWLS supports span-based textual annotation, N-ary relations and freeform, non-textual bounding boxes, all of which can be exported in convenient formats for training multi-modal machine learning models. A read-only PAWLS server is available athttps://pawls.apps.allenai.org/, and the source code is available athttps://github.com/allenai/pawls.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_32", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_32.flac", "type": "audio", "duration": 224.003}, {"from": "Assistant", "value": "We present TweeNLP, a one-stop portal that organizes Twitter’s natural language processing (NLP) data and builds a visualization and exploration platform. It curates 19,395 tweets (as of April 2021) from various NLP conferences and general NLP discussions. It supports multiple features such as TweetExplorer to explore tweets by topics, visualize insights from Twitter activity throughout the organization cycle of conferences, discover popular research papers and researchers. It also builds a timeline of conference and workshop submission deadlines. We envision TweeNLP to function as a collective memory unit for the NLP community by integrating the tweets pertaining to research papers with the NLPExplorer scientific literature search engine. The current system is hosted athttp://nlpexplorer.org/twitter/CFP.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_33", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_33.flac", "type": "audio", "duration": 347.022}, {"from": "Assistant", "value": "We introduce ChrEnTranslate, an online machine translation demonstration system for translation between English and an endangered language Cherokee. It supports both statistical and neural translation models as well as provides quality estimation to inform users of reliability, two user feedback interfaces for experts and common users respectively, example inputs to collect human translations for monolingual data, word alignment visualization, and relevant terms from the Cherokee English dictionary. The quantitative evaluation demonstrates that our backbone translation models achieve state-of-the-art translation performance and our quality estimation well correlates with both BLEU and human judgment. By analyzing 216 pieces of expert feedback, we find that NMT is preferable because it copies less than SMT, and, in general, current models can translate fragments of the source sentence but make major mistakes. When we add these 216 expert-corrected parallel texts into the training set and retrain models, equal or slightly better performance is observed, which demonstrates indicates the potential of human-in-the-loop learning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_34", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_34.flac", "type": "audio", "duration": 300.8}, {"from": "Assistant", "value": "With the rapid development of NLP research, leaderboards have emerged as one tool to track the performance of various systems on various NLP tasks. They are effective in this goal to some extent, but generally present a rather simplistic one-dimensional view of the submitted systems, communicated only through holistic accuracy numbers. In this paper, we present a new conceptualization and implementation of NLP evaluation: the ExplainaBoard, which in addition to inheriting the functionality of the standard leaderboard, also allows researchers to (i) diagnose strengths and weaknesses of a single system (e.g. what is the best-performing system bad at?) (ii) interpret relationships between multiple systems. (e.g. where does system A outperform system B? What if we combine systems A, B and C?) and (iii) examine prediction results closely (e.g. what are common errors made by multiple systems or in what contexts do particular errors occur?). So far, ExplainaBoard covers more than 400 systems, 50 datasets, 40 languages, and 12 tasks. We not only released an online platform at the website but also make our evaluation tool an API with MIT Licence at Github and PyPi that allows users to conveniently assess their models offline. We additionally release all output files from systems that we have run or collected to motivate “output-driven” research in the future.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_35", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_35.flac", "type": "audio", "duration": 287.022}, {"from": "Assistant", "value": "The usage of individual words can change over time, for example, when words experience a semantic shift. As text datasets generally comprise documents that were collected over a longer period of time, examining word usage changes in a corpus can often reveal interesting patterns. In this paper, we introduce a simple and intuitive way to track word usage changes via continuously evolving embeddings, computed as a weighted running average of transformer-based contextualized embeddings. We demonstrate our approach on a corpus of recent New York Times article snippets and provide code for an easy to use web app to conveniently explore semantic shifts with interactive plots.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_36", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_36.flac", "type": "audio", "duration": 300.331}, {"from": "Assistant", "value": "A natural language database interface (NLDB) can democratize data-driven insights for non-technical users. However, existing Text-to-SQL semantic parsers cannot achieve high enough accuracy in the cross-database setting to allow good usability in practice. This work presents TURING, a NLDB system toward bridging this gap. The cross-domain semantic parser of TURING with our novel value prediction method achieves 75.1% execution accuracy, and 78.3% top-5 beam execution accuracy on the Spider validation set (Yu et al., 2018b). To benefit from the higher beam accuracy, we design an interactive system where the SQL hypotheses in the beam are explained step-by-step in natural language, with their differences highlighted. The user can then compare and judge the hypotheses to select which one reflects their intention if any. The English explanations of SQL queries in TURING are produced by our high-precision natural language generation system based on synchronous grammars.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_37", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_37.flac", "type": "audio", "duration": 289.536}, {"from": "Assistant", "value": "While there are more than 7000 languages in the world, most translation research efforts have targeted a few high resource languages. Commercial translation systems support only one hundred languages or fewer, and do not make these models available for transfer to low resource languages. In this work, we present useful tools for machine translation research: MTData, NLCodec and RTG. We demonstrate their usefulness by creating a multilingual neural machine translation model capable of translating from 500 source languages to English. We make this multilingual model readily downloadable and usable as a service, or as a parent model for transfer-learning to even lower-resource languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_38", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_38.flac", "type": "audio", "duration": 273.92}, {"from": "Assistant", "value": "We present LEGOEval, an open-source toolkit that enables researchers to easily evaluate dialogue systems in a few lines of code using the online crowdsource platform, Amazon Mechanical Turk. Compared to existing toolkits, LEGOEval features a flexible task design by providing a Python API that maps to commonly used React.js interface components. Researchers can personalize their evaluation procedures easily with our built-in pages as if playing with LEGO blocks. Thus, LEGOEval provides a fast, consistent method for reproducing human evaluation results. Besides the flexible task design, LEGOEval also offers an easy API to review collected data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_39", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_39.flac", "type": "audio", "duration": 293.013}, {"from": "Assistant", "value": "We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing framework for large scale knowledge base question answering (KBQA). ReTraCk is designed as a modular framework to maintain high flexibility. It includes a retriever to retrieve relevant KB items efficiently, a transducer to generate logical form with syntax correctness guarantees and a checker to improve transduction procedure. ReTraCk is ranked at top1 overall performance on the GrailQA leaderboard and obtains highly competitive performance on the typical WebQuestionsSP benchmark. Our system can interact with users timely, demonstrating the efficiency of the proposed framework.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_41", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_41.flac", "type": "audio", "duration": 294.059}, {"from": "Assistant", "value": "TextFlint is a multilingual robustness evaluation toolkit for NLP tasks that incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. This enables practitioners to automatically evaluate their models from various aspects or to customize their evaluations as desired with just a few lines of code. TextFlint also generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model in terms of its robustness. To guarantee acceptability, all the text transformations are linguistically based and all the transformed data selected (up to 100,000 texts) scored highly under human evaluation. To validate the utility, we performed large-scale empirical evaluations (over 67,000) on state-of-the-art deep learning models, classic supervised methods, and real-world systems. The toolkit is already available athttps://github.com/textflintwith all the evaluation results demonstrated at textflint.io.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-demo_42", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-demo_42.flac", "type": "audio", "duration": 256.043}, {"from": "Assistant", "value": "In visual storytelling, a short story is generated based on a given image sequence. Despite years of work, most visual storytelling models remain limited in terms of the generated stories’ fixed length: most models produce stories with exactly five sentences because five-sentence stories dominate the training data. The fix-length stories carry limited details and provide ambiguous textual information to the readers. Therefore, we propose to “stretch” the stories, which create the potential to present in-depth visual details. This paper presents Stretch-VST, a visual storytelling framework that enables the generation of prolonged stories by adding appropriate knowledge, which is selected by the proposed scoring function. We propose a length-controlled Transformer to generate long stories. This model introduces novel positional encoding methods to maintain story quality with lengthy inputs. Experiments confirm that long stories are generated without deteriorating the quality. The human evaluation further shows that Stretch-VST can provide better focus and detail when stories are prolonged compared to state of the art. We create a webpage to demonstrate our prolonged capability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_case-1_5", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_case-1_5.flac", "type": "audio", "duration": 901.004}, {"from": "Assistant", "value": "Most of the existing information extraction frameworks (Wadden et al., 2019; Veysehet al., 2020) focus on sentence-level tasks and are hardly able to capture the consolidated information from a given document. In our endeavour to generate precise document-level information frames from lengthy textual records, we introduce the task of Information Aggregation or Argument Aggregation. More specifically, our aim is to filter irrelevant and redundant argument mentions that were extracted at a sentence level and render a document level information frame. Majority of the existing works have been observed to resolve related tasks of document-level event argument extraction (Yang et al., 2018; Zheng et al., 2019) and salient entity identification (Jain et al., 2020) using supervised techniques. To remove dependency from large amounts of labelled data, we explore the task of information aggregation using weakly supervised techniques. In particular, we present an extractive algorithm with multiple sieves which adopts active learning strategies to work efficiently in low-resource settings. For this task, we have annotated our own test dataset comprising of 131 document information frames and have released the code and dataset to further research prospects in this new domain. To the best of our knowledge, we are the first to establish baseline results for this task in English. Our data and code are publicly available athttps://github.com/DebanjanaKar/ArgFuse.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_case-1_6", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_case-1_6.flac", "type": "audio", "duration": 712.986}, {"from": "Assistant", "value": "Language provides speakers with a rich system of modality for expressing thoughts about events, without being committed to their actual occurrence. Modality is commonly used in the political news domain, where both actual and possible courses of events are discussed. NLP systems struggle with these semantic phenomena, often incorrectly extracting events which did not happen, which can lead to issues in downstream applications. We present an open-domain, lexicon-based event extraction system that captures various types of modality. This information is valuable for Question Answering, Knowledge Graph construction and Fact-checking tasks, and our evaluation shows that the system is sufficiently strong to be used in downstream applications.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_case-1_7", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_case-1_7.flac", "type": "audio", "duration": 561.952}, {"from": "Assistant", "value": "We apply statistical techniques from natural language processing to a collection of Western and Hong Kong–based English-language newspaper articles spanning the years 1998–2020, studying the difference and evolution of its portrayal. We observe that both content and attitudes differ between Western and Hong Kong–based sources. ANOVA on keyword frequencies reveals that Hong Kong–based papers discuss protests and democracy less often. Topic modeling detects salient aspects of protests and shows that Hong Kong–based papers made fewer references to police violence during the Anti–Extradition Law Amendment Bill Movement. Diachronic shifts in word embedding neighborhoods reveal a shift in the characterization of salient keywords once the Movement emerged. Together, these raise questions about the existence of anodyne reporting from Hong Kong–based media. Likewise, they illustrate the importance of sample selection for protest event analysis.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_case-1_9", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_case-1_9.flac", "type": "audio", "duration": 636.331}, {"from": "Assistant", "value": "Incidents in industries have huge social and political impact and minimizing the consequent damage has been a high priority. However, automated analysis of repositories of incident reports has remained a challenge. In this paper, we focus on automatically extracting events from incident reports. Due to absence of event annotated datasets for industrial incidents we employ a transfer learning based approach which is shown to outperform several baselines. We further provide detailed analysis regarding effect of increase in pre-training data and provide explainability of why pre-training improves the performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_case-1_14", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_case-1_14.flac", "type": "audio", "duration": 182.464}, {"from": "Assistant", "value": "Event Sentence Coreference Identification (ESCI) aims to cluster event sentences that refer to the same event together for information extraction. We describe our ESCI solution developed for the ACL-CASE 2021 shared tasks on the detection and classification of socio-political and crisis event information in a multilingual setting. For a given article, our proposed pipeline comprises of an accurate sentence pair classifier that identifies coreferent sentence pairs and subsequently uses these predicted probabilities to cluster sentences into groups. Sentence pair representations are constructed from fine-tuned BERT embeddings plus POS embeddings fed through a BiLSTM model, and combined with linguistic-based lexical and semantic similarities between sentences. Our best models ranked 2nd, 1st and 2nd and obtained CoNLL F1 scores of 81.20%, 93.03%, 83.15% for the English, Portuguese and Spanish test sets respectively in the ACL-CASE 2021 competition.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_case-1_16", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_case-1_16.flac", "type": "audio", "duration": 475.011}, {"from": "Assistant", "value": "Automatic socio-political and crisis event detection has been a challenge for natural language processing as well as social and political science communities, due to the diversity and nuance in such events and high accuracy requirements. In this paper, we propose an approach which can handle both document and cross-sentence level event detection in a multilingual setting using pretrained transformer models. Our approach became the winning solution in document level predictions and secured the 3rd place in cross-sentence level predictions for the English language. We could also achieve competitive results for other languages to prove the effectiveness and universality of our approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_case-1_20", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_case-1_20.flac", "type": "audio", "duration": 411.616}, {"from": "Assistant", "value": "An ever-increasing amount of text, in the form of social media posts and news articles, gives rise to new challenges and opportunities for the automatic extraction of socio-political events. In this paper, we present our submission to the Shared Tasks on Socio-Political and Crisis Events Detection, Task 1, Multilingual Protest News Detection, Subtask 2, Event Sentence Classification, of CASE @ ACL-IJCNLP 2021. In our submission, we utilize the RoBERTa model with additional pretraining, and achieve the best F1 score of 0.8532 in event sentence classification in English and the second-best F1 score of 0.8700 in Portuguese via simple translation. We analyze the failure cases of our model. We also conduct an ablation study to show the effect of choosing the right pretrained language model, adding additional training data and data augmentation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_dialdoc-1_4", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_dialdoc-1_4.flac", "type": "audio", "duration": 699.014}, {"from": "Assistant", "value": "This paper presents a learning assistant that tests one’s knowledge and gives feedback that helps a person learn at a faster pace. A learning assistant (based on automated question generation) has extensive uses in education, information websites, self-assessment, FAQs, testing ML agents, research, etc. Multiple researchers, and companies have worked on Virtual Assistance, but majorly in English. We built our learning assistant for Telugu language to help with teaching in the mother tongue, which is the most efficient way of learning. Our system is built primarily based on Question Generation in Telugu. Many experiments were conducted on Question Generation in English in multiple ways. We have built the first hybrid machine learning and rule-based solution in Telugu, which proves efficient for short stories or short passages in children’s books. Our work covers the fundamental question forms with question types: adjective, yes/no, adverb, verb, when, where, whose, quotative, and quantitative (how many/how much). We constructed rules for question generation using Part of Speech (POS) tags and Universal Dependency (UD) tags along with linguistic information of the surrounding relevant context of the word. We used keyword matching, multilingual sentence embedding to evaluate the answer. Our system is primarily built on question generation in Telugu, and is also capable of evaluating the user’s answers to the generated questions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_gem-1_15", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_gem-1_15.flac", "type": "audio", "duration": 870.912}, {"from": "Assistant", "value": "In a current experiment we were testing CommonGen dataset for structure-to-text task from GEM living benchmark with the constraint based POINTER model. POINTER represents a hybrid architecture, combining insertion-based and transformer paradigms, predicting the token and the insertion position at the same time. The text is therefore generated gradually in a parallel non-autoregressive manner, given the set of keywords. The pretrained model was fine-tuned on a training split of the CommonGen dataset and the generation result was compared to the validation and challenge splits. The received metrics outputs, which measure lexical equivalence, semantic similarity and diversity, are discussed in details in a present system description.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_iwpt-1_1", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_iwpt-1_1.flac", "type": "audio", "duration": 723.441}, {"from": "Assistant", "value": "When learned without exploration, local models for structured prediction tasks are subject to exposure bias and cannot be trained without detailed guidance. Active Imitation Learning (AIL), also known in NLP as Dynamic Oracle Learning, is a general technique for working around these issues by allowing the exploration of different outputs at training time. AIL requires oracle feedback: an oracle is any algorithm which can, given a partial candidate solution and gold annotation, find the correct (minimum loss) next output to produce. This paper describes a general finite state technique for deriving oracles. The technique describe is also efficient and will greatly expand the tasks for which AIL can be used.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_iwpt-1_2", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_iwpt-1_2.flac", "type": "audio", "duration": 701.483}, {"from": "Assistant", "value": "In this paper, we present the first statistical parser for Lambek categorial grammar (LCG), a grammatical formalism for which the graphical proof method known as *proof nets* is applicable. Our parser incorporates proof net structure and constraints into a system based on self-attention networks via novel model elements. Our experiments on an English LCG corpus show that incorporating term graph structure is helpful to the model, improving both parsing accuracy and coverage. Moreover, we derive novel loss functions by expressing proof net constraints as differentiable functions of our model output, enabling us to train our parser without ground-truth derivations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_iwpt-1_3", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_iwpt-1_3.flac", "type": "audio", "duration": 718.379}, {"from": "Assistant", "value": "The Reading Machine, is a parsing framework that takes as input raw text and performs six standard nlp tasks: tokenization, pos tagging, morphological analysis, lemmatization, dependency parsing and sentence segmentation. It is built upon Transition Based Parsing, and allows to implement a large number of parsing configurations, among which a fully incremental one. Three case studies are presented to highlight the versatility of the framework. The first one explores whether an incremental parser is able to take into account top-down dependencies (i.e. the influence of high level decisions on low level ones), the second compares the performances of an incremental and a pipe-line architecture and the third quantifies the impact of the right context on the predictions made by an incremental parser.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_iwpt-1_4", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_iwpt-1_4.flac", "type": "audio", "duration": 696.899}, {"from": "Assistant", "value": "Strong and affordable in-domain data is a desirable asset when transferring trained semantic parsers to novel domains. As previous methods for semi-automatically constructing such data cannot handle the complexity of realistic SQL queries, we propose to construct SQL queries via context-dependent sampling, and introduce the concept of topic. Along with our SQL query construction method, we propose a novel pipeline of semi-automatic Text-to-SQL dataset construction that covers the broad space of SQL queries. We show that the created dataset is comparable with expert annotation along multiple dimensions, and is capable of improving domain transfer performance for SOTA semantic parsers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_iwpt-1_5", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_iwpt-1_5.flac", "type": "audio", "duration": 308.57}, {"from": "Assistant", "value": "Coupled with biaffine decoders, transformers have been effectively adapted to text-to-graph transduction and achieved state-of-the-art performance on AMR parsing. Many prior works, however, rely on the biaffine decoder for either or both arc and label predictions although most features used by the decoder may be learned by the transformer already. This paper presents a novel approach to AMR parsing by combining heterogeneous data (tokens, concepts, labels) as one input to a transformer to learn attention, and use only attention matrices from the transformer to predict all elements in AMR graphs (concepts, arcs, labels). Although our models use significantly fewer parameters than the previous state-of-the-art graph parser, they show similar or better accuracy on AMR 2.0 and 3.0.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_iwpt-1_7", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_iwpt-1_7.flac", "type": "audio", "duration": 743.015}, {"from": "Assistant", "value": "Broad-coverage meaning representations in NLP mostly focus on explicitly expressed content. More importantly, the scarcity of datasets annotating diverse implicit roles limits empirical studies into their linguistic nuances. For example, in the web review “Great service!”, the provider and consumer are implicit arguments of different types. We examine an annotated corpus of fine-grained implicit arguments (Cui and Hershcovich, 2020) by carefully re-annotating it, resolving several inconsistencies. Subsequently, we present the first transition-based neural parser that can handle implicit arguments dynamically, and experiment with two different transition systems on the improved dataset. We find that certain types of implicit arguments are more difficult to parse than others and that the simpler system is more accurate in recovering implicit arguments, despite having a lower overall parsing score, attesting current reasoning limitations of NLP models. This work will facilitate a better understanding of implicit and underspecified language, by incorporating it holistically into meaning representations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_iwpt-1_10", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_iwpt-1_10.flac", "type": "audio", "duration": 808.657}, {"from": "Assistant", "value": "Domain adaption in syntactic parsing is still a significant challenge. We address the issue of data imbalance between the in-domain and out-of-domain treebank typically used for the problem. We define domain adaptation as a Multi-task learning (MTL) problem, which allows us to train two parsers, one for each do-main. Our results show that the MTL approach is beneficial for the smaller treebank. For the larger treebank, we need to use loss weighting in order to avoid a decrease in performance be-low the single task. In order to determine towhat degree the data imbalance between two domains and the domain differences affect results, we also carry out an experiment with two imbalanced in-domain treebanks and show that loss weighting also improves performance in an in-domain setting. Given loss weighting in MTL, we can improve results for both parsers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_iwpt-1_11", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_iwpt-1_11.flac", "type": "audio", "duration": 635.019}, {"from": "Assistant", "value": "We review two features of mixture of experts (MoE) models which we call averaging and clustering effects in the context of graph-based dependency parsers learned in a supervised probabilistic framework. Averaging corresponds to the ensemble combination of parsers and is responsible for variance reduction which helps stabilizing and improving parsing accuracy. Clustering describes the capacity of MoE models to give more credit to experts believed to be more accurate given an input. Although promising, this is difficult to achieve, especially without additional data. We design an experimental set-up to study the impact of these effects. Whereas averaging is always beneficial, clustering requires good initialization and stabilization techniques, but its advantages over mere averaging seem to eventually vanish when enough experts are present. As a by product, we show how this leads to state-of-the-art results on the PTB and the CoNLL09 Chinese treebank, with low variance across experiments.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_iwpt-1_13", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_iwpt-1_13.flac", "type": "audio", "duration": 471.272}, {"from": "Assistant", "value": "The introduction of pre-trained transformer-based contextualized word embeddings has led to considerable improvements in the accuracy of graph-based parsers for frameworks such as Universal Dependencies (UD). However, previous works differ in various dimensions, including their choice of pre-trained language models and whether they use LSTM layers. With the aims of disentangling the effects of these choices and identifying a simple yet widely applicable architecture, we introduce STEPS, a new modular graph-based dependency parser. Using STEPS, we perform a series of analyses on the UD corpora of a diverse set of languages. We find that the choice of pre-trained embeddings has by far the greatest impact on parser performance and identify XLM-R as a robust choice across the languages in our study. Adding LSTM layers provides no benefits when using transformer-based embeddings. A multi-task training setup outputting additional UD features may contort results. Taking these insights together, we propose a simple but widely applicable parser architecture and configuration, achieving new state-of-the-art results (in terms of LAS) for 10 out of 12 diverse languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_iwpt-1_16", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_iwpt-1_16.flac", "type": "audio", "duration": 491.009}, {"from": "Assistant", "value": "We introduce the COMBO-based approach for EUD parsing and its implementation, which took part in the IWPT 2021 EUD shared task. The goal of this task is to parse raw texts in 17 languages into Enhanced Universal Dependencies (EUD). The proposed approach uses COMBO to predict UD trees and EUD graphs. These structures are then merged into the final EUD graphs. Some EUD edge labels are extended with case information using a single language-independent expansion rule. In the official evaluation, the solution ranked fourth, achieving an average ELAS of 83.79%. The source code is available athttps://gitlab.clarin-pl.eu/syntactic-tools/combo.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_iwpt-1_18", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_iwpt-1_18.flac", "type": "audio", "duration": 642.044}, {"from": "Assistant", "value": "This paper describes a system proposed for the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (EUD). We propose a Graph Rewriting based system for computing Enhanced Universal Dependencies, given the Basic Universal Dependencies (UD).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_iwpt-1_20", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_iwpt-1_20.flac", "type": "audio", "duration": 601.186}, {"from": "Assistant", "value": "This paper describe the system used in our submission to theIWPT 2021 Shared Task. Our system is a graph-based parser with the technique of Automated Concatenation of Embeddings (ACE). Because recent work found that better word representations can be obtained by concatenating different types of embeddings, we use ACE to automatically find the better concatenation of embeddings for the task of enhanced universal dependencies. According to official results averaged on 17 languages, our system rank 2nd over 9 teams.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_iwpt-1_21", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_iwpt-1_21.flac", "type": "audio", "duration": 551.729}, {"from": "Assistant", "value": "This paper presents our multilingual dependency parsing system as used in the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies. Our system consists of an unfactorized biaffine classifier that operates directly on fine-tuned XLM-R embeddings and generates enhanced UD graphs by predicting the best dependency label (or absence of a dependency) for each pair of tokens. To avoid sparsity issues resulting from lexicalized dependency labels, we replace lexical items in relations with placeholders at training and prediction time, later retrieving them from the parse via a hybrid rule-based/machine-learning system. In addition, we utilize model ensembling at prediction time. Our system achieves high parsing accuracy on the blind test data, ranking 3rd out of 9 with an average ELAS F1 score of 86.97.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_iwpt-1_22", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_iwpt-1_22.flac", "type": "audio", "duration": 585.429}, {"from": "Assistant", "value": "We describe the DCU-EPFL submission to the IWPT 2021 Parsing Shared Task: From Raw Text to Enhanced Universal Dependencies. The task involves parsing Enhanced UD graphs, which are an extension of the basic dependency trees designed to be more facilitative towards representing semantic structure. Evaluation is carried out on 29 treebanks in 17 languages and participants are required to parse the data from each language starting from raw strings. Our approach uses the Stanza pipeline to preprocess the text files, XLM-RoBERTa to obtain contextualized token representations, and an edge-scoring and labeling model to predict the enhanced graph. Finally, we run a postprocessing script to ensure all of our outputs are valid Enhanced UD graphs. Our system places 6th out of 9 participants with a coarse Enhanced Labeled Attachment Score (ELAS) of 83.57. We carry out additional post-deadline experiments which include using Trankit for pre-processing, XLM-RoBERTa LARGE, treebank concatenation, and multitask learning between a basic and an enhanced dependency parser. All of these modifications improve our initial score and our final system has a coarse ELAS of 88.04.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_iwpt-1_23", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_iwpt-1_23.flac", "type": "audio", "duration": 555.648}, {"from": "Assistant", "value": "We present our contribution to the IWPT 2021 shared task on parsing into enhanced Universal Dependencies. Our main system component is a hybrid tree-graph parser that integrates (a) predictions of spanning trees for the enhanced graphs with (b) additional graph edges not present in the spanning trees. We also adopt a finetuning strategy where we first train a language-generic parser on the concatenation of data from all available languages, and then, in a second step, finetune on each individual language separately. Additionally, we develop our own complete set of pre-processing modules relevant to the shared task, including tokenization, sentence segmentation, and multiword token expansion, based on pre-trained XLM-R models and our own pre-training of character-level language models. Our submission reaches a macro-average ELAS of 89.24 on the test set. It ranks top among all teams, with a margin of more than 2 absolute ELAS over the next best-performing submission, and best score on 16 out of 17 languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_metanlp-1_2", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_metanlp-1_2.flac", "type": "audio", "duration": 636.018}, {"from": "Assistant", "value": "Multilingual pre-trained contextual embedding models (Devlin et al., 2019) have achieved impressive performance on zero-shot cross-lingual transfer tasks. Finding the most effective fine-tuning strategy to fine-tune these models on high-resource languages so that it transfers well to the zero-shot languages is a non-trivial task. In this paper, we propose a novel meta-optimizer to soft-select which layers of the pre-trained model to freeze during fine-tuning. We train the meta-optimizer by simulating the zero-shot transfer scenario. Results on cross-lingual natural language inference show that our approach improves over the simple fine-tuning baseline and X-MAML (Nooralahzadeh et al., 2020).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_metanlp-1_3", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_metanlp-1_3.flac", "type": "audio", "duration": 869.007}, {"from": "Assistant", "value": "In this paper, we study the problem of recognizing compositional attribute-object concepts within the zero-shot learning (ZSL) framework. We propose an episode-based cross-attention (EpiCA) network which combines merits of cross-attention mechanism and episode-based training strategy to recognize novel compositional concepts. Firstly, EpiCA bases on cross-attention to correlate conceptvisual information and utilizes the gated pooling layer to build contextualized representations for both images and concepts. The updated representations are used for a more indepth multi-modal relevance calculation for concept recognition. Secondly, a two-phase episode training strategy, especially the ransductive phase, is adopted to utilize unlabeled test examples to alleviate the low-resource learning problem. Experiments on two widelyused zero-shot compositional learning (ZSCL) benchmarks have demonstrated the effectiveness of the model compared with recent approaches on both conventional and generalized ZSCL settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_metanlp-1_8", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_metanlp-1_8.flac", "type": "audio", "duration": 376.001}, {"from": "Assistant", "value": "Meta learning aims to optimize the model’s capability to generalize to new tasks and domains. Lacking a data-efficient way to create meta training tasks has prevented the application of meta-learning to the real-world few shot learning scenarios. Recent studies have proposed unsupervised approaches to create meta-training tasks from unlabeled data for free, e.g., the SMLMT method (Bansal et al., 2020a) constructs unsupervised multi-class classification tasks from the unlabeled text by randomly masking words in the sentence and let the meta learner choose which word to fill in the blank. This study proposes a semi-supervised meta-learning approach that incorporates both the representation power of large pre-trained language models and the generalization capability of prototypical networks enhanced by SMLMT. The semi-supervised meta training approach avoids overfitting prototypical networks on a small number of labeled training examples and quickly learns cross-domain task-specific representation only from a few supporting examples. By incorporating SMLMT with prototypical networks, the meta learner generalizes better to unseen domains and gains higher accuracy on out-of-scope examples without the heavy lifting of pre-training. We observe significant improvement in few-shot generalization after training only a few epochs on the intent classification tasks evaluated in a multi-domain setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mwe-1_8", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mwe-1_8.flac", "type": "audio", "duration": 848.039}, {"from": "Assistant", "value": "The paper reports on a corpus study of German light verb constructions (LVCs). LVCs come in families which exemplify systematic interpretation patterns. The paper’s aim is to account for the properties determining these patterns on the basis of a corpus study on German LVCs of the type ‘stehen unter’ NP’ (‘stand under NP’).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_repl4nlp-1_3", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_repl4nlp-1_3.flac", "type": "audio", "duration": 402.923}, {"from": "Assistant", "value": "Current pre-trained language models have lots of knowledge, but a more limited ability to use that knowledge. Bloom’s Taxonomy helps educators teach children how to use knowledge by categorizing comprehension skills, so we use it to analyze and improve the comprehension skills of large pre-trained language models. Our experiments focus on zero-shot question answering, using the taxonomy to provide proximal context that helps the model answer questions by being relevant to those questions. We show targeting context in this manner improves performance across 4 popular common sense question answer datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_repl4nlp-1_10", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_repl4nlp-1_10.flac", "type": "audio", "duration": 369.012}, {"from": "Assistant", "value": "Conventional Knowledge Graph Completion (KGC) assumes that all test entities appear during training. However, in real-world scenarios, Knowledge Graphs (KG) evolve fast with out-of-knowledge-graph (OOKG) entities added frequently, and we need to efficiently represent these entities. Most existing Knowledge Graph Embedding (KGE) methods cannot represent OOKG entities without costly retraining on the whole KG. To enhance efficiency, we propose a simple and effective method that inductively represents OOKG entities by their optimal estimation under translational assumptions. Moreover, given pretrained embeddings of the in-knowledge-graph (IKG) entities, our method even needs no additional learning. Experimental results on two KGC tasks with OOKG entities show that our method outperforms the previous methods by a large margin with higher efficiency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_repl4nlp-1_12", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_repl4nlp-1_12.flac", "type": "audio", "duration": 729.786}, {"from": "Assistant", "value": "Strategies for improving the training and prediction quality of weakly supervised machine learning models vary in how much they are tailored to a specific task or integrated with a specific model architecture. In this work, we introduce Knodle, a software framework that treats weak data annotations, deep learning models, and methods for improving weakly supervised training as separate, modular components. This modularization gives the training process access to fine-grained information such as data set characteristics, matches of heuristic rules, or elements of the deep learning model ultimately used for prediction. Hence, our framework can encompass a wide range of training methods for improving weak supervision, ranging from methods that only look at correlations of rules and output classes (independently of the machine learning model trained with the resulting labels), to those that harness the interplay of neural networks and weakly labeled data. We illustrate the benchmarking potential of the framework with a performance comparison of several reference implementations on a selection of datasets that are already available in Knodle.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_repl4nlp-1_18", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_repl4nlp-1_18.flac", "type": "audio", "duration": 512.832}, {"from": "Assistant", "value": "Word Embedding maps words to vectors of real numbers. It is derived from a large corpus and is known to capture semantic knowledge from the corpus. Word Embedding is a critical component of many state-of-the-art Deep Learning techniques. However, generating good Word Embeddings is a special challenge for low-resource languages such as Nepali due to the unavailability of large text corpus. In this paper, we present NPVec1 which consists of 25 state-of-art Word Embeddings for Nepali that we have derived from a large corpus using Glove, Word2Vec, FastText, and BERT. We further provide intrinsic and extrinsic evaluations of these Embeddings using well established metrics and methods. These models are trained using 279 million word tokens and are the largest Embeddings ever trained for Nepali language. Furthermore, we have made these Embeddings publicly available to accelerate the development of Natural Language Processing (NLP) applications in Nepali.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_repl4nlp-1_20", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_repl4nlp-1_20.flac", "type": "audio", "duration": 419.883}, {"from": "Assistant", "value": "We investigate how sentence-level transformers can be modified into effective sequence labelers at the token level without any direct supervision. Existing approaches to zero-shot sequence labeling do not perform well when applied on transformer-based architectures. As transformers contain multiple layers of multi-head self-attention, information in the sentence gets distributed between many tokens, negatively affecting zero-shot token-level performance. We find that a soft attention module which explicitly encourages sharpness of attention weights can significantly outperform existing methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_repl4nlp-1_31", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_repl4nlp-1_31.flac", "type": "audio", "duration": 440.896}, {"from": "Assistant", "value": "Contrastive learning has been applied successfully to learn vector representations of text. Previous research demonstrated that learning high-quality representations benefits from batch-wise contrastive loss with a large number of negatives. In practice, the technique of in-batch negative is used, where for each example in a batch, other batch examples’ positives will be taken as its negatives, avoiding encoding extra negatives. This, however, still conditions each example’s loss on all batch examples and requires fitting the entire large batch into GPU memory. This paper introduces a gradient caching technique that decouples backpropagation between contrastive loss and the encoder, removing encoder backward pass data dependency along the batch dimension. As a result, gradients can be computed for one subset of the batch at a time, leading to almost constant memory usage.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_semeval-1_1", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_semeval-1_1.flac", "type": "audio", "duration": 702.513}, {"from": "Assistant", "value": "This paper presents the results and main findings of SemEval-2021 Task 1 - Lexical Complexity Prediction. We provided participants with an augmented version of the CompLex Corpus (Shardlow et al. 2020). CompLex is an English multi-domain corpus in which words and multi-word expressions (MWEs) were annotated with respect to their complexity using a five point Likert scale. SemEval-2021 Task 1 featured two Sub-tasks: Sub-task 1 focused on single words and Sub-task 2 focused on MWEs. The competition attracted 198 teams in total, of which 54 teams submitted official runs on the test data to Sub-task 1 and 37 to Sub-task 2.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_semeval-1_2", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_semeval-1_2.flac", "type": "audio", "duration": 388.005}, {"from": "Assistant", "value": "We propose an ensemble model for predicting the lexical complexity of words and multiword expressions (MWEs). The model receives as input a sentence with a target word or MWE and outputs its complexity score. Given that a key challenge with this task is the limited size of annotated data, our model relies on pretrained contextual representations from different state-of-the-art transformer-based language models (i.e., BERT and RoBERTa), and on a variety of training methods for further enhancing model generalization and robustness: multi-step fine-tuning and multi-task learning, and adversarial training. Additionally, we propose to enrich contextual representations by adding hand-crafted features during training. Our model achieved competitive results and ranked among the top-10 systems in both sub-tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_semeval-1_3", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_semeval-1_3.flac", "type": "audio", "duration": 641.033}, {"from": "Assistant", "value": "In this paper, we introduce the first SemEval task on Multilingual and Cross-Lingual Word-in-Context disambiguation (MCL-WiC). This task allows the largely under-investigated inherent ability of systems to discriminate between word senses within and across languages to be evaluated, dropping the requirement of a fixed sense inventory. Framed as a binary classification, our task is divided into two parts. In the multilingual sub-task, participating systems are required to determine whether two target words, each occurring in a different context within the same language, express the same meaning or not. Instead, in the cross-lingual part, systems are asked to perform the task in a cross-lingual scenario, in which the two target words and their corresponding contexts are provided in two different languages. We illustrate our task, as well as the construction of our manually-created dataset including five languages, namely Arabic, Chinese, English, French and Russian, and the results of the participating systems. Datasets and results are available at:https://github.com/SapienzaNLP/mcl-wic.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_semeval-1_6", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_semeval-1_6.flac", "type": "audio", "duration": 569.051}, {"from": "Assistant", "value": "The Toxic Spans Detection task of SemEval-2021 required participants to predict the spans of toxic posts that were responsible for the toxic label of the posts. The task could be addressed as supervised sequence labeling, using training data with gold toxic spans provided by the organisers. It could also be treated as rationale extraction, using classifiers trained on potentially larger external datasets of posts manually annotated as toxic or not, without toxic span annotations. For the supervised sequence labeling approach and evaluation purposes, posts previously labeled as toxic were crowd-annotated for toxic spans. Participants submitted their predicted spans for a held-out test set and were scored using character-based F1. This overview summarises the work of the 36 teams that provided system descriptions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_semeval-1_7", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_semeval-1_7.flac", "type": "audio", "duration": 709.021}, {"from": "Assistant", "value": "We describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems. The task focused on memes and had three subtasks: (i) detecting the techniques in the text, (ii) detecting the text spans where the techniques are used, and (iii) detecting techniques in the entire meme, i.e., both in the text and in the image. It was a popular task, attracting 71 registrations, and 22 teams that eventually made an official submission on the test set. The evaluation results for the third subtask confirmed the importance of both modalities, the text and the image. Moreover, some teams reported benefits when not just combining the two modalities, e.g., by using early or late fusion, but rather modeling the interaction between them in a joint model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_semeval-1_8", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_semeval-1_8.flac", "type": "audio", "duration": 378.726}, {"from": "Assistant", "value": "This paper describes our system participated in Task 6 of SemEval-2021: the task focuses on multimodal propaganda technique classification and it aims to classify given image and text into 22 classes. In this paper, we propose to use transformer based architecture to fuse the clues from both image and text. We explore two branches of techniques including fine-tuning the text pretrained transformer with extended visual features, and fine-tuning the multimodal pretrained transformers. For the visual features, we have tested both grid features based on ResNet and salient region features from pretrained object detector. Among the pretrained multimodal transformers, we choose ERNIE-ViL, a two-steam cross-attended transformers pretrained on large scale image-caption aligned data. Fine-tuing ERNIE-ViL for our task produce a better performance due to general joint multimodal representation for text and image learned by ERNIE-ViL. Besides, as the distribution of the classification labels is very unbalanced, we also make a further attempt on the loss function and the experiment result shows that focal loss would perform better than cross entropy loss. Last we have won first for subtask C in the final competition.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_semeval-1_38", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_semeval-1_38.flac", "type": "audio", "duration": 728.015}, {"from": "Assistant", "value": "We describe MeasEval, a SemEval task of extracting counts, measurements, and related context from scientific documents, which is of significant importance to the creation of Knowledge Graphs that distill information from the scientific literature. This is a new task in 2021, for which over 75 submissions from 25 participants were received. We expect the data developed for this task and the findings reported to be valuable to the scientific knowledge extraction, metrology, and automated knowledge base construction communities.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_semeval-1_39", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_semeval-1_39.flac", "type": "audio", "duration": 694.577}, {"from": "Assistant", "value": "Understanding tables is an important and relevant task that involves understanding table structure as well as being able to compare and contrast information within cells. In this paper, we address this challenge by presenting a new dataset and tasks that addresses this goal in a shared task in SemEval 2020 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS). Our dataset contains 981 manually-generated tables and an auto-generated dataset of 1980 tables providing over 180K statement and over 16M evidence annotations. SEM-TAB-FACTS featured two sub-tasks. In sub-task A, the goal was to determine if a statement is supported, refuted or unknown in relation to a table. In sub-task B, the focus was on identifying the specific cells of a table that provide evidence for the statement. 69 teams signed up to participate in the task with 19 successful submissions to subtask A and 12 successful submissions to subtask B. We present our results and main findings from the competition.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_semeval-1_40", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_semeval-1_40.flac", "type": "audio", "duration": 421.001}, {"from": "Assistant", "value": "Recently, there has been an interest in the research on factual verification and prediction over structured data like tables and graphs. To circumvent any false news incident, it is necessary to not only model and predict over structured data efficiently but also to explain those predictions. In this paper, as the part of the SemEval-2021 Task 9, we tackle the problem of fact verification and evidence finding over tabular data. There are two subtasks, in which given a table and a statement/fact, the subtask A is to determine whether the statement is inferred from the tabular data and the subtask B is to determine which cells in the table provide evidence for the former subtask. We make a comparison of the baselines and state of the art approaches over the given SemTabFact dataset. We also propose a novel approach CellBERT to solve the task of evidence finding, as a form of Natural Language Inference task. We obtain a 3-way F1 score of 0.69 on subtask A and an F1 score of 0.65 on subtask B.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_semeval-1_41", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_semeval-1_41.flac", "type": "audio", "duration": 747.172}, {"from": "Assistant", "value": "Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision. However, most supervised machine learning methods assume that a single preferred interpretation exists for each item, which is at best an idealization. The aim of the SemEval-2021 shared task on learning with disagreements (Le-Wi-Di) was to provide a unified testing framework for methods for learning from data containing multiple and possibly contradictory annotations covering the best-known datasets containing information about disagreements for interpreting language and classifying images. In this paper we describe the shared task and its results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_semeval-1_44", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_semeval-1_44.flac", "type": "audio", "duration": 708.023}, {"from": "Assistant", "value": "There is currently a gap between the natural language expression of scholarly publications and their structured semantic content modeling to enable intelligent content search. With the volume of research growing exponentially every year, a search feature operating over semantically structured content is compelling. The SemEval-2021 Shared Task NLPContributionGraph (a.k.a. ‘the NCG task’) tasks participants to develop automated systems that structure contributions from NLP scholarly articles in the English language. Being the first-of-its-kind in the SemEval series, the task released structured data from NLP scholarly articles at three levels of information granularity, i.e. at sentence-level, phrase-level, and phrases organized as triples toward Knowledge Graph (KG) building. The sentence-level annotations comprised the few sentences about the article’s contribution. The phrase-level annotations were scientific term and predicate phrases from the contribution sentences. Finally, the triples constituted the research overview KG. For the Shared Task, participating systems were then expected to automatically classify contribution sentences, extract scientific terms and relations from the sentences, and organize them as KG triples. Overall, the task drew a strong participation demographic of seven teams and 27 participants. The best end-to-end task system classified contribution sentences at 57.27% F1, phrases at 46.41% F1, and triples at 22.28% F1. While the absolute performance to generate triples remains low, as conclusion to the article, the difficulty of producing such data and as a consequence of modeling it is highlighted.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_semeval-1_45", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_semeval-1_45.flac", "type": "audio", "duration": 419.456}, {"from": "Assistant", "value": "We propose a cascade of neural models that performs sentence classification, phrase recognition, and triple extraction to automatically structure the scholarly contributions of NLP publications. To identify the most important contribution sentences in a paper, we used a BERT-based classifier with positional features (Subtask 1). A BERT-CRF model was used to recognize and characterize relevant phrases in contribution sentences (Subtask 2). We categorized the triples into several types based on whether and how their elements were expressed in text, and addressed each type using separate BERT-based classifiers as well as rules (Subtask 3). Our system was officially ranked second in Phase 1 evaluation and first in both parts of Phase 2 evaluation. After fixing a submission error in Pharse 1, our approach yields the best results overall. In this paper, in addition to a system description, we also provide further analysis of our results, highlighting its strengths and limitations. We make our code publicly available athttps://github.com/Liu-Hy/nlp-contrib-graph.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sigmorphon-1_1", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sigmorphon-1_1.flac", "type": "audio", "duration": 563.733}, {"from": "Assistant", "value": "Reducing communication breakdown is critical to success in interactive NLP applications, such as dialogue systems. To this end, we propose a confusion-mitigation framework for the detection and remediation of communication breakdown. In this work, as a first step towards implementing this framework, we focus on detecting phonemic sources of confusion. As a proof-of-concept, we evaluate two neural architectures in predicting the probability that a listener will misunderstand phonemes in an utterance. We show that both neural models outperform a weighted n-gram baseline, showing early promise for the broader framework.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sigmorphon-1_2", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sigmorphon-1_2.flac", "type": "audio", "duration": 937.769}, {"from": "Assistant", "value": "This paper investigates bounds on the generative capacity of prosodic processes, by focusing on the complexity of recursive prosody in coordination contexts in English (Wagner, 2010). Although all phonological processes and most prosodic processes are computationally regular string languages, we show that recursive prosody is not. The output string language is instead parallel multiple context-free (Seki et al., 1991). We evaluate the complexity of the pattern over strings, and then move on to a characterization over trees that requires the expressivity of multi bottom-up tree transducers. In doing so, we provide a foundation for future mathematically grounded investigations of the syntax-prosody interface.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sigmorphon-1_3", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sigmorphon-1_3.flac", "type": "audio", "duration": 922.496}, {"from": "Assistant", "value": "Raimy (1999; 2000a; 2000b) proposed a graphical formalism for modeling reduplication, originallymostly focused on phonological overapplication in a derivational framework. This framework is now known as Precedence-based phonology or Multiprecedence phonology. Raimy’s idea is that the segments at the input to the phonology are not totally ordered by precedence. This paper tackles a challenge that arose with Raimy’s work, the development of a deterministic serialization algorithm as part of the derivation of surface forms. The Match-Extend algorithm introduced here requires fewer assumptions and sticks tighter to the attested typology. The algorithm also contains no parameter or constraint specific to individual graphs or topologies, unlike previous proposals. Match-Extend requires nothing except knowing the last added set of links.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sigmorphon-1_4", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sigmorphon-1_4.flac", "type": "audio", "duration": 293.593}, {"from": "Assistant", "value": "This paper investigates how the ordering of tone relative to the segmental string influences the calculation of phonotactic probability. Trigram and recurrent neural network models were trained on syllable lexicons of four Asian syllable-tone languages (Mandarin, Thai, Vietnamese, and Cantonese) in which tone was treated as a segment occurring in different positions in the string. For trigram models, the optimal permutation interacted with language, while neural network models were relatively unaffected by tone position in all languages. In addition to providing a baseline for future evaluation, these results suggest that phonotactic probability is robust to choices of how tone is ordered with respect to other elements in the syllable.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sigmorphon-1_5", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sigmorphon-1_5.flac", "type": "audio", "duration": 925.013}, {"from": "Assistant", "value": "Large-scale morphological databases provide essential input to a wide range of NLP applications. Inflectional data is of particular importance for morphologically rich (agglutinative and highly inflecting) languages, and derivations can be used, e.g. to infer the semantics of out-of-vocabulary words. Extending the scope of state-of-the-art multilingual morphological databases, we announce the release of MorphyNet, a high-quality resource with 15 languages, 519k derivational and 10.1M inflectional entries, and a rich set of morphological features. MorphyNet was extracted from Wiktionary using both hand-crafted and automated methods, and was manually evaluated to be of a precision higher than 98%. Both the resource generation logic and the resulting database are made freely available and are reusable as stand-alone tools or in combination with existing resources.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sigmorphon-1_6", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sigmorphon-1_6.flac", "type": "audio", "duration": 631.52}, {"from": "Assistant", "value": "In this work, we analyze the robustness of neural machine translation systems towards grammatical perturbations in the source. In particular, we focus on morphological inflection related perturbations. While this has been recently studied for English→French (MORPHEUS) (Tan et al., 2020), it is unclear how this extends to Any→English translation systems. We propose MORPHEUS-MULTILINGUAL that utilizes UniMorph dictionaries to identify morphological perturbations to source that adversely affect the translation models. Along with an analysis of state-of-the-art pretrained MT systems, we train and analyze systems for 11 language pairs using the multilingual TED corpus (Qi et al., 2018). We also compare this to actual errors of non-native speakers using Grammatical Error Correction datasets. Finally, we present a qualitative and quantitative analysis of the robustness of Any→English translation systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sigmorphon-1_7", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sigmorphon-1_7.flac", "type": "audio", "duration": 811.563}, {"from": "Assistant", "value": "Neural models excel at extracting statistical patterns from large amounts of data, but struggle to learn patterns or reason about language from only a few examples. In this paper, we ask: Can we learn explicit rules that generalize well from only a few examples? We explore this question using program synthesis. We develop a synthesis model to learn phonology rules as programs in a domain-specific language. We test the ability of our models to generalize from few training examples using our new dataset of problems from the Linguistics Olympiad, a challenging set of tasks that require strong linguistic reasoning ability. In addition to being highly sample-efficient, our approach generates human-readable programs, and allows control over the generalizability of the learnt programs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sigmorphon-1_18", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sigmorphon-1_18.flac", "type": "audio", "duration": 817.017}, {"from": "Assistant", "value": "We investigate how abstract processes like suffixation can be learned from morphological inflection task data using an analogical memory-based framework. In this framework, the inflection target form is specified by providing an example inflection of another word in the language. We show that this model is capable of near-baseline performance on the SigMorphon 2020 inflection challenge. Such a model can make predictions for unseen languages, allowing us to perform one-shot inflection on natural languages and investigate morphological transfer with synthetic probes. Accuracy for one-shot transfer can be unexpectedly high for some target languages (88% in Shona) and language families (53% across Romance). Probe experiments show that the model learns partially generalizable representations of prefixation, suffixation and reduplication, aiding its ability to transfer. We argue that the degree of generality of these process representations also helps to explain transfer results from previous research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sigmorphon-1_20", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sigmorphon-1_20.flac", "type": "audio", "duration": 893.016}, {"from": "Assistant", "value": "Total reduplication is common in natural language phonology and morphology. However, formally as copying on reduplicants of unbounded size, unrestricted total reduplication requires computational power beyond context-free, while other phonological and morphological patterns are regular, or even sub-regular. Thus, existing language classes characterizing reduplicated strings inevitably include typologically unattested context-free patterns, such as reversals. This paper extends regular languages to incorporate reduplication by introducing a new computational device: finite state buffered machine (FSBMs). We give its mathematical definitions and discuss some closure properties of the corresponding set of languages. As a result, the class of regular languages and languages derived from them through a copying mechanism is characterized. Suggested by previous literature, this class of languages should approach the characterization of natural language word sets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sigmorphon-1_21", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sigmorphon-1_21.flac", "type": "audio", "duration": 983.017}, {"from": "Assistant", "value": "This paper presents a finite-state morphological analyzer for the Gitksan language. The analyzer draws from a 1250-token Eastern dialect wordlist. It is based on finite-state technology and additionally includes two extensions which can provide analyses for out-of-vocabulary words: rules for generating predictable dialect variants, and a neural guesser component. The pre-neural analyzer, tested against interlinear-annotated texts from multiple dialects, achieves coverage of (75-81%), and maintains high precision (95-100%). The neural extension improves coverage at the cost of lowered precision.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sigmorphon-1_22", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sigmorphon-1_22.flac", "type": "audio", "duration": 915.2}, {"from": "Assistant", "value": "Traditionally, character-level transduction problems have been solved with finite-state models designed to encode structural and linguistic knowledge of the underlying process, whereas recent approaches rely on the power and flexibility of sequence-to-sequence models with attention. Focusing on the less explored unsupervised learning scenario, we compare the two model classes side by side and find that they tend to make different types of errors even when achieving comparable performance. We analyze the distributions of different error classes using two unsupervised tasks as testbeds: converting informally romanized text into the native script of its language (for Russian, Arabic, and Kannada) and translating between a pair of closely related languages (Serbian and Bosnian). Finally, we investigate how combining finite-state and sequence-to-sequence models at decoding time affects the output quantitatively and qualitatively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sigmorphon-1_23", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sigmorphon-1_23.flac", "type": "audio", "duration": 773.247}, {"from": "Assistant", "value": "Shupamem, a language of Western Cameroon, is a tonal language which also exhibits the morpho-phonological process of full reduplication. This creates two challenges for finite-state model of its morpho-syntax and morphophonology: how to manage the full reduplication and the autosegmental nature of lexical tone. Dolatian and Heinz (2020) explain how 2-way finite-state transducers can model full reduplication without an exponential increase in states, and finite-state transducers with multiple tapes have been used to model autosegmental tiers, including tone (Wiebe, 1992; Dolatian and Rawski, 2020a). Here we synthesize 2-way finite-state transducers and multitape transducers, resulting in a finite-state formalism that subsumes both, to account for the full reduplicative processes in Shupamem which also affect tone.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sigmorphon-1_24", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sigmorphon-1_24.flac", "type": "audio", "duration": 865.31}, {"from": "Assistant", "value": "Pronunciation lexicons and prediction models are a key component in several speech synthesis and recognition systems. We know that morphologically related words typically follow a fixed pattern of pronunciation which can be described by language-specific paradigms. In this work we explore how deep recurrent neural networks can be used to automatically learn and exploit this pattern to improve the pronunciation prediction quality of words related by morphological inflection. We propose two novel approaches for supplying morphological information, using the word’s morphological class and its lemma, which are typically annotated in standard lexicons. We report improvements across a number of European languages with varying degrees of phonological and morphological complexity, and two language families, with greater improvements for languages where the pronunciation prediction task is inherently more challenging. We also observe that combining bidirectional LSTM networks with attention mechanisms is an effective neural approach for the computational problem considered, across languages. Our approach seems particularly beneficial in the low resource setting, both by itself and in conjunction with transfer learning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_splurobonlp-1_3", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_splurobonlp-1_3.flac", "type": "audio", "duration": 654.013}, {"from": "Assistant", "value": "In this paper, we define and evaluate a methodology for extracting history-dependent spatial questions from visual dialogues. We say that a question is history-dependent if it requires (parts of) its dialogue history to be interpreted. We argue that some kinds of visual questions define a context upon which a follow-up spatial question relies. We call the question that restricts the context: trigger, and we call the spatial question that requires the trigger question to be answered: zoomer. We automatically extract different trigger and zoomer pairs based on the visual property that the questions rely on (e.g. color, number). We manually annotate the automatically extracted trigger and zoomer pairs to verify which zoomers require their trigger. We implement a simple baseline architecture based on a SOTA multimodal encoder. Our results reveal that there is much room for improvement for answering history-dependent questions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_spnlp-1_2", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_spnlp-1_2.flac", "type": "audio", "duration": 590.623}, {"from": "Assistant", "value": "The de-facto standard decoding method for semantic parsing in recent years has been to autoregressively decode the abstract syntax tree of the target program using a top-down depth-first traversal. In this work, we propose an alternative approach: a Semi-autoregressive Bottom-up Parser (SmBoP) that constructs at decoding step t the top-K sub-trees of height ≤ t. Our parser enjoys several benefits compared to top-down autoregressive parsing. From an efficiency perspective, bottom-up parsing allows to decode all sub-trees of a certain height in parallel, leading to logarithmic runtime complexity rather than linear. From a modeling perspective, a bottom-up parser learns representations for meaningful semantic sub-programs at each step, rather than for semantically-vacuous partial trees. We apply SmBoP on Spider, a challenging zero-shot semantic parsing benchmark, and show that SmBoP leads to a 2.2x speed-up in decoding time and a ~5x speed-up in training time, compared to a semantic parser that uses autoregressive decoding. SmBoP obtains 71.1 denotation accuracy on Spider, establishing a new state-of-the-art, and 69.5 exact match, comparable to the 69.6 exact match of the autoregressive RAT-SQL+Grappa.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_spnlp-1_3", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_spnlp-1_3.flac", "type": "audio", "duration": 698.709}, {"from": "Assistant", "value": "AM dependency parsing is a method for neural semantic graph parsing that exploits the principle of compositionality. While AM dependency parsers have been shown to be fast and accurate across several graphbanks, they require explicit annotations of the compositional tree structures for training. In the past, these were obtained using complex graphbank-specific heuristics written by experts. Here we show how they can instead be trained directly on the graphs with a neural latent-variable model, drastically reducing the amount and complexity of manual heuristics. We demonstrate that our model picks up on several linguistic phenomena on its own and achieves comparable accuracy to supervised training, greatly facilitating the use of AM dependency parsing for new sembanks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_spnlp-1_5", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_spnlp-1_5.flac", "type": "audio", "duration": 612.203}, {"from": "Assistant", "value": "Despite its wide use, recent studies have revealed unexpected and undesirable properties of neural autoregressive sequence models trained with maximum likelihood, such as an unreasonably high affinity to short sequences after training and to infinitely long sequences at decoding time. We propose to study these phenomena by investigating how the modes, or local maxima, of a distribution are maintained throughout the fulllearning chainof the ground-truth, empirical, learned and decoding-induced distributions, via the newly proposedmode recovery cost. We design a tractable testbed where we build three types of ground-truth distributions: (1) an LSTM based structured distribution, (2) an unstructured distribution where probability of a sequence does not depend on its content, and (3) a product of these two which we call a semi-structured distribution. Our study reveals both expected and unexpected findings. First, starting with data collection, mode recovery cost strongly relies on the ground-truth distribution and is most costly with the semi-structured distribution. Second, after learning, mode recovery cost from the ground-truth distribution may increase or decrease compared to data collection, with the largest cost degradation occurring with the semi-structured ground-truth distribution. Finally, the ability of the decoding-induced distribution to recover modes from the learned distribution is highly impacted by the choices made earlier in the learning chain. We conclude that future research must consider the entire learning chain in order to fully understand the potentials and perils and to further improve neural autoregressive sequence models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_spnlp-1_8", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_spnlp-1_8.flac", "type": "audio", "duration": 693.023}, {"from": "Assistant", "value": "In this work, we empirically compare span extraction methods for the task of semantic role labeling (SRL). While recent progress incorporating pre-trained contextualized representations into neural encoders has greatly improved SRL F1 performance on popular benchmarks, the potential costs and benefits of structured decoding in these models have become less clear. With extensive experiments on PropBank SRL datasets, we find that more structured decoding methods outperform BIO-tagging when using static (word type) embeddings across all experimental settings. However, when used in conjunction with pre-trained contextualized word representations, the benefits are diminished. We also experiment in cross-genre and cross-lingual settings and find similar trends. We further perform speed comparisons and provide analysis on the accuracy-efficiency trade-offs among different decoding methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_unimplicit-1_8", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_unimplicit-1_8.flac", "type": "audio", "duration": 574.357}, {"from": "Assistant", "value": "In this report, we describe our transformers for text classification baseline (TTCB) submissions to a shared task on implicit and underspecified language 2021. We cast the task of predicting revision requirements in collaboratively edited instructions as text classification. We considered transformer-based models which are the current state-of-the-art methods for text classification. We explored different training schemes, loss functions, and data augmentations. Our best result of68.45%test accuracy (68.84%validation accuracy), however, consists of an XLNet model with a linear annealing scheduler and a cross-entropy loss. We do not observe any significant gain on any validation metric based on our various design choices except the MiniLM which has a higher validation F1 score and is faster to train by a half but also a lower validation accuracy score.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_1", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_1.flac", "type": "audio", "duration": 412.672}, {"from": "Assistant", "value": "Offensive language detection (OLD) has received increasing attention due to its societal impact. Recent work shows that bidirectional transformer based methods obtain impressive performance on OLD. However, such methods usually rely on large-scale well-labeled OLD datasets for model training. To address the issue of data/label scarcity in OLD, in this paper, we propose a simple yet effective domain adaptation approach to train bidirectional transformers. Our approach introduces domain adaptation (DA) training procedures to ALBERT, such that it can effectively exploit auxiliary data from source domains to improve the OLD performance in a target domain. Experimental results on benchmark datasets show that our approach, ALBERT (DA), obtains the state-of-the-art performance in most cases. Particularly, our approach significantly benefits underrepresented and under-performing classes, with a significant improvement over ALBERT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_2", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_2.flac", "type": "audio", "duration": 571.002}, {"from": "Assistant", "value": "Hate speech and profanity detection suffer from data sparsity, especially for languages other than English, due to the subjective nature of the tasks and the resulting annotation incompatibility of existing corpora. In this study, we identify profane subspaces in word and sentence representations and explore their generalization capability on a variety of similar and distant target tasks in a zero-shot setting. This is done monolingually (German) and cross-lingually to closely-related (English), distantly-related (French) and non-related (Arabic) tasks. We observe that, on both similar and distant target tasks and across all languages, the subspace-based representations transfer more effectively than standard BERT representations in the zero-shot setting, with improvements between F1 +10.9 and F1 +42.9 over the baselines across all tested monolingual and cross-lingual scenarios.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_3", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_3.flac", "type": "audio", "duration": 430.01}, {"from": "Assistant", "value": "We introduce HateBERT, a re-trained BERT model for abusive language detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit comments in English from communities banned for being offensive, abusive, or hateful that we have curated and made available to the public. We present the results of a detailed comparison between a general pre-trained language model and the retrained version on three English datasets for offensive, abusive language and hate speech detection tasks. In all datasets, HateBERT outperforms the corresponding general BERT model. We also discuss a battery of experiments comparing the portability of the fine-tuned models across the datasets, suggesting that portability is affected by compatibility of the annotated phenomena.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_4", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_4.flac", "type": "audio", "duration": 468.256}, {"from": "Assistant", "value": "Hateful memes pose a unique challenge for current machine learning systems because their message is derived from both text- and visual-modalities. To this effect, Facebook released the Hateful Memes Challenge, a dataset of memes with pre-extracted text captions, but it is unclear whether these synthetic examples generalize to ‘memes in the wild’. In this paper, we collect hateful and non-hateful memes from Pinterest to evaluate out-of-sample performance on models pre-trained on the Facebook dataset. We find that ‘memes in the wild’ differ in two key aspects: 1) Captions must be extracted via OCR, injecting noise and diminishing performance of multimodal models, and 2) Memes are more diverse than ‘traditional memes’, including screenshots of conversations or text on a plain background. This paper thus serves as a reality-check for the current benchmark of hateful meme detection and its applicability for detecting real world hate.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_5", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_5.flac", "type": "audio", "duration": 575.019}, {"from": "Assistant", "value": "Content moderation is often performed by a collaboration between humans and machine learning models. However, it is not well understood how to design the collaborative process so as to maximize the combined moderator-model system performance. This work presents a rigorous study of this problem, focusing on an approach that incorporates model uncertainty into the collaborative process. First, we introduce principled metrics to describe the performance of the collaborative system under capacity constraints on the human moderator, quantifying how efficiently the combined system utilizes human decisions. Using these metrics, we conduct a large benchmark study evaluating the performance of state-of-the-art uncertainty models under different collaborative review strategies. We find that an uncertainty-based strategy consistently outperforms the widely used strategy based on toxicity scores, and moreover that the choice of review strategy drastically changes the overall system performance. Our results demonstrate the importance of rigorous metrics for understanding and developing effective moderator-model systems for content moderation, as well as the utility of uncertainty estimation in this domain.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_6", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_6.flac", "type": "audio", "duration": 506.009}, {"from": "Assistant", "value": "As socially unacceptable language become pervasive in social media platforms, the need for automatic content moderation become more pressing. This contribution introduces the Dutch Abusive Language Corpus (DALC v1.0), a new dataset with tweets manually an- notated for abusive language. The resource ad- dress a gap in language resources for Dutch and adopts a multi-layer annotation scheme modeling the explicitness and the target of the abusive messages. Baselines experiments on all annotation layers have been conducted, achieving a macro F1 score of 0.748 for binary classification of the explicitness layer and .489 for target classification.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_7", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_7.flac", "type": "audio", "duration": 594.013}, {"from": "Assistant", "value": "Social media texts such as blog posts, comments, and tweets often contain offensive languages including racial hate speech comments, personal attacks, and sexual harassment. Detecting inappropriate use of language is, therefore, of utmost importance for the safety of the users as well as for suppressing hateful conduct and aggression. Existing approaches to this problem are mostly available for resource-rich languages such as English and German. In this paper, we characterize the offensive language in Nepali, a low-resource language, highlighting the challenges that need to be addressed for processing Nepali social media text. We also present experiments for detecting offensive language using supervised machine learning. Besides contributing the first baseline approaches of detecting offensive language in Nepali, we also release human annotated data sets to encourage future research on this crucial topic.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_8", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_8.flac", "type": "audio", "duration": 391.001}, {"from": "Assistant", "value": "Hate speech-related lexicons have been proved to be useful for many tasks such as data collection and classification. However, existing Portuguese lexicons do not distinguish between European and Brazilian Portuguese, and do not include neutral terms that are potentially useful to detect a broader spectrum of content referring to minorities. In this work, we present MIN_PT, a new European Portuguese Lexicon for Minorities-Related Terms specifically designed to tackle the limitations of existing resources. We describe the data collection and annotation process, discuss the limitation and ethical concerns, and prove the utility of the resource by applying it to a use case for the Portuguese 2021 presidential elections.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_9", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_9.flac", "type": "audio", "duration": 587.0}, {"from": "Assistant", "value": "Current abusive language detection systems have demonstrated unintended bias towards sensitive features such as nationality or gender. This is a crucial issue, which may harm minorities and underrepresented groups if such systems were integrated in real-world applications. In this paper, we create ad hoc tests through the CheckList tool (Ribeiro et al., 2020) to detect biases within abusive language classifiers for English. We compare the behaviour of two BERT-based models, one trained on a generic hate speech dataset and the other on a dataset for misogyny detection. Our evaluation shows that, although BERT-based classifiers achieve high accuracy levels on a variety of natural language processing tasks, they perform very poorly as regards fairness and bias, in particular on samples involving implicit stereotypes, expressions of hate towards minorities and protected attributes such as race or sexual orientation. We release both the notebooks implemented to extend the Fairness tests and the synthetic datasets usable to evaluate systems bias independently of CheckList.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_10", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_10.flac", "type": "audio", "duration": 481.001}, {"from": "Assistant", "value": "Bias mitigation approaches reduce models’ dependence on sensitive features of data, such as social group tokens (SGTs), resulting in equal predictions across the sensitive features. In hate speech detection, however, equalizing model predictions may ignore important differences among targeted social groups, as hate speech can contain stereotypical language specific to each SGT. Here, to take the specific language about each SGT into account, we rely on counterfactual fairness and equalize predictions among counterfactuals, generated by changing the SGTs. Our method evaluates the similarity in sentence likelihoods (via pre-trained language models) among counterfactuals, to treat SGTs equally only within interchangeable contexts. By applying logit pairing to equalize outcomes on the restricted set of counterfactuals for each instance, we improve fairness metrics while preserving model performance on hate speech detection.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_11", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_11.flac", "type": "audio", "duration": 592.039}, {"from": "Assistant", "value": "There have been several attempts to create an accurate and thorough emotion lexicon in English, which identifies the emotional content of words. Of the several commonly used resources, the NRC emotion lexicon (Mohammad and Turney, 2013b) has received the most attention due to its availability, size, and its choice of Plutchik’s expressive 8-class emotion model. In this paper we identify a large number of troubling entries in the NRC lexicon, where words that should in most contexts be emotionally neutral, with no affect (e.g., ‘lesbian’, ‘stone’, ‘mountain’), are associated with emotional labels that are inaccurate, nonsensical, pejorative, or, at best, highly contingent and context-dependent (e.g., ‘lesbian’ labeled as Disgust and Sadness, ‘stone’ as Anger, or ‘mountain’ as Anticipation). We describe a procedure for semi-automatically correcting these problems in the NRC, which includes disambiguating POS categories and aligning NRC entries with other emotion lexicons to infer the accuracy of labels. We demonstrate via an experimental benchmark that the quality of the resources is thus improved. We release the revised resource and our code to enable other researchers to reproduce and build upon results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_12", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_12.flac", "type": "audio", "duration": 420.003}, {"from": "Assistant", "value": "Automatic detection of toxic language plays an essential role in protecting social media users, especially minority groups, from verbal abuse. However, biases toward some attributes, including gender, race, and dialect, exist in most training datasets for toxicity detection. The biases make the learned models unfair and can even exacerbate the marginalization of people. Considering that current debiasing methods for general natural language understanding tasks cannot effectively mitigate the biases in the toxicity detectors, we propose to use invariant rationalization (InvRat), a game-theoretic framework consisting of a rationale generator and a predictor, to rule out the spurious correlation of certain syntactic patterns (e.g., identity mentions, dialect) to toxicity labels. We empirically show that our method yields lower false positive rate in both lexical and dialectal attributes than previous debiasing methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_14", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_14.flac", "type": "audio", "duration": 548.014}, {"from": "Assistant", "value": "Online abuse and offensive language on social media have become widespread problems in today’s digital age. In this paper, we contribute a Reddit-based dataset, consisting of 68,159 insults and 51,102 compliments targeted at individuals instead of targeting a particular community or race. Secondly, we benchmark multiple existing state-of-the-art models for both classification and unsupervised style transfer on the dataset. Finally, we analyse the experimental results and conclude that the transfer task is challenging, requiring the models to understand the high degree of creativity exhibited in the data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_15", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_15.flac", "type": "audio", "duration": 379.019}, {"from": "Assistant", "value": "User posts whose perceived toxicity depends on the conversational context are rare in current toxicity detection datasets. Hence, toxicity detectors trained on current datasets will also disregard context, making the detection of context-sensitive toxicity a lot harder when it occurs. We constructed and publicly release a dataset of 10k posts with two kinds of toxicity labels per post, obtained from annotators who considered (i) both the current post and the previous one as context, or (ii) only the current post. We introduce a new task, context-sensitivity estimation, which aims to identify posts whose perceived toxicity changes if the context (previous post) is also considered. Using the new dataset, we show that systems can be developed for this task. Such systems could be used to enhance toxicity detection datasets with more context-dependent posts or to suggest when moderators should consider the parent posts, which may not always be necessary and may introduce additional costs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_17", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_17.flac", "type": "audio", "duration": 413.653}, {"from": "Assistant", "value": "With the rise of research on toxic comment classification, more and more annotated datasets have been released. The wide variety of the task (different languages, different labeling processes and schemes) has led to a large amount of heterogeneous datasets that can be used for training and testing very specific settings. Despite recent efforts to create web pages that provide an overview, most publications still use only a single dataset. They are not stored in one central database, they come in many different data formats and it is difficult to interpret their class labels and how to reuse these labels in other projects. To overcome these issues, we present a collection of more than thirty datasets in the form of a software tool that automatizes downloading and processing of the data and presents them in a unified data format that also offers a mapping of compatible class labels. Another advantage of that tool is that it gives an overview of properties of available datasets, such as different languages, platforms, and class labels to make it easier to select suitable training and test data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_18", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_18.flac", "type": "audio", "duration": 599.637}, {"from": "Assistant", "value": "Community-level bans are a common tool against groups that enable online harassment and harmful speech. Unfortunately, the efficacy of community bans has only been partially studied and with mixed results. Here, we provide a flexible unsupervised methodology to identify in-group language and track user activity on Reddit both before and after the ban of a community (subreddit). We use a simple word frequency divergence to identify uncommon words overrepresented in a given community, not as a proxy for harmful speech but as a linguistic signature of the community. We apply our method to 15 banned subreddits, and find that community response is heterogeneous between subreddits and between users of a subreddit. Top users were more likely to become less active overall, while random users often reduced use of in-group language without decreasing activity. Finally, we find some evidence that the effectiveness of bans aligns with the content of a community. Users of dark humor communities were largely unaffected by bans while users of communities organized around white supremacy and fascism were the most affected. Altogether, our results show that bans do not affect all groups or users equally, and pave the way to understanding the effect of bans across communities.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_19", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_19.flac", "type": "audio", "duration": 690.731}, {"from": "Assistant", "value": "Mainstream research on hate speech focused so far predominantly on the task of classifying mainly social media posts with respect to predefined typologies of rather coarse-grained hate speech categories. This may be sufficient if the goal is to detect and delete abusive language posts. However, removal is not always possible due to the legislation of a country. Also, there is evidence that hate speech cannot be successfully combated by merely removing hate speech posts; they should be countered by education and counter-narratives. For this purpose, we need to identify (i) who is the target in a given hate speech post, and (ii) what aspects (or characteristics) of the target are attributed to the target in the post. As the first approximation, we propose to adapt a generic state-of-the-art concept extraction model to the hate speech domain. The outcome of the experiments is promising and can serve as inspiration for further work on the task", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_20", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_20.flac", "type": "audio", "duration": 552.054}, {"from": "Assistant", "value": "Abusive language is a growing phenomenon on social media platforms. Its effects can reach beyond the online context, contributing to mental or emotional stress on users. Automatic tools for detecting abuse can alleviate the issue. In practice, developing automated methods to detect abusive language relies on good quality data. However, there is currently a lack of standards for creating datasets in the field. These standards include definitions of what is considered abusive language, annotation guidelines and reporting on the process. This paper introduces an annotation framework inspired by legal concepts to define abusive language in the context of online harassment. The framework uses a 7-point Likert scale for labelling instead of class labels. We also present ALYT – a dataset of Abusive Language on YouTube. ALYT includes YouTube comments in English extracted from videos on different controversial topics and labelled by Law students. The comments were sampled from the actual collected data, without artificial methods for increasing the abusive content. The paper describes the annotation process thoroughly, including all its guidelines and training steps.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_22", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_22.flac", "type": "audio", "duration": 416.363}, {"from": "Assistant", "value": "This paper describes our submission (winning solution for Task A) to the Shared Task on Hateful Meme Detection at WOAH 2021. We build our system on top of a state-of-the-art system for binary hateful meme classification that already uses image tags such as race, gender, and web entities. We add further metadata such as emotions and experiment with data augmentation techniques, as hateful instances are underrepresented in the data set.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_23", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_23.flac", "type": "audio", "duration": 384.012}, {"from": "Assistant", "value": "Memes are the combinations of text and images that are often humorous in nature. But, that may not always be the case, and certain combinations of texts and images may depict hate, referred to as hateful memes. This work presents a multimodal pipeline that takes both visual and textual features from memes into account to (1) identify the protected category (e.g. race, sex etc.) that has been attacked; and (2) detect the type of attack (e.g. contempt, slurs etc.). Our pipeline uses state-of-the-art pre-trained visual and textual representations, followed by a simple logistic regression classifier. We employ our pipeline on the Hateful Memes Challenge dataset with additional newly created fine-grained labels for protected category and type of attack. Our best model achieves an AUROC of 0.96 for identifying the protected category, and 0.97 for detecting the type of attack. We release our code athttps://github.com/harisbinzia/HatefulMemes", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_woah-1_24", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_woah-1_24.flac", "type": "audio", "duration": 394.019}, {"from": "Assistant", "value": "The Shared Task on Hateful Memes is a challenge that aims at the detection of hateful content in memes by inviting the implementation of systems that understand memes, potentially by combining image and textual information. The challenge consists of three detection tasks: hate, protected category and attack type. The first is a binary classification task, while the other two are multi-label classification tasks. Our participation included a text-based BERT baseline (TxtBERT), the same but adding information from the image (ImgBERT), and neural retrieval approaches. We also experimented with retrieval augmented classification models. We found that an ensemble of TxtBERT and ImgBERT achieves the best performance in terms of ROC AUC score in two out of the three tasks on our development set.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_101", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_101.flac", "type": "audio", "duration": 722.048}, {"from": "Assistant", "value": "Multilingual neural machine translation has shown the capability of directly translating between language pairs unseen in training, i.e. zero-shot translation. Despite being conceptually attractive, it often suffers from low output quality. The difficulty of generalizing to new translation directions suggests the model representations are highly specific to those language pairs seen in training. We demonstrate that a main factor causing the language-specific representations is the positional correspondence to input tokens. We show that this can be easily alleviated by removing residual connections in an encoder layer. With this modification, we gain up to 18.5 BLEU points on zero-shot translation while retaining quality on supervised directions. The improvements are particularly prominent between related languages, where our proposed model outperforms pivot-based translation. Moreover, our approach allows easy integration of new languages, which substantially expands translation coverage. By thorough inspections of the hidden layer outputs, we show that our approach indeed leads to more language-independent representations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_102", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_102.flac", "type": "audio", "duration": 719.285}, {"from": "Assistant", "value": "Commonsense reasoning research has so far been limited to English. We aim to evaluate and improve popular multilingual language models (ML-LMs) to help advance commonsense reasoning (CSR) beyond English. We collect the Mickey corpus, consisting of 561k sentences in 11 different languages, which can be used for analyzing and improving ML-LMs. We propose Mickey Probe, a language-general probing task for fairly evaluating the common sense of popular ML-LMs across different languages. In addition, we also create two new datasets, X-CSQA and X-CODAH, by translating their English versions to 14 other languages, so that we can evaluate popular ML-LMs for cross-lingual commonsense reasoning. To improve the performance beyond English, we propose a simple yet effective method — multilingual contrastive pretraining (MCP). It significantly enhances sentence representations, yielding a large performance gain on both benchmarks (e.g., +2.7% accuracy for X-CSQA over XLM-R_L).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_103", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_103.flac", "type": "audio", "duration": 584.405}, {"from": "Assistant", "value": "Attention mechanisms have achieved substantial improvements in neural machine translation by dynamically selecting relevant inputs for different predictions. However, recent studies have questioned the attention mechanisms’ capability for discovering decisive inputs. In this paper, we propose to calibrate the attention weights by introducing a mask perturbation model that automatically evaluates each input’s contribution to the model outputs. We increase the attention weights assigned to the indispensable tokens, whose removal leads to a dramatic performance decrease. The extensive experiments on the Transformer-based translation have demonstrated the effectiveness of our model. We further find that the calibrated attention weights are more uniform at lower layers to collect multiple information while more concentrated on the specific inputs at higher layers. Detailed analyses also show a great need for calibration in the attention weights with high entropy where the model is unconfident about its decision.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_104", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_104.flac", "type": "audio", "duration": 763.101}, {"from": "Assistant", "value": "We propose a new architecture for adapting a sentence-level sequence-to-sequence transformer by incorporating multiple pre-trained document context signals and assess the impact on translation performance of (1) different pretraining approaches for generating these signals, (2) the quantity of parallel data for which document context is available, and (3) conditioning on source, target, or source and target contexts. Experiments on the NIST Chinese-English, and IWSLT and WMT English-German tasks support four general conclusions: that using pre-trained context representations markedly improves sample efficiency, that adequate parallel data resources are crucial for learning to use document context, that jointly conditioning on multiple context representations outperforms any single representation, and that source context is more valuable for translation performance than target side context. Our best multi-context model consistently outperforms the best existing context-aware transformers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_105", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_105.flac", "type": "audio", "duration": 689.052}, {"from": "Assistant", "value": "Recent research in multilingual language models (LM) has demonstrated their ability to effectively handle multiple languages in a single model. This holds promise for low web-resource languages (LRL) as multilingual models can enable transfer of supervision from high resource languages to LRLs. However, incorporating a new language in an LM still remains a challenge, particularly for languages with limited corpora and in unseen scripts. In this paper we argue that relatedness among languages in a language family may be exploited to overcome some of the corpora limitations of LRLs, and propose RelateLM. We focus on Indian languages, and exploit relatedness along two dimensions: (1) script (since many Indic scripts originated from the Brahmic script), and (2) sentence structure. RelateLM uses transliteration to convert the unseen script of limited LRL text into the script of a Related Prominent Language (RPL) (Hindi in our case). While exploiting similar sentence structures, RelateLM utilizes readily available bilingual dictionaries to pseudo translate RPL text into LRL corpora. Experiments on multiple real-world benchmark datasets provide validation to our hypothesis that using a related language as pivot, along with transliteration and pseudo translation based data augmentation, can be an effective way to adapt LMs for LRLs, rather than direct training or pivoting through English.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_106", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_106.flac", "type": "audio", "duration": 425.691}, {"from": "Assistant", "value": "The connection between the maximum spanning tree in a directed graph and the best dependency tree of a sentence has been exploited by the NLP community. However, for many dependency parsing schemes, an important detail of this approach is that the spanning tree must have exactly one edge emanating from the root. While work has been done to efficiently solve this problem for finding the one-best dependency tree, no research has attempted to extend this solution to finding the K-best dependency trees. This is arguably a more important extension as a larger proportion of decoded trees will not be subject to the root constraint of dependency trees. Indeed, we show that the rate of root constraint violations increases by an average of 13 times when decoding with K=50 as opposed to K=1. In this paper, we provide a simplification of the K-best spanning tree algorithm of Camerini et al. (1980). Our simplification allows us to obtain a constant time speed-up over the original algorithm. Furthermore, we present a novel extension of the algorithm for decoding the K-best dependency trees of a graph which are subject to a root constraint.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_107", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_107.flac", "type": "audio", "duration": 709.857}, {"from": "Assistant", "value": "This survey builds an interdisciplinary picture of Argument Mining (AM), with a strong focus on its potential to address issues related to Social and Political Science. More specifically, we focus on AM challenges related to its applications to social media and in the multilingual domain, and then proceed to the widely debated notion of argument quality. We propose a novel definition of argument quality which is integrated with that of deliberative quality from the Social Science literature. Under our definition, the quality of a contribution needs to be assessed at multiple levels: the contribution itself, its preceding context, and the consequential effect on the development of the upcoming discourse. The latter has not received the deserved attention within the community. We finally define an application of AM for Social Good: (semi-)automatic moderation, a highly integrative application which (a) represents a challenging testbed for the integrated notion of quality we advocate, (b) allows the empirical quantification of argument/deliberative quality to benefit from the developments in other NLP fields (i.e. hate speech detection, fact checking, debiasing), and (c) has a clearly beneficial potential at the level of its societal thanks to its real-world application (even if extremely ambitious).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_108", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_108.flac", "type": "audio", "duration": 711.483}, {"from": "Assistant", "value": "Research on the application of NLP in symbol-based Augmentative and Alternative Communication (AAC) tools for improving social interaction support is scarce. We contribute a novel method for generating context-related vocabulary from photographs of personally relevant events aimed at supporting people with language impairments in retelling their past experiences. Performance was calculated with information retrieval concepts on the relevance of vocabulary generated for communicating a corpus of 9730 narrative phrases about events depicted in 1946 photographs. In comparison to a baseline generation composed of frequent English words, our method generated vocabulary with a 4.6 gain in mean average precision, regardless of the level of contextual information in the input photographs, and 6.9 for photographs in which contextual information was extracted correctly. We conclude by discussing how our findings provide insights for system optimization and usage.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_109", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_109.flac", "type": "audio", "duration": 735.84}, {"from": "Assistant", "value": "Continuity of care is crucial to ensuring positive health outcomes for patients discharged from an inpatient hospital setting, and improved information sharing can help. To share information, caregivers write discharge notes containing action items to share with patients and their future caregivers, but these action items are easily lost due to the lengthiness of the documents. In this work, we describe our creation of a dataset of clinical action items annotated over MIMIC-III, the largest publicly available dataset of real clinical notes. This dataset, which we call CLIP, is annotated by physicians and covers 718 documents representing 100K sentences. We describe the task of extracting the action items from these documents as multi-aspect extractive summarization, with each aspect representing a type of action to be taken. We evaluate several machine learning models on this task, and show that the best models exploit in-domain language model pre-training on 59K unannotated documents, and incorporate context from neighboring sentences. We also propose an approach to pre-training data selection that allows us to explore the trade-off between size and domain-specificity of pre-training datasets for this task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_110", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_110.flac", "type": "audio", "duration": 740.856}, {"from": "Assistant", "value": "Emojis have become ubiquitous in digital communication, due to their visual appeal as well as their ability to vividly convey human emotion, among other factors. This also leads to an increased need for systems and tools to operate on text containing emojis. In this study, we assess this support by considering test sets of tweets with emojis, based on which we perform a series of experiments investigating the ability of prominent NLP and text processing tools to adequately process them. In particular, we consider tokenization, part-of-speech tagging, dependency parsing, as well as sentiment analysis. Our findings show that many systems still have notable shortcomings when operating on text containing emojis.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_111", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_111.flac", "type": "audio", "duration": 707.721}, {"from": "Assistant", "value": "Natural language processing techniques have demonstrated promising results in keyphrase generation. However, one of the major challenges inneuralkeyphrase generation is processing long documents using deep neural networks. Generally, documents are truncated before given as inputs to neural networks. Consequently, the models may miss essential points conveyed in the target document. To overcome this limitation, we proposeSEG-Net, a neural keyphrase generation model that is composed of two major components, (1) a selector that selects the salient sentences in a document and (2) an extractor-generator that jointly extracts and generates keyphrases from the selected sentences. SEG-Net uses Transformer, a self-attentive architecture, as the basic building block with a novellayer-wisecoverage attention to summarize most of the points discussed in the document. The experimental results on seven keyphrase generation benchmarks from scientific and web documents demonstrate that SEG-Net outperforms the state-of-the-art neural generative methods by a large margin.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_112", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_112.flac", "type": "audio", "duration": 559.04}, {"from": "Assistant", "value": "We propose a method for generating paraphrases of English questions that retain the original intent but use a different surface form. Our model combines a careful choice of training objective with a principled information bottleneck, to induce a latent encoding space that disentangles meaning and form. We train an encoder-decoder model to reconstruct a question from a paraphrase with the same meaning and an exemplar with the same surface form, leading to separated encoding spaces. We use a Vector-Quantized Variational Autoencoder to represent the surface form as a set of discrete latent variables, allowing us to use a classifier to select a different surface form at test time. Crucially, our method does not require access to an external source of target exemplars. Extensive experiments and a human evaluation show that we are able to generate paraphrases with a better tradeoff between semantic preservation and syntactic novelty compared to previous methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_113", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_113.flac", "type": "audio", "duration": 684.408}, {"from": "Assistant", "value": "We present AggGen (pronounced ‘again’) a data-to-text model which re-introduces two explicit sentence planning stages into neural data-to-text systems: input ordering and input aggregation. In contrast to previous work using sentence planning, our model is still end-to-end: AggGen performs sentence planning at the same time as generating text by learning latent alignments (via semantic facts) between input representation and target text. Experiments on the WebNLG and E2E challenge data show that by using fact-based alignments our approach is more interpretable, expressive, robust to noise, and easier to control, while retaining the advantages of end-to-end systems in terms of fluency. Our code is available athttps://github.com/XinnuoXu/AggGen.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_114", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_114.flac", "type": "audio", "duration": 647.721}, {"from": "Assistant", "value": "Publicly available, large pretrained Language Models (LMs) generate text with remarkable quality, but only sequentially from left to right. As a result, they are not immediately applicable to generation tasks that break the unidirectional assumption, such as paraphrasing or text-infilling, necessitating task-specific supervision. In this paper, we present Reflective Decoding, a novel unsupervised algorithm that allows for direct application of unidirectional LMs to non-sequential tasks. Our 2-step approach requires no supervision or even parallel corpora, only two off-the-shelf pretrained LMs in opposite directions: forward and backward. First, in the contextualization step, we use LMs to generate ensembles of past and future contexts which collectively capture the input (e.g. the source sentence for paraphrasing). Second, in the reflection step, we condition on these “context ensembles”, generating outputs that are compatible with them. Comprehensive empirical results demonstrate that Reflective Decoding outperforms strong unsupervised baselines on both paraphrasing and abductive text infilling, significantly narrowing the gap between unsupervised and supervised methods. Reflective Decoding surpasses multiple supervised baselines on various metrics including human evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_115", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_115.flac", "type": "audio", "duration": 673.045}, {"from": "Assistant", "value": "Recent neural text generation models have shown significant improvement in generating descriptive text from structured data such as table formats. One of the remaining important challenges is generating more analytical descriptions that can be inferred from facts in a data source. The use of a template-based generator and a pointer-generator is among the potential alternatives for table-to-text generators. In this paper, we propose a framework consisting of a pre-trained model and a copy mechanism. The pre-trained models are fine-tuned to produce fluent text that is enriched with numerical reasoning. However, it still lacks fidelity to the table contents. The copy mechanism is incorporated in the fine-tuning step by using general placeholders to avoid producing hallucinated phrases that are not supported by a table while preserving high fluency. In summary, our contributions are (1) a new dataset for numerical table-to-text generation using pairs of a table and a paragraph of a table description with richer inference from scientific papers, and (2) a table-to-text generation framework enriched with numerical reasoning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_116", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_116.flac", "type": "audio", "duration": 704.052}, {"from": "Assistant", "value": "In this paper, we focus on the problem of citing sentence generation, which entails generating a short text to capture the salient information in a cited paper and the connection between the citing and cited paper. We present BACO, a BAckground knowledge- and COntent-based framework for citing sentence generation, which considers two types of information: (1) background knowledge by leveraging structural information from a citation network; and (2) content, which represents in-depth information about what to cite and why to cite. First, a citation network is encoded to provide background knowledge. Second, we apply salience estimation to identify what to cite by estimating the importance of sentences in the cited paper. During the decoding stage, both types of information are combined to facilitate the text generation, and then we conduct a joint training for the generator and citation function classification to make the model aware of why to cite. Our experimental results show that our framework outperforms comparative baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_117", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_117.flac", "type": "audio", "duration": 551.451}, {"from": "Assistant", "value": "Current dialogue summarization systems usually encode the text with a number of general semantic features (e.g., keywords and topics) to gain more powerful dialogue modeling capabilities. However, these features are obtained via open-domain toolkits that are dialog-agnostic or heavily relied on human annotations. In this paper, we show how DialoGPT, a pre-trained model for conversational response generation, can be developed as an unsupervised dialogue annotator, which takes advantage of dialogue background knowledge encoded in DialoGPT. We apply DialoGPT to label three types of features on two dialogue summarization datasets, SAMSum and AMI, and employ pre-trained and non pre-trained models as our summarizers. Experimental results show that our proposed method can obtain remarkable improvements on both datasets and achieves new state-of-the-art performance on the SAMSum dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_118", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_118.flac", "type": "audio", "duration": 728.062}, {"from": "Assistant", "value": "Recent pretrained language models “solved” many reading comprehension benchmarks, where questions are written with access to the evidence document. However, datasets containing information-seeking queries where evidence documents are provided after the queries are written independently remain challenging. We analyze why answering information-seeking queries is more challenging and where their prevalent unanswerabilities arise, on Natural Questions and TyDi QA. Our controlled experiments suggest two headrooms – paragraph selection and answerability prediction, i.e. whether the paired evidence document contains the answer to the query or not. When provided with a gold paragraph and knowing when to abstain from answering, existing models easily outperform a human annotator. However, predicting answerability itself remains challenging. We manually annotate 800 unanswerable examples across six languages on what makes them challenging to answer. With this new data, we conduct per-category answerability prediction, revealing issues in the current dataset collection as well as task formulation. Together, our study points to avenues for future research in information-seeking question answering, both for dataset creation and model development. Our code and annotated data is publicly available athttps://github.com/AkariAsai/unanswerable_qa.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_119", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_119.flac", "type": "audio", "duration": 713.131}, {"from": "Assistant", "value": "Users of medical question answering systems often submit long and detailed questions, making it hard to achieve high recall in answer retrieval. To alleviate this problem, we propose a novel Multi-Task Learning (MTL) method with data augmentation for medical question understanding. We first establish an equivalence between the tasks of question summarization and Recognizing Question Entailment (RQE) using their definitions in the medical domain. Based on this equivalence, we propose a data augmentation algorithm to use just one dataset to optimize for both tasks, with a weighted MTL loss. We introduce gradually soft parameter-sharing: a constraint for decoder parameters to be close, that is gradually loosened as we move to the highest layer. We show through ablation studies that our proposed novelties improve performance. Our method outperforms existing MTL methods across 4 datasets of medical question pairs, in ROUGE scores, RQE accuracy and human evaluation. Finally, we show that our method fares better than single-task learning under 4 low-resource settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_120", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_120.flac", "type": "audio", "duration": 699.968}, {"from": "Assistant", "value": "A common issue in real-world applications of named entity recognition and classification (NERC) is the absence of annotated data for the target entity classes during training. Zero-shot learning approaches address this issue by learning models from classes with training data that can predict classes without it. This paper presents the first approach for zero-shot NERC, introducing novel architectures that leverage the fact that textual descriptions for many entity classes occur naturally. We address the zero-shot NERC specific challenge that the not-an-entity class is not well defined as different entity classes are considered in training and testing. For evaluation, we adapt two datasets, OntoNotes and MedMentions, emulating the difficulty of real-world zero-shot learning by testing models on the rarest entity classes. Our proposed approach outperforms baselines adapted from machine reading comprehension and zero-shot text classification. Furthermore, we assess the effect of different class descriptions for this task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_121", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_121.flac", "type": "audio", "duration": 411.806}, {"from": "Assistant", "value": "Recently, word enhancement has become very popular for Chinese Named Entity Recognition (NER), reducing segmentation errors and increasing the semantic and boundary information of Chinese words. However, these methods tend to ignore the information of the Chinese character structure after integrating the lexical information. Chinese characters have evolved from pictographs since ancient times, and their structure often reflects more information about the characters. This paper presents a novel Multi-metadata Embedding based Cross-Transformer (MECT) to improve the performance of Chinese NER by fusing the structural information of Chinese characters. Specifically, we use multi-metadata embedding in a two-stream Transformer to integrate Chinese character features with the radical-level embedding. With the structural characteristics of Chinese characters, MECT can better capture the semantic information of Chinese characters for NER. The experimental results obtained on several well-known benchmarking datasets demonstrate the merits and superiority of the proposed MECT method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_122", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_122.flac", "type": "audio", "duration": 707.326}, {"from": "Assistant", "value": "As the sources of information that we consume everyday rapidly diversify, it is becoming increasingly important to develop NLP tools that help to evaluate the credibility of the information we receive. A critical step towards this goal is to determine the factuality of events in text. In this paper, we frame factuality assessment as a modal dependency parsing task that identifies the events and their sources, formally known as conceivers, and then determine the level of certainty that the sources are asserting with respect to the events. We crowdsource the first large-scale data set annotated with modal dependency structures that consists of 353 Covid-19 related news articles, 24,016 events, and 2,938 conceivers. We also develop the first modal dependency parser that jointly extracts events, conceivers and constructs the modal dependency structure of a text. We evaluate the joint model against a pipeline model and demonstrate the advantage of the joint model in conceiver extraction and modal dependency structure construction when events and conceivers are automatically extracted. We believe the dataset and the models will be a valuable resource for a whole host of NLP applications such as fact checking and rumor detection.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_123", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_123.flac", "type": "audio", "duration": 450.049}, {"from": "Assistant", "value": "The modeling of conversational context plays a vital role in emotion recognition from conversation (ERC). In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network, namely DAG-ERC, to implement this idea. In an attempt to combine the strengths of conventional graph-based neural models and recurrence-based neural models, DAG-ERC provides a more intuitive way to model the information flow between long-distance conversation background and nearby context. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison. The empirical results demonstrate the superiority of this new model and confirm the motivation of the directed acyclic graph architecture for ERC.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_124", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_124.flac", "type": "audio", "duration": 723.046}, {"from": "Assistant", "value": "Models pre-trained on large-scale regular text corpora often do not work well for user-generated data where the language styles differ significantly from the mainstream text. Here we present Context-Aware Rule Injection (CARI), an innovative method for formality style transfer (FST) by injecting multiple rules into an end-to-end BERT-based encoder and decoder model. CARI is able to learn to select optimal rules based on context. The intrinsic evaluation showed that CARI achieved the new highest performance on the FST benchmark dataset. Our extrinsic evaluation showed that CARI can greatly improve the regular pre-trained models’ performance on several tweet sentiment analysis tasks. Our contributions are as follows: 1.We propose a new method, CARI, to integrate rules for pre-trained language models. CARI is context-aware and can trained end-to-end with the downstream NLP applications. 2.We have achieved new state-of-the-art results for FST on the benchmark GYAFC dataset. 3.We are the first to evaluate FST methods with extrinsic evaluation and specifically on sentiment classification tasks. We show that CARI outperformed existing rule-based FST approaches for sentiment classification.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_125", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_125.flac", "type": "audio", "duration": 725.322}, {"from": "Assistant", "value": "Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intricate transition patterns between the affective states. In this paper, we propose a Topic-Driven Knowledge-Aware Transformer to handle the challenges above. We firstly design a topic-augmented language model (LM) with an additional layer specialized for topic detection. The topic-augmented LM is then combined with commonsense statements derived from a knowledge base based on the dialogue contextual information. Finally, a transformer-based encoder-decoder architecture fuses the topical and commonsense information, and performs the emotion label sequence prediction. The model has been experimented on four datasets in dialogue emotion detection, demonstrating its superiority empirically over the existing state-of-the-art approaches. Quantitative and qualitative results show that the model can discover topics which help in distinguishing emotion categories.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_126", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_126.flac", "type": "audio", "duration": 584.052}, {"from": "Assistant", "value": "Approaches to computational argumentation tasks such as stance detection and aspect detection have largely focused on the text of independent claims, losing out on potentially valuable context provided by the rest of the collection. We introduce a general approach to these tasks motivated by syntopical reading, a reading process that emphasizes comparing and contrasting viewpoints in order to improve topic understanding. To capture collection-level context, we introduce the syntopical graph, a data structure for linking claims within a collection. A syntopical graph is a typed multi-graph where nodes represent claims and edges represent different possible pairwise relationships, such as entailment, paraphrase, or support. Experiments applying syntopical graphs to the problems of detecting stance and aspects demonstrate state-of-the-art performance in each domain, significantly outperforming approaches that do not utilize collection-level information.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_127", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_127.flac", "type": "audio", "duration": 718.332}, {"from": "Assistant", "value": "The prevalence of the COVID-19 pandemic in day-to-day life has yielded large amounts of stance detection data on social media sites, as users turn to social media to share their views regarding various issues related to the pandemic, e.g. stay at home mandates and wearing face masks when out in public. We set out to make use of this data by collecting the stance expressed by Twitter users, with respect to topics revolving around the pandemic. We annotate a new stance detection dataset, called COVID-19-Stance. Using this newly annotated dataset, we train several established stance detection models to ascertain a baseline performance for this specific task. To further improve the performance, we employ self-training and domain adaptation approaches to take advantage of large amounts of unlabeled data and existing stance detection datasets. The dataset, code, and other resources are available on GitHub.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_128", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_128.flac", "type": "audio", "duration": 606.621}, {"from": "Assistant", "value": "Fact verification is a challenging task that requires simultaneously reasoning and aggregating over multiple retrieved pieces of evidence to evaluate the truthfulness of a claim. Existing approaches typically (i) explore the semantic interaction between the claim and evidence at different granularity levels but fail to capture their topical consistency during the reasoning process, which we believe is crucial for verification; (ii) aggregate multiple pieces of evidence equally without considering their implicit stances to the claim, thereby introducing spurious information. To alleviate the above issues, we propose a novel topic-aware evidence reasoning and stance-aware aggregation model for more accurate fact verification, with the following four key properties: 1) checking topical consistency between the claim and evidence; 2) maintaining topical coherence among multiple pieces of evidence; 3) ensuring semantic similarity between the global topic information and the semantic representation of evidence; 4) aggregating evidence based on their implicit stances to the claim. Extensive experiments conducted on the two benchmark datasets demonstrate the superiority of the proposed model over several state-of-the-art approaches for fact verification. The source code can be obtained fromhttps://github.com/jasenchn/TARSA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_129", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_129.flac", "type": "audio", "duration": 698.062}, {"from": "Assistant", "value": "We introduce the well-established social scientific concept of social solidarity and its contestation, anti-solidarity, as a new problem setting to supervised machine learning in NLP to assess how European solidarity discourses changed before and after the COVID-19 outbreak was declared a global pandemic. To this end, we annotate 2.3k English and German tweets for (anti-)solidarity expressions, utilizing multiple human annotators and two annotation approaches (experts vs. crowds). We use these annotations to train a BERT model with multiple data augmentation strategies. Our augmented BERT model that combines both expert and crowd annotations outperforms the baseline BERT classifier trained with expert annotations only by over 25 points, from 58% macro-F1 to almost 85%. We use this high-quality model to automatically label over 270k tweets between September 2019 and December 2020. We then assess the automatically labeled data for how statements related to European (anti-)solidarity discourses developed over time and in relation to one another, before and during the COVID-19 crisis. Our results show that solidarity became increasingly salient and contested during the crisis. While the number of solidarity tweets remained on a higher level and dominated the discourse in the scrutinized time frame, anti-solidarity tweets initially spiked, then decreased to (almost) pre-COVID-19 values before rising to a stable higher level until the end of 2020.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_130", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_130.flac", "type": "audio", "duration": 733.054}, {"from": "Assistant", "value": "In conversation, uptake happens when a speaker builds on the contribution of their interlocutor by, for example, acknowledging, repeating or reformulating what they have said. In education, teachers’ uptake of student contributions has been linked to higher student achievement. Yet measuring and improving teachers’ uptake at scale is challenging, as existing methods require expensive annotation by experts. We propose a framework for computationally measuring uptake, by (1) releasing a dataset of student-teacher exchanges extracted from US math classroom transcripts annotated for uptake by experts; (2) formalizing uptake as pointwise Jensen-Shannon Divergence (pJSD), estimated via next utterance classification; (3) conducting a linguistically-motivated comparison of different unsupervised measures and (4) correlating these measures with educational outcomes. We find that although repetition captures a significant part of uptake, pJSD outperforms repetition-based baselines, as it is capable of identifying a wider range of uptake phenomena like question answering and reformulation. We apply our uptake measure to three different educational datasets with outcome indicators. Unlike baseline measures, pJSD correlates significantly with instruction quality in all three, providing evidence for its generalizability and for its potential to serve as an automated professional development tool for teachers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_131", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_131.flac", "type": "audio", "duration": 272.64}, {"from": "Assistant", "value": "The analysis of data in which multiple languages are represented has gained popularity among computational linguists in recent years. So far, much of this research focuses mainly on the improvement of computational methods and largely ignores linguistic and social aspects of C-S discussed across a wide range of languages within the long-established literature in linguistics. To fill this gap, we offer a survey of code-switching (C-S) covering the literature in linguistics with a reflection on the key issues in language technologies. From the linguistic perspective, we provide an overview of structural and functional patterns of C-S focusing on the literature from European and Indian contexts as highly multilingual areas. From the language technologies perspective, we discuss how massive language models fail to represent diverse C-S types due to lack of appropriate training data, lack of robust evaluation benchmarks for C-S (across multilingual situations and types of C-S) and lack of end-to- end systems that cover sociolinguistic aspects of C-S as well. Our survey will be a step to- wards an outcome of mutual benefit for computational scientists and linguists with a shared interest in multilingualism and C-S.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_132", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_132.flac", "type": "audio", "duration": 821.058}, {"from": "Assistant", "value": "We present a human-and-model-in-the-loop process for dynamically generating datasets and training better performing and more robust hate detection models. We provide a new dataset of 40,000 entries, generated and labelled by trained annotators over four rounds of dynamic data creation. It includes 15,000 challenging perturbations and each hateful entry has fine-grained labels for the type and target of hate. Hateful entries make up 54% of the dataset, which is substantially higher than comparable datasets. We show that model performance is substantially improved using this approach. Models trained on later rounds of data collection perform better on test sets and are harder for annotators to trick. They also have better performance on HateCheck, a suite of functional tests for online hate detection. We provide the code, dataset and annotation guidelines for other researchers to use.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_133", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_133.flac", "type": "audio", "duration": 621.291}, {"from": "Assistant", "value": "To defend against machine-generated fake news, an effective mechanism is urgently needed. We contribute a novel benchmark for fake news detection at the knowledge element level, as well as a solution for this task which incorporates cross-media consistency checking to detect the fine-grained knowledge elements making news articles misinformative. Due to training data scarcity, we also formulate a novel data synthesis method by manipulating knowledge elements within the knowledge graph to generate noisy training data with specific, hard to detect, known inconsistencies. Our detection approach outperforms the state-of-the-art (up to 16.8% accuracy gain), and more critically, yields fine-grained explanations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_134", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_134.flac", "type": "audio", "duration": 761.045}, {"from": "Assistant", "value": "To quantify how well natural language understanding models can capture consistency in a general conversation, we introduce the DialoguE COntradiction DEtection task (DECODE) and a new conversational dataset containing both human-human and human-bot contradictory dialogues. We show that: (i) our newly collected dataset is notably more effective at providing supervision for the dialogue contradiction detection task than existing NLI data including those aimed to cover the dialogue domain; (ii) Transformer models that explicitly hinge on utterance structures for dialogue contradiction detection are more robust and generalize well on both analysis and out-of-distribution dialogues than standard (unstructured) Transformers. We also show that our best contradiction detection model correlates well with human judgments and further provide evidence for its usage in both automatically evaluating and improving the consistency of state-of-the-art generative chatbots.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_135", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_135.flac", "type": "audio", "duration": 640.363}, {"from": "Assistant", "value": "This paper is concerned with dialogue state tracking (DST) in a task-oriented dialogue system. Building a DST module that is highly effective is still a challenging issue, although significant progresses have been made recently. This paper proposes a new approach to dialogue state tracking, referred to as Seq2Seq-DU, which formalizes DST as a sequence-to-sequence problem. Seq2Seq-DU employs two BERT-based encoders to respectively encode the utterances in the dialogue and the descriptions of schemas, an attender to calculate attentions between the utterance embeddings and the schema embeddings, and a decoder to generate pointers to represent the current state of dialogue. Seq2Seq-DU has the following advantages. It can jointly model intents, slots, and slot values; it can leverage the rich representations of utterances and schemas based on BERT; it can effectively deal with categorical and non-categorical slots, and unseen schemas. In addition, Seq2Seq-DU can also be used in the NLU (natural language understanding) module of a dialogue system. Experimental results on benchmark datasets in different settings (SGD, MultiWOZ2.2, MultiWOZ2.1, WOZ2.0, DSTC2, M2M, SNIPS, and ATIS) show that Seq2Seq-DU outperforms the existing methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_136", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_136.flac", "type": "audio", "duration": 717.056}, {"from": "Assistant", "value": "Learning discrete dialog structure graph from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation. However, this problem is less studied in open-domain dialogue. In this paper, we conduct unsupervised discovery of discrete dialog structure from chitchat corpora, and then leverage it to facilitate coherent dialog generation in downstream systems. To this end, we present an unsupervised model, Discrete Variational Auto-Encoder with Graph Neural Network (DVAE-GNN), to discover discrete hierarchical latent dialog states (at the level of both session and utterance) and their transitions from corpus as a dialog structure graph. Then we leverage it as background knowledge to facilitate dialog management in a RL based dialog system. Experimental results on two benchmark corpora confirm that DVAE-GNN can discover meaningful dialog structure graph, and the use of dialog structure as background knowledge can significantly improve multi-turn coherence.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_137", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_137.flac", "type": "audio", "duration": 645.933}, {"from": "Assistant", "value": "We study the learning of a matching model for dialogue response selection. Motivated by the recent finding that models trained with random negative samples are not ideal in real-world scenarios, we propose a hierarchical curriculum learning framework that trains the matching model in an “easy-to-difficult” scheme. Our learning framework consists of two complementary curricula: (1) corpus-level curriculum (CC); and (2) instance-level curriculum (IC). In CC, the model gradually increases its ability in finding the matching clues between the dialogue context and a response candidate. As for IC, it progressively strengthens the model’s ability in identifying the mismatching information between the dialogue context and a response candidate. Empirical studies on three benchmark datasets with three state-of-the-art matching models demonstrate that the proposed learning framework significantly improves the model performance across various evaluation metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_138", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_138.flac", "type": "audio", "duration": 542.059}, {"from": "Assistant", "value": "In this paper, we present a neural model for joint dropped pronoun recovery (DPR) and conversational discourse parsing (CDP) in Chinese conversational speech. We show that DPR and CDP are closely related, and a joint model benefits both tasks. We refer to our model as DiscProReco, and it first encodes the tokens in each utterance in a conversation with a directed Graph Convolutional Network (GCN). The token states for an utterance are then aggregated to produce a single state for each utterance. The utterance states are then fed into a biaffine classifier to construct a conversational discourse graph. A second (multi-relational) GCN is then applied to the utterance states to produce a discourse relation-augmented representation for the utterances, which are then fused together with token states in each utterance as input to a dropped pronoun recovery layer. The joint model is trained and evaluated on a new Structure Parsing-enhanced Dropped Pronoun Recovery (SPDPR) data set that we annotated with both two types of information. Experimental results on the SPDPR dataset and other benchmarks show that DiscProReco significantly outperforms the state-of-the-art baselines of both tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_139", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_139.flac", "type": "audio", "duration": 664.044}, {"from": "Assistant", "value": "Knowledge bases (KBs) and text often contain complementary knowledge: KBs store structured knowledge that can support long range reasoning, while text stores more comprehensive and timely knowledge in an unstructured way. Separately embedding the individual knowledge sources into vector spaces has demonstrated tremendous successes in encoding the respective knowledge, but how to jointly embed and reason with both knowledge sources to fully leverage the complementary information is still largely an open problem. We conduct a large-scale, systematic investigation of aligning KB and text embeddings for joint reasoning. We set up a novel evaluation framework with two evaluation tasks, few-shot link prediction and analogical reasoning, and evaluate an array of KB-text embedding alignment methods. We also demonstrate how such alignment can infuse textual information into KB embeddings for more accurate link prediction on emerging entities and events, using COVID-19 as a case study.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_140", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_140.flac", "type": "audio", "duration": 706.003}, {"from": "Assistant", "value": "Weak supervision has shown promising results in many natural language processing tasks, such as Named Entity Recognition (NER). Existing work mainly focuses on learning deep NER models only with weak supervision, i.e., without any human annotation, and shows that by merely using weakly labeled data, one can achieve good performance, though still underperforms fully supervised NER with manually/strongly labeled data. In this paper, we consider a more practical scenario, where we have both a small amount of strongly labeled data and a large amount of weakly labeled data. Unfortunately, we observe that weakly labeled data does not necessarily improve, or even deteriorate the model performance (due to the extensive noise in the weak labels) when we train deep NER models over a simple or weighted combination of the strongly labeled and weakly labeled data. To address this issue, we propose a new multi-stage computational framework – NEEDLE with three essential ingredients: (1) weak label completion, (2) noise-aware loss function, and (3) final fine-tuning over the strongly labeled data. Through experiments on E-commerce query NER and Biomedical NER, we demonstrate that NEEDLE can effectively suppress the noise of the weak labels and outperforms existing methods. In particular, we achieve new SOTA F1-scores on 3 Biomedical NER datasets: BC5CDR-chem 93.74, BC5CDR-disease 90.69, NCBI-disease 92.28.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_141", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_141.flac", "type": "audio", "duration": 683.051}, {"from": "Assistant", "value": "Recently, there is an effort to extend fine-grained entity typing by using a richer and ultra-fine set of types, and labeling noun phrases including pronouns and nominal nouns instead of just named entity mentions. A key challenge for this ultra-fine entity typing task is that human annotated data are extremely scarce, and the annotation ability of existing distant or weak supervision approaches is very limited. To remedy this problem, in this paper, we propose to obtain training data for ultra-fine entity typing by using a BERT Masked Language Model (MLM). Given a mention in a sentence, our approach constructs an input for the BERT MLM so that it predicts context dependent hypernyms of the mention, which can be used as type labels. Experimental results demonstrate that, with the help of these automatically generated labels, the performance of an ultra-fine entity typing model can be improved substantially. We also show that our approach can be applied to improve traditional fine-grained entity typing after performing simple type mapping.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_142", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_142.flac", "type": "audio", "duration": 760.896}, {"from": "Assistant", "value": "Recent advances in Named Entity Recognition (NER) show that document-level contexts can significantly improve model performance. In many application scenarios, however, such contexts are not available. In this paper, we propose to find external contexts of a sentence by retrieving and selecting a set of semantically relevant texts through a search engine, with the original sentence as the query. We find empirically that the contextual representations computed on the retrieval-based input view, constructed through the concatenation of a sentence and its external contexts, can achieve significantly improved performance compared to the original input view based only on the sentence. Furthermore, we can improve the model performance of both input views by Cooperative Learning, a training method that encourages the two input views to produce similar contextual representations or output label distributions. Experiments show that our approach can achieve new state-of-the-art performance on 8 NER data sets across 5 domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_143", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_143.flac", "type": "audio", "duration": 705.051}, {"from": "Assistant", "value": "Does the effectiveness of neural language models derive entirely from accurate modeling of surface word co-occurrence statistics, or do these models represent and reason about the world they describe? In BART and T5 transformer language models, we identify contextual word representations that function as *models of entities and situations* as they evolve throughout a discourse. These neural representations have functional similarities to linguistic models of dynamic semantics: they support a linear readout of each entity’s current properties and relations, and can be manipulated with predictable effects on language generation. Our results indicate that prediction in pretrained neural language models is supported, at least in part, by dynamic representations of meaning and implicit simulation of entity state, and that this behavior can be learned with only text as training data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_144", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_144.flac", "type": "audio", "duration": 564.288}, {"from": "Assistant", "value": "Targeted syntactic evaluations have demonstrated the ability of language models to perform subject-verb agreement given difficult contexts. To elucidate the mechanisms by which the models accomplish this behavior, this study applies causal mediation analysis to pre-trained neural language models. We investigate the magnitude of models’ preferences for grammatical inflections, as well as whether neurons process subject-verb agreement similarly across sentences with different syntactic structures. We uncover similarities and differences across architectures and model sizes—notably, that larger models do not necessarily learn stronger preferences. We also observe two distinct mechanisms for producing subject-verb agreement depending on the syntactic structure of the input sentence. Finally, we find that language models rely on similar sets of neurons when given sentences with similar syntactic structure.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_145", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_145.flac", "type": "audio", "duration": 703.054}, {"from": "Assistant", "value": "NLP has a rich history of representing our prior understanding of language in the form of graphs. Recent work on analyzing contextualized text representations has focused on hand-designed probe models to understand how and to what extent do these representations encode a particular linguistic phenomenon. However, due to the inter-dependence of various phenomena and randomness of training probe models, detecting how these representations encode the rich information in these linguistic graphs remains a challenging problem. In this paper, we propose a new information-theoretic probe, Bird’s Eye, which is a fairly simple probe method for detecting if and how these representations encode the information in these linguistic graphs. Instead of using model performance, our probe takes an information-theoretic view of probing and estimates the mutual information between the linguistic graph embedded in a continuous space and the contextualized word representations. Furthermore, we also propose an approach to use our probe to investigate localized linguistic information in the linguistic graphs using perturbation analysis. We call this probing setup Worm’s Eye. Using these probes, we analyze the BERT models on its ability to encode a syntactic and a semantic graph structure, and find that these models encode to some degree both syntactic as well as semantic information; albeit syntactic information to a greater extent.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_146", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_146.flac", "type": "audio", "duration": 760.059}, {"from": "Assistant", "value": "Previous literatures show that pre-trained masked language models (MLMs) such as BERT can achieve competitive factual knowledge extraction performance on some datasets, indicating that MLMs can potentially be a reliable knowledge source. In this paper, we conduct a rigorous study to explore the underlying predicting mechanisms of MLMs over different extraction paradigms. By investigating the behaviors of MLMs, we find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts. Furthermore, incorporating illustrative cases and external contexts improve knowledge prediction mainly due to entity type guidance and golden answer leakage. Our findings shed light on the underlying predicting mechanisms of MLMs, and strongly question the previous conclusion that current MLMs can potentially serve as reliable factual knowledge bases.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_147", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_147.flac", "type": "audio", "duration": 712.063}, {"from": "Assistant", "value": "We study the problem of generating data poisoning attacks against Knowledge Graph Embedding (KGE) models for the task of link prediction in knowledge graphs. To poison KGE models, we propose to exploit their inductive abilities which are captured through the relationship patterns like symmetry, inversion and composition in the knowledge graph. Specifically, to degrade the model’s prediction confidence on target facts, we propose to improve the model’s prediction confidence on a set of decoy facts. Thus, we craft adversarial additions that can improve the model’s prediction confidence on decoy facts through different inference patterns. Our experiments demonstrate that the proposed poisoning attacks outperform state-of-art baselines on four KGE models for two publicly available datasets. We also find that the symmetry pattern based attacks generalize across all model-dataset combinations which indicates the sensitivity of KGE models to this pattern.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_148", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_148.flac", "type": "audio", "duration": 689.052}, {"from": "Assistant", "value": "A common factor in bias measurement methods is the use of hand-curated seed lexicons, but there remains little guidance for their selection. We gather seeds used in prior work, documenting their common sources and rationales, and in case studies of three English-language corpora, we enumerate the different types of social biases and linguistic features that, once encoded in the seeds, can affect subsequent bias measurements. Seeds developed in one context are often re-used in other contexts, but documentation and evaluation remain necessary precursors to relying on seeds for sensitive measurements.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_149", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_149.flac", "type": "audio", "duration": 714.048}, {"from": "Assistant", "value": "Despite inextricable ties between race and language, little work has considered race in NLP research and development. In this work, we survey 79 papers from the ACL anthology that mention race. These papers reveal various types of race-related bias in all stages of NLP model development, highlighting the need for proactive consideration of how NLP systems can uphold racial hierarchies. However, persistent gaps in research on race and NLP remain: race has been siloed as a niche topic and remains ignored in many NLP tasks; most work operationalizes race as a fixed single-dimensional variable with a ground-truth label, which risks reinforcing differences produced by historical racism; and the voices of historically marginalized people are nearly absent in NLP literature. By identifying where and how NLP literature has and has not considered race, especially in comparison to related fields, our work calls for inclusion and racial justice in NLP research practices.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_150", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_150.flac", "type": "audio", "duration": 621.041}, {"from": "Assistant", "value": "Natural Language Processing (NLP) systems learn harmful societal biases that cause them to amplify inequality as they are deployed in more and more situations. To guide efforts at debiasing these systems, the NLP community relies on a variety of metrics that quantify bias in models. Some of these metrics are intrinsic, measuring bias in word embedding spaces, and some are extrinsic, measuring bias in downstream tasks that the word embeddings enable. Do these intrinsic and extrinsic metrics correlate with each other? We compare intrinsic and extrinsic metrics across hundreds of trained models covering different tasks and experimental conditions. Our results show no reliable correlation between these metrics that holds in all scenarios across tasks and languages. We urge researchers working on debiasing to focus on extrinsic measures of bias, and to make using these measures more feasible via creation of new challenge sets and annotated test data. To aid this effort, we release code, a new intrinsic metric, and an annotated test set focused on gender bias in hate speech.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_151", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_151.flac", "type": "audio", "duration": 695.04}, {"from": "Assistant", "value": "Text representation models are prone to exhibit a range of societal biases, reflecting the non-controlled and biased nature of the underlying pretraining data, which consequently leads to severe ethical issues and even bias amplification. Recent work has predominantly focused on measuring and mitigating bias in pretrained language models. Surprisingly, the landscape of bias measurements and mitigation resources and methods for conversational language models is still very scarce: it is limited to only a few types of bias, artificially constructed resources, and completely ignores the impact that debiasing methods may have on the final perfor mance in dialog tasks, e.g., conversational response generation. In this work, we present REDDITBIAS, the first conversational data set grounded in the actual human conversations from Reddit, allowing for bias measurement and mitigation across four important bias dimensions: gender,race,religion, and queerness. Further, we develop an evaluation framework which simultaneously 1)measures bias on the developed REDDITBIAS resource, and 2)evaluates model capability in dialog tasks after model debiasing. We use the evaluation framework to benchmark the widely used conversational DialoGPT model along with the adaptations of four debiasing methods. Our results indicate that DialoGPT is biased with respect to religious groups and that some debiasing techniques can remove this bias while preserving downstream task performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_152", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_152.flac", "type": "audio", "duration": 690.051}, {"from": "Assistant", "value": "This paper studies the relative importance of attention heads in Transformer-based models to aid their interpretability in cross-lingual and multi-lingual tasks. Prior research has found that only a few attention heads are important in each mono-lingual Natural Language Processing (NLP) task and pruning the remaining heads leads to comparable or improved performance of the model. However, the impact of pruning attention heads is not yet clear in cross-lingual and multi-lingual tasks. Through extensive experiments, we show that (1) pruning a number of attention heads in a multi-lingual Transformer-based model has, in general, positive effects on its performance in cross-lingual and multi-lingual tasks and (2) the attention heads to be pruned can be ranked using gradients and identified with a few trial experiments. Our experiments focus on sequence labeling tasks, with potential applicability on other cross-lingual and multi-lingual tasks. For comprehensiveness, we examine two pre-trained multi-lingual models, namely multi-lingual BERT (mBERT) and XLM-R, on three tasks across 9 languages each. We also discuss the validity of our findings and their extensibility to truly resource-scarce languages and other task settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_153", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_153.flac", "type": "audio", "duration": 551.04}, {"from": "Assistant", "value": "Effective adversary generation for neural machine translation (NMT) is a crucial prerequisite for building robust machine translation systems. In this work, we investigate veritable evaluations of NMT adversarial attacks, and propose a novel method to craft NMT adversarial examples. We first show the current NMT adversarial attacks may be improperly estimated by the commonly used mono-directional translation, and we propose to leverage the round-trip translation technique to build valid metrics for evaluating NMT adversarial attacks. Our intuition is that an effective NMT adversarial example, which imposes minor shifting on the source and degrades the translation dramatically, would naturally lead to a semantic-destroyed round-trip translation result. We then propose a promising black-box attack method called Word Saliency speedup Local Search (WSLS) that could effectively attack the mainstream NMT architectures. Comprehensive experiments demonstrate that the proposed metrics could accurately evaluate the attack effectiveness, and the proposed WSLS could significantly break the state-of-art NMT models with small perturbation. Besides, WSLS exhibits strong transferability on attacking Baidu and Bing online translators.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_154", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_154.flac", "type": "audio", "duration": 684.06}, {"from": "Assistant", "value": "Transfer learning has yielded state-of-the-art (SoTA) results in many supervised NLP tasks. However, annotated data for every target task in every target language is rare, especially for low-resource languages. We propose UXLA, a novel unsupervised data augmentation framework for zero-resource transfer learning scenarios. In particular, UXLA aims to solve cross-lingual adaptation problems from a source language task distribution to an unknown target language task distribution, assuming no training label in the target language. At its core, UXLA performs simultaneous self-training with data augmentation and unsupervised sample selection. To show its effectiveness, we conduct extensive experiments on three diverse zero-resource cross-lingual transfer tasks. UXLA achieves SoTA results in all the tasks, outperforming the baselines by a good margin. With an in-depth framework dissection, we demonstrate the cumulative contributions of different components to its success.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_155", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_155.flac", "type": "audio", "duration": 653.056}, {"from": "Assistant", "value": "Recent work on non-autoregressive neural machine translation (NAT) aims at improving the efficiency by parallel decoding without sacrificing the quality. However, existing NAT methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup. We propose the Glancing Language Model (GLM) for single-pass parallel generation models. With GLM, we develop Glancing Transformer (GLAT) for machine translation. With only single-pass parallel decoding, GLAT is able to generate high-quality translation with 8×-15× speedup. Note that GLAT does not modify the network architecture, which is a training method to learn word interdependency. Experiments on multiple WMT language directions show that GLAT outperforms all previous single pass non-autoregressive methods, and is nearly comparable to Transformer, reducing the gap to 0.25-0.9 BLEU points.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_156", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_156.flac", "type": "audio", "duration": 734.571}, {"from": "Assistant", "value": "Dense video event captioning aims to generate a sequence of descriptive captions for each event in a long untrimmed video. Video-level context provides important information and facilities the model to generate consistent and less redundant captions between events. In this paper, we introduce a novel Hierarchical Context-aware Network for dense video event captioning (HCN) to capture context from various aspects. In detail, the model leverages local and global context with different mechanisms to jointly learn to generate coherent captions. The local context module performs full interaction between neighbor frames and the global context module selectively attends to previous or future events. According to our extensive experiment on both Youcook2 and Activitynet Captioning datasets, the video-level HCN model outperforms the event-level context-agnostic model by a large margin. The code is available athttps://github.com/KirkGuo/HCN.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_157", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_157.flac", "type": "audio", "duration": 681.041}, {"from": "Assistant", "value": "Generating image captions with user intention is an emerging need. The recently published Localized Narratives dataset takes mouse traces as another input to the image captioning task, which is an intuitive and efficient way for a user to control what to describe in the image. However, how to effectively employ traces to improve generation quality and controllability is still under exploration. This paper aims to solve this problem by proposing a novel model called LoopCAG, which connects Contrastive constraints and Attention Guidance in a Loop manner, engaged explicit spatial and temporal constraints to the generating process. Precisely, each generated sentence is temporally aligned to the corresponding trace sequence through a contrastive learning strategy. Besides, each generated text token is supervised to attend to the correct visual objects under heuristic spatial attention guidance. Comprehensive experimental results demonstrate that our LoopCAG model learns better correspondence among the three modalities (vision, language, and traces) and achieves SOTA performance on trace-controlled image captioning task. Moreover, the controllability and explainability of LoopCAG are validated by analyzing spatial and temporal sensitivity during the generation process.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_158", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_158.flac", "type": "audio", "duration": 383.06}, {"from": "Assistant", "value": "Understanding manipulated media, from automatically generated ‘deepfakes’ to manually edited ones, raises novel research challenges. Because the vast majority of edited or manipulated images are benign, such as photoshopped images for visual enhancements, the key challenge is to understand the complex layers of underlying intents of media edits and their implications with respect to disinformation. In this paper, we study Edited Media Frames, a new formalism to understand visual media manipulation as structured annotations with respect to the intents, emotional reactions, attacks on individuals, and the overall implications of disinformation. We introduce a dataset for our task, EMU, with 56k question-answer pairs written in rich natural language. We evaluate a wide variety of vision-and-language models for our task, and introduce a new model PELICAN, which builds upon recent progress in pretrained multimodal representations. Our model obtains promising results on our dataset, with humans rating its answers as accurate 48.2% of the time. At the same time, there is still much work to be done – and we provide analysis that highlights areas for further progress.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_159", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_159.flac", "type": "audio", "duration": 710.902}, {"from": "Assistant", "value": "We propose PIGLeT: a model that learns physical commonsense knowledge through interaction, and then uses this knowledge to ground language. We factorize PIGLeT into a physical dynamics model, and a separate language model. Our dynamics model learns not just what objects are but also what they do: glass cups break when thrown, plastic ones don’t. We then use it as the interface to our language model, giving us a unified model of linguistic form and grounded meaning. PIGLeT can read a sentence, simulate neurally what might happen next, and then communicate that result through a literal symbolic representation, or natural language. Experimental results show that our model effectively learns world dynamics, along with how to communicate them. It is able to correctly forecast what happens next given an English sentence over 80% of the time, outperforming a 100x larger, text-to-text approach by over 10%. Likewise, its natural language summaries of physical interactions are also judged by humans as more accurate than LM alternatives. We present comprehensive analysis showing room for future work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_160", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_160.flac", "type": "audio", "duration": 766.05}, {"from": "Assistant", "value": "Neural entity typing models typically represent fine-grained entity types as vectors in a high-dimensional space, but such spaces are not well-suited to modeling these types’ complex interdependencies. We study the ability of box embeddings, which embed concepts as d-dimensional hyperrectangles, to capture hierarchies of types even when these relationships are not defined explicitly in the ontology. Our model represents both types and entity mentions as boxes. Each mention and its context are fed into a BERT-based model to embed that mention in our box space; essentially, this model leverages typological clues present in the surface text to hypothesize a type representation for the mention. Box containment can then be used to derive both the posterior probability of a mention exhibiting a given type and the conditional probability relations between types themselves. We compare our approach with a vector-based typing model and observe state-of-the-art performance on several entity typing benchmarks. In addition to competitive typing performance, our box-based model shows better performance in prediction consistency (predicting a supertype and a subtype together) and confidence (i.e., calibration), demonstrating that the box-based model captures the latent type hierarchies better than the vector-based model does.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_161", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_161.flac", "type": "audio", "duration": 611.056}, {"from": "Assistant", "value": "Recent pretraining models in Chinese neglect two important aspects specific to the Chinese language: glyph and pinyin, which carry significant syntax and semantic information for language understanding. In this work, we propose ChineseBERT, which incorporates both theglyphandpinyininformation of Chinese characters into language model pretraining. The glyph embedding is obtained based on different fonts of a Chinese character, being able to capture character semantics from the visual features, and the pinyin embedding characterizes the pronunciation of Chinese characters, which handles the highly prevalent heteronym phenomenon in Chinese (the same character has different pronunciations with different meanings). Pretrained on large-scale unlabeled Chinese corpus, the proposed ChineseBERT model yields significant performance boost over baseline models with fewer training steps. The proposed model achieves new SOTA performances on a wide range of Chinese NLP tasks, including machine reading comprehension, natural language inference, text classification, sentence pair matching, and competitive performances in named entity recognition and word segmentation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_162", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_162.flac", "type": "audio", "duration": 496.128}, {"from": "Assistant", "value": "Knowledge distillation has been proven to be effective in model acceleration and compression. It transfers knowledge from a large neural network to a small one by using the large neural network predictions as targets of the small neural network. But this way ignores the knowledge inside the large neural networks, e.g., parameters. Our preliminary study as well as the recent success in pre-training suggests that transferring parameters are more effective in distilling knowledge. In this paper, we propose Weight Distillation to transfer the knowledge in parameters of a large neural network to a small neural network through a parameter generator. On the WMT16 En-Ro, NIST12 Zh-En, and WMT14 En-De machine translation tasks, our experiments show that weight distillation learns a small network that is 1.88 2.94x faster than the large network but with competitive BLEU performance. When fixing the size of small networks, weight distillation outperforms knowledge distillation by 0.51 1.82 BLEU points.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_163", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_163.flac", "type": "audio", "duration": 710.043}, {"from": "Assistant", "value": "It is a common belief that training deep transformers from scratch requires large datasets. Consequently, for small datasets, people usually use shallow and simple additional layers on top of pre-trained models during fine-tuning. This work shows that this does not always need to be the case: with proper initialization and optimization, the benefits of very deep transformers can carry over to challenging tasks with small datasets, including Text-to-SQL semantic parsing and logical reading comprehension. In particular, we successfully train 48 layers of transformers, comprising 24 fine-tuned layers from pre-trained RoBERTa and 24 relation-aware layers trained from scratch. With fewer training steps and no task-specific pre-training, we obtain the state of the art performance on the challenging cross-domain Text-to-SQL parsing benchmark Spider. We achieve this by deriving a novel Data dependent Transformer Fixed-update initialization scheme (DT-Fixup), inspired by the prior T-Fixup work. Further error analysis shows that increasing depth can help improve generalization on small datasets for hard cases that require reasoning and structural understanding.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_164", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_164.flac", "type": "audio", "duration": 718.933}, {"from": "Assistant", "value": "Transformer-based language models (TLMs), such as BERT, ALBERT and GPT-3, have shown strong performance in a wide range of NLP tasks and currently dominate the field of NLP. However, many researchers wonder whether these models can maintain their dominance forever. Of course, we do not have answers now, but, as an attempt to find better neural architectures and training schemes, we pretrain a simple CNN using a GAN-style learning scheme and Wikipedia data, and then integrate it with standard TLMs. We show that on the GLUE tasks, the combination of our pretrained CNN with ALBERT outperforms the original ALBERT and achieves a similar performance to that of SOTA. Furthermore, on open-domain QA (Quasar-T and SearchQA), the combination of the CNN with ALBERT or RoBERTa achieved stronger performance than SOTA and the original TLMs. We hope that this work provides a hint for developing a novel strong network architecture along with its training scheme. Our source code and models are available athttps://github.com/nict-wisdom/bertac.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_165", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_165.flac", "type": "audio", "duration": 703.616}, {"from": "Assistant", "value": "We introduce a FEVER-like dataset COVID-Fact of 4,086 claims concerning the COVID-19 pandemic. The dataset contains claims, evidence for the claims, and contradictory claims refuted by the evidence. Unlike previous approaches, we automatically detect true claims and their source articles and then generate counter-claims using automatic methods rather than employing human annotators. Along with our constructed resource, we formally present the task of identifying relevant evidence for the claims and verifying whether the evidence refutes or supports a given claim. In addition to scientific claims, our data contains simplified general claims from media sources, making it better suited for detecting general misinformation regarding COVID-19. Our experiments indicate that COVID-Fact will provide a challenging testbed for the development of new systems and our approach will reduce the costs of building domain-specific datasets for detecting misinformation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_166", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_166.flac", "type": "audio", "duration": 660.608}, {"from": "Assistant", "value": "We address the task of explaining relationships between two scientific documents using natural language text. This task requires modeling the complex content of long technical documents, deducing a relationship between these documents, and expressing the details of that relationship in text. In addition to the theoretical interest of this task, successful solutions can help improve researcher efficiency in search and review. In this paper we establish a dataset of 622K examples from 154K documents. We pretrain a large language model to serve as the foundation for autoregressive approaches to the task. We explore the impact of taking different views on the two documents, including the use of dense representations extracted with scientific IE systems. We provide extensive automatic and human evaluations which show the promise of such models, but make clear challenges for future work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_167", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_167.flac", "type": "audio", "duration": 679.211}, {"from": "Assistant", "value": "Existing software-based energy measurements of NLP models are not accurate because they do not consider the complex interactions between energy consumption and model execution. We present IrEne, an interpretable and extensible energy prediction system that accurately predicts the inference energy consumption of a wide range of Transformer-based NLP models. IrEne constructs a model tree graph that breaks down the NLP model into modules that are further broken down into low-level machine learning (ML) primitives. IrEne predicts the inference energy consumption of the ML primitives as a function of generalizable features and fine-grained runtime resource usage. IrEne then aggregates these low-level predictions recursively to predict the energy of each module and finally of the entire model. Experiments across multiple Transformer models show IrEne predicts inference energy consumption of transformer models with an error of under 7% compared to the ground truth. In contrast, existing energy models see an error of over 50%. We also show how IrEne can be used to conduct energy bottleneck analysis and to easily evaluate the energy impact of different architectural choices. We release the code and data athttps://github.com/StonyBrookNLP/irene.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_168", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_168.flac", "type": "audio", "duration": 703.007}, {"from": "Assistant", "value": "The element of repetition in cyberbullying behavior has directed recent computational studies toward detecting cyberbullying based on a social media session. In contrast to a single text, a session may consist of an initial post and an associated sequence of comments. Yet, emerging efforts to enhance the performance of session-based cyberbullying detection have largely overlooked unintended social biases in existing cyberbullying datasets. For example, a session containing certain demographic-identity terms (e.g., “gay” or “black”) is more likely to be classified as an instance of cyberbullying. In this paper, we first show evidence of such bias in models trained on sessions collected from different social media platforms (e.g., Instagram). We then propose a context-aware and model-agnostic debiasing strategy that leverages a reinforcement learning technique, without requiring any extra resources or annotations apart from a pre-defined set of sensitive triggers commonly used for identifying cyberbullying instances. Empirical evaluations show that the proposed strategy can simultaneously alleviate the impacts of the unintended biases and improve the detection performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_169", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_169.flac", "type": "audio", "duration": 718.336}, {"from": "Assistant", "value": "Creating effective visualization is an important part of data analytics. While there are many libraries for creating visualization, writing such code remains difficult given the myriad of parameters that users need to provide. In this paper, we propose the new task of synthesizing visualization programs from a combination of natural language utterances and code context. To tackle the learning problem, we introduce PlotCoder, a new hierarchical encoder-decoder architecture that models both the code context and the input utterance. We use PlotCoder to first determine the template of the visualization code, followed by predicting the data to be plotted. We use Jupyter notebooks containing visualization programs crawled from GitHub to train PlotCoder. On a comprehensive set of test samples from those notebooks, we show that PlotCoder correctly predicts the plot type of about 70% samples, and synthesizes the correct programs for 35% samples, performing 3-4.5% better than the baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_170", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_170.flac", "type": "audio", "duration": 734.656}, {"from": "Assistant", "value": "NLP community is currently investing a lot more research and resources into development of deep learning models than training data. While we have made a lot of progress, it is now clear that our models learn all kinds of spurious patterns, social biases, and annotation artifacts. Algorithmic solutions have so far had limited success. An alternative that is being actively discussed is more careful design of datasets so as to deliver specific signals. This position paper maps out the arguments for and against data curation, and argues that fundamentally the point is moot: curation already is and will be happening, and it is changing the world. The question is only how much thought we want to invest into that process.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_171", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_171.flac", "type": "audio", "duration": 643.925}, {"from": "Assistant", "value": "Heavily overparameterized language models such as BERT, XLNet and T5 have achieved impressive success in many NLP tasks. However, their high model complexity requires enormous computation resources and extremely long training time for both pre-training and fine-tuning. Many works have studied model compression on large NLP models, but only focusing on reducing inference time while still requiring an expensive training process. Other works use extremely large batch sizes to shorten the pre-training time, at the expense of higher computational resource demands. In this paper, inspired by the Early-Bird Lottery Tickets recently studied for computer vision tasks, we propose EarlyBERT, a general computationally-efficient training algorithm applicable to both pre-training and fine-tuning of large-scale language models. By slimming the self-attention and fully-connected sub-layers inside a transformer, we are the first to identify structured winning tickets in the early stage of BERT training. We apply those tickets towards efficient BERT training, and conduct comprehensive pre-training and fine-tuning experiments on GLUE and SQuAD downstream tasks. Our results show that EarlyBERT achieves comparable performance to standard BERT, with 35 45% less training time. Code is available athttps://github.com/VITA-Group/EarlyBERT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_172", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_172.flac", "type": "audio", "duration": 707.048}, {"from": "Assistant", "value": "Adapter-based tuning has recently arisen as an alternative to fine-tuning. It works by adding light-weight adapter modules to a pretrained language model (PrLM) and only updating the parameters of adapter modules when learning on a downstream task. As such, it adds only a few trainable parameters per new task, allowing a high degree of parameter sharing. Prior studies have shown that adapter-based tuning often achieves comparable results to fine-tuning. However, existing work only focuses on the parameter-efficient aspect of adapter-based tuning while lacking further investigation on its effectiveness. In this paper, we study the latter. We first show that adapter-based tuning better mitigates forgetting issues than fine-tuning since it yields representations with less deviation from those generated by the initial PrLM. We then empirically compare the two tuning methods on several downstream NLP tasks and settings. We demonstrate that 1) adapter-based tuning outperforms fine-tuning on low-resource and cross-lingual tasks; 2) it is more robust to overfitting and less sensitive to changes in learning rates.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_173", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_173.flac", "type": "audio", "duration": 504.732}, {"from": "Assistant", "value": "Data augmentation is an effective way to improve the performance of many neural text generation models. However, current data augmentation methods need to define or choose proper data mapping functions that map the original samples into the augmented samples. In this work, we derive an objective to formulate the problem of data augmentation on text generation tasks without any use of augmented data constructed by specific mapping functions. Our proposed objective can be efficiently optimized and applied to popular loss functions on text generation tasks with a convergence rate guarantee. Experiments on five datasets of two text generation tasks show that our approach can approximate or even surpass popular data augmentation methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_174", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_174.flac", "type": "audio", "duration": 701.057}, {"from": "Assistant", "value": "With the need of fast retrieval speed and small memory footprint, document hashing has been playing a crucial role in large-scale information retrieval. To generate high-quality hashing code, both semantics and neighborhood information are crucial. However, most existing methods leverage only one of them or simply combine them via some intuitive criteria, lacking a theoretical principle to guide the integration process. In this paper, we encode the neighborhood information with a graph-induced Gaussian distribution, and propose to integrate the two types of information with a graph-driven generative model. To deal with the complicated correlations among documents, we further propose a tree-structured approximation method for learning. Under the approximation, we prove that the training objective can be decomposed into terms involving only singleton or pairwise documents, enabling the model to be trained as efficiently as uncorrelated ones. Extensive experimental results on three benchmark datasets show that our method achieves superior performance over state-of-the-art methods, demonstrating the effectiveness of the proposed model for simultaneously preserving semantic and neighborhood information.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_175", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_175.flac", "type": "audio", "duration": 646.049}, {"from": "Assistant", "value": "The open-ended nature of visual captioning makes it a challenging area for evaluation. The majority of proposed models rely on specialized training to improve human-correlation, resulting in limited adoption, generalizability, and explainabilty. We introduce “typicality”, a new formulation of evaluation rooted in information theory, which is uniquely suited for problems lacking a definite ground truth. Typicality serves as our framework to develop a novel semantic comparison, SPARCS, as well as referenceless fluency evaluation metrics. Over the course of our analysis, two separate dimensions of fluency naturally emerge: style, captured by metric SPURTS, and grammar, captured in the form of grammatical outlier penalties. Through extensive experiments and ablation studies on benchmark datasets, we show how these decomposed dimensions of semantics and fluency provide greater system-level insight into captioner differences. Our proposed metrics along with their combination, SMURF, achieve state-of-the-art correlation with human judgment when compared with other rule-based evaluation metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_176", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_176.flac", "type": "audio", "duration": 675.093}, {"from": "Assistant", "value": "The goal of database question answering is to enable natural language querying of real-life relational databases in diverse application domains. Recently, large-scale datasets such as Spider and WikiSQL facilitated novel modeling techniques for text-to-SQL parsing, improving zero-shot generalization to unseen databases. In this work, we examine the challenges that still prevent these techniques from practical deployment. First, we present KaggleDBQA, a new cross-domain evaluation dataset of real Web databases, with domain-specific data types, original formatting, and unrestricted questions. Second, we re-examine the choice of evaluation tasks for text-to-SQL parsers as applied in real-life settings. Finally, we augment our in-domain evaluation task with database documentation, a naturally occurring source of implicit domain knowledge. We show that KaggleDBQA presents a challenge to state-of-the-art zero-shot parsers but a more realistic evaluation setting and creative use of associated database documentation boosts their accuracy by over 13.2%, doubling their performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_177", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_177.flac", "type": "audio", "duration": 814.251}, {"from": "Assistant", "value": "We introduce the largest transcribed Arabic speech corpus, QASR, collected from the broadcast domain. This multi-dialect speech dataset contains 2,000 hours of speech sampled at 16kHz crawled from Aljazeera news channel. The dataset is released with lightly supervised transcriptions, aligned with the audio segments. Unlike previous datasets, QASR contains linguistically motivated segmentation, punctuation, speaker information among others. QASR is suitable for training and evaluating speech recognition systems, acoustics- and/or linguistics- based Arabic dialect identification, punctuation restoration, speaker identification, speaker linking, and potentially other NLP modules for spoken data. In addition to QASR transcription, we release a dataset of 130M words to aid in designing and training a better language model. We show that end-to-end automatic speech recognition trained on QASR reports a competitive word error rate compared to the previous MGB-2 corpus. We report baseline results for downstream natural language processing tasks such as named entity recognition using speech transcript. We also report the first baseline for Arabic punctuation restoration. We make the corpus available for the research community.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_178", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_178.flac", "type": "audio", "duration": 693.162}, {"from": "Assistant", "value": "The performance of fine-tuning pre-trained language models largely depends on the hyperparameter configuration. In this paper, we investigate the performance of modern hyperparameter optimization methods (HPO) on fine-tuning pre-trained language models. First, we study and report three HPO algorithms’ performances on fine-tuning two state-of-the-art language models on the GLUE dataset. We find that using the same time budget, HPO often fails to outperform grid search due to two reasons: insufficient time budget and overfitting. We propose two general strategies and an experimental procedure to systematically troubleshoot HPO’s failure cases. By applying the procedure, we observe that HPO can succeed with more appropriate settings in the search space and time budget; however, in certain cases overfitting remains. Finally, we make suggestions for future work. Our implementation can be found inhttps://github.com/microsoft/FLAML/tree/main/flaml/nlp/", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_179", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_179.flac", "type": "audio", "duration": 664.043}, {"from": "Assistant", "value": "Evaluation in NLP is usually done by comparing the scores of competing systems independently averaged over a common set of test instances. In this work, we question the use of averages for aggregating evaluation scores into a final number used to decide which system is best, since the average, as well as alternatives such as the median, ignores the pairing arising from the fact that systems are evaluated on the same test instances. We illustrate the importance of taking the instancelevel pairing of evaluation scores into account and demonstrate, both theoretically and empirically, the advantages of aggregation methods based on pairwise comparisons, such as the Bradley–Terry (BT) model, a mechanism based on the estimated probability that a given system scores better than another on the test set. By re-evaluating 296 real NLP evaluation setups across four tasks and 18 evaluation metrics, we show that the choice of aggregation mechanism matters and yields different conclusions as to which systems are state of the art in about 30% of the setups. To facilitate the adoption of pairwise evaluation, we release a practical tool for performing the full analysis of evaluation scores with the mean, median, BT, and two variants of BT (Elo and TrueSkill), alongside functionality for appropriate statistical testing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_180", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_180.flac", "type": "audio", "duration": 706.048}, {"from": "Assistant", "value": "The cross-database context-dependent Text-to-SQL (XDTS) problem has attracted considerable attention in recent years due to its wide range of potential applications. However, we identify two biases in existing datasets for XDTS: (1) a high proportion of context-independent questions and (2) a high proportion of easy SQL queries. These biases conceal the major challenges in XDTS to some extent. In this work, we present Chase, a large-scale and pragmatic Chinese dataset for XDTS. It consists of 5,459 coherent question sequences (17,940 questions with their SQL queries annotated) over 280 databases, in which only 35% of questions are context-independent, and 28% of SQL queries are easy. We experiment on Chase with three state-of-the-art XDTS approaches. The best approach only achieves an exact match accuracy of 40% over all questions and 16% over all question sequences, indicating that Chase highlights the challenging problems of XDTS. We believe that XDTS can provide fertile soil for addressing the problems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_181", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_181.flac", "type": "audio", "duration": 559.019}, {"from": "Assistant", "value": "Despite pre-trained language models have proven useful for learning high-quality semantic representations, these models are still vulnerable to simple perturbations. Recent works aimed to improve the robustness of pre-trained models mainly focus on adversarial training from perturbed examples with similar semantics, neglecting the utilization of different or even opposite semantics. Different from the image processing field, the text is discrete and few word substitutions can cause significant semantic changes. To study the impact of semantics caused by small perturbations, we conduct a series of pilot experiments and surprisingly find that adversarial training is useless or even harmful for the model to detect these semantic changes. To address this problem, we propose Contrastive Learning with semantIc Negative Examples (CLINE), which constructs semantic negative examples unsupervised to improve the robustness under semantically adversarial attacking. By comparing with similar and opposite semantic examples, the model can effectively perceive the semantic changes caused by small perturbations. Empirical results show that our approach yields substantial improvements on a range of sentiment analysis, reasoning, and reading comprehension tasks. And CLINE also ensures the compactness within the same semantics and separability across different semantics in sentence-level.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_182", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_182.flac", "type": "audio", "duration": 628.843}, {"from": "Assistant", "value": "Topic modeling has been widely used for discovering the latent semantic structure of documents, but most existing methods learn topics with a flat structure. Although probabilistic models can generate topic hierarchies by introducing nonparametric priors like Chinese restaurant process, such methods have data scalability issues. In this study, we develop a tree-structured topic model by leveraging nonparametric neural variational inference. Particularly, the latent components of the stick-breaking process are first learned for each document, then the affiliations of latent components are modeled by the dependency matrices between network layers. Utilizing this network structure, we can efficiently extract a tree-structured topic hierarchy with reasonable structure, low redundancy, and adaptable widths. Experiments on real-world datasets validate the effectiveness of our method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_183", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_183.flac", "type": "audio", "duration": 545.576}, {"from": "Assistant", "value": "Prior work infers the causation between events mainly based on the knowledge induced from the annotated causal event pairs. However, additional evidence information intermediate to the cause and effect remains unexploited. By incorporating such information, the logical law behind the causality can be unveiled, and the interpretability and stability of the causal reasoning system can be improved. To facilitate this, we present anEvent graph knowledge enhanced explainableCAusalReasoning framework (ExCAR). ExCAR first acquires additional evidence information from a large-scale causal event graph as logical rules for causal reasoning. To learn the conditional probabilistic of logical rules, we propose the Conditional Markov Neural Logic Network (CMNLN) that combines the representation learning and structure learning of logical rules in an end-to-end differentiable manner. Experimental results demonstrate that ExCAR outperforms previous state-of-the-art methods. Adversarial evaluation shows the improved stability of ExCAR over baseline systems. Human evaluation shows that ExCAR can achieve a promising explainable performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_184", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_184.flac", "type": "audio", "duration": 676.843}, {"from": "Assistant", "value": "Emotion category is usually divided into different ones by human beings, but it is indeed difficult to clearly distinguish and define the boundaries between different emotion categories. The existing studies working on emotion detection usually focus on how to improve the performance of model prediction, in which emotions are represented with one-hot vectors. However, emotion relations are ignored in one-hot representations. In this article, we first propose a general framework to learn the distributed representations for emotion categories in emotion space from a given emotion classification dataset. Furthermore, based on the soft labels predicted by the pre-trained neural network model, we derive a simple and effective algorithm. Experiments have validated that the proposed representations in emotion space can express emotion relations much better than word vectors in semantic space.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_185", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_185.flac", "type": "audio", "duration": 732.566}, {"from": "Assistant", "value": "Every natural text is written in some style. Style is formed by a complex combination of different stylistic factors, including formality markers, emotions, metaphors, etc. One cannot form a complete understanding of a text without considering these factors. The factors combine and co-vary in complex ways to form styles. Studying the nature of the covarying combinations sheds light on stylistic language in general, sometimes called cross-style language understanding. This paper provides the benchmark corpus (XSLUE) that combines existing datasets and collects a new one for sentence-level cross-style language understanding and evaluation. The benchmark contains text in 15 different styles under the proposed four theoretical groupings: figurative, personal, affective, and interpersonal groups. For valid evaluation, we collect an additional diagnostic set by annotating all 15 styles on the same text. Using XSLUE, we propose three interesting cross-style applications in classification, correlation, and generation. First, our proposed cross-style classifier trained with multiple styles together helps improve overall classification performance against individually-trained style classifiers. Second, our study shows that some styles are highly dependent on each other in human-written text. Finally, we find that combinations of some contradictive styles likely generate stylistically less appropriate text. We believe our benchmark and case studies help explore interesting future directions for cross-style research. The preprocessed datasets and code are publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_186", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_186.flac", "type": "audio", "duration": 718.681}, {"from": "Assistant", "value": "We introduce DynaSent (‘Dynamic Sentiment’), a new English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis. DynaSent combines naturally occurring sentences with sentences created using the open-source Dynabench Platform, which facilities human-and-model-in-the-loop dataset creation. DynaSent has a total of 121,634 sentences, each validated by five crowdworkers, and its development and test splits are designed to produce chance performance for even the best models we have been able to develop; when future models solve this task, we will use them to create DynaSent version 2, continuing the dynamic evolution of this benchmark. Here, we report on the dataset creation effort, focusing on the steps we took to increase quality and reduce artifacts. We also present evidence that DynaSent’s Neutral category is more coherent than the comparable category in other benchmarks, and we motivate training models from scratch for each round over successive fine-tuning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_187", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_187.flac", "type": "audio", "duration": 712.411}, {"from": "Assistant", "value": "In this digital age, online users expect personalized content. To cater to diverse group of audiences across online platforms it is necessary to generate multiple variants of same content with differing degree of characteristics (sentiment, style, formality, etc.). Though text-style transfer is a well explored related area, it focuses on flipping the style attribute polarity instead of regulating a fine-grained attribute transfer. In this paper we propose a hierarchical architecture for finer control over the at- tribute, preserving content using attribute dis- entanglement. We demonstrate the effective- ness of the generative process for two different attributes with varied complexity, namely sentiment and formality. With extensive experiments and human evaluation on five real-world datasets, we show that the framework can generate natural looking sentences with finer degree of control of intensity of a given attribute.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_188", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_188.flac", "type": "audio", "duration": 589.611}, {"from": "Assistant", "value": "Aspect-based Sentiment Analysis (ABSA) aims to identify the aspect terms, their corresponding sentiment polarities, and the opinion terms. There exist seven subtasks in ABSA. Most studies only focus on the subsets of these subtasks, which leads to various complicated ABSA models while hard to solve these subtasks in a unified framework. In this paper, we redefine every subtask target as a sequence mixed by pointer indexes and sentiment class indexes, which converts all ABSA subtasks into a unified generative formulation. Based on the unified formulation, we exploit the pre-training sequence-to-sequence model BART to solve all ABSA subtasks in an end-to-end framework. Extensive experiments on four ABSA datasets for seven subtasks demonstrate that our framework achieves substantial performance gain and provides a real unified end-to-end solution for the whole ABSA subtasks, which could benefit multiple tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_189", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_189.flac", "type": "audio", "duration": 500.096}, {"from": "Assistant", "value": "Task-oriented dialogue systems typically require manual annotation of dialogue slots in training data, which is costly to obtain. We propose a method that eliminates this requirement: We use weak supervision from existing linguistic annotation models to identify potential slot candidates, then automatically identify domain-relevant slots by using clustering algorithms. Furthermore, we use the resulting slot annotation to train a neural-network-based tagger that is able to perform slot tagging with no human intervention. This tagger is trained solely on the outputs of our method and thus does not rely on any labeled data. Our model demonstrates state-of-the-art performance in slot tagging without labeled training data on four different dialogue domains. Moreover, we find that slot annotations discovered by our model significantly improve the performance of an end-to-end dialogue response generation model, compared to using no slot annotation at all.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_190", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_190.flac", "type": "audio", "duration": 720.043}, {"from": "Assistant", "value": "Intent classification is a major task in spoken language understanding (SLU). Since most models are built with pre-collected in-domain (IND) training utterances, their ability to detect unsupported out-of-domain (OOD) utterances has a critical effect in practical use. Recent works have shown that using extra data and labels can improve the OOD detection performance, yet it could be costly to collect such data. This paper proposes to train a model with only IND data while supporting both IND intent classification and OOD detection. Our method designs a novel domain-regularized module (DRM) to reduce the overconfident phenomenon of a vanilla classifier, achieving a better generalization in both cases. Besides, DRM can be used as a drop-in replacement for the last layer in any neural network-based intent classifier, providing a low-cost strategy for a significant improvement. The evaluation on four datasets shows that our method built on BERT and RoBERTa models achieves state-of-the-art performance against existing approaches and the strong baselines we created for the comparisons.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_191", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_191.flac", "type": "audio", "duration": 610.88}, {"from": "Assistant", "value": "Recent research considers few-shot intent detection as a meta-learning problem: the model is learning to learn from a consecutive set of small tasks named episodes. In this work, we propose ProtAugment, a meta-learning algorithm for short texts classification (the intent detection task). ProtAugment is a novel extension of Prototypical Networks, that limits overfitting on the bias introduced by the few-shots classification objective at each episode. It relies on diverse paraphrasing: a conditional language model is first fine-tuned for paraphrasing, and diversity is later introduced at the decoding stage at each meta-learning episode. The diverse paraphrasing is unsupervised as it is applied to unlabelled data, and then fueled to the Prototypical Network training objective as a consistency loss. ProtAugment is the state-of-the-art method for intent detection meta-learning, at no extra labeling efforts and without the need to fine-tune a conditional language model on a given application domain.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_192", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_192.flac", "type": "audio", "duration": 638.059}, {"from": "Assistant", "value": "Most language understanding models in task-oriented dialog systems are trained on a small amount of annotated training data, and evaluated in a small set from the same distribution. However, these models can lead to system failure or undesirable output when being exposed to natural language perturbation or variation in practice. In this paper, we conduct comprehensive evaluation and analysis with respect to the robustness of natural language understanding models, and introduce three important aspects related to language understanding in real-world dialog systems, namely, language variety, speech characteristics, and noise perturbation. We propose a model-agnostic toolkit LAUG to approximate natural language perturbations for testing the robustness issues in task-oriented dialog. Four data augmentation approaches covering the three aspects are assembled in LAUG, which reveals critical robustness issues in state-of-the-art models. The augmented dataset through LAUG can be used to facilitate future research on the robustness testing of language understanding in task-oriented dialog.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_193", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_193.flac", "type": "audio", "duration": 610.048}, {"from": "Assistant", "value": "Dialogue state tracking (DST) plays a key role in task-oriented dialogue systems to monitor the user’s goal. In general, there are two strategies to track a dialogue state: predicting it from scratch and updating it from previous state. The scratch-based strategy obtains each slot value by inquiring all the dialogue history, and the previous-based strategy relies on the current turn dialogue to update the previous dialogue state. However, it is hard for the scratch-based strategy to correctly track short-dependency dialogue state because of noise; meanwhile, the previous-based strategy is not very useful for long-dependency dialogue state tracking. Obviously, it plays different roles for the context information of different granularity to track different kinds of dialogue states. Thus, in this paper, we will study and discuss how the context information of different granularity affects dialogue state tracking. First, we explore how greatly different granularities affect dialogue state tracking. Then, we further discuss how to combine multiple granularities for dialogue state tracking. Finally, we apply the findings about context granularity to few-shot learning scenario. Besides, we have publicly released all codes.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_194", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_194.flac", "type": "audio", "duration": 746.057}, {"from": "Assistant", "value": "Mixed initiative in open-domain dialogue requires a system to pro-actively introduce new topics. The one-turn topic transition task explores how a system connects two topics in a cooperative and coherent manner. The goal of the task is to generate a “bridging” utterance connecting the new topic to the topic of the previous conversation turn. We are especially interested in commonsense explanations of how a new topic relates to what has been mentioned before. We first collect a new dataset of human one-turn topic transitions, which we callOTTers. We then explore different strategies used by humans when asked to complete such a task, and notice that the use of a bridging utterance to connect the two topics is the approach used the most. We finally show how existing state-of-the-art text generation models can be adapted to this task and examine the performance of these baselines on different splits of the OTTers data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_195", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_195.flac", "type": "audio", "duration": 710.571}, {"from": "Assistant", "value": "Recently, there has been significant progress in studying neural networks to translate text descriptions into SQL queries. Despite achieving good performance on some public benchmarks, existing text-to-SQL models typically rely on the lexical matching between words in natural language (NL) questions and tokens in table schemas, which may render the models vulnerable to attacks that break the schema linking mechanism. In this work, we investigate the robustness of text-to-SQL models to synonym substitution. In particular, we introduce Spider-Syn, a human-curated dataset based on the Spider benchmark for text-to-SQL translation. NL questions in Spider-Syn are modified from Spider, by replacing their schema-related words with manually selected synonyms that reflect real-world question paraphrases. We observe that the accuracy dramatically drops by eliminating such explicit correspondence between NL questions and table schemas, even if the synonyms are not adversarially selected to conduct worst-case attacks. Finally, we present two categories of approaches to improve the model robustness. The first category of approaches utilizes additional synonym annotations for table schemas by modifying the model input, while the second category is based on adversarial training. We demonstrate that both categories of approaches significantly outperform their counterparts without the defense, and the first category of approaches are more effective.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_196", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_196.flac", "type": "audio", "duration": 728.178}, {"from": "Assistant", "value": "In order to better understand the reason behind model behaviors (i.e., making predictions), most recent works have exploited generative models to provide complementary explanations. However, existing approaches in NLP mainly focus on “WHY A” rather than contrastive “WHY A NOT B”, which is shown to be able to better distinguish confusing candidates and improve data efficiency in other research fields. In this paper, we focus on generating contrastive explanations with counterfactual examples in NLI and propose a novelKnowledge-AwareContrastiveExplanation generation framework (KACE).Specifically, we first identify rationales (i.e., key phrases) from input sentences, and use them as key perturbations for generating counterfactual examples. After obtaining qualified counterfactual examples, we take them along with original examples and external knowledge as input, and employ a knowledge-aware generative pre-trained language model to generate contrastive explanations. Experimental results show that contrastive explanations are beneficial to fit the scenarios by clarifying the difference between the predicted answer and other possible wrong ones. Moreover, we train an NLI model enhanced with contrastive explanations and achieves an accuracy of 91.9% on SNLI, gaining improvements of 5.7% against ETPA (“Explain-Then-Predict-Attention”) and 0.6% against NILE (“WHY A”).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_197", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_197.flac", "type": "audio", "duration": 742.04}, {"from": "Assistant", "value": "Although BERT and its variants have reshaped the NLP landscape, it still remains unclear how best to derive sentence embeddings from such pre-trained Transformers. In this work, we propose a contrastive learning method that utilizes self-guidance for improving the quality of BERT sentence representations. Our method fine-tunes BERT in a self-supervised fashion, does not rely on data augmentation, and enables the usual [CLS] token embeddings to function as sentence vectors. Moreover, we redesign the contrastive learning objective (NT-Xent) and apply it to sentence representation learning. We demonstrate with extensive experiments that our approach is more effective than competitive baselines on diverse sentence-related tasks. We also show it is efficient at inference and robust to domain shifts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_198", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_198.flac", "type": "audio", "duration": 690.048}, {"from": "Assistant", "value": "This work aims to tackle the challenging heterogeneous graph encoding problem in the text-to-SQL task. Previous methods are typically node-centric and merely utilize different weight matrices to parameterize edge types, which 1) ignore the rich semantics embedded in the topological structure of edges, and 2) fail to distinguish local and non-local relations for each node. To this end, we propose a Line Graph Enhanced Text-to-SQL (LGESQL) model to mine the underlying relational features without constructing meta-paths. By virtue of the line graph, messages propagate more efficiently through not only connections between nodes, but also the topology of directed edges. Furthermore, both local and non-local relations are integrated distinctively during the graph iteration. We also design an auxiliary task called graph pruning to improve the discriminative capability of the encoder. Our framework achieves state-of-the-art results (62.8% with Glove, 72.0% with Electra) on the cross-domain text-to-SQL benchmark Spider at the time of writing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_199", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_199.flac", "type": "audio", "duration": 562.005}, {"from": "Assistant", "value": "Multimodal pre-training models, such as LXMERT, have achieved excellent results in downstream tasks. However, current pre-trained models require large amounts of training data and have huge model sizes, which make them impossible to apply in low-resource situations. How to obtain similar or even better performance than a larger model under the premise of less pre-training data and smaller model size has become an important problem. In this paper, we propose a new Multi-stage Pre-training (MSP) method, which uses information at different granularities from word, phrase to sentence in both texts and images to pre-train a model in stages. We also design several different pre-training tasks suitable for the information granularity in different stage in order to efficiently capture the diverse knowledge from a limited corpus. We take a Simplified LXMERT (LXMERT-S) which is with 45.9% parameters of the original LXMERT model and only 11.44% of the original pre-training data as the testbed of our MSP method. Experimental results show that our method achieves comparable performance to the original LXMERT model in all downstream tasks, and even outperforms the original model in Image-Text Retrieval task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_200", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_200.flac", "type": "audio", "duration": 687.334}, {"from": "Assistant", "value": "Document-level contextual information has shown benefits to text-based machine translation, but whether and how context helps end-to-end (E2E) speech translation (ST) is still under-studied. We fill this gap through extensive experiments using a simple concatenation-based context-aware ST model, paired with adaptive feature selection on speech encodings for computational efficiency. We investigate several decoding approaches, and introduce in-model ensemble decoding which jointly performs document- and sentence-level translation using the same model. Our results on the MuST-C benchmark with Transformer demonstrate the effectiveness of context to E2E ST. Compared to sentence-level ST, context-aware ST obtains better translation quality (+0.18-2.61 BLEU), improves pronoun and homophone translation, shows better robustness to (artificial) audio segmentation errors, and reduces latency and flicker to deliver higher quality for simultaneous translation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_201", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_201.flac", "type": "audio", "duration": 532.085}, {"from": "Assistant", "value": "Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents. We propose LayoutLMv2 architecture with new pre-training tasks to model the interaction among text, layout, and image in a single multi-modal framework. Specifically, with a two-stream multi-modal Transformer encoder, LayoutLMv2 uses not only the existing masked visual-language modeling task but also the new text-image alignment and text-image matching tasks, which make it better capture the cross-modality interaction in the pre-training stage. Meanwhile, it also integrates a spatial-aware self-attention mechanism into the Transformer architecture so that the model can fully understand the relative positional relationship among different text blocks. Experiment results show that LayoutLMv2 outperforms LayoutLM by a large margin and achieves new state-of-the-art results on a wide variety of downstream visually-rich document understanding tasks, including FUNSD (0.7895 to 0.8420), CORD (0.9493 to 0.9601), SROIE (0.9524 to 0.9781), Kleister-NDA (0.8340 to 0.8520), RVL-CDIP (0.9443 to 0.9564), and DocVQA (0.7295 to 0.8672).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_202", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_202.flac", "type": "audio", "duration": 610.058}, {"from": "Assistant", "value": "Existed pre-training methods either focus on single-modal tasks or multi-modal tasks, and cannot effectively adapt to each other. They can only utilize single-modal data (i.e., text or image) or limited multi-modal data (i.e., image-text pairs). In this work, we propose a UNIfied-MOdal pre-training architecture, namely UNIMO, which can effectively adapt to both single-modal and multi-modal understanding and generation tasks. Large scale of free text corpus and image collections are utilized to improve the capability of visual and textual understanding, and cross-modal contrastive learning (CMCL) is leveraged to align the textual and visual information into a unified semantic space, over a corpus of image-text pairs augmented with related images and texts. With the help of rich non-paired single-modal data, our model is able to learn more generalizable representations, by allowing textual knowledge and visual knowledge to enhance each other in the unified semantic space. The experimental results show that UNIMO greatly improves the performance of several single-modal and multi-modal downstream tasks. Our code and pre-trained models are public athttps://github.com/PaddlePaddle/Research/tree/master/NLP/UNIMO.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_203", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_203.flac", "type": "audio", "duration": 719.052}, {"from": "Assistant", "value": "Multimodal fusion has been proved to improve emotion recognition performance in previous works. However, in real-world applications, we often encounter the problem of missing modality, and which modalities will be missing is uncertain. It makes the fixed multimodal fusion fail in such cases. In this work, we propose a unified model, Missing Modality Imagination Network (MMIN), to deal with the uncertain missing modality problem. MMIN learns robust joint multimodal representations, which can predict the representation of any missing modality given available modalities under different missing modality conditions. Comprehensive experiments on two benchmark datasets demonstrate that the unified MMIN model significantly improves emotion recognition performance under both uncertain missing-modality testing conditions and full-modality ideal testing condition. The code will be available athttps://github.com/AIM3-RUC/MMIN.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_204", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_204.flac", "type": "audio", "duration": 647.047}, {"from": "Assistant", "value": "Encoder pre-training is promising in end-to-end Speech Translation (ST), given the fact that speech-to-translation data is scarce. But ST encoders are not simple instances of Automatic Speech Recognition (ASR) or Machine Translation (MT) encoders. For example, we find that ASR encoders lack the global context representation, which is necessary for translation, whereas MT encoders are not designed to deal with long but locally attentive acoustic sequences. In this work, we propose a Stacked Acoustic-and-Textual Encoding (SATE) method for speech translation. Our encoder begins with processing the acoustic sequence as usual, but later behaves more like an MT encoder for a global representation of the input sequence. In this way, it is straightforward to incorporate the pre-trained models into the system. Also, we develop an adaptor module to alleviate the representation inconsistency between the pre-trained ASR encoder and MT encoder, and develop a multi-teacher knowledge distillation method to preserve the pre-training knowledge. Experimental results on the LibriSpeech En-Fr and MuST-C En-De ST tasks show that our method achieves state-of-the-art BLEU scores of 18.3 and 25.2. To our knowledge, we are the first to develop an end-to-end ST system that achieves comparable or even better BLEU performance than the cascaded ST counterpart when large-scale ASR and MT data is available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_205", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_205.flac", "type": "audio", "duration": 716.057}, {"from": "Assistant", "value": "In this paper, we study the task of graph-based constituent parsing in the setting that binarization is not conducted as a pre-processing step, where a constituent tree may consist of nodes with more than two children. Previous graph-based methods on this setting typically generate hidden nodes with the dummy label inside the n-ary nodes, in order to transform the tree into a binary tree for prediction. The limitation is that the hidden nodes break the sibling relations of the n-ary node’s children. Consequently, the dependencies of such sibling constituents might not be accurately modeled and is being ignored. To solve this limitation, we propose a novel graph-based framework, which is called “recursive semi-Markov model”. The main idea is to utilize 1-order semi-Markov model to predict the immediate children sequence of a constituent candidate, which then recursively serves as a child candidate of its parent. In this manner, the dependencies of sibling constituents can be described by 1-order transition features, which solves the above limitation. Through experiments, the proposed framework obtains the F1 of 95.92% and 92.50% on the datasets of PTB and CTB 5.1 respectively. Specially, the recursive semi-Markov model shows advantages in modeling nodes with more than two children, whose average F1 can be improved by 0.3-1.1 points in PTB and 2.3-6.8 points in CTB 5.1.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_206", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_206.flac", "type": "audio", "duration": 765.483}, {"from": "Assistant", "value": "Pretrained contextualized embeddings are powerful word representations for structured prediction tasks. Recent work found that better word representations can be obtained by concatenating different types of embeddings. However, the selection of embeddings to form the best concatenated representation usually varies depending on the task and the collection of candidate embeddings, and the ever-increasing number of embedding types makes it a more difficult problem. In this paper, we propose Automated Concatenation of Embeddings (ACE) to automate the process of finding better concatenations of embeddings for structured prediction tasks, based on a formulation inspired by recent progress on neural architecture search. Specifically, a controller alternately samples a concatenation of embeddings, according to its current belief of the effectiveness of individual embedding types in consideration for a task, and updates the belief based on a reward. We follow strategies in reinforcement learning to optimize the parameters of the controller and compute the reward based on the accuracy of a task model, which is fed with the sampled concatenation as input and trained on a task dataset. Empirical results on 6 tasks and 21 datasets show that our approach outperforms strong baselines and achieves state-of-the-art performance with fine-tuned embeddings in all the evaluations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_207", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_207.flac", "type": "audio", "duration": 622.899}, {"from": "Assistant", "value": "In structured prediction problems, cross-lingual transfer learning is an efficient way to train quality models for low-resource languages, and further improvement can be obtained by learning from multiple source languages. However, not all source models are created equal and some may hurt performance on the target language. Previous work has explored the similarity between source and target sentences as an approximate measure of strength for different source models. In this paper, we propose a multi-view framework, by leveraging a small number of labeled target sentences, to effectively combine multiple source models into an aggregated source view at different granularity levels (language, sentence, or sub-structure), and transfer it to a target view based on a task-specific model. By encouraging the two views to interact with each other, our framework can dynamically adjust the confidence level of each source model and improve the performance of both views during training. Experiments for three structured prediction tasks on sixteen data sets show that our framework achieves significant improvement over all existing approaches, including these with access to additional source language data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_208", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_208.flac", "type": "audio", "duration": 718.528}, {"from": "Assistant", "value": "Incorporating syntax into neural approaches in NLP has a multitude of practical and scientific benefits. For instance, a language model that is syntax-aware is likely to be able to produce better samples; even a discriminative model like BERT with a syntax module could be used for core NLP tasks like unsupervised syntactic parsing. Rapid progress in recent years was arguably spurred on by the empirical success of the Parsing-Reading-Predict architecture of (Shen et al., 2018a), later simplified by the Order Neuron LSTM of (Shen et al., 2019). Most notably, this is the first time neural approaches were able to successfully perform unsupervised syntactic parsing (evaluated by various metrics like F-1 score). However, even heuristic (much less fully mathematical) understanding of why and when these architectures work is lagging severely behind. In this work, we answer representational questions raised by the architectures in (Shen et al., 2018a, 2019), as well as some transition-based syntax-aware language models (Dyer et al., 2016): what kind of syntactic structure can current neural approaches to syntax represent? Concretely, we ground this question in the sandbox of probabilistic context-free-grammars (PCFGs), and identify a key aspect of the representational power of these approaches: the amount and directionality of context that the predictor has access to when forced to make parsing decision. We show that with limited context (either bounded, or unidirectional), there are PCFGs, for which these approaches cannot represent the max-likelihood parse; conversely, if the context is unlimited, they can represent the max-likelihood parse of any PCFG.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_209", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_209.flac", "type": "audio", "duration": 700.058}, {"from": "Assistant", "value": "Neural lexicalized PCFGs (L-PCFGs) have been shown effective in grammar induction. However, to reduce computational complexity, they make a strong independence assumption on the generation of the child word and thus bilexical dependencies are ignored. In this paper, we propose an approach to parameterize L-PCFGs without making implausible independence assumptions. Our approach directly models bilexical dependencies and meanwhile reduces both learning and representation complexities of L-PCFGs. Experimental results on the English WSJ dataset confirm the effectiveness of our approach in improving both running speed and unsupervised parsing performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_210", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_210.flac", "type": "audio", "duration": 701.057}, {"from": "Assistant", "value": "On social media platforms, hateful and offensive language negatively impact the mental well-being of users and the participation of people from diverse backgrounds. Automatic methods to detect offensive language have largely relied on datasets with categorical labels. However, comments can vary in their degree of offensiveness. We create the first dataset of English language Reddit comments that has fine-grained, real-valued scores between -1 (maximally supportive) and 1 (maximally offensive). The dataset was annotated using Best–Worst Scaling, a form of comparative annotation that has been shown to alleviate known biases of using rating scales. We show that the method produces highly reliable offensiveness scores. Finally, we evaluate the ability of widely-used neural models to predict offensiveness scores on this new dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_211", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_211.flac", "type": "audio", "duration": 486.016}, {"from": "Assistant", "value": "Automatic dialogue coherence evaluation has attracted increasing attention and is crucial for developing promising dialogue systems. However, existing metrics have two major limitations: (a) they are mostly trained in a simplified two-level setting (coherent vs. incoherent), while humans give Likert-type multi-level coherence scores, dubbed as “quantifiable”; (b) their predicted coherence scores cannot align with the actual human rating standards due to the absence of human guidance during training. To address these limitations, we propose Quantifiable Dialogue Coherence Evaluation (QuantiDCE), a novel framework aiming to train a quantifiable dialogue coherence metric that can reflect the actual human rating standards. Specifically, QuantiDCE includes two training stages, Multi-Level Ranking (MLR) pre-training and Knowledge Distillation (KD) fine-tuning. During MLR pre-training, a new MLR loss is proposed for enabling the model to learn the coarse judgement of coherence degrees. Then, during KD fine-tuning, the pretrained model is further finetuned to learn the actual human rating standards with only very few human-annotated data. To advocate the generalizability even with limited fine-tuning data, a novel KD regularization is introduced to retain the knowledge learned at the pre-training stage. Experimental results show that the model trained by QuantiDCE presents stronger correlations with human judgements than the other state-of-the-art metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_212", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_212.flac", "type": "audio", "duration": 482.688}, {"from": "Assistant", "value": "Accurate assessment of the ability of embedding models to capture idiomaticity may require evaluation at token rather than type level, to account for degrees of idiomaticity and possible ambiguity between literal and idiomatic usages. However, most existing resources with annotation of idiomaticity include ratings only at type level. This paper presents the Noun Compound Type and Token Idiomaticity (NCTTI) dataset, with human annotations for 280 noun compounds in English and 180 in Portuguese at both type and token level. We compiled 8,725 and 5,091 token level annotations for English and Portuguese, respectively, which are strongly correlated with the corresponding scores obtained at type level. The NCTTI dataset is used to explore how vector space models reflect the variability of idiomaticity across sentences. Several experiments using state-of-the-art contextualised models suggest that their representations are not capturing the noun compounds idiomaticity as human annotators. This new multilingual resource also contains suggestions for paraphrases of the noun compounds both at type and token levels, with uses for lexical substitution or disambiguation in context.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_213", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_213.flac", "type": "audio", "duration": 719.851}, {"from": "Assistant", "value": "Statutory reasoning is the task of determining whether a legal statute, stated in natural language, applies to the text description of a case. Prior work introduced a resource that approached statutory reasoning as a monolithic textual entailment problem, with neural baselines performing nearly at-chance. To address this challenge, we decompose statutory reasoning into four types of language-understanding challenge problems, through the introduction of concepts and structure found in Prolog programs. Augmenting an existing benchmark, we provide annotations for the four tasks, and baselines for three of them. Models for statutory reasoning are shown to benefit from the additional structure, improving on prior baselines. Further, the decomposition into subtasks facilitates finer-grained model diagnostics and clearer incremental progress.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_214", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_214.flac", "type": "audio", "duration": 720.492}, {"from": "Assistant", "value": "Ordinal Classification (OC) is an important classification task where the classes are ordinal. For example, an OC task for sentiment analysis could have the following classes: highly positive, positive, neutral, negative, highly negative. Clearly, evaluation measures for an OC task should penalise misclassifications by considering the ordinal nature of the classes. Ordinal Quantification (OQ) is a related task where the gold data is a distribution over ordinal classes, and the system is required to estimate this distribution. Evaluation measures for an OQ task should also take the ordinal nature of the classes into account. However, for both OC and OQ, there are only a small number of known evaluation measures that meet this basic requirement. In the present study, we utilise data from the SemEval and NTCIR communities to clarify the properties of nine evaluation measures in the context of OC tasks, and six measures in the context of OQ tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_215", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_215.flac", "type": "audio", "duration": 685.056}, {"from": "Assistant", "value": "Entity Matching (EM) aims at recognizing entity records that denote the same real-world object. Neural EM models learn vector representation of entity descriptions and match entities end-to-end. Though robust, these methods require many annotated resources for training, and lack of interpretability. In this paper, we propose a novel EM framework that consists of Heterogeneous Information Fusion (HIF) and Key Attribute Tree (KAT) Induction to decouple feature representation from matching decision. Using self-supervised learning and mask mechanism in pre-trained language modeling, HIF learns the embeddings of noisy attribute values by inter-attribute attention with unlabeled data. Using a set of comparison features and a limited amount of annotated data, KAT Induction learns an efficient decision tree that can be interpreted by generating entity matching rules whose structure is advocated by domain experts. Experiments on 6 public datasets and 3 industrial datasets show that our method is highly efficient and outperforms SOTA EM models in most cases. We will release the codes upon acceptance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_216", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_216.flac", "type": "audio", "duration": 711.761}, {"from": "Assistant", "value": "Named entity recognition (NER) is a well-studied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. The span-based methods treat entity recognition as a span classification task. Although these methods have the innate ability to handle nested NER, they suffer from high computational cost, ignorance of boundary information, under-utilization of the spans that partially match with entities, and difficulties in long entity recognition. To tackle these issues, we propose a two-stage entity identifier. First we generate span proposals by filtering and boundary regression on the seed spans to locate the entities, and then label the boundary-adjusted span proposals with the corresponding categories. Our method effectively utilizes the boundary information of entities and partially matched spans during training. Through boundary regression, entities of any length can be covered theoretically, which improves the ability to recognize long entities. In addition, many low-quality seed spans are filtered out in the first stage, which reduces the time complexity of inference. Experiments on nested NER datasets demonstrate that our proposed method outperforms previous state-of-the-art models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_217", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_217.flac", "type": "audio", "duration": 790.895}, {"from": "Assistant", "value": "Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event. Traditional methods usually extract event records by decomposing the complex structure prediction task into multiple subtasks. In this paper, we propose Text2Event, a sequence-to-structure generation paradigm that can directly extract events from the text in an end-to-end manner. Specifically, we design a sequence-to-structure network for unified event extraction, a constrained decoding algorithm for event knowledge injection during inference, and a curriculum learning algorithm for efficient model learning. Experimental results show that, by uniformly modeling all tasks in a single model and universally predicting different labels, our method can achieve competitive performance using only record-level annotations in both supervised learning and transfer learning settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_218", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_218.flac", "type": "audio", "duration": 506.048}, {"from": "Assistant", "value": "In this paper, we aim to explore an uncharted territory, which is Chinese multimodal named entity recognition (NER) with both textual and acoustic contents. To achieve this, we construct a large-scale human-annotated Chinese multimodal NER dataset, named CNERTA. Our corpus totally contains 42,987 annotated sentences accompanying by 71 hours of speech data. Based on this dataset, we propose a family of strong and representative baseline models, which can leverage textual features or multimodal features. Upon these baselines, to capture the natural monotonic alignment between the textual modality and the acoustic modality, we further propose a simple multimodal multitask model by introducing a speech-to-text alignment auxiliary task. Through extensive experiments, we observe that: (1) Progressive performance boosts as we move from unimodal to multimodal, verifying the necessity of integrating speech clues into Chinese NER. (2) Our proposed model yields state-of-the-art (SoTA) results on CNERTA, demonstrating its effectiveness. For further research, the annotated dataset is publicly available athttp://github.com/DianboWork/CNERTA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_219", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_219.flac", "type": "audio", "duration": 641.045}, {"from": "Assistant", "value": "Disease is one of the fundamental entities in biomedical research. Recognizing such entities from biomedical text and then normalizing them to a standardized disease vocabulary offer a tremendous opportunity for many downstream applications. Previous studies have demonstrated that joint modeling of the two sub-tasks has superior performance than the pipelined counterpart. Although the neural joint model based on multi-task learning framework has achieved state-of-the-art performance, it suffers from the boundary inconsistency problem due to the separate decoding procedures. Moreover, it ignores the rich information (e.g., the text surface form) of each candidate concept in the vocabulary, which is quite essential for entity normalization. In this work, we propose a neural transition-based joint model to alleviate these two issues. We transform the end-to-end disease recognition and normalization task as an action sequence prediction task, which not only jointly learns the model with shared representations of the input, but also jointly searches the output by state transitions in one search space. Moreover, we introduce attention mechanisms to take advantage of the text surface form of each candidate concept for better normalization performance. Experimental results conducted on two publicly available datasets show the effectiveness of the proposed method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_220", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_220.flac", "type": "audio", "duration": 717.938}, {"from": "Assistant", "value": "Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most current methods to ED rely heavily on training instances, and almost ignore the correlation of event types. Hence, they tend to suffer from data scarcity and fail to handle new unseen event types. To address these problems, we formulate ED as a process of event ontology population: linking event instances to pre-defined event types in event ontology, and propose a novel ED framework entitled OntoED with ontology embedding. We enrich event ontology with linkages among event types, and further induce more event-event correlations. Based on the event ontology, OntoED can leverage and propagate correlation knowledge, particularly from data-rich to data-poor event types. Furthermore, OntoED can be applied to new unseen event types, by establishing linkages to existing ones. Experiments indicate that OntoED is more predominant and robust than previous approaches to ED, especially in data-scarce scenarios.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_221", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_221.flac", "type": "audio", "duration": 716.173}, {"from": "Assistant", "value": "Self-training has proven effective for improving NMT performance by augmenting model training with synthetic parallel data. The common practice is to construct synthetic data based on a randomly sampled subset of large-scale monolingual data, which we empirically show is sub-optimal. In this work, we propose to improve the sampling procedure by selecting the most informative monolingual sentences to complement the parallel data. To this end, we compute the uncertainty of monolingual sentences using the bilingual dictionary extracted from the parallel data. Intuitively, monolingual sentences with lower uncertainty generally correspond to easy-to-translate patterns which may not provide additional gains. Accordingly, we design an uncertainty-based sampling strategy to efficiently exploit the monolingual data for self-training, in which monolingual sentences with higher uncertainty would be sampled with higher probability. Experimental results on large-scale WMT English⇒German and English⇒Chinese datasets demonstrate the effectiveness of the proposed approach. Extensive analyses suggest that emphasizing the learning on uncertain monolingual sentences by our approach does improve the translation quality of high-uncertainty sentences and also benefits the prediction of low-frequency words at the target side.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_222", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_222.flac", "type": "audio", "duration": 636.053}, {"from": "Assistant", "value": "Context-aware neural machine translation (NMT) remains challenging due to the lack of large-scale document-level parallel corpora. To break the corpus bottleneck, in this paper we aim to improve context-aware NMT by taking the advantage of the availability of both large-scale sentence-level parallel dataset and source-side monolingual documents. To this end, we propose two pre-training tasks. One learns to translate a sentence from source language to target language on the sentence-level parallel dataset while the other learns to translate a document from deliberately noised to original on the monolingual documents. Importantly, the two pre-training tasks are jointly and simultaneously learned via the same model, thereafter fine-tuned on scale-limited parallel documents from both sentence-level and document-level perspectives. Experimental results on four translation tasks show that our approach significantly improves translation performance. One nice property of our approach is that the fine-tuned model can be used to translate both sentences and documents.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_223", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_223.flac", "type": "audio", "duration": 650.577}, {"from": "Assistant", "value": "Although teacher forcing has become the main training paradigm for neural machine translation, it usually makes predictions only conditioned on past information, and hence lacks global planning for the future. To address this problem, we introduce another decoder, called seer decoder, into the encoder-decoder framework during training, which involves future information in target predictions. Meanwhile, we force the conventional decoder to simulate the behaviors of the seer decoder via knowledge distillation. In this way, at test the conventional decoder can perform like the seer decoder without the attendance of it. Experiment results on the Chinese-English, English-German and English-Romanian translation tasks show our method can outperform competitive baselines significantly and achieves greater improvements on the bigger data sets. Besides, the experiments also prove knowledge distillation the best way to transfer knowledge from the seer decoder to the conventional decoder compared to adversarial learning and L2 regularization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_224", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_224.flac", "type": "audio", "duration": 671.057}, {"from": "Assistant", "value": "Five years after the first published proofs of concept, direct approaches to speech translation (ST) are now competing with traditional cascade solutions. In light of this steady progress, can we claim that the performance gap between the two is closed? Starting from this question, we present a systematic comparison between state-of-the-art systems representative of the two paradigms. Focusing on three language directions (English-German/Italian/Spanish), we conduct automatic and manual evaluations, exploiting high-quality professional post-edits and annotations. Our multi-faceted analysis on one of the few publicly available ST benchmarks attests for the first time that: i) the gap between the two paradigms is now closed, and ii) the subtle differences observed in their behavior are not sufficient for humans neither to distinguish them nor to prefer one over the other.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_225", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_225.flac", "type": "audio", "duration": 711.041}, {"from": "Assistant", "value": "Unsupervised machine translation, which utilizes unpaired monolingual corpora as training data, has achieved comparable performance against supervised machine translation. However, it still suffers from data-scarce domains. To address this issue, this paper presents a novel meta-learning algorithm for unsupervised neural machine translation (UNMT) that trains the model to adapt to another domain by utilizing only a small amount of training data. We assume that domain-general knowledge is a significant factor in handling data-scarce domains. Hence, we extend the meta-learning algorithm, which utilizes knowledge learned from high-resource domains, to boost the performance of low-resource UNMT. Our model surpasses a transfer learning-based approach by up to 2-3 BLEU scores. Extensive experimental results show that our proposed algorithm is pertinent for fast adaptation and consistently outperforms other baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_226", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_226.flac", "type": "audio", "duration": 714.325}, {"from": "Assistant", "value": "Large-scale models for learning fixed-dimensional cross-lingual sentence representations like LASER (Artetxe and Schwenk, 2019b) lead to significant improvement in performance on downstream tasks. However, further increases and modifications based on such large-scale models are usually impractical due to memory limitations. In this work, we introduce a lightweight dual-transformer architecture with just 2 layers for generating memory-efficient cross-lingual sentence representations. We explore different training tasks and observe that current cross-lingual training tasks leave a lot to be desired for this shallow architecture. To ameliorate this, we propose a novel cross-lingual language model, which combines the existing single-word masked language model with the newly proposed cross-lingual token-level reconstruction task. We further augment the training task by the introduction of two computationally-lite sentence-level contrastive learning tasks to enhance the alignment of cross-lingual sentence representation space, which compensates for the learning bottleneck of the lightweight transformer for generative tasks. Our comparisons with competing models on cross-lingual sentence retrieval and multilingual document classification confirm the effectiveness of the newly proposed training tasks for a shallow model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_228", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_228.flac", "type": "audio", "duration": 719.052}, {"from": "Assistant", "value": "Recently, knowledge distillation (KD) has shown great success in BERT compression. Instead of only learning from the teacher’s soft label as in conventional KD, researchers find that the rich information contained in the hidden layers of BERT is conducive to the student’s performance. To better exploit the hidden knowledge, a common practice is to force the student to deeply mimic the teacher’s hidden states of all the tokens in a layer-wise manner. In this paper, however, we observe that although distilling the teacher’s hidden state knowledge (HSK) is helpful, the performance gain (marginal utility) diminishes quickly as more HSK is distilled. To understand this effect, we conduct a series of analysis. Specifically, we divide the HSK of BERT into three dimensions, namely depth, length and width. We first investigate a variety of strategies to extract crucial knowledge for each single dimension and then jointly compress the three dimensions. In this way, we show that 1) the student’s performance can be improved by extracting and distilling the crucial HSK, and 2) using a tiny fraction of HSK can achieve the same performance as extensive HSK distillation. Based on the second finding, we further propose an efficient KD paradigm to compress BERT, which does not require loading the teacher during the training of student. For two kinds of student models and computing devices, the proposed KD paradigm gives rise to training speedup of 2.7x 3.4x.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_229", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_229.flac", "type": "audio", "duration": 673.173}, {"from": "Assistant", "value": "Lifelong learning (LL) aims to train a neural network on a stream of tasks while retaining knowledge from previous tasks. However, many prior attempts in NLP still suffer from the catastrophic forgetting issue, where the model completely forgets what it just learned in the previous tasks. In this paper, we introduce Rational LAMOL, a novel end-to-end LL framework for language models. In order to alleviate catastrophic forgetting, Rational LAMOL enhances LAMOL, a recent LL model, by applying critical freezing guided by human rationales. When the human rationales are not available, we propose exploiting unsupervised generated rationales as substitutions. In the experiment, we tested Rational LAMOL on permutations of three datasets from the ERASER benchmark. The results show that our proposed framework outperformed vanilla LAMOL on most permutations. Furthermore, unsupervised rationale generation was able to consistently improve the overall LL performance from the baseline without relying on human-annotated rationales.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_230", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_230.flac", "type": "audio", "duration": 684.06}, {"from": "Assistant", "value": "Natural language processing (NLP) often faces the problem of data diversity such as different domains, themes, styles, and so on. Therefore, a single language model (LM) is insufficient to learn all knowledge from diverse samples. To solve this problem, we firstly propose an autoencoding topic model with a mixture prior (mATM) to perform clustering for the data, where the clusters defined in semantic space describes the data diversity. Having obtained the clustering assignment for each sample, we develop the ensemble LM (EnsLM) with the technique of weight modulation. Specifically, EnsLM contains a backbone that is adjusted by a few modulated weights to fit for different sample clusters. As a result, the backbone learns the shared knowledge among all clusters while modulated weights extract the cluster-specific features. EnsLM can be trained jointly with mATM with a flexible LM backbone. We evaluate the effectiveness of both mATM and EnsLM on various tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_231", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_231.flac", "type": "audio", "duration": 681.685}, {"from": "Assistant", "value": "Pre-trained language models like BERT are performant in a wide range of natural language tasks. However, they are resource exhaustive and computationally expensive for industrial scenarios. Thus, early exits are adopted at each layer of BERT to perform adaptive computation by predicting easier samples with the first few layers to speed up the inference. In this work, to improve efficiency without performance drop, we propose a novel training scheme called Learned Early Exit for BERT (LeeBERT). First, we ask each exit to learn from each other, rather than learning only from the last layer. Second, the weights of different loss terms are learned, thus balancing off different objectives. We formulate the optimization of LeeBERT as a bi-level optimization problem, and we propose a novel cross-level optimization (CLO) algorithm to improve the optimization results. Experiments on the GLUE benchmark show that our proposed methods improve the performance of the state-of-the-art (SOTA) early exit methods for pre-trained models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_232", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_232.flac", "type": "audio", "duration": 752.448}, {"from": "Assistant", "value": "We pioneer the first extractive summarization-based collaborative filtering model called ESCOFILT. Our proposed model specifically produces extractive summaries for each item and user. Unlike other types of explanations, summary-level explanations closely resemble real-life explanations. The strength of ESCOFILT lies in the fact that it unifies representation and explanation. In other words, extractive summaries both represent and explain the items and users. Our model uniquely integrates BERT, K-Means embedding clustering, and multilayer perceptron to learn sentence embeddings, representation-explanations, and user-item interactions, respectively. We argue that our approach enhances both rating prediction accuracy and user/item explainability. Our experiments illustrate that ESCOFILT’s prediction accuracy is better than the other state-of-the-art recommender models. Furthermore, we propose a comprehensive set of criteria that assesses the real-life explainability of explanations. Our explainability study demonstrates the superiority of and preference for summary-level explanations over other explanation types.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_233", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_233.flac", "type": "audio", "duration": 577.062}, {"from": "Assistant", "value": "Chinese spelling correction (CSC) is a task to detect and correct spelling errors in texts. CSC is essentially a linguistic problem, thus the ability of language understanding is crucial to this task. In this paper, we propose a Pre-trained masked Language model with Misspelled knowledgE (PLOME) for CSC, which jointly learns how to understand language and correct spelling errors. To this end, PLOME masks the chosen tokens with similar characters according to a confusion set rather than the fixed token “[MASK]” as in BERT. Besides character prediction, PLOME also introduces pronunciation prediction to learn the misspelled knowledge on phonic level. Moreover, phonological and visual similarity knowledge is important to this task. PLOME utilizes GRU networks to model such knowledge based on characters’ phonics and strokes. Experiments are conducted on widely used benchmarks. Our method achieves superior performance against state-of-the-art approaches by a remarkable margin. We release the source code and pre-trained model for further use by the community (https://github.com/liushulinle/PLOME).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_234", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_234.flac", "type": "audio", "duration": 718.699}, {"from": "Assistant", "value": "Medical report generation task, which targets to produce long and coherent descriptions of medical images, has attracted growing research interests recently. Different from the general image captioning tasks, medical report generation is more challenging for data-driven neural models. This is mainly due to 1) the serious data bias and 2) the limited medical data. To alleviate the data bias and make best use of available data, we propose a Competence-based Multimodal Curriculum Learning framework (CMCL). Specifically, CMCL simulates the learning process of radiologists and optimizes the model in a step by step manner. Firstly, CMCL estimates the difficulty of each training instance and evaluates the competence of current model; Secondly, CMCL selects the most suitable batch of training instances considering current model competence. By iterating above two steps, CMCL can gradually improve the model’s performance. The experiments on the public IU-Xray and MIMIC-CXR datasets show that CMCL can be incorporated into existing models to improve their performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_235", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_235.flac", "type": "audio", "duration": 604.053}, {"from": "Assistant", "value": "Deep learning models for automatic readability assessment generally discard linguistic features traditionally used in machine learning models for the task. We propose to incorporate linguistic features into neural network models by learning syntactic dense embeddings based on linguistic features. To cope with the relationships between the features, we form a correlation graph among features and use it to learn their embeddings so that similar features will be represented by similar embeddings. Experiments with six data sets of two proficiency levels demonstrate that our proposed methodology can complement BERT-only model to achieve significantly better performances for automatic readability assessment.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_236", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_236.flac", "type": "audio", "duration": 392.022}, {"from": "Assistant", "value": "Pre-trained language models have been applied to various NLP tasks with considerable performance gains. However, the large model sizes, together with the long inference time, limit the deployment of such models in real-time applications. One line of model compression approaches considers knowledge distillation to distill large teacher models into small student models. Most of these studies focus on single-domain only, which ignores the transferable knowledge from other domains. We notice that training a teacher with transferable knowledge digested across domains can achieve better generalization capability to help knowledge distillation. Hence we propose a Meta-Knowledge Distillation (Meta-KD) framework to build a meta-teacher model that captures transferable knowledge across domains and passes such knowledge to students. Specifically, we explicitly force the meta-teacher to capture transferable knowledge at both instance-level and feature-level from multiple domains, and then propose a meta-distillation algorithm to learn single-domain student models with guidance from the meta-teacher. Experiments on public multi-domain NLP tasks show the effectiveness and superiority of the proposed Meta-KD framework. Further, we also demonstrate the capability of Meta-KD in the settings where the training data is scarce.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_237", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_237.flac", "type": "audio", "duration": 580.058}, {"from": "Assistant", "value": "Unsupervised commonsense question answering is appealing since it does not rely on any labeled task data. Among existing work, a popular solution is to use pre-trained language models to score candidate choices directly conditioned on the question or context. However, such scores from language models can be easily affected by irrelevant factors, such as word frequencies, sentence structures, etc. These distracting factors may not only mislead the model to choose a wrong answer but also make it oversensitive to lexical perturbations in candidate answers. In this paper, we present a novel SEmantic-based Question Answering method (SEQA) for unsupervised commonsense question answering. Instead of directly scoring each answer choice, our method first generates a set of plausible answers with generative models (e.g., GPT-2), and then uses these plausible answers to select the correct choice by considering the semantic similarity between each plausible answer and each choice. We devise a simple, yet sound formalism for this idea and verify its effectiveness and robustness with extensive experiments. We evaluate the proposed method on four benchmark datasets, and our method achieves the best results in unsupervised settings. Moreover, when attacked by TextFooler with synonym replacement, SEQA demonstrates much less performance drops than baselines, thereby indicating stronger robustness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_238", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_238.flac", "type": "audio", "duration": 698.048}, {"from": "Assistant", "value": "CommonsenseQA (CQA) (Talmor et al., 2019) dataset was recently released to advance the research on common-sense question answering (QA) task. Whereas the prior work has mostly focused on proposing QA models for this dataset, our aim is to retrieve as well as generate explanation for a given (question, correct answer choice, incorrect answer choices) tuple from this dataset. Our explanation definition is based on certain desiderata, and translates an explanation into a set of positive and negative common-sense properties (aka facts) which not only explain the correct answer choice but also refute the incorrect ones. We human-annotate a first-of-its-kind dataset (called ECQA) of positive and negative properties, as well as free-flow explanations, for11KQA pairs taken from the CQA dataset. We propose a latent representation based property retrieval model as well as a GPT-2 based property generation model with a novel two step fine-tuning procedure. We also propose a free-flow explanation generation model. Extensive experiments show that our retrieval model beats BM25 baseline by a relative gain of 100% inF1score, property generation model achieves a respectableF1score of 36.4, and free-flow generation model achieves a similarity score of 61.9, where last two scores are based on a human correlated semantic similarity metric.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_239", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_239.flac", "type": "audio", "duration": 589.044}, {"from": "Assistant", "value": "In several question answering benchmarks, pretrained models have reached human parity through fine-tuning on an order of 100,000 annotated questions and answers. We explore the more realistic few-shot setting, where only a few hundred training examples are available, and observe that standard models perform poorly, highlighting the discrepancy between current pretraining objectives and question answering. We propose a new pretraining scheme tailored for question answering: recurring span selection. Given a passage with multiple sets of recurring spans, we mask in each set all recurring spans but one, and ask the model to select the correct span in the passage for each masked span. Masked spans are replaced with a special token, viewed as a question representation, that is later used during fine-tuning to select the answer span. The resulting model obtains surprisingly good results on multiple benchmarks (e.g., 72.7 F1 on SQuAD with only 128 training examples), while maintaining competitive performance in the high-resource setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_240", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_240.flac", "type": "audio", "duration": 707.048}, {"from": "Assistant", "value": "To date, most of recent work under the retrieval-reader framework for open-domain QA focuses on either extractive or generative reader exclusively. In this paper, we study a hybrid approach for leveraging the strengths of both models. We apply novel techniques to enhance both extractive and generative readers built upon recent pretrained neural language models, and find that proper training methods can provide large improvement over previous state-of-the-art models. We demonstrate that a simple hybrid approach by combining answers from both readers can efficiently take advantages of extractive and generative answer inference strategies and outperforms single models as well as homogeneous ensembles. Our approach outperforms previous state-of-the-art models by 3.3 and 2.7 points in exact match on NaturalQuestions and TriviaQA respectively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_241", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_241.flac", "type": "audio", "duration": 695.893}, {"from": "Assistant", "value": "Neural models have shown impressive performance gains in answering queries from natural language text. However, existing works are unable to support database queries, such as “List/Count all female athletes who were born in 20th century”, which require reasoning over sets of relevant facts with operations such as join, filtering and aggregation. We show that while state-of-the-art transformer models perform very well for small databases, they exhibit limitations in processing noisy data, numerical operations, and queries that aggregate facts. We propose a modular architecture to answer these database-style queries over multiple spans from text and aggregating these at scale. We evaluate the architecture using WikiNLDB, a novel dataset for exploring such queries. Our architecture scales to databases containing thousands of facts whereas contemporary models are limited by how many facts can be encoded. In direct comparison on small databases, our approach increases overall answer accuracy from 85% to 90%. On larger databases, our approach retains its accuracy whereas transformer baselines could not encode the context.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_242", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_242.flac", "type": "audio", "duration": 694.443}, {"from": "Assistant", "value": "In Machine Translation, assessing the quality of a large amount of automatic translations can be challenging. Automatic metrics are not reliable when it comes to high performing systems. In addition, resorting to human evaluators can be expensive, especially when evaluating multiple systems. To overcome the latter challenge, we propose a novel application of online learning that, given an ensemble of Machine Translation systems, dynamically converges to the best systems, by taking advantage of the human feedback available. Our experiments on WMT’19 datasets show that our online approach quickly converges to the top-3 ranked systems for the language pairs considered, despite the lack of human feedback for many translations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_243", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_243.flac", "type": "audio", "duration": 709.045}, {"from": "Assistant", "value": "In this work, we provide a systematic and comprehensive empirical comparison of pretrained multilingual language models versus their monolingual counterparts with regard to their monolingual task performance. We study a set of nine typologically diverse languages with readily available pretrained monolingual models on a set of five diverse monolingual downstream tasks. We first aim to establish, via fair and controlled comparisons, if a gap between the multilingual and the corresponding monolingual representation of that language exists, and subsequently investigate the reason for any performance difference. To disentangle conflating factors, we train new monolingual models on the same data, with monolingually and multilingually trained tokenizers. We find that while the pretraining data size is an important factor, a designated monolingual tokenizer plays an equally important role in the downstream performance. Our results show that languages that are adequately represented in the multilingual model’s vocabulary exhibit negligible performance decreases over their monolingual counterparts. We further find that replacing the original multilingual tokenizer with the specialized monolingual tokenizer improves the downstream performance of the multilingual model for almost every task and language.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_244", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_244.flac", "type": "audio", "duration": 649.044}, {"from": "Assistant", "value": "Cross-lingual transfer has improved greatly through multi-lingual language model pretraining, reducing the need for parallel data and increasing absolute performance. However, this progress has also brought to light the differences in performance across languages. Specifically, certain language families and typologies seem to consistently perform worse in these models. In this paper, we address what effects morphological typology has on zero-shot cross-lingual transfer for two tasks: Part-of-speech tagging and sentiment analysis. We perform experiments on 19 languages from four language typologies (fusional, isolating, agglutinative, and introflexive) and find that transfer to another morphological type generally implies a higher loss than transfer to another language with the same morphological typology. Furthermore, POS tagging is more sensitive to morphological typology than sentiment analysis and, on this task, models perform much better on fusional languages than on the other typologies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_245", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_245.flac", "type": "audio", "duration": 701.568}, {"from": "Assistant", "value": "Generating code-switched text is a problem of growing interest, especially given the scarcity of corpora containing large volumes of real code-switched text. In this work, we adapt a state-of-the-art neural machine translation model to generate Hindi-English code-switched sentences starting from monolingual Hindi sentences. We outline a carefully designed curriculum of pretraining steps, including the use of synthetic code-switched text, that enable the model to generate high-quality code-switched text. Using text generated from our model as data augmentation, we show significant reductions in perplexity on a language modeling task, compared to using text from other generative models of CS text. We also show improvements using our text for a downstream code-switched natural language inference task. Our generated text is further subjected to a rigorous evaluation using a human evaluation study and a range of objective metrics, where we show performance comparable (and sometimes even superior) to code-switched text obtained via crowd workers who are native Hindi speakers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_246", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_246.flac", "type": "audio", "duration": 657.101}, {"from": "Assistant", "value": "It is generally believed that a translation memory (TM) should be beneficial for machine translation tasks. Unfortunately, existing wisdom demonstrates the superiority of TM-based neural machine translation (NMT) only on the TM-specialized translation tasks rather than general tasks, with a non-negligible computational overhead. In this paper, we propose a fast and accurate approach to TM-based NMT within the Transformer framework: the model architecture is simple and employs a single bilingual sentence as its TM, leading to efficient training and inference; and its parameters are effectively optimized through a novel training criterion. Extensive experiments on six TM-specialized tasks show that the proposed approach substantially surpasses several strong baselines that use multiple TMs, in terms of BLEU and running time. In particular, the proposed approach also advances the strong baselines on two general tasks (WMT news Zh->En and En->De).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_248", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_248.flac", "type": "audio", "duration": 409.771}, {"from": "Assistant", "value": "Recently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark data specifically focused on the practical and challenging task. Current approaches collect existing supervised NER datasets and re-organize them to the few-shot setting for empirical study. These strategies conventionally aim to recognize coarse-grained entity types with few examples, while in practice, most unseen entity types are fine-grained. In this paper, we present Few-NERD, a large-scale human-annotated few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 fine-grained entity types. Few-NERD consists of 188,238 sentences from Wikipedia, 4,601,160 words are included and each is annotated as context or a part of the two-level entity type. To the best of our knowledge, this is the first few-shot NER dataset and the largest human-crafted NER dataset. We construct benchmark tasks with different emphases to comprehensively assess the generalization capability of models. Extensive empirical results and analysis show that Few-NERD is challenging and the problem requires further research. The Few-NERD dataset and the baselines will be publicly available to facilitate the research on this problem.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_249", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_249.flac", "type": "audio", "duration": 688.683}, {"from": "Assistant", "value": "Metaphor involves not only a linguistic phenomenon, but also a cognitive phenomenon structuring human thought, which makes understanding it challenging. As a means of cognition, metaphor is rendered by more than texts alone, and multimodal information in which vision/audio content is integrated with the text can play an important role in expressing and understanding metaphor. However, previous metaphor processing and understanding has focused on texts, partly due to the unavailability of large-scale datasets with ground truth labels of multimodal metaphor. In this paper, we introduce MultiMET, a novel multimodal metaphor dataset to facilitate understanding metaphorical information from multimodal text and image. It contains 10,437 text-image pairs from a range of sources with multimodal annotations of the occurrence of metaphors, domain relations, sentiments metaphors convey, and author intents. MultiMET opens the door to automatic metaphor understanding by investigating multimodal cues and their interplay. Moreover, we propose a range of strong baselines and show the importance of combining multimodal cues for metaphor understanding. MultiMET will be released publicly for research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_250", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_250.flac", "type": "audio", "duration": 719.052}, {"from": "Assistant", "value": "Undermining the impact of hateful content with informed and non-aggressive responses, called counter narratives, has emerged as a possible solution for having healthier online communities. Thus, some NLP studies have started addressing the task of counter narrative generation. Although such studies have made an effort to build hate speech / counter narrative (HS/CN) datasets for neural generation, they fall short in reaching either high-quality and/or high-quantity. In this paper, we propose a novel human-in-the-loop data collection methodology in which a generative language model is refined iteratively by using its own data from the previous loops to generate new training samples that experts review and/or post-edit. Our experiments comprised several loops including diverse dynamic variations. Results show that the methodology is scalable and facilitates diverse, novel, and cost-effective data collection. To our knowledge, the resulting dataset is the only expert-based multi-target HS/CN dataset available to the community.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_251", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_251.flac", "type": "audio", "duration": 712.528}, {"from": "Assistant", "value": "Recent work has investigated the interesting question using pre-trained language models (PLMs) as knowledge bases for answering open questions. However, existing work is limited in using small benchmarks with high test-train overlaps. We construct a new dataset of closed-book QA using SQuAD, and investigate the performance of BART. Experiments show that it is challenging for BART to remember training facts in high precision, and also challenging to answer closed-book questions even if relevant knowledge is retained. Some promising directions are found, including decoupling the knowledge memorizing process and the QA finetune process, forcing the model to recall relevant knowledge when question answering.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_252", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_252.flac", "type": "audio", "duration": 708.046}, {"from": "Assistant", "value": "This paper studies joint models for selecting correct answer sentences among the top k provided by answer sentence selection (AS2) modules, which are core components of retrieval-based Question Answering (QA) systems. Our work shows that a critical step to effectively exploiting an answer set regards modeling the interrelated information between pair of answers. For this purpose, we build a three-way multi-classifier, which decides if an answer supports, refutes, or is neutral with respect to another one. More specifically, our neural architecture integrates a state-of-the-art AS2 module with the multi-classifier, and a joint layer connecting all components. We tested our models on WikiQA, TREC-QA, and a real-world dataset. The results show that our models obtain the new state of the art in AS2.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_253", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_253.flac", "type": "audio", "duration": 657.171}, {"from": "Assistant", "value": "In open-domain question answering, questions are highly likely to be ambiguous because users may not know the scope of relevant topics when formulating them. Therefore, a system needs to find possible interpretations of the question, and predict one or multiple plausible answers. When multiple plausible answers are found, the system should rewrite the question for each answer to resolve the ambiguity. In this paper, we present a model that aggregates and combines evidence from multiple passages to adaptively predict a single answer or a set of question-answer pairs for ambiguous questions. In addition, we propose a novel round-trip prediction approach to iteratively generate additional interpretations that our model fails to find in the first pass, and then verify and filter out the incorrect question-answer pairs to arrive at the final disambiguated output. Our model, named Refuel, achieves a new state-of-the-art performance on the AmbigQA dataset, and shows competitive performance on NQ-Open and TriviaQA. The proposed round-trip prediction is a model-agnostic general approach for answering ambiguous open-domain questions, which improves our Refuel as well as several baseline models. We release source code for our models and experiments athttps://github.com/amzn/refuel-open-domain-qa.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_254", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_254.flac", "type": "audio", "duration": 707.051}, {"from": "Assistant", "value": "Hybrid data combining both tabular and textual content (e.g., financial reports) are quite pervasive in the real world. However, Question Answering (QA) over such hybrid data is largely neglected in existing research. In this work, we extract samples from real financial reports to build a new large-scale QA dataset containing both Tabular And Textual data, named TAT-QA, where numerical reasoning is usually required to infer the answer, such as addition, subtraction, multiplication, division, counting, comparison/sorting, and the compositions. We further propose a novel QA model termed TAGOP, which is capable of reasoning over both tables and text. It adopts sequence tagging to extract relevant cells from the table along with relevant spans from the text to infer their semantics, and then applies symbolic reasoning over them with a set of aggregation operators to arrive at the final answer. TAGOP achieves 58.0% inF1, which is an 11.1% absolute increase over the previous best baseline model, according to our experiments on TAT-QA. But this result still lags far behind performance of expert human, i.e.90.8% in F1. It is demonstrated that our TAT-QA is very challenging and can serve as a benchmark for training and testing powerful QA models that address hybrid form data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_255", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_255.flac", "type": "audio", "duration": 719.052}, {"from": "Assistant", "value": "Conversational KBQA is about answering a sequence of questions related to a KB. Follow-up questions in conversational KBQA often have missing information referring to entities from the conversation history. In this paper, we propose to model these implied entities, which we refer to as the focal entities of the conversation. We propose a novel graph-based model to capture the transitions of focal entities and apply a graph neural network to derive a probability distribution of focal entities for each question, which is then combined with a standard KBQA module to perform answer ranking. Our experiments on two datasets demonstrate the effectiveness of our proposed method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_256", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_256.flac", "type": "audio", "duration": 723.371}, {"from": "Assistant", "value": "This paper introduces the task of factual error correction: performing edits to a claim so that the generated rewrite is better supported by evidence. This extends the well-studied task of fact verification by providing a mechanism to correct written texts that are refuted or only partially supported by evidence. We demonstrate that it is feasible to train factual error correction systems from existing fact checking datasets which only contain labeled claims accompanied by evidence, but not the correction. We achieve this by employing a two-stage distant supervision approach that incorporates evidence into masked claims when generating corrections. Our approach, based on the T5 transformer and using retrieved evidence, achieved better results than existing work which used a pointer copy network and gold evidence, producing accurate factual error corrections for 5x more instances in human evaluation and a .125 increase in SARI score. The evaluation is conducted on a dataset of 65,000 instances based on a recent fact verification shared task and we release it to enable further work on the task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_257", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_257.flac", "type": "audio", "duration": 677.205}, {"from": "Assistant", "value": "We present algorithms for aligning components of Abstract Meaning Representation (AMR) graphs to spans in English sentences. We leverage unsupervised learning in combination with heuristics, taking the best of both worlds from previous AMR aligners. Our unsupervised models, however, are more sensitive to graph substructures, without requiring a separate syntactic parse. Our approach covers a wider variety of AMR substructures than previously considered, achieves higher coverage of nodes and edges, and does so with higher accuracy. We will release our LEAMR datasets and aligner for use in research on AMR parsing, generation, and evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_258", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_258.flac", "type": "audio", "duration": 726.854}, {"from": "Assistant", "value": "Natural language is compositional; the meaning of a sentence is a function of the meaning of its parts. This property allows humans to create and interpret novel sentences, generalizing robustly outside their prior experience. Neural networks have been shown to struggle with this kind of generalization, in particular performing poorly on tasks designed to assess compositional generalization (i.e. where training and testing distributions differ in ways that would be trivial for a compositional strategy to resolve). Their poor performance on these tasks may in part be due to the nature of supervised learning which assumes training and testing data to be drawn from the same distribution. We implement a meta-learning augmented version of supervised learning whose objective directly optimizes for out-of-distribution generalization. We construct pairs of tasks for meta-learning by sub-sampling existing training data. Each pair of tasks is constructed to contain relevant examples, as determined by a similarity metric, in an effort to inhibit models from memorizing their input. Experimental results on the COGS and SCAN datasets show that our similarity-driven meta-learning can improve generalization performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_259", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_259.flac", "type": "audio", "duration": 705.004}, {"from": "Assistant", "value": "Large pre-trained models such as BERT are known to improve different downstream NLP tasks, even when such a model is trained on a generic domain. Moreover, recent studies have shown that when large domain-specific corpora are available, continued pre-training on domain-specific data can further improve the performance of in-domain tasks. However, this practice requires significant domain-specific data and computational resources which may not always be available. In this paper, we aim to adapt a generic pretrained model with a relatively small amount of domain-specific data. We demonstrate that by explicitly incorporating multi-granularity information of unseen and domain-specific words via the adaptation of (word based) n-grams, the performance of a generic pretrained model can be greatly improved. Specifically, we introduce a Transformer-based Domain-aware N-gram Adaptor, T-DNA, to effectively learn and incorporate the semantic representation of different combinations of words in the new domain. Experimental results illustrate the effectiveness of T-DNA on eight low-resource downstream tasks from four domains. We show that T-DNA is able to achieve significant improvements compared to existing methods on most tasks using limited data with lower computational costs. Moreover, further analyses demonstrate the importance and effectiveness of both unseen words and the information of different granularities. Our code is available athttps://github.com/shizhediao/T-DNA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_260", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_260.flac", "type": "audio", "duration": 677.582}, {"from": "Assistant", "value": "Pre-trained Language Models (PLMs) have shown superior performance on various downstream Natural Language Processing (NLP) tasks. However, conventional pre-training objectives do not explicitly model relational facts in text, which are crucial for textual understanding. To address this issue, we propose a novel contrastive learning framework ERICA to obtain a deep understanding of the entities and their relations in text. Specifically, we define two novel pre-training tasks to better understand entities and relations: (1) the entity discrimination task to distinguish which tail entity can be inferred by the given head entity and relation; (2) the relation discrimination task to distinguish whether two relations are close or not semantically, which involves complex relational reasoning. Experimental results demonstrate that ERICA can improve typical PLMs (BERT and RoBERTa) on several language understanding tasks, including relation extraction, entity typing and question answering, especially under low-resource settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_261", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_261.flac", "type": "audio", "duration": 691.328}, {"from": "Assistant", "value": "The Emotion Cause Extraction (ECE) task aims to identify clauses which contain emotion-evoking information for a particular emotion expressed in text. We observe that a widely-used ECE dataset exhibits a bias that the majority of annotated cause clauses are either directly before their associated emotion clauses or are the emotion clauses themselves. Existing models for ECE tend to explore such relative position information and suffer from the dataset bias. To investigate the degree of reliance of existing ECE models on clause relative positions, we propose a novel strategy to generate adversarial examples in which the relative position information is no longer the indicative feature of cause clauses. We test the performance of existing models on such adversarial examples and observe a significant performance drop. To address the dataset bias, we propose a novel graph-based method to explicitly model the emotion triggering paths by leveraging the commonsense knowledge to enhance the semantic dependencies between a candidate clause and an emotion clause. Experimental results show that our proposed approach performs on par with the existing state-of-the-art methods on the original ECE dataset, and is more robust against adversarial attacks compared to existing models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_262", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_262.flac", "type": "audio", "duration": 709.045}, {"from": "Assistant", "value": "Previous work on review summarization focused on measuring the sentiment toward the main aspects of the reviewed product or business, or on creating a textual summary. These approaches provide only a partial view of the data: aspect-based sentiment summaries lack sufficient explanation or justification for the aspect rating, while textual summaries do not quantify the significance of each element, and are not well-suited for representing conflicting views. Recently, Key Point Analysis (KPA) has been proposed as a summarization framework that provides both textual and quantitative summary of the main points in the data. We adapt KPA to review data by introducing Collective Key Point Mining for better key point extraction; integrating sentiment analysis into KPA; identifying good key point candidates for review summaries; and leveraging the massive amount of available reviews and their metadata. We show empirically that these novel extensions of KPA substantially improve its performance. We demonstrate that promising results can be achieved without any domain-specific annotation, while human supervision can lead to further improvement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_263", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_263.flac", "type": "audio", "duration": 778.56}, {"from": "Assistant", "value": "Structured sentiment analysis attempts to extract full opinion tuples from a text, but over time this task has been subdivided into smaller and smaller sub-tasks, e.g., target extraction or targeted polarity classification. We argue that this division has become counterproductive and propose a new unified framework to remedy the situation. We cast the structured sentiment problem as dependency graph parsing, where the nodes are spans of sentiment holders, targets and expressions, and the arcs are the relations between them. We perform experiments on five datasets in four languages (English, Norwegian, Basque, and Catalan) and show that this approach leads to strong improvements over state-of-the-art baselines. Our analysis shows that refining the sentiment graphs with syntactic dependency information further improves results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_264", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_264.flac", "type": "audio", "duration": 679.044}, {"from": "Assistant", "value": "Fine-tuning pre-trained cross-lingual language models can transfer task-specific supervision from one language to the others. In this work, we propose to improve cross-lingual fine-tuning with consistency regularization. Specifically, we use example consistency regularization to penalize the prediction sensitivity to four types of data augmentations, i.e., subword sampling, Gaussian noise, code-switch substitution, and machine translation. In addition, we employ model consistency to regularize the models trained with two augmented versions of the same training set. Experimental results on the XTREME benchmark show that our method significantly improves cross-lingual fine-tuning across various tasks, including text classification, question answering, and sequence labeling.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_265", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_265.flac", "type": "audio", "duration": 718.699}, {"from": "Assistant", "value": "The cross-lingual language models are typically pretrained with masked language modeling on multilingual text or parallel sentences. In this paper, we introduce denoising word alignment as a new cross-lingual pre-training task. Specifically, the model first self-label word alignments for parallel sentences. Then we randomly mask tokens in a bitext pair. Given a masked token, the model uses a pointer network to predict the aligned token in the other language. We alternately perform the above two steps in an expectation-maximization manner. Experimental results show that our method improves cross-lingual transferability on various datasets, especially on the token-level tasks, such as question answering, and structured prediction. Moreover, the model can serve as a pretrained word aligner, which achieves reasonably low error rate on the alignment benchmarks. The code and pretrained parameters are available at github.com/CZWin32768/XLM-Align.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_266", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_266.flac", "type": "audio", "duration": 712.017}, {"from": "Assistant", "value": "Knowledge distillation (KD) is commonly used to construct synthetic data for training non-autoregressive translation (NAT) models. However, there exists a discrepancy on low-frequency words between the distilled and the original data, leading to more errors on predicting low-frequency words. To alleviate the problem, we directly expose the raw data into NAT by leveraging pretraining. By analyzing directed alignments, we found that KD makes low-frequency source words aligned with targets more deterministically but fails to align sufficient low-frequency words from target to source. Accordingly, we propose reverse KD to rejuvenate more alignments for low-frequency target words. To make the most of authentic and synthetic data, we combine these complementary approaches as a new training strategy for further boosting NAT performance. We conduct experiments on five translation benchmarks over two advanced architectures. Results demonstrate that the proposed approach can significantly and universally improve translation quality by reducing translation errors on low-frequency words. Encouragingly, our approach achieves 28.2 and 33.9 BLEU points on the WMT14 English-German and WMT16 Romanian-English datasets, respectively. Our code, data, and trained models are available athttps://github.com/longyuewangdcu/RLFW-NAT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_267", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_267.flac", "type": "audio", "duration": 666.041}, {"from": "Assistant", "value": "Document-level MT models are still far from satisfactory. Existing work extend translation unit from single sentence to multiple sentences. However, study shows that when we further enlarge the translation unit to a whole document, supervised training of Transformer can fail. In this paper, we find such failure is not caused by overfitting, but by sticking around local minima during training. Our analysis shows that the increased complexity of target-to-source attention is a reason for the failure. As a solution, we propose G-Transformer, introducing locality assumption as an inductive bias into Transformer, reducing the hypothesis space of the attention from target to source. Experiments show that G-Transformer converges faster and more stably than Transformer, achieving new state-of-the-art BLEU scores for both nonpretraining and pre-training settings on three benchmark datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_268", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_268.flac", "type": "audio", "duration": 591.041}, {"from": "Assistant", "value": "The Neural Machine Translation (NMT) model is essentially a joint language model conditioned on both the source sentence and partial translation. Therefore, the NMT model naturally involves the mechanism of the Language Model (LM) that predicts the next token only based on partial translation. Despite its success, NMT still suffers from the hallucination problem, generating fluent but inadequate translations. The main reason is that NMT pays excessive attention to the partial translation while neglecting the source sentence to some extent, namely overconfidence of the LM. Accordingly, we define the Margin between the NMT and the LM, calculated by subtracting the predicted probability of the LM from that of the NMT model for each token. The Margin is negatively correlated to the overconfidence degree of the LM. Based on the property, we propose a Margin-based Token-level Objective (MTO) and a Margin-based Sentence-level Objective (MSO) to maximize the Margin for preventing the LM from being overconfident. Experiments on WMT14 English-to-German, WMT19 Chinese-to-English, and WMT14 English-to-French translation tasks demonstrate the effectiveness of our approach, with 1.36, 1.50, and 0.63 BLEU improvements, respectively, compared to the Transformer baseline. The human evaluation further verifies that our approaches improve translation adequacy as well as fluency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_269", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_269.flac", "type": "audio", "duration": 693.673}, {"from": "Assistant", "value": "Emotional support is a crucial ability for many conversation scenarios, including social interactions, mental health support, and customer service chats. Following reasonable procedures and using various support skills can help to effectively provide support. However, due to the lack of a well-designed task and corpora of effective emotional support conversations, research on building emotional support into dialog systems remains lacking. In this paper, we define the Emotional Support Conversation (ESC) task and propose an ESC Framework, which is grounded on the Helping Skills Theory. We construct an Emotion Support Conversation dataset (ESConv) with rich annotation (especially support strategy) in a help-seeker and supporter mode. To ensure a corpus of high-quality conversations that provide examples of effective emotional support, we take extensive effort to design training tutorials for supporters and several mechanisms for quality control during data collection. Finally, we evaluate state-of-the-art dialog models with respect to the ability to provide emotional support. Our results show the importance of support strategies in providing effective emotional support and the utility of ESConv in training more emotional support systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_270", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_270.flac", "type": "audio", "duration": 586.048}, {"from": "Assistant", "value": "Existing slot filling models can only recognize pre-defined in-domain slot types from a limited slot set. In the practical application, a reliable dialogue system should know what it does not know. In this paper, we introduce a new task, Novel Slot Detection (NSD), in the task-oriented dialogue system. NSD aims to discover unknown or out-of-domain slot types to strengthen the capability of a dialogue system based on in-domain training data. Besides, we construct two public NSD datasets, propose several strong NSD baselines, and establish a benchmark for future work. Finally, we conduct exhaustive experiments and qualitative analysis to comprehend key challenges and provide new guidance for future directions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_271", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_271.flac", "type": "audio", "duration": 613.248}, {"from": "Assistant", "value": "Generating some appealing questions in open-domain conversations is an effective way to improve human-machine interactions and lead the topic to a broader or deeper direction. To avoid dull or deviated questions, some researchers tried to utilize answer, the “future” information, to guide question generation. However, they separate a post-question-answer (PQA) triple into two parts: post-question (PQ) and question-answer (QA) pairs, which may hurt the overall coherence. Besides, the QA relationship is modeled as a one-to-one mapping that is not reasonable in open-domain conversations. To tackle these problems, we propose a generative triple-wise model with hierarchical variations for open-domain conversational question generation (CQG). Latent variables in three hierarchies are used to represent the shared background of a triple and one-to-many semantic mappings in both PQ and QA pairs. Experimental results on a large-scale CQG dataset show that our method significantly improves the quality of questions in terms of fluency, coherence and diversity over competitive baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_272", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_272.flac", "type": "audio", "duration": 539.691}, {"from": "Assistant", "value": "Neural dialogue generation models trained with the one-hot target distribution suffer from the over-confidence issue, which leads to poor generation diversity as widely reported in the literature. Although existing approaches such as label smoothing can alleviate this issue, they fail to adapt to diverse dialog contexts. In this paper, we propose an Adaptive Label Smoothing (AdaLabel) approach that can adaptively estimate a target label distribution at each time step for different contexts. The maximum probability in the predicted distribution is used to modify the soft target distribution produced by a novel light-weight bi-directional decoder module. The resulting target distribution is aware of both previous and future contexts and is adjusted to avoid over-training the dialogue model. Our model can be trained in an endto-end manner. Extensive experiments on two benchmark datasets show that our approach outperforms various competitive baselines in producing diverse responses.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_273", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_273.flac", "type": "audio", "duration": 709.045}, {"from": "Assistant", "value": "Out-of-scope intent detection is of practical importance in task-oriented dialogue systems. Since the distribution of outlier utterances is arbitrary and unknown in the training stage, existing methods commonly rely on strong assumptions on data distribution such as mixture of Gaussians to make inference, resulting in either complex multi-step training procedures or hand-crafted rules such as confidence threshold selection for outlier detection. In this paper, we propose a simple yet effective method to train an out-of-scope intent classifier in a fully end-to-end manner by simulating the test scenario in training, which requires no assumption on data distribution and no additional post-processing or threshold setting. Specifically, we construct a set of pseudo outliers in the training stage, by generating synthetic outliers using inliner features via self-supervision and sampling out-of-scope sentences from easily available open-domain datasets. The pseudo outliers are used to train a discriminative classifier that can be directly applied to and generalize well on the test task. We evaluate our method extensively on four benchmark dialogue datasets and observe significant improvements over state-of-the-art approaches. Our code has been released athttps://github.com/liam0949/DCLOOS.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_274", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_274.flac", "type": "audio", "duration": 697.323}, {"from": "Assistant", "value": "Document-level event extraction aims to recognize event information from a whole piece of article. Existing methods are not effective due to two challenges of this task: a) the target event arguments are scattered across sentences; b) the correlation among events in a document is non-trivial to model. In this paper, we propose Heterogeneous Graph-based Interaction Model with a Tracker (GIT) to solve the aforementioned two challenges. For the first challenge, GIT constructs a heterogeneous graph interaction network to capture global interactions among different sentences and entity mentions. For the second, GIT introduces a Tracker module to track the extracted events and hence capture the interdependency among the events. Experiments on a large-scale dataset (Zheng et al, 2019) show GIT outperforms the previous methods by 2.8 F1. Further analysis reveals is effective in extracting multiple correlated events and event arguments that scatter across the document.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_275", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_275.flac", "type": "audio", "duration": 588.045}, {"from": "Assistant", "value": "This paper presents a novel method for nested named entity recognition. As a layered method, our method extends the prior second-best path recognition method by explicitly excluding the influence of the best path. Our method maintains a set of hidden states at each time step and selectively leverages them to build a different potential function for recognition at each level. In addition, we demonstrate that recognizing innermost entities first results in better performance than the conventional outermost entities first scheme. We provide extensive experimental results on ACE2004, ACE2005, and GENIA datasets to show the effectiveness and efficiency of our proposed method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_276", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_276.flac", "type": "audio", "duration": 669.056}, {"from": "Assistant", "value": "Modern models for event causality identification (ECI) are mainly based on supervised learning, which are prone to the data lacking problem. Unfortunately, the existing NLP-related augmentation methods cannot directly produce available data required for this task. To solve the data lacking problem, we introduce a new approach to augment training data for event causality identification, by iteratively generating new examples and classifying event causality in a dual learning framework. On the one hand, our approach is knowledge guided, which can leverage existing knowledge bases to generate well-formed new sentences. On the other hand, our approach employs a dual mechanism, which is a learnable augmentation framework, and can interactively adjust the generation process to generate task-related sentences. Experimental results on two benchmarks EventStoryLine and Causal-TimeBank show that 1) our method can augment suitable task-related training data for ECI; 2) our method outperforms previous methods on EventStoryLine and Causal-TimeBank (+2.5 and +2.1 points on F1 value respectively).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_277", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_277.flac", "type": "audio", "duration": 630.059}, {"from": "Assistant", "value": "Distantly supervision automatically generates plenty of training samples for relation extraction. However, it also incurs two major problems: noisy labels and imbalanced training data. Previous works focus more on reducing wrongly labeled relations (false positives) while few explore the missing relations that are caused by incompleteness of knowledge base (false negatives). Furthermore, the quantity of negative labels overwhelmingly surpasses the positive ones in previous problem formulations. In this paper, we first provide a thorough analysis of the above challenges caused by negative data. Next, we formulate the problem of relation extraction into as a positive unlabeled learning task to alleviate false negative problem. Thirdly, we propose a pipeline approach, dubbed ReRe, that first performs sentence classification with relational labels and then extracts the subjects/objects. Experimental results show that the proposed method consistently outperforms existing approaches and remains excellent performance even learned with a large quantity of false positive samples. Source code is available online athttps://github.com/redreamality/RERE-relation-extraction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_278", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_278.flac", "type": "audio", "duration": 715.456}, {"from": "Assistant", "value": "This paper studies a new problem setting of entity alignment for knowledge graphs (KGs). Since KGs possess different sets of entities, there could be entities that cannot find alignment across them, leading to the problem of dangling entities. As the first attempt to this problem, we construct a new dataset and design a multi-task learning framework for both entity alignment and dangling entity detection. The framework can opt to abstain from predicting alignment for the detected dangling entities. We propose three techniques for dangling entity detection that are based on the distribution of nearest-neighbor distances, i.e., nearest neighbor classification, marginal ranking and background ranking. After detecting and removing dangling entities, an incorporated entity alignment model in our framework can provide more robust alignment for remaining entities. Comprehensive experiments and analyses demonstrate the effectiveness of our framework. We further discover that the dangling entity detection module can, in turn, improve alignment learning and the final performance. The contributed resource is publicly available to foster further research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_279", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_279.flac", "type": "audio", "duration": 683.061}, {"from": "Assistant", "value": "How does the input segmentation of pretrained language models (PLMs) affect their interpretations of complex words? We present the first study investigating this question, taking BERT as the example PLM and focusing on its semantic representations of English derivatives. We show that PLMs can be interpreted as serial dual-route models, i.e., the meanings of complex words are either stored or else need to be computed from the subwords, which implies that maximally meaningful input tokens should allow for the best generalization on new words. This hypothesis is confirmed by a series of semantic probing tasks on which DelBERT (Derivation leveraging BERT), a model with derivational input segmentation, substantially outperforms BERT with WordPiece segmentation. Our results suggest that the generalization capabilities of PLMs could be further improved if a morphologically-informed vocabulary of input tokens were used.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_280", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_280.flac", "type": "audio", "duration": 596.056}, {"from": "Assistant", "value": "Analogies play a central role in human commonsense reasoning. The ability to recognize analogies such as “eye is to seeing what ear is to hearing”, sometimes referred to as analogical proportions, shape how we structure knowledge and understand language. Surprisingly, however, the task of identifying such analogies has not yet received much attention in the language model era. In this paper, we analyze the capabilities of transformer-based language models on this unsupervised task, using benchmarks obtained from educational settings, as well as more commonly used datasets. We find that off-the-shelf language models can identify analogies to a certain extent, but struggle with abstract and complex relations, and results are highly sensitive to model architecture and hyperparameters. Overall the best results were obtained with GPT-2 and RoBERTa, while configurations using BERT were not able to outperform word embedding models. Our results raise important questions for future work about how, and to what extent, pre-trained language models capture knowledge about abstract semantic relations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_281", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_281.flac", "type": "audio", "duration": 558.928}, {"from": "Assistant", "value": "This paper presents a multilingual study of word meaning representations in context. We assess the ability of both static and contextualized models to adequately represent different lexical-semantic relations, such as homonymy and synonymy. To do so, we created a new multilingual dataset that allows us to perform a controlled evaluation of several factors such as the impact of the surrounding context or the overlap between words, conveying the same or different senses. A systematic assessment on four scenarios shows that the best monolingual models based on Transformers can adequately disambiguate homonyms in context. However, as they rely heavily on context, these models fail at representing words with different senses when occurring in similar sentences. Experiments are performed in Galician, Portuguese, English, and Spanish, and both the dataset (with more than 3,000 evaluation items) and new models are freely released with this study.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_282", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_282.flac", "type": "audio", "duration": 700.523}, {"from": "Assistant", "value": "We propose to measure fine-grained domain relevance– the degree that a term is relevant to a broad (e.g., computer science) or narrow (e.g., deep learning) domain. Such measurement is crucial for many downstream tasks in natural language processing. To handle long-tail terms, we build a core-anchored semantic graph, which uses core terms with rich description information to bridge the vast remaining fringe terms semantically. To support a fine-grained domain without relying on a matching corpus for supervision, we develop hierarchical core-fringe learning, which learns core and fringe terms jointly in a semi-supervised manner contextualized in the hierarchy of the domain. To reduce expensive human efforts, we employ automatic annotation and hierarchical positive-unlabeled learning. Our approach applies to big or small domains, covers head or tail terms, and requires little human effort. Extensive experiments demonstrate that our methods outperform strong baselines and even surpass professional human performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_283", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_283.flac", "type": "audio", "duration": 701.76}, {"from": "Assistant", "value": "Open-domain dialog systems have a user-centric goal: to provide humans with an engaging conversation experience. User engagement is one of the most important metrics for evaluating open-domain dialog systems, and could also be used as real-time feedback to benefit dialog policy learning. Existing work on detecting user disengagement typically requires hand-labeling many dialog samples. We propose HERALD, an efficient annotation framework that reframes the training data annotation process as a denoising problem. Specifically, instead of manually labeling training samples, we first use a set of labeling heuristics to label training samples automatically. We then denoise the weakly labeled data using the Shapley algorithm. Finally, we use the denoised data to train a user engagement detector. Our experiments show that HERALD improves annotation efficiency significantly and achieves 86% user disengagement detection accuracy in two dialog corpora.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_284", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_284.flac", "type": "audio", "duration": 723.441}, {"from": "Assistant", "value": "Conversational semantic parsers map user utterances to executable programs given dialogue histories composed of previous utterances, programs, and system responses. Existing parsers typically condition on rich representations of history that include the complete set of values and computations previously discussed. We propose a model that abstracts over values to focus prediction on type- and function-level context. This approach provides a compact encoding of dialogue histories and predicted programs, improving generalization and computational efficiency. Our model incorporates several other components, including an atomic span copy operation and structural enforcement of well-formedness constraints on predicted programs, that are particularly advantageous in the low-data regime. Trained on the SMCalFlow and TreeDST datasets, our model outperforms prior work by 7.3% and 10.6% respectively in terms of absolute accuracy. Trained on only a thousand examples from each dataset, it outperforms strong baselines by 12.4% and 6.4%. These results indicate that simple representations are key to effective generalization in conversational semantic parsing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_285", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_285.flac", "type": "audio", "duration": 695.043}, {"from": "Assistant", "value": "Recently, various neural models for multi-party conversation (MPC) have achieved impressive improvements on a variety of tasks such as addressee recognition, speaker identification and response prediction. However, these existing methods on MPC usually represent interlocutors and utterances individually and ignore the inherent complicated structure in MPC which may provide crucial interlocutor and utterance semantics and would enhance the conversation understanding process. To this end, we present MPC-BERT, a pre-trained model for MPC understanding that considers learning who says what to whom in a unified model with several elaborated self-supervised tasks. Particularly, these tasks can be generally categorized into (1) interlocutor structure modeling including reply-to utterance recognition, identical speaker searching and pointer consistency distinction, and (2) utterance semantics modeling including masked shared utterance restoration and shared node detection. We evaluate MPC-BERT on three downstream tasks including addressee recognition, speaker identification and response selection. Experimental results show that MPC-BERT outperforms previous methods by large margins and achieves new state-of-the-art performance on all three downstream tasks at two benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_286", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_286.flac", "type": "audio", "duration": 699.78}, {"from": "Assistant", "value": "While Transformer-based text classifiers pre-trained on large volumes of text have yielded significant improvements on a wide range of computational linguistics tasks, their implementations have been unsuitable for live incremental processing thus far, operating only on the level of complete sentence inputs. We address the challenge of introducing methods for word-by-word left-to-right incremental processing to Transformers such as BERT, models without an intrinsic sense of linear order. We modify the training method and live decoding of non-incremental models to detect speech disfluencies with minimum latency and without pre-segmentation of dialogue acts. We experiment with several decoding methods to predict the rightward context of the word currently being processed using a GPT-2 language model and apply a BERT-based disfluency detector to sequences, including predicted words. We show our method of incrementalising Transformers maintains most of their high non-incremental performance while operating strictly incrementally. We also evaluate our models’ incremental performance to establish the trade-off between incremental performance and final performance, using different prediction strategies. We apply our system to incremental speech recognition results as they arrive into a live system and achieve state-of-the-art results in this setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_287", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_287.flac", "type": "audio", "duration": 718.054}, {"from": "Assistant", "value": "We propose NeuralWOZ, a novel dialogue collection framework that uses model-based dialogue simulation. NeuralWOZ has two pipelined models, Collector and Labeler. Collector generates dialogues from (1) user’s goal instructions, which are the user context and task constraints in natural language, and (2) system’s API call results, which is a list of possible query responses for user requests from the given knowledge base. Labeler annotates the generated dialogue by formulating the annotation as a multiple-choice problem, in which the candidate labels are extracted from goal instructions and API call results. We demonstrate the effectiveness of the proposed method in the zero-shot domain transfer learning for dialogue state tracking. In the evaluation, the synthetic dialogue corpus generated from NeuralWOZ achieves a new state-of-the-art with improvements of 4.4% point joint goal accuracy on average across domains, and improvements of 5.7% point of zero-shot coverage against the MultiWOZ 2.1 dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_288", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_288.flac", "type": "audio", "duration": 709.045}, {"from": "Assistant", "value": "The human mind is a dynamical system, yet many analysis techniques used to study it are limited in their ability to capture the complex dynamics that may characterize mental processes. This study proposes the continuous-time deconvolutional regressive neural network (CDRNN), a deep neural extension of continuous-time deconvolutional regression (Shain & Schuler, 2021) that jointly captures time-varying, non-linear, and delayed influences of predictors (e.g. word surprisal) on the response (e.g. reading time). Despite this flexibility, CDRNN is interpretable and able to illuminate patterns in human cognition that are otherwise difficult to study. Behavioral and fMRI experiments reveal detailed and plausible estimates of human language processing dynamics that generalize better than CDR and other baselines, supporting a potential role for CDRNN in studying human language processing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_289", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_289.flac", "type": "audio", "duration": 689.045}, {"from": "Assistant", "value": "Transformer-based language models pre-trained on large amounts of text data have proven remarkably successful in learning generic transferable linguistic representations. Here we study whether structural guidance leads to more human-like systematic linguistic generalization in Transformer language models without resorting to pre-training on very large amounts of data. We explore two general ideas. The “Generative Parsing” idea jointly models the incremental parse and word sequence as part of the same sequence modeling task. The “Structural Scaffold” idea guides the language model’s representation via additional structure loss that separately predicts the incremental constituency parse. We train the proposed models along with a vanilla Transformer language model baseline on a 14 million-token and a 46 million-token subset of the BLLIP dataset, and evaluate models’ syntactic generalization performances on SG Test Suites and sized BLiMP. Experiment results across two benchmarks suggest converging evidence that generative structural supervisions can induce more robust and humanlike linguistic generalization in Transformer language models without the need for data intensive pre-training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_290", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_290.flac", "type": "audio", "duration": 717.525}, {"from": "Assistant", "value": "While the use of character models has been popular in NLP applications, it has not been explored much in the context of psycholinguistic modeling. This paper presents a character model that can be applied to a structural parser-based processing model to calculate word generation probabilities. Experimental results show that surprisal estimates from a structural processing model using this character model deliver substantially better fits to self-paced reading, eye-tracking, and fMRI data than those from large-scale language models trained on much more data. This may suggest that the proposed processing model provides a more humanlike account of sentence processing, which assumes a larger role of morphology, phonotactics, and orthographic complexity than was previously thought.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_291", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_291.flac", "type": "audio", "duration": 713.062}, {"from": "Assistant", "value": "Most previous studies integrate cognitive language processing signals (e.g., eye-tracking or EEG data) into neural models of natural language processing (NLP) just by directly concatenating word embeddings with cognitive features, ignoring the gap between the two modalities (i.e., textual vs. cognitive) and noise in cognitive features. In this paper, we propose a CogAlign approach to these issues, which learns to align textual neural representations to cognitive features. In CogAlign, we use a shared encoder equipped with a modality discriminator to alternatively encode textual and cognitive inputs to capture their differences and commonalities. Additionally, a text-aware attention mechanism is proposed to detect task-related information and to avoid using noise in cognitive features. Experimental results on three NLP tasks, namely named entity recognition, sentiment analysis and relation extraction, show that CogAlign achieves significant improvements with multiple cognitive features over state-of-the-art models on public datasets. Moreover, our model is able to transfer cognitive information to other datasets that do not have any cognitive processing signals.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_292", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_292.flac", "type": "audio", "duration": 461.888}, {"from": "Assistant", "value": "Despite their impressive performance in NLP, self-attention networks were recently proved to be limited for processing formal languages with hierarchical structure, such as Dyck-k, the language consisting of well-nested parentheses of k types. This suggested that natural language can be approximated well with models that are too weak for formal languages, or that the role of hierarchy and recursion in natural language might be limited. We qualify this implication by proving that self-attention networks can process Dyck-(k, D), the subset of Dyck-k with depth bounded by D, which arguably better captures the bounded hierarchical structure of natural language. Specifically, we construct a hard-attention network with D+1 layers and O(log k) memory size (per token per layer) that recognizes Dyck-(k, D), and a soft-attention network with two layers and O(log k) memory size that generates Dyck-(k, D). Experiments show that self-attention networks trained on Dyck-(k, D) generalize to longer inputs with near-perfect accuracy, and also verify the theoretical memory advantage of self-attention networks over recurrent networks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_293", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_293.flac", "type": "audio", "duration": 693.046}, {"from": "Assistant", "value": "We present a novel approach to the problem of text style transfer. Unlike previous approaches requiring style-labeled training data, our method makes use of readily-available unlabeled text by relying on the implicit connection in style between adjacent sentences, and uses labeled data only at inference time. We adapt T5 (Raffel et al., 2020), a strong pretrained text-to-text model, to extract a style vector from text and use it to condition the decoder to perform style transfer. As our label-free training results in a style vector space encoding many facets of style, we recast transfers as “targeted restyling” vector operations that adjust specific attributes of the input while preserving others. We demonstrate that training on unlabeled Amazon reviews data results in a model that is competitive on sentiment transfer, even compared to models trained fully on labeled data. Furthermore, applying our novel method to a diverse corpus of unlabeled web text results in a single model capable of transferring along multiple dimensions of style (dialect, emotiveness, formality, politeness, sentiment) despite no additional training and using only a handful of exemplars at inference time.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_294", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_294.flac", "type": "audio", "duration": 691.051}, {"from": "Assistant", "value": "We describe an efficient hierarchical method to compute attention in the Transformer architecture. The proposed attention mechanism exploits a matrix structure similar to the Hierarchical Matrix (H-Matrix) developed by the numerical analysis community, and has linear run time and memory complexity. We perform extensive experiments to show that the inductive bias embodied by our hierarchical attention is effective in capturing the hierarchical structure in the sequences typical for natural language and vision tasks. Our method is superior to alternative sub-quadratic proposals by over +6 points on average on the Long Range Arena benchmark. It also sets a new SOTA test perplexity on One-Billion Word dataset with 5x fewer model parameters than that of the previous-best Transformer-based models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_295", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_295.flac", "type": "audio", "duration": 719.808}, {"from": "Assistant", "value": "The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot performance solely by leveraging a natural-language prompt and a few task demonstrations as input context. Inspired by their findings, we study few-shot learning in a more practical scenario, where we use smaller language models for which fine-tuning is computationally efficient. We present LM-BFF—better few-shot fine-tuning of language models—a suite of simple and complementary techniques for fine-tuning language models on a small number of annotated examples. Our approach includes (1) prompt-based fine-tuning together with a novel pipeline for automating prompt generation; and (2) a refined strategy for dynamically and selectively incorporating demonstrations into each context. Finally, we present a systematic evaluation for analyzing few-shot performance on a range of NLP tasks, including classification and regression. Our experiments demonstrate that our methods combine to dramatically outperform standard fine-tuning procedures in this low resource setting, achieving up to 30% absolute improvement, and 11% on average across all tasks. Our approach makes minimal assumptions on task resources and domain expertise, and hence constitutes a strong task-agnostic method for few-shot learning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_296", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_296.flac", "type": "audio", "duration": 712.899}, {"from": "Assistant", "value": "The Universal Trigger (UniTrigger) is a recently-proposed powerful adversarial textual attack method. Utilizing a learning-based mechanism, UniTrigger generates a fixed phrase that, when added to any benign inputs, can drop the prediction accuracy of a textual neural network (NN) model to near zero on a target class. To defend against this attack that can cause significant harm, in this paper, we borrow the “honeypot” concept from the cybersecurity community and propose DARCY, a honeypot-based defense framework against UniTrigger. DARCY greedily searches and injects multiple trapdoors into an NN model to “bait and catch” potential attacks. Through comprehensive experiments across four public datasets, we show that DARCY detects UniTrigger’s adversarial attacks with up to 99% TPR and less than 2% FPR in most cases, while maintaining the prediction accuracy (in F1) for clean inputs within a 1% margin. We also demonstrate that DARCY with multiple trapdoors is also robust to a diverse set of attack scenarios with attackers’ varying levels of knowledge and skills. We release the source code of DARCY at:https://github.com/lethaiq/ACL2021-DARCY-HoneypotDefenseNLP.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_297", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_297.flac", "type": "audio", "duration": 699.968}, {"from": "Assistant", "value": "Detecting rumors on social media is a very critical task with significant implications to the economy, public health, etc. Previous works generally capture effective features from texts and the propagation structure. However, the uncertainty caused by unreliable relations in the propagation structure is common and inevitable due to wily rumor producers and the limited collection of spread data. Most approaches neglect it and may seriously limit the learning of features. Towards this issue, this paper makes the first attempt to explore propagation uncertainty for rumor detection. Specifically, we propose a novel Edge-enhanced Bayesian Graph Convolutional Network (EBGCN) to capture robust structural features. The model adaptively rethinks the reliability of latent relations by adopting a Bayesian approach. Besides, we design a new edge-wise consistency training framework to optimize the model by enforcing consistency on relations. Experiments on three public benchmark datasets demonstrate that the proposed model achieves better performance than baseline methods on both rumor detection and early rumor detection tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_298", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_298.flac", "type": "audio", "duration": 643.2}, {"from": "Assistant", "value": "Multi-label text classification is one of the fundamental tasks in natural language processing. Previous studies have difficulties to distinguish similar labels well because they learn the same document representations for different labels, that is they do not explicitly extract label-specific semantic components from documents. Moreover, they do not fully explore the high-order interactions among these semantic components, which is very helpful to predict tail labels. In this paper, we propose a novel label-specific dual graph neural network (LDGN), which incorporates category information to learn label-specific components from documents, and employs dual Graph Convolution Network (GCN) to model complete and adaptive interactions among these components based on the statistical label co-occurrence and dynamic reconstruction graph in a joint way. Experimental results on three benchmark datasets demonstrate that LDGN significantly outperforms the state-of-the-art models, and also achieves better performance with respect to tail labels.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_299", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_299.flac", "type": "audio", "duration": 712.299}, {"from": "Assistant", "value": "Topic models have been widely used to learn text representations and gain insight into document corpora. To perform topic discovery, most existing neural models either take document bag-of-words (BoW) or sequence of tokens as input followed by variational inference and BoW reconstruction to learn topic-word distribution. However, leveraging topic-word distribution for learning better features during document encoding has not been explored much. To this end, we develop a framework TAN-NTM, which processes document as a sequence of tokens through a LSTM whose contextual outputs are attended in a topic-aware manner. We propose a novel attention mechanism which factors in topic-word distribution to enable the model to attend on relevant words that convey topic related cues. The output of topic attention module is then used to carry out variational inference. We perform extensive ablations and experiments resulting in ~9-15 percentage improvement over score of existing SOTA topic models in NPMI coherence on several benchmark datasets - 20Newsgroups, Yelp Review Polarity and AGNews. Further, we show that our method learns better latent document-topic features compared to existing topic models through improvement on two downstream tasks: document classification and topic guided keyphrase generation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_300", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_300.flac", "type": "audio", "duration": 707.048}, {"from": "Assistant", "value": "This paper proposes an approach to cross-language sentence selection in a low-resource setting. It uses data augmentation and negative sampling techniques on noisy parallel sentence data to directly learn a cross-lingual embedding-based query relevance model. Results show that this approach performs as well as or better than multiple state-of-the-art machine translation + monolingual retrieval systems trained on the same parallel data. Moreover, when a rationale training secondary objective is applied to encourage the model to match word alignment hints from a phrase-based statistical machine translation model, consistent improvements are seen across three language pairs (English-Somali, English-Swahili and English-Tagalog) over a variety of state-of-the-art baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_301", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_301.flac", "type": "audio", "duration": 705.817}, {"from": "Assistant", "value": "Question answering (QA) systems for large document collections typically use pipelines that (i) retrieve possibly relevant documents, (ii) re-rank them, (iii) rank paragraphs or other snippets of the top-ranked documents, and (iv) select spans of the top-ranked snippets as exact answers. Pipelines are conceptually simple, but errors propagate from one component to the next, without later components being able to revise earlier decisions. We present an architecture for joint document and snippet ranking, the two middle stages, which leverages the intuition that relevant documents have good snippets and good snippets come from relevant documents. The architecture is general and can be used with any neural text relevance ranker. We experiment with two main instantiations of the architecture, based on POSIT-DRMM (PDRMM) and a BERT-based ranker. Experiments on biomedical data from BIOASQ show that our joint models vastly outperform the pipelines in snippet retrieval, the main goal for QA, with fewer trainable parameters, also remaining competitive in document retrieval. Furthermore, our joint PDRMM-based model is competitive with BERT-based models, despite using orders of magnitude fewer parameters. These claims are also supported by human evaluation on two test batches of BIOASQ. To test our key findings on another dataset, we modified the Natural Questions dataset so that it can also be used for document and snippet retrieval. Our joint PDRMM-based model again outperforms the corresponding pipeline in snippet retrieval on the modified Natural Questions dataset, even though it performs worse than the pipeline in document retrieval. We make our code and the modified Natural Questions dataset publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_302", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_302.flac", "type": "audio", "duration": 718.054}, {"from": "Assistant", "value": "Aiming for a better integration of data-driven and linguistically-inspired approaches, we explore whether RST Nuclearity, assigning a binary assessment of importance between text segments, can be replaced by automatically generated, real-valued scores, in what we call a Weighted-RST framework. In particular, we find that weighted discourse trees from auxiliary tasks can benefit key NLP downstream applications, compared to nuclearity-centered approaches. We further show that real-valued importance distributions partially and interestingly align with the assessment and uncertainty of human annotators.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_303", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_303.flac", "type": "audio", "duration": 676.053}, {"from": "Assistant", "value": "Atomic clauses are fundamental text units for understanding complex sentences. Identifying the atomic sentences within complex sentences is important for applications such as summarization, argument mining, discourse analysis, discourse parsing, and question answering. Previous work mainly relies on rule-based methods dependent on parsing. We propose a new task to decompose each complex sentence into simple sentences derived from the tensed clauses in the source, and a novel problem formulation as a graph edit task. Our neural model learns to Accept, Break, Copy or Drop elements of a graph that combines word adjacency and grammatical dependencies. The full processing pipeline includes modules for graph construction, graph editing, and sentence generation from the output graph. We introduce DeSSE, a new dataset designed to train and evaluate complex sentence decomposition, and MinWiki, a subset of MinWikiSplit. ABCD achieves comparable performance as two parsing baselines on MinWiki. On DeSSE, which has a more even balance of complex sentence types, our model achieves higher accuracy on the number of atomic sentences than an encoder-decoder baseline. Results include a detailed error analysis.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_304", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_304.flac", "type": "audio", "duration": 720.491}, {"from": "Assistant", "value": "Many Question-Answering (QA) datasets contain unanswerable questions, but their treatment in QA systems remains primitive. Our analysis of the Natural Questions (Kwiatkowski et al. 2019) dataset reveals that a substantial portion of unanswerable questions (~21%) can be explained based on the presence of unverifiable presuppositions. Through a user preference study, we demonstrate that the oracle behavior of our proposed system—which provides responses based on presupposition failure—is preferred over the oracle behavior of existing QA systems. Then, we present a novel framework for implementing such a system in three steps: presupposition generation, presupposition verification, and explanation generation, reporting progress on each. Finally, we show that a simple modification of adding presuppositions and their verifiability to the input of a competitive end-to-end QA system yields modest gains in QA performance and unanswerability detection, demonstrating the promise of our approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_305", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_305.flac", "type": "audio", "duration": 720.043}, {"from": "Assistant", "value": "Text-level discourse rhetorical structure (DRS) parsing is known to be challenging due to the notorious lack of training data. Although recent top-down DRS parsers can better leverage global document context and have achieved certain success, the performance is still far from perfect. To our knowledge, all previous DRS parsers make local decisions for either bottom-up node composition or top-down split point ranking at each time step, and largely ignore DRS parsing from the global view point. Obviously, it is not sufficient to build an entire DRS tree only through these local decisions. In this work, we present our insight on evaluating the pros and cons of the entire DRS tree for global optimization. Specifically, based on recent well-performing top-down frameworks, we introduce a novel method to transform both gold standard and predicted constituency trees into tree diagrams with two color channels. After that, we learn an adversarial bot between gold and fake tree diagrams to estimate the generated DRS trees from a global perspective. We perform experiments on both RST-DT and CDTB corpora and use the original Parseval for performance evaluation. The experimental results show that our parser can substantially improve the performance when compared with previous state-of-the-art parsers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_306", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_306.flac", "type": "audio", "duration": 626.091}, {"from": "Assistant", "value": "Discourse relations among arguments reveal logical structures of a debate conversation. However, no prior work has explicitly studied how the sequence of discourse relations influence a claim’s impact. This paper empirically shows that the discourse relations between two arguments along the context path are essential factors for identifying the persuasive power of an argument. We further propose DisCOC to inject and fuse the sentence-level structural discourse information with contextualized features derived from large-scale language models. Experimental results and extensive analysis show that the attention and gate mechanisms that explicitly model contexts and texts can indeed help the argument impact classification task defined by Durmus et al. (2019), and discourse structures among the context path of the claim to be classified can further boost the performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_307", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_307.flac", "type": "audio", "duration": 637.016}, {"from": "Assistant", "value": "This paper proposes a sophisticated neural architecture to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models. By introducing three novel components: Pointer, Disambiguator, and Copier, our method PDC achieves the following merits inherently compared with previous efforts: (1) Pointer leverages the semantic information from bilingual dictionaries, for the first time, to better locate source words whose translation in dictionaries can potentially be used; (2) Disambiguator synthesizes contextual information from the source view and the target view, both of which contribute to distinguishing the proper translation of a specific source word from multiple candidates in dictionaries; (3) Copier systematically connects Pointer and Disambiguator based on a hierarchical copy mechanism seamlessly integrated with Transformer, thereby building an end-to-end architecture that could avoid error propagation problems in alternative pipe-line methods. The experimental results on Chinese-English and English-Japanese benchmarks demonstrate the PDC’s overall superiority and effectiveness of each component.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_308", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_308.flac", "type": "audio", "duration": 657.28}, {"from": "Assistant", "value": "Existing work in multilingual pretraining has demonstrated the potential of cross-lingual transferability by training a unified Transformer encoder for multiple languages. However, much of this work only relies on the shared vocabulary and bilingual contexts to encourage the correlation across languages, which is loose and implicit for aligning the contextual representations between languages. In this paper, we plug a cross-attention module into the Transformer encoder to explicitly build the interdependence between languages. It can effectively avoid the degeneration of predicting masked words only conditioned on the context in its own language. More importantly, when fine-tuning on downstream tasks, the cross-attention module can be plugged in or out on-demand, thus naturally benefiting a wider range of cross-lingual tasks, from language understanding to generation. As a result, the proposed cross-lingual model delivers new state-of-the-art results on various cross-lingual understanding tasks of the XTREME benchmark, covering text classification, sequence labeling, question answering, and sentence retrieval. For cross-lingual generation tasks, it also outperforms all existing cross-lingual models and state-of-the-art Transformer variants on WMT14 English-to-German and English-to-French translation datasets, with gains of up to 1 2 BLEU.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_309", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_309.flac", "type": "audio", "duration": 684.06}, {"from": "Assistant", "value": "The sentence is a fundamental unit of text processing. Yet sentences in the wild are commonly encountered not in isolation, but unsegmented within larger paragraphs and documents. Therefore, the first step in many NLP pipelines issentence segmentation. Despite its importance, this step is the subject of relatively little research. There are no standard test sets or even methods for evaluation, leaving researchers and engineers without a clear footing for evaluating and selecting models for the task. Existing tools have relatively small language coverage, and efforts to extend them to other languages are often ad hoc. We introduce a modern context-based modeling approach that provides a solution to the problem of segmenting punctuated text in many languages, and show how it can be trained on noisily-annotated data. We also establish a new 23-language multilingual evaluation set. Our approach exceeds high baselines set by existing methods on prior English corpora (WSJ and Brown corpora), and also performs well on average on our new evaluation set. We release our tool, ersatz, as open source.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_310", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_310.flac", "type": "audio", "duration": 581.163}, {"from": "Assistant", "value": "A good translation should not only translate the original content semantically, but also incarnate personal traits of the original text. For a real-world neural machine translation (NMT) system, these user traits (e.g., topic preference, stylistic characteristics and expression habits) can be preserved in user behavior (e.g., historical inputs). However, current NMT systems marginally consider the user behavior due to: 1) the difficulty of modeling user portraits in zero-shot scenarios, and 2) the lack of user-behavior annotated parallel dataset. To fill this gap, we introduce a novel framework called user-driven NMT. Specifically, a cache-based module and a user-driven contrastive learning method are proposed to offer NMT the ability to capture potential user traits from their historical inputs under a zero-shot learning fashion. Furthermore, we contribute the first Chinese-English parallel corpus annotated with user behavior called UDT-Corpus. Experimental results confirm that the proposed user-driven NMT can generate user-specific translations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_311", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_311.flac", "type": "audio", "duration": 716.173}, {"from": "Assistant", "value": "Lexically constrained machine translation allows the user to manipulate the output sentence by enforcing the presence or absence of certain words and phrases. Although current approaches can enforce terms to appear in the translation, they often struggle to make the constraint word form agree with the rest of the generated output. Our manual analysis shows that 46% of the errors in the output of a baseline constrained model for English to Czech translation are related to agreement. We investigate mechanisms to allow neural machine translation to infer the correct word inflection given lemmatized constraints. In particular, we focus on methods based on training the model with constraints provided as part of the input sequence. Our experiments on English-Czech language pair show that this approach improves translation of constrained terms in both automatic and manual evaluation by reducing errors in agreement. Our approach thus eliminates inflection errors, without introducing new errors or decreasing overall quality of the translation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_312", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_312.flac", "type": "audio", "duration": 668.053}, {"from": "Assistant", "value": "Technical logbooks are a challenging and under-explored text type in automated event identification. These texts are typically short and written in non-standard yet technical language, posing challenges to off-the-shelf NLP pipelines. The granularity of issue types described in these datasets additionally leads to class imbalance, making it challenging for models to accurately predict which issue each logbook entry describes. In this paper we focus on the problem of technical issue classification by considering logbook datasets from the automotive, aviation, and facilities maintenance domains. We adapt a feedback strategy from computer vision for handling extreme class imbalance, which resamples the training data based on its error in the prediction process. Our experiments show that with statistical significance this feedback strategy provides the best results for four different neural network models trained across a suite of seven different technical logbook datasets from distinct technical domains. The feedback strategy is also generic and could be applied to any learning problem with substantial class imbalances.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_313", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_313.flac", "type": "audio", "duration": 718.059}, {"from": "Assistant", "value": "An automated system that could assist a judge in predicting the outcome of a case would help expedite the judicial process. For such a system to be practically useful, predictions by the system should be explainable. To promote research in developing such a system, we introduce ILDC (Indian Legal Documents Corpus). ILDC is a large corpus of 35k Indian Supreme Court cases annotated with original court decisions. A portion of the corpus (a separate test set) is annotated with gold standard explanations by legal experts. Based on ILDC, we propose the task of Court Judgment Prediction and Explanation (CJPE). The task requires an automated system to predict an explainable outcome of a case. We experiment with a battery of baseline models for case predictions and propose a hierarchical occlusion based model for explainability. Our best prediction model has an accuracy of 78% versus 94% for human legal experts, pointing towards the complexity of the prediction task. The analysis of explanations by the proposed algorithm reveals a significant difference in the point of view of the algorithm and legal experts for explaining the judgments, pointing towards scope for future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_314", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_314.flac", "type": "audio", "duration": 644.096}, {"from": "Assistant", "value": "We present an annotation approach to capturing emotional and cognitive empathy in student-written peer reviews on business models in German. We propose an annotation scheme that allows us to model emotional and cognitive empathy scores based on three types of review components. Also, we conducted an annotation study with three annotators based on 92 student essays to evaluate our annotation scheme. The obtained inter-rater agreement of α=0.79 for the components and the multi-π=0.41 for the empathy scores indicate that the proposed annotation scheme successfully guides annotators to a substantial to moderate agreement. Moreover, we trained predictive models to detect the annotated empathy structures and embedded them in an adaptive writing support system for students to receive individual empathy feedback independent of an instructor, time, and location. We evaluated our tool in a peer learning exercise with 58 students and found promising results for perceived empathy skill learning, perceived feedback accuracy, and intention to use. Finally, we present our freely available corpus of 500 empathy-annotated, student-written peer reviews on business models and our annotation guidelines to encourage future research on the design and development of empathy support systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_316", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_316.flac", "type": "audio", "duration": 660.608}, {"from": "Assistant", "value": "We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts without external resources as supervision. We demonstrate that the generated contexts substantially enrich the semantics of the queries and GAR with sparse representations (BM25) achieves comparable or better performance than state-of-the-art dense retrieval methods such as DPR. We show that generating diverse contexts for a query is beneficial as fusing their results consistently yields better retrieval accuracy. Moreover, as sparse and dense representations are often complementary, GAR can be easily combined with DPR to achieve even better performance. GAR achieves state-of-the-art performance on Natural Questions and TriviaQA datasets under the extractive QA setup when equipped with an extractive reader, and consistently outperforms other retrieval methods when the same generative reader is used.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_317", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_317.flac", "type": "audio", "duration": 713.062}, {"from": "Assistant", "value": "While sophisticated neural-based models have achieved remarkable success in Visual Question Answering (VQA), these models tend to answer questions only according to superficial correlations between question and answer. Several recent approaches have been developed to address this language priors problem. However, most of them predict the correct answer according to one best output without checking the authenticity of answers. Besides, they only explore the interaction between image and question, ignoring the semantics of candidate answers. In this paper, we propose a select-and-rerank (SAR) progressive framework based on Visual Entailment. Specifically, we first select the candidate answers relevant to the question or the image, then we rerank the candidate answers by a visual entailment task, which verifies whether the image semantically entails the synthetic statement of the question and each candidate answer. Experimental results show the effectiveness of our proposed framework, which establishes a new state-of-the-art accuracy on VQA-CP v2 with a 7.55% improvement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_318", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_318.flac", "type": "audio", "duration": 705.813}, {"from": "Assistant", "value": "Weakly supervised question answering usually has only the final answers as supervision signals while the correct solutions to derive the answers are not provided. This setting gives rise to the spurious solution problem: there may exist many spurious solutions that coincidentally derive the correct answer, but training on such solutions can hurt model performance (e.g., producing wrong solutions or answers). For example, for discrete reasoning tasks as on DROP, there may exist many equations to derive a numeric answer, and typically only one of them is correct. Previous learning methods mostly filter out spurious solutions with heuristics or using model confidence, but do not explicitly exploit the semantic correlations between a question and its solution. In this paper, to alleviate the spurious solution problem, we propose to explicitly exploit such semantic correlations by maximizing the mutual information between question-answer pairs and predicted solutions. Extensive experiments on four question answering datasets show that our method significantly outperforms previous learning methods in terms of task performance and is more effective in training models to produce correct solutions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_319", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_319.flac", "type": "audio", "duration": 757.013}, {"from": "Assistant", "value": "Privacy plays a crucial role in preserving democratic ideals and personal autonomy. The dominant legal approach to privacy in many jurisdictions is the “Notice and Choice” paradigm, where privacy policies are the primary instrument used to convey information to users. However, privacy policies are long and complex documents that are difficult for users to read and comprehend. We discuss how language technologies can play an important role in addressing this information gap, reporting on initial progress towards helping three specific categories of stakeholders take advantage of digital privacy policies: consumers, enterprises, and regulators. Our goal is to provide a roadmap for the development and use of language technologies to empower users to reclaim control over their privacy, limit privacy harms, and rally research efforts from the community towards addressing an issue with large social impact. We highlight many remaining opportunities to develop language technologies that are more precise or nuanced in the way in which they use the text of privacy policies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_320", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_320.flac", "type": "audio", "duration": 702.4}, {"from": "Assistant", "value": "Open pit mines left many regions worldwide inhospitable or uninhabitable. Many sites are left behind in a hazardous or contaminated state, show remnants of waste, or have other restrictions imposed upon them, e.g., for the protection of human or nature. Such information has to be permanently managed in order to reuse those areas in the future. In this work we present and evaluate an automated workflow for supporting the post-mining management of former lignite open pit mines in the eastern part of Germany, where prior to any planned land reuse, aforementioned information has to be acquired to ensure the safety and validity of such an endeavor. Usually, this information is found in expert reports, either in the form of paper documents, or in the best case as digitized unstructured text—all of them in German language. However, due to the size and complexity of these documents, any inquiry is tedious and time-consuming, thereby slowing down or even obstructing the reuse of related areas. Since no training data is available, we employ active learning in order to perform multi-label sentence classification for two categories of restrictions and seven categories of topics. The final system integrates optical character recognition (OCR), active-learning-based text classification, and geographic information system visualization in order to effectively extract, query, and visualize this information for any area of interest. Active learning and text classification results are twofold: Whereas the restriction categories were reasonably accurate (>0.85 F1), the seven topic-oriented categories seemed to be complex even for human annotators and achieved mediocre evaluation scores (<0.70 F1).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_321", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_321.flac", "type": "audio", "duration": 714.411}, {"from": "Assistant", "value": "Questions of fairness, robustness, and transparency are paramount to address before deploying NLP systems. Central to these concerns is the question of reliability: Can NLP systems reliably treat different demographics fairly and function correctly in diverse and noisy environments? To address this, we argue for the need for reliability testing and contextualize it among existing work on improving accountability. We show how adversarial attacks can be reframed for this goal, via a framework for developing reliability tests. We argue that reliability testing — with an emphasis on interdisciplinary collaboration — will enable rigorous and targeted testing, and aid in the enactment and enforcement of industry standards.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_322", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_322.flac", "type": "audio", "duration": 783.68}, {"from": "Assistant", "value": "Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care. The ability to accurately and efficiently predict mood from easily collectible data has several important implications for the early detection, intervention, and treatment of mental health disorders. One promising data source to help monitor human behavior is daily smartphone usage. However, care must be taken to summarize behaviors without identifying the user through personal (e.g., personally identifiable information) or protected (e.g., race, gender) attributes. In this paper, we study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors. Using computational models, we find that language and multimodal representations of mobile typed text (spanning typed characters, words, keystroke timings, and app usage) are predictive of daily mood. However, we find that models trained to predict mood often also capture private user identities in their intermediate representations. To tackle this problem, we evaluate approaches that obfuscate user identity while remaining predictive. By combining multimodal representations with privacy-preserving learning, we are able to push forward the performance-privacy frontier.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_323", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_323.flac", "type": "audio", "duration": 720.051}, {"from": "Assistant", "value": "This position paper investigates the problem of automated text anonymisation, which is a prerequisite for secure sharing of documents containing sensitive information about individuals. We summarise the key concepts behind text anonymisation and provide a review of current approaches. Anonymisation methods have so far been developed in two fields with little mutual interaction, namely natural language processing and privacy-preserving data publishing. Based on a case study, we outline the benefits and limitations of these approaches and discuss a number of open challenges, such as (1) how to account for multiple types of semantic inferences, (2) how to strike a balance between disclosure risk and data utility and (3) how to evaluate the quality of the resulting anonymisation. We lay out a case for moving beyond sequence labelling models and incorporate explicit measures of disclosure risk into the text anonymisation process.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_324", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_324.flac", "type": "audio", "duration": 577.062}, {"from": "Assistant", "value": "Although parsing to Abstract Meaning Representation (AMR) has become very popular and AMR has been shown effective on the many sentence-level downstream tasks, little work has studied how to generate AMRs that can represent multi-sentence information. We introduce the first end-to-end AMR coreference resolution model in order to build multi-sentence AMRs. Compared with the previous pipeline and rule-based approaches, our model alleviates error propagation and it is more robust for both in-domain and out-domain situations. Besides, the document-level AMRs obtained by our model can significantly improve over the AMRs generated by a rule-based method (Liu et al., 2015) on text summarization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_325", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_325.flac", "type": "audio", "duration": 644.736}, {"from": "Assistant", "value": "Transformer language models have shown remarkable ability in detecting when a word is anomalous in context, but likelihood scores offer no information about the cause of the anomaly. In this work, we use Gaussian models for density estimation at intermediate layers of three language models (BERT, RoBERTa, and XLNet), and evaluate our method on BLiMP, a grammaticality judgement benchmark. In lower layers, surprisal is highly correlated to low token frequency, but this correlation diminishes in upper layers. Next, we gather datasets of morphosyntactic, semantic, and commonsense anomalies from psycholinguistic studies; we find that the best performing model RoBERTa exhibits surprisal in earlier layers when the anomaly is morphosyntactic than when it is semantic, while commonsense anomalies do not exhibit surprisal at any intermediate layer. These results suggest that language models employ separate mechanisms to detect different types of linguistic anomalies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_326", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_326.flac", "type": "audio", "duration": 622.059}, {"from": "Assistant", "value": "Most of the recent work on personality detection from online posts adopts multifarious deep neural networks to represent the posts and builds predictive models in a data-driven manner, without the exploitation of psycholinguistic knowledge that may unveil the connections between one’s language use and his psychological traits. In this paper, we propose a psycholinguistic knowledge-based tripartite graph network, TrigNet, which consists of a tripartite graph network and a BERT-based graph initializer. The graph network injects structural psycholinguistic knowledge in LIWC, a computerized instrument for psycholinguistic analysis, by constructing a heterogeneous tripartite graph. The initializer is employed to provide initial embeddings for the graph nodes. To reduce the computational cost in graph learning, we further propose a novel flow graph attention network (GAT) that only transmits messages between neighboring parties in the tripartite graph. Benefiting from the tripartite graph, TrigNet can aggregate post information from a psychological perspective, which is a novel way of exploiting domain knowledge. Extensive experiments on two datasets show that TrigNet outperforms the existing state-of-art model by 3.47 and 2.10 points in average F1. Moreover, the flow GAT reduces the FLOPS and Memory measures by 38% and 32%, respectively, in comparison to the original GAT in our setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_327", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_327.flac", "type": "audio", "duration": 666.807}, {"from": "Assistant", "value": "Correct natural language understanding requires computers to distinguish the literal and metaphorical senses of a word. Recent neu- ral models achieve progress on verb metaphor detection by viewing it as sequence labeling. In this paper, we argue that it is appropriate to view this task as relation classification between a verb and its various contexts. We propose the Metaphor-relation BERT (Mr-BERT) model, which explicitly models the relation between a verb and its grammatical, sentential and semantic contexts. We evaluate our method on the VUA, MOH-X and TroFi datasets. Our method gets competitive results compared with state-of-the-art approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_328", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_328.flac", "type": "audio", "duration": 679.044}, {"from": "Assistant", "value": "Pretraining and multitask learning are widely used to improve the speech translation performance. In this study, we are interested in training a speech translation model along with an auxiliary text translation task. We conduct a detailed analysis to understand the impact of the auxiliary task on the primary task within the multitask learning framework. Our analysis confirms that multitask learning tends to generate similar decoder representations from different modalities and preserve more information from the pretrained text translation modules. We observe minimal negative transfer effect between the two tasks and sharing more parameters is helpful to transfer knowledge from the text task to the speech task. The analysis also reveals that the modality representation difference at the top decoder layers is still not negligible, and those layers are critical for the translation quality. Inspired by these findings, we propose three methods to improve translation quality. First, a parameter sharing and initialization strategy is proposed to enhance information sharing between the tasks. Second, a novel attention-based regularization is proposed for the encoders and pulls the representations from different modalities closer. Third, an online knowledge distillation is proposed to enhance the knowledge transfer from the text to the speech task. Our experiments show that the proposed approach improves translation performance by more than 2 BLEU over a strong baseline and achieves state-of-the-art results on the MuST-C English-German, English-French and English-Spanish language pairs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_329", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_329.flac", "type": "audio", "duration": 595.615}, {"from": "Assistant", "value": "Large pre-trained language models (PTLMs) have been shown to carry biases towards different social groups which leads to the reproduction of stereotypical and toxic content by major NLP systems. We propose a method based on logistic regression classifiers to probe English, French, and Arabic PTLMs and quantify the potentially harmful content that they convey with respect to a set of templates. The templates are prompted by a name of a social group followed by a cause-effect relation. We use PTLMs to predict masked tokens at the end of a sentence in order to examine how likely they enable toxicity towards specific communities. We shed the light on how such negative content can be triggered within unrelated and benign contexts based on evidence from a large-scale study, then we explain how to take advantage of our methodology to assess and mitigate the toxicity transmitted by PTLMs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_330", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_330.flac", "type": "audio", "duration": 710.043}, {"from": "Assistant", "value": "Technology for language generation has advanced rapidly, spurred by advancements in pre-training large models on massive amounts of data and the need for intelligent agents to communicate in a natural manner. While techniques can effectively generate fluent text, they can also produce undesirable societal biases that can have a disproportionately negative impact on marginalized populations. Language generation presents unique challenges for biases in terms of direct user interaction and the structure of decoding techniques. To better understand these challenges, we present a survey on societal biases in language generation, focusing on how data and techniques contribute to biases and progress towards reducing biases. Motivated by a lack of studies on biases from decoding techniques, we also conduct experiments to quantify the effects of these techniques. By further discussing general trends and open challenges, we call to attention promising directions for research and the importance of fairness and inclusivity considerations for language generation applications.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_331", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_331.flac", "type": "audio", "duration": 352.061}, {"from": "Assistant", "value": "We demonstrate that transformers obtain impressive performance even when some of the layers are randomly initialized and never updated. Inspired by old and well-established ideas in machine learning, we explore a variety of non-linear “reservoir” layers interspersed with regular transformer layers, and show improvements in wall-clock compute time until convergence, as well as overall performance, on various machine translation and (masked) language modelling tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_332", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_332.flac", "type": "audio", "duration": 718.016}, {"from": "Assistant", "value": "Active Learning (AL) has been successfully applied to Deep Learning in order to drastically reduce the amount of data required to achieve high performance. Previous works have shown that lightweight architectures for Named Entity Recognition (NER) can achieve optimal performance with only 25% of the original training data. However, these methods do not exploit the sequential nature of language and the heterogeneity of uncertainty within each instance, requiring the labelling of whole sentences. Additionally, this standard method requires that the annotator has access to the full sentence when labelling. In this work, we overcome these limitations by allowing the AL algorithm to query subsequences within sentences, and propagate their labels to other sentences. We achieve highly efficient results on OntoNotes 5.0, only requiring 13% of the original training data, and CoNLL 2003, requiring only 27%. This is an improvement of 39% and 37% compared to querying full sentences.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_333", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_333.flac", "type": "audio", "duration": 651.041}, {"from": "Assistant", "value": "In this paper, we detail the relationship between convolutions and self-attention in natural language tasks. We show that relative position embeddings in self-attention layers are equivalent to recently-proposed dynamic lightweight convolutions, and we consider multiple new ways of integrating convolutions into Transformer self-attention. Specifically, we propose composite attention, which unites previous relative position encoding methods under a convolutional framework. We conduct experiments by training BERT with composite attention, finding that convolutions consistently improve performance on multiple downstream tasks, replacing absolute position embeddings. To inform future work, we present results comparing lightweight convolutions, dynamic convolutions, and depthwise-separable convolutions in language model pre-training, considering multiple injection points for convolutions in self-attention layers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_334", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_334.flac", "type": "audio", "duration": 661.002}, {"from": "Assistant", "value": "The rapid development of large pre-trained language models has greatly increased the demand for model compression techniques, among which quantization is a popular solution. In this paper, we propose BinaryBERT, which pushes BERT quantization to the limit by weight binarization. We find that a binary BERT is hard to be trained directly than a ternary counterpart due to its complex and irregular loss landscape. Therefore, we propose ternary weight splitting, which initializes BinaryBERT by equivalently splitting from a half-sized ternary network. The binary model thus inherits the good performance of the ternary one, and can be further enhanced by fine-tuning the new architecture after splitting. Empirical results show that our BinaryBERT has only a slight performance drop compared with the full-precision model while being 24x smaller, achieving the state-of-the-art compression results on the GLUE and SQuAD benchmarks. Code will be released.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_335", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_335.flac", "type": "audio", "duration": 727.017}, {"from": "Assistant", "value": "In the era of pre-trained language models, Transformers are the de facto choice of model architectures. While recent research has shown promise in entirely convolutional, or CNN, architectures, they have not been explored using the pre-train-fine-tune paradigm. In the context of language models, are convolutional models competitive to Transformers when pre-trained? This paper investigates this research question and presents several interesting findings. Across an extensive set of experiments on 8 datasets/tasks, we find that CNN-based pre-trained models are competitive and outperform their Transformer counterpart in certain scenarios, albeit with caveats. Overall, the findings outlined in this paper suggest that conflating pre-training and architectural advances is misguided and that both advances should be considered independently. We believe our research paves the way for a healthy amount of optimism in alternative architectures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_336", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_336.flac", "type": "audio", "duration": 588.208}, {"from": "Assistant", "value": "Distance based knowledge graph embedding methods show promising results on link prediction task, on which two topics have been widely studied: one is the ability to handle complex relations, such as N-to-1, 1-to-N and N-to-N, the other is to encode various relation patterns, such as symmetry/antisymmetry. However, the existing methods fail to solve these two problems at the same time, which leads to unsatisfactory results. To mitigate this problem, we propose PairRE, a model with paired vectors for each relation representation. The paired vectors enable an adaptive adjustment of the margin in loss function to fit for different complex relations. Besides, PairRE is capable of encoding three important relation patterns, symmetry/antisymmetry, inverse and composition. Given simple constraints on relation representations, PairRE can encode subrelation further. Experiments on link prediction benchmarks demonstrate the proposed key capabilities of PairRE. Moreover, We set a new state-of-the-art on two knowledge graph datasets of the challenging Open Graph Benchmark.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_337", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_337.flac", "type": "audio", "duration": 687.055}, {"from": "Assistant", "value": "Hierarchical text classification is an important yet challenging task due to the complex structure of the label hierarchy. Existing methods ignore the semantic relationship between text and labels, so they cannot make full use of the hierarchical information. To this end, we formulate the text-label semantics relationship as a semantic matching problem and thus propose a hierarchy-aware label semantics matching network (HiMatch). First, we project text semantics and label semantics into a joint embedding space. We then introduce a joint embedding loss and a matching learning loss to model the matching relationship between the text semantics and the label semantics. Our model captures the text-label semantics matching relationship among coarse-grained labels and fine-grained labels in a hierarchy-aware manner. The experimental results on various benchmark datasets verify that our model achieves state-of-the-art results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_338", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_338.flac", "type": "audio", "duration": 813.056}, {"from": "Assistant", "value": "Fine-tuning large pre-trained models with task-specific data has achieved great success in NLP. However, it has been demonstrated that the majority of information within the self-attention networks is redundant and not utilized effectively during the fine-tuning stage. This leads to inferior results when generalizing the obtained models to out-of-domain distributions. To this end, we propose a simple yet effective data augmentation technique, HiddenCut, to better regularize the model and encourage it to learn more generalizable features. Specifically, contiguous spans within the hidden space are dynamically and strategically dropped during training. Experiments show that our HiddenCut method outperforms the state-of-the-art augmentation methods on the GLUE benchmark, and consistently exhibits superior generalization performances on out-of-distribution and challenging counterexamples. We have publicly released our code athttps://github.com/GT-SALT/HiddenCut.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_339", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_339.flac", "type": "audio", "duration": 577.062}, {"from": "Assistant", "value": "Generating open-domain conversational responses in the desired style usually suffers from the lack of parallel data in the style. Meanwhile, using monolingual stylistic data to increase style intensity often leads to the expense of decreasing content relevance. In this paper, we propose to disentangle the content and style in latent space by diluting sentence-level information in style representations. Combining the desired style representation and a response content representation will then obtain a stylistic response. Our approach achieves a higher BERT-based style intensity score and comparable BLEU scores, compared with baselines. Human evaluation results show that our approach significantly improves style intensity and maintains content relevance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_340", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_340.flac", "type": "audio", "duration": 652.69}, {"from": "Assistant", "value": "Understanding privacy policies is crucial for users as it empowers them to learn about the information that matters to them. Sentences written in a privacy policy document explain privacy practices, and the constituent text spans convey further specific information about that practice. We refer to predicting the privacy practice explained in a sentence as intent classification and identifying the text spans sharing specific information as slot filling. In this work, we propose PolicyIE, an English corpus consisting of 5,250 intent and 11,788 slot annotations spanning 31 privacy policies of websites and mobile applications. PolicyIE corpus is a challenging real-world benchmark with limited labeled examples reflecting the cost of collecting large-scale annotations from domain experts. We present two alternative neural approaches as baselines, (1) intent classification and slot filling as a joint sequence tagging and (2) modeling them as a sequence-to-sequence (Seq2Seq) learning task. The experiment results show that both approaches perform comparably in intent classification, while the Seq2Seq method outperforms the sequence tagging approach in slot filling by a large margin. We perform a detailed error analysis to reveal the challenges of the proposed corpus.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_341", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_341.flac", "type": "audio", "duration": 654.06}, {"from": "Assistant", "value": "For task-oriented dialog systems to be maximally useful, it must be able to process conversations in a way that is (1) generalizable with a small number of training examples for new task domains, and (2) robust to user input in various styles, modalities, or domains. In pursuit of these goals, we introduce the RADDLE benchmark, a collection of corpora and tools for evaluating the performance of models across a diverse set of domains. By including tasks with limited training data, RADDLE is designed to favor and encourage models with a strong generalization ability. RADDLE also includes a diagnostic checklist that facilitates detailed robustness analysis in aspects such as language variations, speech errors, unseen entities, and out-of-domain utterances. We evaluate recent state-of-the-art systems based on pre-training and fine-tuning, and find that grounded pre-training on heterogeneous dialog corpora performs better than training a separate model per domain. Adversarial training is also proposed to improve model robustness against noisy inputs. Overall, existing models are less than satisfactory in robustness evaluation, which suggests opportunities for future improvement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_342", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_342.flac", "type": "audio", "duration": 716.22}, {"from": "Assistant", "value": "Although neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities. To this end, we exploit Abstract Meaning Representation (AMR) to help dialogue modeling. Compared with the textual input, AMR explicitly provides core semantic knowledge and reduces data sparsity. We develop an algorithm to construct dialogue-level AMR graphs from sentence-level AMRs and explore two ways to incorporate AMRs into dialogue systems. Experimental results on both dialogue understanding and response generation tasks show the superiority of our model. To our knowledge, we are the first to leverage a formal semantic representation into neural dialogue modeling.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_343", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_343.flac", "type": "audio", "duration": 710.461}, {"from": "Assistant", "value": "Recently, many studies are emerging towards building a retrieval-based dialogue system that is able to effectively leverage background knowledge (e.g., documents) when conversing with humans. However, it is non-trivial to collect large-scale dialogues that are naturally grounded on the background documents, which hinders the effective and adequate training of knowledge selection and response matching. To overcome the challenge, we consider decomposing the training of the knowledge-grounded response selection into three tasks including: 1) query-passage matching task; 2) query-dialogue history matching task; 3) multi-turn response matching task, and joint learning all these tasks in a unified pre-trained language model. The former two tasks could help the model in knowledge selection and comprehension, while the last task is designed for matching the proper response with the given query and background knowledge (dialogue history). By this means, the model can be learned to select relevant knowledge and distinguish proper response, with the help of ad-hoc retrieval corpora and a large number of ungrounded multi-turn dialogues. Experimental results on two benchmarks of knowledge-grounded response selection indicate that our model can achieve comparable performance with several existing methods that rely on crowd-sourced data for training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_344", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_344.flac", "type": "audio", "duration": 717.891}, {"from": "Assistant", "value": "Syntactic information, especially dependency trees, has been widely used by existing studies to improve relation extraction with better semantic guidance for analyzing the context information associated with the given entities. However, most existing studies suffer from the noise in the dependency trees, especially when they are automatically generated, so that intensively leveraging dependency information may introduce confusions to relation classification and necessary pruning is of great importance in this task. In this paper, we propose a dependency-driven approach for relation extraction with attentive graph convolutional networks (A-GCN). In this approach, an attention mechanism upon graph convolutional networks is applied to different contextual words in the dependency tree obtained from an off-the-shelf dependency parser, to distinguish the importance of different word dependencies. Consider that dependency types among words also contain important contextual guidance, which is potentially helpful for relation extraction, we also include the type information in A-GCN modeling. Experimental results on two English benchmark datasets demonstrate the effectiveness of our A-GCN, which outperforms previous studies and achieves state-of-the-art performance on both datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_345", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_345.flac", "type": "audio", "duration": 735.051}, {"from": "Assistant", "value": "Retrieval is a core component for open-domain NLP tasks. In open-domain tasks, multiple entities can share a name, making disambiguation an inherent yet under-explored problem. We propose an evaluation benchmark for assessing the entity disambiguation capabilities of these retrievers, which we call Ambiguous Entity Retrieval (AmbER) sets. We define an AmbER set as a collection of entities that share a name along with queries about those entities. By covering the set of entities for polysemous names, AmbER sets act as a challenging test of entity disambiguation. We create AmbER sets for three popular open-domain tasks: fact checking, slot filling, and question answering, and evaluate a diverse set of retrievers. We find that the retrievers exhibit popularity bias, significantly under-performing on rarer entities that share a name, e.g., they are twice as likely to retrieve erroneous documents on queries for the less popular entity under the same name. These experiments on AmbER sets show their utility as an evaluation tool and highlight the weaknesses of popular retrieval systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_346", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_346.flac", "type": "audio", "duration": 710.531}, {"from": "Assistant", "value": "Leaderboards are widely used in NLP and push the field forward. While leaderboards are a straightforward ranking of NLP models, this simplicity can mask nuances in evaluation items (examples) and subjects (NLP models). Rather than replace leaderboards, we advocate a re-imagining so that they better highlight if and where progress is made. Building on educational testing, we create a Bayesian leaderboard model where latent subject skill and latent item difficulty predict correct responses. Using this model, we analyze the ranking reliability of leaderboards. Afterwards, we show the model can guide what to annotate, identify annotation errors, detect overfitting, and identify informative examples. We conclude with recommendations for future benchmark tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_347", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_347.flac", "type": "audio", "duration": 645.259}, {"from": "Assistant", "value": "Manual fact-checking does not scale well to serve the needs of the internet. This issue is further compounded in non-English contexts. In this paper, we discuss claim matching as a possible solution to scale fact-checking. We define claim matching as the task of identifying pairs of textual messages containing claims that can be served with one fact-check. We construct a novel dataset of WhatsApp tipline and public group messages alongside fact-checked claims that are first annotated for containing “claim-like statements” and then matched with potentially similar items and annotated for claim matching. Our dataset contains content in high-resource (English, Hindi) and lower-resource (Bengali, Malayalam, Tamil) languages. We train our own embedding model using knowledge distillation and a high-quality “teacher” model in order to address the imbalance in embedding quality between the low- and high-resource languages in our dataset. We provide evaluations on the performance of our solution and compare with baselines and existing state-of-the-art multilingual embedding models, namely LASER and LaBSE. We demonstrate that our performance exceeds LASER and LaBSE in all settings. We release our annotated datasets, codebooks, and trained embedding model to allow for further research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_348", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_348.flac", "type": "audio", "duration": 762.009}, {"from": "Assistant", "value": "While pre-training techniques are working very well in natural language processing, how to pre-train a decoder and effectively use it for neural machine translation (NMT) still remains a tricky issue. The main reason is that the cross-attention module between the encoder and decoder cannot be pre-trained, and the combined encoder-decoder model cannot work well in the fine-tuning stage because the inputs of the decoder cross-attention come from unknown encoder outputs. In this paper, we propose a better pre-training method for NMT by defining a semantic interface (SemFace) between the pre-trained encoder and the pre-trained decoder. Specifically, we propose two types of semantic interfaces, including CL-SemFace which regards cross-lingual embeddings as an interface, and VQ-SemFace which employs vector quantized embeddings to constrain the encoder outputs and decoder inputs in the same language-independent space. We conduct massive experiments on six supervised translation pairs and three unsupervised pairs. Experimental results demonstrate that our proposed SemFace can effectively connect the pre-trained encoder and decoder, and achieves significant improvement by 3.7 and 1.5 BLEU points on the two tasks respectively compared with previous pre-training-based NMT models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_349", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_349.flac", "type": "audio", "duration": 744.13}, {"from": "Assistant", "value": "The discrepancy between maximum likelihood estimation (MLE) and task measures such as BLEU score has been studied before for autoregressive neural machine translation (NMT) and resulted in alternative training algorithms (Ranzato et al., 2016; Norouzi et al., 2016; Shen et al., 2016; Wu et al., 2018). However, MLE training remains the de facto approach for autoregressive NMT because of its computational efficiency and stability. Despite this mismatch between the training objective and task measure, we notice that the samples drawn from an MLE-based trained NMT support the desired distribution – there are samples with much higher BLEU score comparing to the beam decoding output. To benefit from this observation, we train an energy-based model to mimic the behavior of the task measure (i.e., the energy-based model assigns lower energy to samples with higher BLEU score), which is resulted in a re-ranking algorithm based on the samples drawn from NMT: energy-based re-ranking (EBR). We use both marginal energy models (over target sentence) and joint energy models (over both source and target sentences). Our EBR with the joint energy model consistently improves the performance of the Transformer-based NMT: +3.7 BLEU points on IWSLT’14 German-English, +3.37 BELU points on Sinhala-English, +1.4 BLEU points on WMT’16 English-German tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_350", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_350.flac", "type": "audio", "duration": 683.061}, {"from": "Assistant", "value": "In recent years, we have seen a colossal effort in pre-training multilingual text encoders using large-scale corpora in many languages to facilitate cross-lingual transfer learning. However, due to typological differences across languages, the cross-lingual transfer is challenging. Nevertheless, language syntax, e.g., syntactic dependencies, can bridge the typological gap. Previous works have shown that pre-trained multilingual encoders, such as mBERT (CITATION), capture language syntax, helping cross-lingual transfer. This work shows that explicitly providing language syntax and training mBERT using an auxiliary objective to encode the universal dependency tree structure helps cross-lingual transfer. We perform rigorous experiments on four NLP tasks, including text classification, question answering, named entity recognition, and task-oriented semantic parsing. The experiment results show that syntax-augmented mBERT improves cross-lingual transfer on popular benchmarks, such as PAWS-X and MLQA, by 1.4 and 1.6 points on average across all languages. In thegeneralizedtransfer setting, the performance boosted significantly, with 3.9 and 3.1 points on average in PAWS-X and MLQA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_351", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_351.flac", "type": "audio", "duration": 581.962}, {"from": "Assistant", "value": "Pretrained multilingual models (PMMs) enable zero-shot learning via cross-lingual transfer, performing best for languages seen during pretraining. While methods exist to improve performance for unseen languages, they have almost exclusively been evaluated using amounts of raw text only available for a small fraction of the world’s languages. In this paper, we evaluate the performance of existing methods to adapt PMMs to new languages using a resource available for close to 1600 languages: the New Testament. This is challenging for two reasons: (1) the small corpus size, and (2) the narrow domain. While performance drops for all approaches, we surprisingly still see gains of up to 17.69% accuracy for part-of-speech tagging and 6.29 F1 for NER on average over all languages as compared to XLM-R. Another unexpected finding is that continued pretraining, the simplest approach, performs best. Finally, we perform a case study to disentangle the effects of domain and size and to shed light on the influence of the finetuning source language.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_352", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_352.flac", "type": "audio", "duration": 666.041}, {"from": "Assistant", "value": "We study the problem of building entity tagging systems by using a few rules as weak supervision. Previous methods mostly focus on disambiguating entity types based on contexts and expert-provided rules, while assuming entity spans are given. In this work, we propose a novel method TALLOR that bootstraps high-quality logical rules to train a neural tagger in a fully automated manner. Specifically, we introduce compound rules that are composed from simple rules to increase the precision of boundary detection and generate more diverse pseudo labels. We further design a dynamic label selection strategy to ensure pseudo label quality and therefore avoid overfitting the neural tagger. Experiments on three datasets demonstrate that our method outperforms other weakly supervised methods and even rivals a state-of-the-art distantly supervised tagger with a lexicon of over 2,000 terms when starting from only 20 simple rules. Our method can serve as a tool for rapidly building taggers in emerging domains and tasks. Case studies show that learned rules can potentially explain the predicted entities.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_353", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_353.flac", "type": "audio", "duration": 721.932}, {"from": "Assistant", "value": "Fine-tuning is the de facto way of leveraging large pretrained language models for downstream tasks. However, fine-tuning modifies all the language model parameters and therefore necessitates storing a full copy for each task. In this paper, we propose prefix-tuning, a lightweight alternative to fine-tuning for natural language generation tasks, which keeps language model parameters frozen and instead optimizes a sequence of continuous task-specific vectors, which we call the prefix. Prefix-tuning draws inspiration from prompting for language models, allowing subsequent tokens to attend to this prefix as if it were “virtual tokens”. We apply prefix-tuning to GPT-2 for table-to-text generation and to BART for summarization. We show that by learning only 0.1% of the parameters, prefix-tuning obtains comparable performance in the full data setting, outperforms fine-tuning in low-data settings, and extrapolates better to examples with topics that are unseen during training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_354", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_354.flac", "type": "audio", "duration": 637.063}, {"from": "Assistant", "value": "Recently, the sequence-to-sequence models have made remarkable progress on the task of keyphrase generation (KG) by concatenating multiple keyphrases in a predefined order as a target sequence during training. However, the keyphrases are inherently an unordered set rather than an ordered sequence. Imposing a predefined order will introduce wrong bias during training, which can highly penalize shifts in the order between keyphrases. In this work, we propose a new training paradigm One2Set without predefining an order to concatenate the keyphrases. To fit this paradigm, we propose a novel model that utilizes a fixed set of learned control codes as conditions to generate a set of keyphrases in parallel. To solve the problem that there is no correspondence between each prediction and target during training, we propose a K-step label assignment mechanism via bipartite matching, which greatly increases the diversity and reduces the repetition rate of generated keyphrases. The experimental results on multiple benchmarks demonstrate that our approach significantly outperforms the state-of-the-art methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_355", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_355.flac", "type": "audio", "duration": 732.056}, {"from": "Assistant", "value": "Recent years have witnessed various types of generative models for natural language generation (NLG), especially RNNs or transformer based sequence-to-sequence models, as well as variational autoencoder (VAE) and generative adversarial network (GAN) based models. However, flow-based generative models, which achieve strong performance in image generation due to their invertibility and exact density estimation properties, have been less explored for NLG. In this paper, we propose a flow-based language generation model by adapting previous flow generative models to language generation via continuous input embeddings, adapted affine coupling structures, and a novel architecture for autoregressive text generation. We also apply our framework to Sequence-to-Sequence generation, including text- and video-based Question Generation (QG) and Neural Machine Translation (NMT), and data augmentation for Question Answering (QA). We use our language flow model to provide extra input features for QG and NMT, which achieves improvements over the strong QG baselines on SQuAD and TVQA and NMT baseline on WMT16. We also augment QA data with new context by injecting noise to the latent features of the language flow and show this augmentation leads to a large performance improvement from strong baselines on SQuAD and TVQA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_356", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_356.flac", "type": "audio", "duration": 656.057}, {"from": "Assistant", "value": "Wikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multi-document summarization techniques. However, previous works generally view the abstract as plain text, ignoring the fact that it is a description of a certain entity and can be decomposed into different topics. In this paper, we propose a two-stage model TWAG that guides the abstract generation with topical information. First, we detect the topic of each input paragraph with a classifier trained on existing Wikipedia articles to divide input documents into different topics. Then, we predict the topic distribution of each abstract sentence, and decode the sentence from topic-aware representations with a Pointer-Generator network. We evaluate our model on the WikiCatSum dataset, and the results show that TWAG outperforms various existing baselines and is capable of generating comprehensive abstracts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_357", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_357.flac", "type": "audio", "duration": 633.301}, {"from": "Assistant", "value": "Event forecasting is a challenging, yet important task, as humans seek to constantly plan for the future. Existing automated forecasting studies rely mostly on structured data, such as time-series or event-based knowledge graphs, to help predict future events. In this work, we aim to formulate a task, construct a dataset, and provide benchmarks for developing methods for event forecasting with large volumes of unstructured text data. To simulate the forecasting scenario on temporal news documents, we formulate the problem as a restricted-domain, multiple-choice, question-answering (QA) task. Unlike existing QA tasks, our task limits accessible information, and thus a model has to make a forecasting judgement. To showcase the usefulness of this task formulation, we introduce ForecastQA, a question-answering dataset consisting of 10,392 event forecasting questions, which have been collected and verified via crowdsourcing efforts. We present our experiments on ForecastQA using BERTbased models and find that our best model achieves 61.0% accuracy on the dataset, which still lags behind human performance by about 19%. We hope ForecastQA will support future research efforts in bridging this gap.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_358", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_358.flac", "type": "audio", "duration": 717.822}, {"from": "Assistant", "value": "Syntactic structure is an important component of natural language text. Recent top-performing models in Answer Sentence Selection (AS2) use self-attention and transfer learning, but not syntactic structure. Tree structures have shown strong performance in tasks with sentence pair input like semantic relatedness. We investigate whether tree structures can boost performance in AS2. We introduce the Tree Aggregation Transformer: a novel recursive, tree-structured self-attention model for AS2. The recursive nature of our model is able to represent all levels of syntactic parse trees with only one additional self-attention layer. Without transfer learning, we establish a new state of the art on the popular TrecQA and WikiQA benchmark datasets. Additionally, we evaluate our method on four Community Question Answering datasets, and find that tree-structured representations have limitations with noisy user-generated text. We conduct probing experiments to evaluate how our models leverage tree structures across datasets. Our findings show that the ability of tree-structured models to successfully absorb syntactic information is strongly correlated with a higher performance in AS2.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_359", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_359.flac", "type": "audio", "duration": 668.41}, {"from": "Assistant", "value": "Knowledge Graph (KG) and attention mechanism have been demonstrated effective in introducing and selecting useful information for weakly supervised methods. However, only qualitative analysis and ablation study are provided as evidence. In this paper, we contribute a dataset and propose a paradigm to quantitatively evaluate the effect of attention and KG on bag-level relation extraction (RE). We find that (1) higher attention accuracy may lead to worse performance as it may harm the model’s ability to extract entity mention features; (2) the performance of attention is largely influenced by various noise distribution patterns, which is closely related to real-world datasets; (3) KG-enhanced attention indeed improves RE performance, while not through enhanced attention but by incorporating entity prior; and (4) attention mechanism may exacerbate the issue of insufficient training data. Based on these findings, we show that a straightforward variant of RE model can achieve significant improvements (6% AUC on average) on two real-world datasets as compared with three state-of-the-art baselines. Our codes and datasets are available athttps://github.com/zig-kwin-hu/how-KG-ATT-help.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_360", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_360.flac", "type": "audio", "duration": 599.725}, {"from": "Assistant", "value": "Implicit Event Argument Extraction seeks to identify arguments that play direct or implicit roles in a given event. However, most prior works focus on capturing direct relations between arguments and the event trigger. The lack of reasoning ability brings many challenges to the extraction of implicit arguments. In this work, we present a Frame-aware Event Argument Extraction (FEAE) learning framework to tackle this issue through reasoning in event frame-level scope. The proposed method leverages related arguments of the expected one as clues to guide the reasoning process. To bridge the gap between oracle knowledge used in the training phase and the imperfect related arguments in the test stage, we further introduce a curriculum knowledge distillation strategy to drive a final model that could operate without extra inputs through mimicking the behavior of a well-informed teacher model. Experimental results demonstrate FEAE obtains new state-of-the-art performance on the RAMS dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_361", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_361.flac", "type": "audio", "duration": 738.046}, {"from": "Assistant", "value": "Open relation extraction aims to cluster relation instances referring to the same underlying relation, which is a critical step for general relation extraction. Current OpenRE models are commonly trained on the datasets generated from distant supervision, which often results in instability and makes the model easily collapsed. In this paper, we revisit the procedure of OpenRE from a causal view. By formulating OpenRE using a structural causal model, we identify that the above-mentioned problems stem from the spurious correlations from entities and context to the relation type. To address this issue, we conductElement Intervention, which intervene on the context and entities respectively to obtain the underlying causal effects of them. We also provide two specific implementations of the interventions based on entity ranking and context contrasting. Experimental results on unsupervised relation extraction datasets show our method to outperform previous state-of-the-art methods and is robust across different datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_362", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_362.flac", "type": "audio", "duration": 684.736}, {"from": "Assistant", "value": "Automatic extraction of product attribute values is an important enabling technology in e-Commerce platforms. This task is usually modeled using sequence labeling architectures, with several extensions to handle multi-attribute extraction. One line of previous work constructs attribute-specific models, through separate decoders or entirely separate models. However, this approach constrains knowledge sharing across different attributes. Other contributions use a single multi-attribute model, with different techniques to embed attribute information. But sharing the entire network parameters across all attributes can limit the model’s capacity to capture attribute-specific characteristics. In this paper we present AdaTag, which uses adaptive decoding to handle extraction. We parameterize the decoder with pretrained attribute embeddings, through a hypernetwork and a Mixture-of-Experts (MoE) module. This allows for separate, but semantically correlated, decoders to be generated on the fly for different attributes. This approach facilitates knowledge sharing, while maintaining the specificity of each attribute. Our experiments on a real-world e-Commerce dataset show marked improvements over previous methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_363", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_363.flac", "type": "audio", "duration": 569.051}, {"from": "Assistant", "value": "Integrating extracted knowledge from the Web to knowledge graphs (KGs) can facilitate tasks like question answering. We study relation integration that aims to align free-text relations in subject-relation-object extractions to relations in a target KG. To address the challenge that free-text relations are ambiguous, previous methods exploit neighbor entities and relations for additional context. However, the predictions are made independently, which can be mutually inconsistent. We propose a two-stage Collective Relation Integration (CoRI) model, where the first stage independently makes candidate predictions, and the second stage employs a collective model that accesses all candidate predictions to make globally coherent predictions. We further improve the collective model with augmented data from the portion of the target KG that is otherwise unused. Experiment results on two datasets show that CoRI can significantly outperform the baselines, improving AUC from .677 to .748 and from .716 to .780, respectively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_364", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_364.flac", "type": "audio", "duration": 774.618}, {"from": "Assistant", "value": "Streaming cross document entity coreference (CDC) systems disambiguate mentions of named entities in a scalable manner via incremental clustering. Unlike other approaches for named entity disambiguation (e.g., entity linking), streaming CDC allows for the disambiguation of entities that are unknown at inference time. Thus, it is well-suited for processing streams of data where new entities are frequently introduced. Despite these benefits, this task is currently difficult to study, as existing approaches are either evaluated on datasets that are no longer available, or omit other crucial details needed to ensure fair comparison. In this work, we address this issue by compiling a large benchmark adapted from existing free datasets, and performing a comprehensive evaluation of a number of novel and existing baseline models. We investigate: how to best encode mentions, which clustering algorithms are most effective for grouping mentions, how models transfer to different domains, and how bounding the number of mentions tracked during inference impacts performance. Our results show that the relative performance of neural and feature-based mention encoders varies across different domains, and in most cases the best performance is achieved using a combination of both approaches. We also find that performance is minimally impacted by limiting the number of tracked mentions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_365", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_365.flac", "type": "audio", "duration": 760.291}, {"from": "Assistant", "value": "Temporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenges to existing models. When facing a prediction task, human beings usually search useful historical information (i.e., clues) in their memories and then reason for future meticulously. Inspired by this mechanism, we propose CluSTeR to predict future facts in a two-stage manner, Clue Searching and Temporal Reasoning, accordingly. Specifically, at the clue searching stage, CluSTeR learns a beam search policy via reinforcement learning (RL) to induce multiple clues from historical facts. At the temporal reasoning stage, it adopts a graph convolution network based sequence method to deduce answers from clues. Experiments on four datasets demonstrate the substantial advantages of CluSTeR compared with the state-of-the-art methods. Moreover, the clues found by CluSTeR further provide interpretability for the results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_366", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_366.flac", "type": "audio", "duration": 410.9}, {"from": "Assistant", "value": "Generating high-quality arguments, while being challenging, may benefit a wide range of downstream applications, such as writing assistants and argument search engines. Motivated by the effectiveness of utilizing knowledge graphs for supporting general text generation tasks, this paper investigates the usage of argumentation-related knowledge graphs to control the generation of arguments. In particular, we construct and populate three knowledge graphs, employing several compositions of them to encode various knowledge into texts of debate portals and relevant paragraphs from Wikipedia. Then, the texts with the encoded knowledge are used to fine-tune a pre-trained text generation model, GPT-2. We evaluate the newly created arguments manually and automatically, based on several dimensions important in argumentative contexts, including argumentativeness and plausibility. The results demonstrate the positive impact of encoding the graphs’ knowledge into debate portal texts for generating arguments with superior quality than those generated without knowledge.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_367", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_367.flac", "type": "audio", "duration": 521.381}, {"from": "Assistant", "value": "Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA which outputs triplets of an aspect target, its associated sentiment, and the corresponding opinion term. Recent models perform the triplet extraction in an end-to-end manner but heavily rely on the interactions between each target word and opinion word. Thereby, they cannot perform well on targets and opinions which contain multiple words. Our proposed span-level approach explicitly considers the interaction between the whole spans of targets and opinions when predicting their sentiment relation. Thus, it can make predictions with the semantics of whole spans, ensuring better sentiment consistency. To ease the high computational cost caused by span enumeration, we propose a dual-channel span pruning strategy by incorporating supervision from the Aspect Term Extraction (ATE) and Opinion Term Extraction (OTE) tasks. This strategy not only improves computational efficiency but also distinguishes the opinion and target spans more properly. Our framework simultaneously achieves strong performance for the ASTE as well as ATE and OTE tasks. In particular, our analysis shows that our span-level approach achieves more significant improvements over the baselines on triplets with multi-word targets or opinions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_368", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_368.flac", "type": "audio", "duration": 716.288}, {"from": "Assistant", "value": "Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks such as WMT. However, there still exist significant issues such as robustness, domain generalization, etc. In this paper, we study NMT models from the perspective of compositional generalization by building a benchmark dataset, CoGnition, consisting of 216k clean and consistent sentence pairs. We quantitatively analyze effects of various factors using compound translation error rate, then demonstrate that the NMT model fails badly on compositional generalization, although it performs remarkably well under traditional metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_369", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_369.flac", "type": "audio", "duration": 636.928}, {"from": "Assistant", "value": "Word alignment, which aims to align translationally equivalent words between source and target sentences, plays an important role in many natural language processing tasks. Current unsupervised neural alignment methods focus on inducing alignments from neural machine translation models, which does not leverage the full context in the target sequence. In this paper, we propose Mask-Align, a self-supervised word alignment model that takes advantage of the full context on the target side. Our model masks out each target token and predicts it conditioned on both source and the remaining target tokens. This two-step process is based on the assumption that the source token contributing most to recovering the masked target token should be aligned. We also introduce an attention variant called leaky attention, which alleviates the problem of unexpected high cross-attention weights on special tokens such as periods. Experiments on four language pairs show that our model outperforms previous unsupervised neural aligners and obtains new state-of-the-art results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_370", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_370.flac", "type": "audio", "duration": 680.768}, {"from": "Assistant", "value": "Computer-aided translation (CAT), the use of software to assist a human translator in the translation process, has been proven to be useful in enhancing the productivity of human translators. Autocompletion, which suggests translation results according to the text pieces provided by human translators, is a core function of CAT. There are two limitations in previous research in this line. First, most research works on this topic focus on sentence-level autocompletion (i.e., generating the whole translation as a sentence based on human input), but word-level autocompletion is under-explored so far. Second, almost no public benchmarks are available for the autocompletion task of CAT. This might be among the reasons why research progress in CAT is much slower compared to automatic MT. In this paper, we propose the task of general word-level autocompletion (GWLAN) from a real-world CAT scenario, and construct the first public benchmark to facilitate research in this topic. In addition, we propose an effective method for GWLAN and compare it with several strong baselines. Experiments demonstrate that our proposed method can give significantly more accurate predictions than the baseline methods on our benchmark datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_371", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_371.flac", "type": "audio", "duration": 721.049}, {"from": "Assistant", "value": "Distant supervision tackles the data bottleneck in NER by automatically generating training instances via dictionary matching. Unfortunately, the learning of DS-NER is severely dictionary-biased, which suffers from spurious correlations and therefore undermines the effectiveness and the robustness of the learned models. In this paper, we fundamentally explain the dictionary bias via a Structural Causal Model (SCM), categorize the bias into intra-dictionary and inter-dictionary biases, and identify their causes. Based on the SCM, we learn de-biased DS-NER via causal interventions. For intra-dictionary bias, we conduct backdoor adjustment to remove the spurious correlations introduced by the dictionary confounder. For inter-dictionary bias, we propose a causal invariance regularizer which will make DS-NER models more robust to the perturbation of dictionaries. Experiments on four datasets and three DS-NER models show that our method can significantly improve the performance of DS-NER.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_372", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_372.flac", "type": "audio", "duration": 665.045}, {"from": "Assistant", "value": "Research on overlapped and discontinuous named entity recognition (NER) has received increasing attention. The majority of previous work focuses on either overlapped or discontinuous entities. In this paper, we propose a novel span-based model that can recognize both overlapped and discontinuous entities jointly. The model includes two major steps. First, entity fragments are recognized by traversing over all possible text spans, thus, overlapped entities can be recognized. Second, we perform relation classification to judge whether a given pair of entity fragments to be overlapping or succession. In this way, we can recognize not only discontinuous entities, and meanwhile doubly check the overlapped entities. As a whole, our model can be regarded as a relation extraction paradigm essentially. Experimental results on multiple benchmark datasets (i.e., CLEF, GENIA and ACE05) show that our model is highly competitive for overlapped and discontinuous NER.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_373", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_373.flac", "type": "audio", "duration": 473.045}, {"from": "Assistant", "value": "We consider the problem of collectively detecting multiple events, particularly in cross-sentence settings. The key to dealing with the problem is to encode semantic information and model event inter-dependency at a document-level. In this paper, we reformulate it as a Seq2Seq task and propose aMulti-LayerBidirectionalNetwork (MLBiNet) to capture the document-level association of events and semantic information simultaneously. Specifically, a bidirectional decoder is firstly devised to model event inter-dependency within a sentence when decoding the event tag vector sequence. Secondly, an information aggregation module is employed to aggregate sentence-level semantic and event tag information. Finally, we stack multiple bidirectional decoders and feed cross-sentence information, forming a multi-layer bidirectional tagging architecture to iteratively propagate information across sentences. We show that our approach provides significant improvement in performance compared to the current state-of-the-art results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_374", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_374.flac", "type": "audio", "duration": 700.053}, {"from": "Assistant", "value": "We study the problem of event coreference resolution (ECR) that seeks to group coreferent event mentions into the same clusters. Deep learning methods have recently been applied for this task to deliver state-of-the-art performance. However, existing deep learning models for ECR are limited in that they cannot exploit important interactions between relevant objects for ECR, e.g., context words and entity mentions, to support the encoding of document-level context. In addition, consistency constraints between golden and predicted clusters of event mentions have not been considered to improve representation learning in prior deep learning models for ECR. This work addresses such limitations by introducing a novel deep learning model for ECR. At the core of our model are document structures to explicitly capture relevant objects for ECR. Our document structures introduce diverse knowledge sources (discourse, syntax, semantics) to compute edges/interactions between structure nodes for document-level representation learning. We also present novel regularization techniques based on consistencies of golden and predicted clusters for event mentions in documents. Extensive experiments show that our model achieve state-of-the-art performance on two benchmark datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_375", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_375.flac", "type": "audio", "duration": 717.76}, {"from": "Assistant", "value": "Relational triple extraction is critical to understanding massive text corpora and constructing large-scale knowledge graph, which has attracted increasing research interest. However, existing studies still face some challenging issues, including information loss, error propagation and ignoring the interaction between entity and relation. To intuitively explore the above issues and address them, in this paper, we provide a revealing insight into relational triple extraction from a stereoscopic perspective, which rationalizes the occurrence of these issues and exposes the shortcomings of existing methods. Further, a novel model is proposed for relational triple extraction, which maps relational triples to a three-dimension (3-D) space and leverages three decoders to extract them, aimed at simultaneously handling the above issues. A series of experiments are conducted on five public datasets, demonstrating that the proposed model outperforms the recent advanced baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_376", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_376.flac", "type": "audio", "duration": 647.047}, {"from": "Assistant", "value": "Identifying causal relations of events is an important task in natural language processing area. However, the task is very challenging, because event causality is usually expressed in diverse forms that often lack explicit causal clues. Existing methods cannot handle well the problem, especially in the condition of lacking training data. Nonetheless, humans can make a correct judgement based on their background knowledge, including descriptive knowledge and relational knowledge. Inspired by it, we propose a novel Latent Structure Induction Network (LSIN) to incorporate the external structural knowledge into this task. Specifically, to make use of the descriptive knowledge, we devise a Descriptive Graph Induction module to obtain and encode the graph-structured descriptive knowledge. To leverage the relational knowledge, we propose a Relational Graph Induction module which is able to automatically learn a reasoning structure for event causality reasoning. Experimental results on two widely used datasets indicate that our approach significantly outperforms previous state-of-the-art methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_377", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_377.flac", "type": "audio", "duration": 685.128}, {"from": "Assistant", "value": "Recent studies show that neural natural language processing (NLP) models are vulnerable to backdoor attacks. Injected with backdoors, models perform normally on benign examples but produce attacker-specified predictions when the backdoor is activated, presenting serious security threats to real-world applications. Since existing textual backdoor attacks pay little attention to the invisibility of backdoors, they can be easily detected and blocked. In this work, we present invisible backdoors that are activated by a learnable combination of word substitution. We show that NLP models can be injected with backdoors that lead to a nearly 100% attack success rate, whereas being highly invisible to existing defense strategies and even human inspections. The results raise a serious alarm to the security of NLP models, which requires further research to be resolved. All the data and code of this paper are released athttps://github.com/thunlp/BkdAtk-LWS.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_378", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_378.flac", "type": "audio", "duration": 696.619}, {"from": "Assistant", "value": "The large size of pretrained networks makes them difficult to deploy for multiple tasks in storage-constrained settings. Diff pruning enables parameter-efficient transfer learning that scales well with new tasks. The approach learns a task-specific “diff” vector that extends the original pretrained parameters. This diff vector is adaptively pruned during training with a differentiable approximation to the L0-norm penalty to encourage sparsity. As the number of tasks increases, diff pruning remains parameter-efficient, as it requires storing only a small diff vector for each task. Since it does not require access to all tasks during training, it is attractive in on-device deployment settings where tasks arrive in stream or even from different providers. Diff pruning can match the performance of finetuned baselines on the GLUE benchmark while only modifying 0.5% of the pretrained model’s parameters per task and scales favorably in comparison to popular pruning approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_379", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_379.flac", "type": "audio", "duration": 713.28}, {"from": "Assistant", "value": "Human language understanding operates at multiple levels of granularity (e.g., words, phrases, and sentences) with increasing levels of abstraction that can be hierarchically combined. However, existing deep models with stacked layers do not explicitly model any sort of hierarchical process. In this paper, we propose a recursive Transformer model based on differentiable CKY style binary trees to emulate this composition process, and we extend the bidirectional language model pre-training objective to this architecture, attempting to predict each word given its left and right abstraction nodes. To scale up our approach, we also introduce an efficient pruning and growing algorithm to reduce the time complexity and enable encoding in linear time. Experimental results on language modeling and unsupervised parsing show the effectiveness of our approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_380", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_380.flac", "type": "audio", "duration": 583.053}, {"from": "Assistant", "value": "Zero-shot sequence labeling aims to build a sequence labeler without human-annotated datasets. One straightforward approach is utilizing existing systems (source models) to generate pseudo-labeled datasets and train a target sequence labeler accordingly. However, due to the gap between the source and the target languages/domains, this approach may fail to recover the true labels. In this paper, we propose a novel unified framework for zero-shot sequence labeling with minimum risk training and design a new decomposable risk function that models the relations between the predicted labels from the source models and the true labels. By making the risk function trainable, we draw a connection between minimum risk training and latent variable model learning. We propose a unified learning algorithm based on the expectation maximization (EM) algorithm. We extensively evaluate our proposed approaches on cross-lingual/domain sequence labeling tasks over twenty-one datasets. The results show that our approaches outperform state-of-the-art baseline systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_381", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_381.flac", "type": "audio", "duration": 556.779}, {"from": "Assistant", "value": "Transfer learning from pretrained language models recently became the dominant approach for solving many NLP tasks. A common approach to transfer learning for multiple tasks that maximize parameter sharing trains one or more task-specific layers on top of the language model. In this paper, we present an alternative approach based on adversarial reprogramming, which extends earlier work on automatic prompt generation. Adversarial reprogramming attempts to learn task-specific word embeddings that, when concatenated to the input text, instruct the language model to solve the specified task. Using up to 25K trainable parameters per task, this approach outperforms all existing methods with up to 25M trainable parameters on the public leaderboard of the GLUE benchmark. Our method, initialized with task-specific human-readable prompts, also works in a few-shot setting, outperforming GPT-3 on two SuperGLUE tasks with just 32 training samples.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_382", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_382.flac", "type": "audio", "duration": 763.179}, {"from": "Assistant", "value": "Sequence-to-sequence transduction is the core problem in language processing applications as diverse as semantic parsing, machine translation, and instruction following. The neural network models that provide the dominant solution to these problems are brittle, especially in low-resource settings: they fail to generalize correctly or systematically from small datasets. Past work has shown that many failures of systematic generalization arise from neural models’ inability to disentangle lexical phenomena from syntactic ones. To address this, we augment neural decoders with a lexical translation mechanism that generalizes existing copy mechanisms to incorporate learned, decontextualized, token-level translation rules. We describe how to initialize this mechanism using a variety of lexicon learning algorithms, and show that it improves systematic generalization on a diverse set of sequence modeling tasks drawn from cognitive science, formal semantics, and machine translation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_383", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_383.flac", "type": "audio", "duration": 639.808}, {"from": "Assistant", "value": "Personalization of natural language generation plays a vital role in a large spectrum of tasks, such as explainable recommendation, review summarization and dialog systems. In these tasks, user and item IDs are important identifiers for personalization. Transformer, which is demonstrated with strong language modeling capability, however, is not personalized and fails to make use of the user and item IDs since the ID tokens are not even in the same semantic space as the words. To address this problem, we present a PErsonalized Transformer for Explainable Recommendation (PETER), on which we design a simple and effective learning objective that utilizes the IDs to predict the words in the target explanation, so as to endow the IDs with linguistic meanings and to achieve personalized Transformer. Besides generating explanations, PETER can also make recommendations, which makes it a unified model for the whole recommendation-explanation pipeline. Extensive experiments show that our small unpretrained model outperforms fine-tuned BERT on the generation task, in terms of both effectiveness and efficiency, which highlights the importance and the nice utility of our design.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_384", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_384.flac", "type": "audio", "duration": 650.925}, {"from": "Assistant", "value": "Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes. While invaluable to clinicians and researchers, creating digital SOAP notes is burdensome, contributing to physician burnout. In this paper, we introduce the first complete pipelines to leverage deep summarization models to generate these notes based on transcripts of conversations between physicians and patients. After exploring a spectrum of methods across the extractive-abstractive spectrum, we propose Cluster2Sent, an algorithm that (i) extracts important utterances relevant to each summary section; (ii) clusters together related utterances; and then (iii) generates one summary sentence per cluster. Cluster2Sent outperforms its purely abstractive counterpart by 8 ROUGE-1 points, and produces significantly more factual and coherent sentences as assessed by expert human evaluators. For reproducibility, we demonstrate similar benefits on the publicly available AMI dataset. Our results speak to the benefits of structuring summaries into sections and annotating supporting evidence when constructing summarization corpora.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_385", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_385.flac", "type": "audio", "duration": 695.04}, {"from": "Assistant", "value": "We investigate the problem of Chinese Grammatical Error Correction (CGEC) and present a new framework named Tail-to-Tail (TtT) non-autoregressive sequence prediction to address the deep issues hidden in CGEC. Considering that most tokens are correct and can be conveyed directly from source to target, and the error positions can be estimated and corrected based on the bidirectional context information, thus we employ a BERT-initialized Transformer Encoder as the backbone model to conduct information modeling and conveying. Considering that only relying on the same position substitution cannot handle the variable-length correction cases, various operations such substitution, deletion, insertion, and local paraphrasing are required jointly. Therefore, a Conditional Random Fields (CRF) layer is stacked on the up tail to conduct non-autoregressive sequence prediction by modeling the token dependencies. Since most tokens are correct and easily to be predicted/conveyed to the target, then the models may suffer from a severe class imbalance issue. To alleviate this problem, focal loss penalty strategies are integrated into the loss functions. Moreover, besides the typical fix-length error correction datasets, we also construct a variable-length corpus to conduct experiments. Experimental results on standard datasets, especially on the variable-length datasets, demonstrate the effectiveness of TtT in terms of sentence-level Accuracy, Precision, Recall, and F1-Measure on tasks of error Detection and Correction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_386", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_386.flac", "type": "audio", "duration": 701.684}, {"from": "Assistant", "value": "An important risk that children face today is online grooming, where a so-called sexual predator establishes an emotional connection with a minor online with the objective of sexual abuse. Prior work has sought to automatically identify grooming chats, but only after an incidence has already happened in the context of legal prosecution. In this work, we instead investigate this problem from the point of view of prevention. We define and study the task of early sexual predator detection (eSPD) in chats, where the goal is to analyze a running chat from its beginning and predict grooming attempts as early and as accurately as possible. We survey existing datasets and their limitations regarding eSPD, and create a new dataset called PANC for more realistic evaluations. We present strong baselines built on BERT that also reach state-of-the-art results for conventional SPD. Finally, we consider coping with limited computational resources, as real-life applications require eSPD on mobile devices.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_387", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_387.flac", "type": "audio", "duration": 715.285}, {"from": "Assistant", "value": "Medical report generation is one of the most challenging tasks in medical image analysis. Although existing approaches have achieved promising results, they either require a predefined template database in order to retrieve sentences or ignore the hierarchical nature of medical report generation. To address these issues, we propose MedWriter that incorporates a novel hierarchical retrieval mechanism to automatically extract both report and sentence-level templates for clinically accurate report generation. MedWriter first employs the Visual-Language Retrieval (VLR) module to retrieve the most relevant reports for the given images. To guarantee the logical coherence between generated sentences, the Language-Language Retrieval (LLR) module is introduced to retrieve relevant sentences based on the previous generated description. At last, a language decoder fuses image features and features from retrieved reports and sentences to generate meaningful medical reports. We verified the effectiveness of our model by automatic evaluation and human evaluation on two datasets, i.e., Open-I and MIMIC-CXR.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_388", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_388.flac", "type": "audio", "duration": 656.01}, {"from": "Assistant", "value": "Hierarchical Text Classification (HTC) is a challenging task that categorizes a textual description within a taxonomic hierarchy. Most of the existing methods focus on modeling the text. Recently, researchers attempt to model the class representations with some resources (e.g., external dictionaries). However, the concept shared among classes which is a kind of domain-specific and fine-grained information has been ignored in previous work. In this paper, we propose a novel concept-based label embedding method that can explicitly represent the concept and model the sharing mechanism among classes for the hierarchical text classification. Experimental results on two widely used datasets prove that the proposed model outperforms several state-of-the-art methods. We release our complementary resources (concepts and definitions of classes) for these two datasets to benefit the research on HTC.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_389", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_389.flac", "type": "audio", "duration": 719.772}, {"from": "Assistant", "value": "Text-to-image retrieval is an essential task in cross-modal information retrieval, i.e., retrieving relevant images from a large and unlabelled dataset given textual queries. In this paper, we propose VisualSparta, a novel (Visual-text Sparse Transformer Matching) model that shows significant improvement in terms of both accuracy and efficiency. VisualSparta is capable of outperforming previous state-of-the-art scalable methods in MSCOCO and Flickr30K. We also show that it achieves substantial retrieving speed advantages, i.e., for a 1 million image index, VisualSparta using CPU gets ~391X speedup compared to CPU vector search and ~5.4X speedup compared to vector search with GPU acceleration. Experiments show that this speed advantage even gets bigger for larger datasets because VisualSparta can be efficiently implemented as an inverted index. To the best of our knowledge, VisualSparta is the first transformer-based text-to-image retrieval model that can achieve real-time searching for large-scale datasets, with significant accuracy improvement compared to previous state-of-the-art methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_390", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_390.flac", "type": "audio", "duration": 686.057}, {"from": "Assistant", "value": "The effectiveness of Neural Information Retrieval (Neu-IR) often depends on a large scale of in-domain relevance training signals, which are not always available in real-world ranking scenarios. To democratize the benefits of Neu-IR, this paper presents MetaAdaptRank, a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot target domains. Drawing on source-domain massive relevance supervision, MetaAdaptRank contrastively synthesizes a large number of weak supervision signals for target domains and meta-learns to reweight these synthetic “weak” data based on their benefits to the target-domain ranking accuracy of Neu-IR models. Experiments on three TREC benchmarks in the web, news, and biomedical domains show that MetaAdaptRank significantly improves the few-shot ranking accuracy of Neu-IR models. Further analyses indicate that MetaAdaptRank thrives from both its contrastive weak data synthesis and meta-reweighted data selection. The code and data of this paper can be obtained fromhttps://github.com/thunlp/MetaAdaptRank.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_391", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_391.flac", "type": "audio", "duration": 665.043}, {"from": "Assistant", "value": "Semi-Supervised Text Classification (SSTC) mainly works under the spirit of self-training. They initialize the deep classifier by training over labeled texts; and then alternatively predict unlabeled texts as their pseudo-labels and train the deep classifier over the mixture of labeled and pseudo-labeled texts. Naturally, their performance is largely affected by the accuracy of pseudo-labels for unlabeled texts. Unfortunately, they often suffer from low accuracy because of the margin bias problem caused by the large difference between representation distributions of labels in SSTC. To alleviate this problem, we apply the angular margin loss, and perform Gaussian linear transformation to achieve balanced label angle variances, i.e., the variance of label angles of texts within the same label. More accuracy of predicted pseudo-labels can be achieved by constraining all label angle variances balanced, where they are estimated over both labeled and pseudo-labeled texts during self-training loops. With this insight, we propose a novel SSTC method, namely Semi-Supervised Text Classification with Balanced Deep representation Distributions (S2TC-BDD). To evaluate S2TC-BDD, we compare it against the state-of-the-art SSTC methods. Empirical results demonstrate the effectiveness of S2TC-BDD, especially when the labeled texts are scarce.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_392", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_392.flac", "type": "audio", "duration": 513.365}, {"from": "Assistant", "value": "Recently, the retrieval models based on dense representations have been gradually applied in the first stage of the document retrieval tasks, showing better performance than traditional sparse vector space models. To obtain high efficiency, the basic structure of these models is Bi-encoder in most cases. However, this simple structure may cause serious information loss during the encoding of documents since the queries are agnostic. To address this problem, we design a method to mimic the queries to each of the documents by an iterative clustering process and represent the documents by multiple pseudo queries (i.e., the cluster centroids). To boost the retrieval process using approximate nearest neighbor search library, we also optimize the matching function with a two-step score calculation procedure. Experimental results on several popular ranking and QA datasets show that our model can achieve state-of-the-art results while still remaining high efficiency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_393", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_393.flac", "type": "audio", "duration": 692.326}, {"from": "Assistant", "value": "Learning high-quality sentence representations benefits a wide range of natural language processing tasks. Though BERT-based pre-trained language models achieve high performance on many downstream tasks, the native derived sentence representations are proved to be collapsed and thus produce a poor performance on the semantic textual similarity (STS) tasks. In this paper, we present ConSERT, a Contrastive Framework for Self-Supervised SEntence Representation Transfer, that adopts contrastive learning to fine-tune BERT in an unsupervised and effective way. By making use of unlabeled texts, ConSERT solves the collapse issue of BERT-derived sentence representations and make them more applicable for downstream tasks. Experiments on STS datasets demonstrate that ConSERT achieves an 8% relative improvement over the previous state-of-the-art, even comparable to the supervised SBERT-NLI. And when further incorporating NLI supervision, we achieve new state-of-the-art performance on STS tasks. Moreover, ConSERT obtains comparable results with only 1000 samples available, showing its robustness in data scarcity scenarios.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_394", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_394.flac", "type": "audio", "duration": 363.369}, {"from": "Assistant", "value": "Due to the great potential in facilitating software development, code generation has attracted increasing attention recently. Generally, dominant models are Seq2Tree models, which convert the input natural language description into a sequence of tree-construction actions corresponding to the pre-order traversal of an Abstract Syntax Tree (AST). However, such a traversal order may not be suitable for handling all multi-branch nodes. In this paper, we propose to equip the Seq2Tree model with a context-based Branch Selector, which is able to dynamically determine optimal expansion orders of branches for multi-branch nodes. Particularly, since the selection of expansion orders is a non-differentiable multi-step operation, we optimize the selector through reinforcement learning, and formulate the reward function as the difference of model losses obtained through different expansion orders. Experimental results and in-depth analysis on several commonly-used datasets demonstrate the effectiveness and generality of our approach. We have released our code athttps://github.com/DeepLearnXMU/CG-RL.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_395", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_395.flac", "type": "audio", "duration": 851.058}, {"from": "Assistant", "value": "Despite recent successes of large pre-trained language models in solving reasoning tasks, their inference capabilities remain opaque. We posit that such models can be made more interpretable by explicitly generating interim inference rules, and using them to guide the generation of task-specific textual outputs. In this paper we present Coins, a recursive inference framework that i) iteratively reads context sentences, ii) dynamically generates contextualized inference rules, encodes them, and iii) uses them to guide task-specific output generation. We apply to aNarrative Story Completiontask that asks a model to complete a story with missing sentences, to produce a coherent story with plausible logical connections, causal relationships, and temporal dependencies. By modularizing inference and sentence generation steps in a recurrent model, we aim to make reasoning steps and their effects on next sentence generation transparent. Our automatic and manual evaluations show that the model generates better story sentences than SOTA baselines, especially in terms of coherence. We further demonstrate improved performance over strong pre-trained LMs in generating commonsense inference rules. The recursive nature of holds the potential for controlled generation of longer sequences.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_396", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_396.flac", "type": "audio", "duration": 586.652}, {"from": "Assistant", "value": "Procedural text understanding aims at tracking the states (e.g., create, move, destroy) and locations of the entities mentioned in a given paragraph. To effectively track the states and locations, it is essential to capture the rich semantic relations between entities, actions, and locations in the paragraph. Although recent works have achieved substantial progress, most of them focus on leveraging the inherent constraints or incorporating external knowledge for state prediction. The rich semantic relations in the given paragraph are largely overlooked. In this paper, we propose a novel approach (REAL) to procedural text understanding, where we build a general framework to systematically model the entity-entity, entity-action, and entity-location relations using a graph neural network. We further develop algorithms for graph construction, representation learning, and state and location tracking. We evaluate the proposed approach on two benchmark datasets, ProPara, and Recipes. The experimental results show that our method outperforms strong baselines by a large margin, i.e., 5.0% on ProPara and 3.2% on Recipes, illustrating the utility of semantic relations and the effectiveness of the graph-based reasoning model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_397", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_397.flac", "type": "audio", "duration": 636.25}, {"from": "Assistant", "value": "Semantic parsing is challenging due to the structure gap and the semantic gap between utterances and logical forms. In this paper, we propose an unsupervised semantic parsing method - Synchronous Semantic Decoding (SSD), which can simultaneously resolve the semantic gap and the structure gap by jointly leveraging paraphrasing and grammar-constrained decoding. Specifically, we reformulate semantic parsing as a constrained paraphrasing problem: given an utterance, our model synchronously generates its canonical utterancel and meaning representation. During synchronously decoding: the utterance paraphrasing is constrained by the structure of the logical form, therefore the canonical utterance can be paraphrased controlledly; the semantic decoding is guided by the semantics of the canonical utterance, therefore its logical form can be generated unsupervisedly. Experimental results show that SSD is a promising approach and can achieve state-of-the-art unsupervised semantic parsing performance on multiple datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_398", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_398.flac", "type": "audio", "duration": 588.053}, {"from": "Assistant", "value": "Despite the well-developed cut-edge representation learning for language, most language representation models usually focus on specific levels of linguistic units. This work introduces universal language representation learning, i.e., embeddings of different levels of linguistic units or text with quite diverse lengths in a uniform vector space. We propose the training objective MiSAD that utilizes meaningful n-grams extracted from large unlabeled corpus by a simple but effective algorithm for pre-trained language models. Then we empirically verify that well designed pre-training scheme may effectively yield universal language representation, which will bring great convenience when handling multiple layers of linguistic objects in a unified way. Especially, our model achieves the highest accuracy on analogy tasks in different language levels and significantly improves the performance on downstream tasks in the GLUE benchmark and a question answering dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_399", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_399.flac", "type": "audio", "duration": 545.045}, {"from": "Assistant", "value": "Pre-trained language models (PrLMs) have demonstrated superior performance due to their strong ability to learn universal language representations from self-supervised pre-training. However, even with the help of the powerful PrLMs, it is still challenging to effectively capture task-related knowledge from dialogue texts which are enriched by correlations among speaker-aware utterances. In this work, we present SPIDER, Structural Pre-traIned DialoguE Reader, to capture dialogue exclusive features. To simulate the dialogue-like features, we propose two training objectives in addition to the original LM objectives: 1) utterance order restoration, which predicts the order of the permuted utterances in dialogue context; 2) sentence backbone regularization, which regularizes the model to improve the factual correctness of summarized subject-verb-object triplets. Experimental results on widely used dialogue benchmarks verify the effectiveness of the newly introduced self-supervised tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_400", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_400.flac", "type": "audio", "duration": 723.046}, {"from": "Assistant", "value": "Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. Few studies have been conducted to explore the design of architecture hyper-parameters in BERT, especially for the more efficient PLMs with tiny sizes, which are essential for practical deployment on resource-constrained devices. In this paper, we adopt the one-shot Neural Architecture Search (NAS) to automatically search architecture hyper-parameters. Specifically, we carefully design the techniques of one-shot learning and the search space to provide an adaptive and efficient development way of tiny PLMs for various latency constraints. We name our method AutoTinyBERT and evaluate its effectiveness on the GLUE and SQuAD benchmarks. The extensive experiments show that our method outperforms both the SOTA search-based baseline (NAS-BERT) and the SOTA distillation-based methods (such as DistilBERT, TinyBERT, MiniLM, and MobileBERT). In addition, based on the obtained architectures, we propose a more efficient development method that is even faster than the development of a single PLM. The source code and models will be publicly available upon publication.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_401", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_401.flac", "type": "audio", "duration": 684.176}, {"from": "Assistant", "value": "Due to recent pretrained multilingual representation models, it has become feasible to exploit labeled data from one language to train a cross-lingual model that can then be applied to multiple new languages. In practice, however, we still face the problem of scarce labeled data, leading to subpar results. In this paper, we propose a novel data augmentation strategy for better cross-lingual natural language inference by enriching the data to reflect more diversity in a semantically faithful way. To this end, we propose two methods of training a generative model to induce synthesized examples, and then leverage the resulting data using an adversarial training regimen for more robustness. In a series of detailed experiments, we show that this fruitful combination leads to substantial gains in cross-lingual inference.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_402", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_402.flac", "type": "audio", "duration": 644.331}, {"from": "Assistant", "value": "As high-quality labeled data is scarce, unsupervised sentence representation learning has attracted much attention. In this paper, we propose a new framework with a two-branch Siamese Network which maximizes the similarity between two augmented views of each sentence. Specifically, given one augmented view of the input sentence, the online network branch is trained by predicting the representation yielded by the target network of the same sentence under another augmented view. Meanwhile, the target network branch is bootstrapped with a moving average of the online network. The proposed method significantly outperforms other state-of-the-art unsupervised methods on semantic textual similarity (STS) and classification tasks. It can be adopted as a post-training procedure to boost the performance of the supervised methods. We further extend our method for learning multilingual sentence representations and demonstrate its effectiveness on cross-lingual STS tasks. Our code is available athttps://github.com/yanzhangnlp/BSL.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_403", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_403.flac", "type": "audio", "duration": 544.253}, {"from": "Assistant", "value": "Abductive reasoning aims at inferring the most plausible explanation for observed events, which would play critical roles in various NLP applications, such as reading comprehension and question answering. To facilitate this task, a narrative text based abductive reasoning task𝛼NLI is proposed, together with explorations about building reasoning framework using pretrained language models. However, abundant event commonsense knowledge is not well exploited for this task. To fill this gap, we propose a variational autoencoder based model ege-RoBERTa, which employs a latent variable to capture the necessary commonsense knowledge from event graph for guiding the abductive reasoning task. Experimental results show that through learning the external event graph knowledge, our approach outperforms the baseline methods on the𝛼NLI task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_404", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_404.flac", "type": "audio", "duration": 605.888}, {"from": "Assistant", "value": "The uniform information density (UID) hypothesis, which posits that speakers behaving optimally tend to distribute information uniformly across a linguistic signal, has gained traction in psycholinguistics as an explanation for certain syntactic, morphological, and prosodic choices. In this work, we explore whether the UID hypothesis can be operationalized as an inductive bias for statistical language modeling. Specifically, we augment the canonical MLE objective for training language models with a regularizer that encodes UID. In experiments on ten languages spanning five language families, we find that using UID regularization consistently improves perplexity in language models, having a larger effect when training data is limited. Moreover, via an analysis of generated sequences, we find that UID-regularized language models have other desirable properties, e.g., they generate text that is more lexically diverse. Our results not only suggest that UID is a reasonable inductive bias for language modeling, but also provide an alternative validation of the UID hypothesis using modern-day NLP tools.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_405", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_405.flac", "type": "audio", "duration": 690.004}, {"from": "Assistant", "value": "In computational psycholinguistics, various language models have been evaluated against human reading behavior (e.g., eye movement) to build human-like computational models. However, most previous efforts have focused almost exclusively on English, despite the recent trend towards linguistic universal within the general community. In order to fill the gap, this paper investigates whether the established results in computational psycholinguistics can be generalized across languages. Specifically, we re-examine an established generalization —the lower perplexity a language model has, the more human-like the language model is— in Japanese with typologically different structures from English. Our experiments demonstrate that this established generalization exhibits a surprising lack of universality; namely, lower perplexity is not always human-like. Moreover, this discrepancy between English and Japanese is further explored from the perspective of (non-)uniform information density. Overall, our results suggest that a cross-lingual evaluation will be necessary to construct human-like computational models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_406", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_406.flac", "type": "audio", "duration": 699.524}, {"from": "Assistant", "value": "Lately proposed Word Sense Disambiguation (WSD) systems have approached the estimated upper bound of the task on standard evaluation benchmarks. However, these systems typically implement the disambiguation of words in a document almost independently, underutilizing sense and word dependency in context. In this paper, we convert the nearly isolated decisions into interrelated ones by exposing senses in context when learning sense embeddings in a similarity-based Sense Aware Context Exploitation (SACE) architecture. Meanwhile, we enhance the context embedding learning with selected sentences from the same document, rather than utilizing only the sentence where each ambiguous word appears. Experiments on both English and multilingual WSD datasets have shown the effectiveness of our approach, surpassing previous state-of-the-art by large margins (3.7% and 1.2% respectively), especially on few-shot (14.3%) and zero-shot (35.9%) scenarios.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_407", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_407.flac", "type": "audio", "duration": 716.011}, {"from": "Assistant", "value": "Frame Identification (FI) is a fundamental and challenging task in frame semantic parsing. The task aims to find the exact frame evoked by a target word in a given sentence. It is generally regarded as a classification task in existing work, where frames are treated as discrete labels or represented using onehot embeddings. However, the valuable knowledge about frames is neglected. In this paper, we propose a Knowledge-Guided Frame Identification framework (KGFI) that integrates three types frame knowledge, including frame definitions, frame elements and frame-to-frame relations, to learn better frame representation, which guides the KGFI to jointly map target words and frames into the same embedding space and subsequently identify the best frame by calculating the dot-product similarity scores between the target word embedding and all of the frame embeddings. The extensive experimental results demonstrate KGFI significantly outperforms the state-of-the-art methods on two benchmark datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_408", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_408.flac", "type": "audio", "duration": 624.779}, {"from": "Assistant", "value": "The advent of contextual word embeddings — representations of words which incorporate semantic and syntactic information from their context—has led to tremendous improvements on a wide variety of NLP tasks. However, recent contextual models have prohibitively high computational cost in many use-cases and are often hard to interpret. In this work, we demonstrate that our proposed distillation method, which is a simple extension of CBOW-based training, allows to significantly improve computational efficiency of NLP applications, while outperforming the quality of existing static embeddings trained from scratch as well as those distilled from previously proposed methods. As a side-effect, our approach also allows a fair comparison of both contextual and static embeddings via standard lexical evaluation tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_409", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_409.flac", "type": "audio", "duration": 729.014}, {"from": "Assistant", "value": "A critical challenge faced by supervised word sense disambiguation (WSD) is the lack of large annotated datasets with sufficient coverage of words in their diversity of senses. This inspired recent research on few-shot WSD using meta-learning. While such work has successfully applied meta-learning to learn new word senses from very few examples, its performance still lags behind its fully-supervised counterpart. Aiming to further close this gap, we propose a model of semantic memory for WSD in a meta-learning setting. Semantic memory encapsulates prior experiences seen throughout the lifetime of the model, which aids better generalization in limited data settings. Our model is based on hierarchical variational inference and incorporates an adaptive memory update rule via a hypernetwork. We show our model advances the state of the art in few-shot WSD, supports effective learning in extremely data scarce (e.g. one-shot) scenarios and produces meaning prototypes that capture similar senses of distinct words.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_410", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_410.flac", "type": "audio", "duration": 744.13}, {"from": "Assistant", "value": "Transformer-based language models (LMs) pretrained on large text collections implicitly store a wealth of lexical semantic knowledge, but it is non-trivial to extract that knowledge effectively from their parameters. Inspired by prior work on semantic specialization of static word embedding (WE) models, we show that it is possible to expose and enrich lexical knowledge from the LMs, that is, to specialize them to serve as effective and universal “decontextualized” word encoders even when fed input words “in isolation” (i.e., without any context). Their transformation into such word encoders is achieved through a simple and efficient lexical fine-tuning procedure (termed LexFit) based on dual-encoder network structures. Further, we show that LexFit can yield effective word encoders even with limited lexical supervision and, via cross-lingual transfer, in different languages without any readily available external knowledge. Our evaluation over four established, structurally different lexical-level tasks in 8 languages indicates the superiority of LexFit-based WEs over standard static WEs (e.g., fastText) and WEs from vanilla LMs. Other extensive experiments and ablation studies further profile the LexFit framework, and indicate best practices and performance variations across LexFit variants, languages, and lexical tasks, also directly questioning the usefulness of traditional WE models in the era of large neural models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_411", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_411.flac", "type": "audio", "duration": 560.251}, {"from": "Assistant", "value": "In this paper we present the first model for directly synthesizing fluent, natural-sounding spoken audio captions for images that does not require natural language text as an intermediate representation or source of supervision. Instead, we connect the image captioning module and the speech synthesis module with a set of discrete, sub-word speech units that are discovered with a self-supervised visual grounding task. We conduct experiments on the Flickr8k spoken caption dataset in addition to a novel corpus of spoken audio captions collected for the popular MSCOCO dataset, demonstrating that our generated captions also capture diverse visual semantics of the images they describe. We investigate several different intermediate speech representations, and empirically find that the representation must satisfy several important properties to serve as drop-in replacements for text.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_412", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_412.flac", "type": "audio", "duration": 613.056}, {"from": "Assistant", "value": "Multimodal sentiment analysis is the challenging research area that attends to the fusion of multiple heterogeneous modalities. The main challenge is the occurrence of some missing modalities during the multimodal fusion procedure. However, the existing techniques require all modalities as input, thus are sensitive to missing modalities at predicting time. In this work, the coupled-translation fusion network (CTFN) is firstly proposed to model bi-direction interplay via couple learning, ensuring the robustness in respect to missing modalities. Specifically, the cyclic consistency constraint is presented to improve the translation performance, allowing us directly to discard decoder and only embraces encoder of Transformer. This could contribute to a much lighter model. Due to the couple learning, CTFN is able to conduct bi-direction cross-modality intercorrelation parallelly. Based on CTFN, a hierarchical architecture is further established to exploit multiple bi-direction translations, leading to double multimodal fusing embeddings compared with traditional translation methods. Moreover, the convolution block is utilized to further highlight explicit interactions among those translations. For evaluation, CTFN was verified on two multimodal benchmarks with extensive ablation studies. The experiments demonstrate that the proposed framework achieves state-of-the-art or often competitive performance. Additionally, CTFN still maintains robustness when considering missing modality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_413", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_413.flac", "type": "audio", "duration": 532.062}, {"from": "Assistant", "value": "In this work, we demonstrate that the contextualized word vectors derived from pretrained masked language model-based encoders share a common, perhaps undesirable pattern across layers. Namely, we find cases of persistent outlier neurons within BERT and RoBERTa’s hidden state vectors that consistently bear the smallest or largest values in said vectors. In an attempt to investigate the source of this information, we introduce a neuron-level analysis method, which reveals that the outliers are closely related to information captured by positional embeddings. We also pre-train the RoBERTa-base models from scratch and find that the outliers disappear without using positional embeddings. These outliers, we find, are the major cause of anisotropy of encoders’ raw vector spaces, and clipping them leads to increased similarity across vectors. We demonstrate this in practice by showing that clipped vectors can more accurately distinguish word senses, as well as lead to better sentence embeddings when mean pooling. In three supervised tasks, we find that clipping does not affect the performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_414", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_414.flac", "type": "audio", "duration": 706.096}, {"from": "Assistant", "value": "We propose an alternate approach to quantifying how well language models learn natural language: we ask how well they match the statistical tendencies of natural language. To answer this question, we analyze whether text generated from language models exhibits the statistical tendencies present in the human-generated text on which they were trained. We provide a framework–paired with significance tests–for evaluating the fit of language models to these trends. We find that neural language models appear to learn only a subset of the tendencies considered, but align much more closely with empirical trends than proposed theoretical distributions (when present). Further, the fit to different distributions is highly-dependent on both model architecture and generation strategy. As concrete examples, text generated under the nucleus sampling scheme adheres more closely to the type–token relationship of natural language than text produced using standard ancestral sampling; text from LSTMs reflects the natural language distributions over length, stopwords, and symbols surprisingly well.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_415", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_415.flac", "type": "audio", "duration": 568.576}, {"from": "Assistant", "value": "The importance of explaining the outcome of a machine learning model, especially a black-box model, is widely acknowledged. Recent approaches explain an outcome by identifying the contributions of input features to this outcome. In environments involving large black-box models or complex inputs, this leads to computationally demanding algorithms. Further, these algorithms often suffer from low stability, with explanations varying significantly across similar examples. In this paper, we propose a Learning to Explain (L2E) approach that learns the behaviour of an underlying explanation algorithm simultaneously from all training examples. Once the explanation algorithm is distilled into an explainer network, it can be used to explain new instances. Our experiments on three classification tasks, which compare our approach to six explanation algorithms, show that L2E is between 5 and 7.5×10ˆ4 times faster than these algorithms, while generating more stable explanations, and having comparable faithfulness to the black-box model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_416", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_416.flac", "type": "audio", "duration": 658.773}, {"from": "Assistant", "value": "A stereotype is an over-generalized belief about a particular group of people, e.g., Asians are good at math or African Americans are athletic. Such beliefs (biases) are known to hurt target groups. Since pretrained language models are trained on large real-world data, they are known to capture stereotypical biases. It is important to quantify to what extent these biases are present in them. Although this is a rapidly growing area of research, existing literature lacks in two important aspects: 1) they mainly evaluate bias of pretrained language models on a small set of artificial sentences, even though these models are trained on natural data 2) current evaluations focus on measuring bias without considering the language modeling ability of a model, which could lead to misleading trust on a model even if it is a poor language model. We address both these problems. We present StereoSet, a large-scale natural English dataset to measure stereotypical biases in four domains: gender, profession, race, and religion. We contrast both stereotypical bias and language modeling ability of popular models like BERT, GPT-2, RoBERTa, and XLnet. We show that these models exhibit strong stereotypical biases. Our data and code are available athttps://stereoset.mit.edu.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_417", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_417.flac", "type": "audio", "duration": 673.054}, {"from": "Assistant", "value": "Deep learning models have achieved great success on the task of Natural Language Inference (NLI), though only a few attempts try to explain their behaviors. Existing explanation methods usually pick prominent features such as words or phrases from the input text. However, for NLI, alignments among words or phrases are more enlightening clues to explain the model. To this end, this paper presents AREC, a post-hoc approach to generate alignment rationale explanations for co-attention based models in NLI. The explanation is based on feature selection, which keeps few but sufficient alignments while maintaining the same prediction of the target model. Experimental results show that our method is more faithful and human-readable compared with many existing approaches. We further study and re-evaluate three typical models through our explanation beyond accuracy, and propose a simple method that greatly improves the model robustness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_418", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_418.flac", "type": "audio", "duration": 691.049}, {"from": "Assistant", "value": "This paper presents a novel pre-trained language models (PLM) compression approach based on the matrix product operator (short as MPO) from quantum many-body physics. It can decompose an original matrix into central tensors (containing the core information) and auxiliary tensors (with only a small proportion of parameters). With the decomposed MPO structure, we propose a novel fine-tuning strategy by only updating the parameters from the auxiliary tensors, and design an optimization algorithm for MPO-based approximation over stacked network architectures. Our approach can be applied to the original or the compressed PLMs in a general way, which derives a lighter network and significantly reduces the parameters to be fine-tuned. Extensive experiments have demonstrated the effectiveness of the proposed approach in model compression, especially the reduction in fine-tuning parameters (91% reduction on average). The code to reproduce the results of this paper can be found athttps://github.com/RUCAIBox/MPOP.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_419", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_419.flac", "type": "audio", "duration": 574.044}, {"from": "Assistant", "value": "In the recent advances of natural language processing, the scale of the state-of-the-art models and datasets is usually extensive, which challenges the application of sample-based explanation methods in many aspects, such as explanation interpretability, efficiency, and faithfulness. In this work, for the first time, we can improve the interpretability of explanations by allowing arbitrary text sequences as the explanation unit. On top of this, we implement a hessian-free method with a model faithfulness guarantee. Finally, to compare our method with the others, we propose a semantic-based evaluation metric that can better align with humans’ judgment of explanations than the widely adopted diagnostic or re-training measures. The empirical results on multiple real data sets demonstrate the proposed method’s superior performance to popular explanation techniques such as Influence Function or TracIn on semantic evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_420", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_420.flac", "type": "audio", "duration": 685.013}, {"from": "Assistant", "value": "We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. Such a problem would lead to the necessity of having human-annotated syntactic information, which limits the application of existing methods to broader scenarios. To address this, we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages. Our model is based on Transformer with a syntax-aware attention layer that considers the dependency tree of the text. We further introduce a new pre-training task of predicting the syntactic distance among tokens in the dependency tree. We evaluate the model on three downstream tasks, including relation classification, entity typing, and question answering. Results show that our model achieves state-of-the-art performance on six public benchmark datasets. We have two major findings. First, we demonstrate that infusing automatically produced syntax of text improves pre-trained models. Second, global syntactic distances among tokens bring larger performance gains compared to local head relations between contiguous tokens.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_421", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_421.flac", "type": "audio", "duration": 615.211}, {"from": "Assistant", "value": "Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge of source domain to the unlabeled target domain. Existing methods typically require to learn to adapt the target model by exploiting the source data and sharing the network architecture across domains. However, this pipeline makes the source data risky and is inflexible for deploying the target model. This paper tackles a novel setting where only a trained source model is available and different network architectures can be adapted for target domain in terms of deployment environments. We propose a generic framework namedCross-domain Knowledge Distillation(CdKD) without needing any source data. CdKD matches the joint distributions between a trained source model and a set of target data during distilling the knowledge from the source model to the target domain. As a type of important knowledge in the source domain, for the first time, the gradient information is exploited to boost the transfer performance. Experiments on cross-domain text classification demonstrate that CdKD achieves superior performance, which verifies the effectiveness in this novel setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_422", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_422.flac", "type": "audio", "duration": 559.09}, {"from": "Assistant", "value": "Today’s text classifiers inevitably suffer from unintended dataset biases, especially the document-level label bias and word-level keyword bias, which may hurt models’ generalization. Many previous studies employed data-level manipulations or model-level balancing mechanisms to recover unbiased distributions and thus prevent models from capturing the two types of biases. Unfortunately, they either suffer from the extra cost of data collection/selection/annotation or need an elaborate design of balancing strategies. Different from traditional factual inference in which debiasing occurs before or during training, counterfactual inference mitigates the influence brought by unintended confounders after training, which can make unbiased decisions with biased observations. Inspired by this, we propose a model-agnostic text classification debiasing framework – Corsair, which can effectively avoid employing data manipulations or designing balancing mechanisms. Concretely, Corsair first trains a base model on a training set directly, allowing the dataset biases ‘poison’ the trained model. In inference, given a factual input document, Corsair imagines its two counterfactual counterparts to distill and mitigate the two biases captured by the poisonous model. Extensive experiments demonstrate Corsair’s effectiveness, generalizability and fairness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_423", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_423.flac", "type": "audio", "duration": 711.253}, {"from": "Assistant", "value": "User interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually learn a single user embedding for each user from their previous behaviors to represent their overall interest. However, user interest is usually diverse and multi-grained, which is difficult to be accurately modeled by a single user embedding. In this paper, we propose a news recommendation method with hierarchical user interest modeling, named HieRec. Instead of a single user embedding, in our method each user is represented in a hierarchical interest tree to better capture their diverse and multi-grained interest in news. We use a three-level hierarchy to represent 1) overall user interest; 2) user interest in coarse-grained topics like sports; and 3) user interest in fine-grained topics like football. Moreover, we propose a hierarchical user interest matching framework to match candidate news with different levels of user interest for more accurate user interest targeting. Extensive experiments on two real-world datasets validate our method can effectively improve the performance of user modeling for personalized news recommendation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_424", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_424.flac", "type": "audio", "duration": 679.403}, {"from": "Assistant", "value": "Personalized news recommendation methods are widely used in online news services. These methods usually recommend news based on the matching between news content and user interest inferred from historical behaviors. However, these methods usually have difficulties in making accurate recommendations to cold-start users, and tend to recommend similar news with those users have read. In general, popular news usually contain important information and can attract users with different interests. Besides, they are usually diverse in content and topic. Thus, in this paper we propose to incorporate news popularity information to alleviate the cold-start and diversity problems for personalized news recommendation. In our method, the ranking score for recommending a candidate news to a target user is the combination of a personalized matching score and a news popularity score. The former is used to capture the personalized user interest in news. The latter is used to measure time-aware popularity of candidate news, which is predicted based on news content, recency, and real-time CTR using a unified framework. Besides, we propose a popularity-aware user encoder to eliminate the popularity bias in user behaviors for accurate interest modeling. Experiments on two real-world datasets show our method can effectively improve the accuracy and diversity for news recommendation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_425", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_425.flac", "type": "audio", "duration": 710.368}, {"from": "Assistant", "value": "False claims that have been previously fact-checked can still spread on social media. To mitigate their continual spread, detecting previously fact-checked claims is indispensable. Given a claim, existing works focus on providing evidence for detection by reranking candidate fact-checking articles (FC-articles) retrieved by BM25. However, these performances may be limited because they ignore the following characteristics of FC-articles: (1) claims are often quoted to describe the checked events, providing lexical information besides semantics; (2) sentence templates to introduce or debunk claims are common across articles, providing pattern information. Models that ignore the two aspects only leverage semantic relevance and may be misled by sentences that describe similar but irrelevant events. In this paper, we propose a novel reranker, MTM (Memory-enhanced Transformers for Matching) to rank FC-articles using key sentences selected with event (lexical and semantic) and pattern information. For event information, we propose a ROUGE-guided Transformer which is finetuned with regression of ROUGE. For pattern information, we generate pattern vectors for matching with sentences. By fusing event and pattern information, we select key sentences to represent an article and then predict if the article fact-checks the given claim using the claim, key sentences, and patterns. Experiments on two real-world datasets show that MTM outperforms existing methods. Human evaluation proves that MTM can capture key sentences for explanations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_426", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_426.flac", "type": "audio", "duration": 652.063}, {"from": "Assistant", "value": "Although deep neural networks have achieved prominent performance on many NLP tasks, they are vulnerable to adversarial examples. We propose Dirichlet Neighborhood Ensemble (DNE), a randomized method for training a robust model to defense synonym substitution-based attacks. During training, DNE forms virtual sentences by sampling embedding vectors for each word in an input sentence from a convex hull spanned by the word and its synonyms, and it augments them with the training data. In such a way, the model is robust to adversarial attacks while maintaining the performance on the original clean data. DNE is agnostic to the network architectures and scales to large models (e.g., BERT) for NLP applications. Through extensive experimentation, we demonstrate that our method consistently outperforms recently proposed defense methods by a significant margin across different network architectures and multiple data sets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_427", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_427.flac", "type": "audio", "duration": 715.058}, {"from": "Assistant", "value": "Increasing the input length has been a driver of progress in language modeling with transformers. We identify conditions where shorter inputs are not harmful, and achieve perplexity and efficiency improvements through two new methods that decrease input length. First, we show that initially training a model on short subsequences before moving on to longer ones both reduces overall training time and, surprisingly, substantially improves perplexity. Second, we show how to improve the efficiency of recurrence methods in transformers, which let models condition on previously processed tokens when generating sequences that exceed the maximal length the transformer can handle at once. Existing methods require computationally expensive relative position embeddings; we introduce a simple alternative of adding absolute position embeddings to queries and keys instead of to word embeddings, which efficiently produces superior results. We show that these recurrent models also benefit from short input lengths. Combining these techniques speeds up training by a factor of 1.65, reduces memory usage, and substantially improves perplexity on WikiText-103, without adding any parameters.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_428", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_428.flac", "type": "audio", "duration": 635.051}, {"from": "Assistant", "value": "Task variance regularization, which can be used to improve the generalization of Multi-task Learning (MTL) models, remains unexplored in multi-task text classification. Accordingly, to fill this gap, this paper investigates how the task might be effectively regularized, and consequently proposes a multi-task learning method based on adversarial multi-armed bandit. The proposed method, named BanditMTL, regularizes the task variance by means of a mirror gradient ascent-descent algorithm. Adopting BanditMTL in the multi-task text classification context is found to achieve state-of-the-art performance. The results of extensive experiments back up our theoretical analysis and validate the superiority of our proposals.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_429", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_429.flac", "type": "audio", "duration": 600.05}, {"from": "Assistant", "value": "In knowledge graph embedding, the theoretical relationship between the softmax cross-entropy and negative sampling loss functions has not been investigated. This makes it difficult to fairly compare the results of the two different loss functions. We attempted to solve this problem by using the Bregman divergence to provide a unified interpretation of the softmax cross-entropy and negative sampling loss functions. Under this interpretation, we can derive theoretical findings for fair comparison. Experimental results on the FB15k-237 and WN18RR datasets show that the theoretical findings are valid in practical settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_430", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_430.flac", "type": "audio", "duration": 740.809}, {"from": "Assistant", "value": "Logical table-to-text generation aims to automatically generate fluent and logically faithful text from tables. The task remains challenging where deep learning models often generated linguistically fluent but logically inconsistent text. The underlying reason may be that deep learning models often capture surface-level spurious correlations rather than the causal relationships between the tablexand the sentencey. Specifically, in the training stage, a model can get a low empirical loss without understandingxand use spurious statistical cues instead. In this paper, we propose a de-confounded variational encoder-decoder (DCVED) based on causal intervention, learning the objectivep(y|do(x)). Firstly, we propose to use variational inference to estimate the confounders in the latent space and cooperate with the causal intervention based on Pearl’s do-calculus to alleviate the spurious correlations. Secondly, to make the latent confounder meaningful, we propose a back-prediction process to predict the not-used entities but linguistically similar to the exactly selected ones. Finally, since our variational model can generate multiple candidates, we train a table-text selector to find out the best candidate sentence for the given table. An extensive set of experiments show that our model outperforms the baselines and achieves new state-of-the-art performance on two logical table-to-text datasets in terms of logical fidelity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_431", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_431.flac", "type": "audio", "duration": 718.123}, {"from": "Assistant", "value": "Recent researches have shown that large natural language processing (NLP) models are vulnerable to a kind of security threat called the Backdoor Attack. Backdoor attacked models can achieve good performance on clean test sets but perform badly on those input sentences injected with designed trigger words. In this work, we point out a potential problem of current backdoor attacking research: its evaluation ignores the stealthiness of backdoor attacks, and most of existing backdoor attacking methods are not stealthy either to system deployers or to system users. To address this issue, we first propose two additional stealthiness-based metrics to make the backdoor attacking evaluation more credible. We further propose a novel word-based backdoor attacking method based on negative data augmentation and modifying word embeddings, making an important step towards achieving stealthy backdoor attacking. Experiments on sentiment analysis and toxic detection tasks show that our method is much stealthier while maintaining pretty good attacking performance. Our code is available athttps://github.com/lancopku/SOS.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_432", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_432.flac", "type": "audio", "duration": 693.928}, {"from": "Assistant", "value": "Crowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers. Previous studies focus on reducing the influences from the noises of the crowdsourced annotations for supervised models. We take a different point in this work, regarding all crowdsourced annotations as gold-standard with respect to the individual annotators. In this way, we find that crowdsourcing could be highly similar to domain adaptation, and then the recent advances of cross-domain methods can be almost directly applied to crowdsourcing. Here we take named entity recognition (NER) as a study case, suggesting an annotator-aware representation learning model that inspired by the domain adaptation methods which attempt to capture effective domain-aware features. We investigate both unsupervised and supervised crowdsourcing learning, assuming that no or only small-scale expert annotations are available. Experimental results on a benchmark crowdsourced NER dataset show that our method is highly effective, leading to a new state-of-the-art performance. In addition, under the supervised setting, we can achieve impressive performance gains with only a very small scale of expert annotations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_433", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_433.flac", "type": "audio", "duration": 721.328}, {"from": "Assistant", "value": "Recent studies strive to incorporate various human rationales into neural networks to improve model performance, but few pay attention to the quality of the rationales. Most existing methods distribute their models’ focus to distantly-labeled rationale words entirely and equally, while ignoring the potential important non-rationale words and not distinguishing the importance of different rationale words. In this paper, we propose two novel auxiliary loss functions to make better use of distantly-labeled rationales, which encourage models to maintain their focus on important words beyond labeled rationales (PINs) and alleviate redundant training on non-helpful rationales (NoIRs). Experiments on two representative classification tasks show that our proposed methods can push a classification model to effectively learn crucial clues from non-perfect rationales while maintaining the ability to spread its focus to other unlabeled important words, thus significantly outperform existing methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_434", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_434.flac", "type": "audio", "duration": 640.043}, {"from": "Assistant", "value": "QA models based on pretrained language models have achieved remarkable performance on various benchmark datasets. However, QA models do not generalize well to unseen data that falls outside the training distribution, due to distributional shifts. Data augmentation (DA) techniques which drop/replace words have shown to be effective in regularizing the model from overfitting to the training data. Yet, they may adversely affect the QA tasks since they incur semantic changes that may lead to wrong answers for the QA task. To tackle this problem, we propose a simple yet effective DA method based on a stochastic noise generator, which learns to perturb the word embedding of the input questions and context without changing their semantics. We validate the performance of the QA models trained with our word embedding perturbation on a single source dataset, on five different target domains. The results show that our method significantly outperforms the baseline DA methods. Notably, the model trained with ours outperforms the model trained with more than 240K artificially generated QA pairs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_435", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_435.flac", "type": "audio", "duration": 547.016}, {"from": "Assistant", "value": "Arguably, the visual perception of conversational agents to the physical world is a key way for them to exhibit the human-like intelligence. Image-grounded conversation is thus proposed to address this challenge. Existing works focus on exploring the multimodal dialog models that ground the conversation on a given image. In this paper, we take a step further to study image-grounded conversation under a fully open-ended setting where no paired dialog and image are assumed available. Specifically, we present Maria, a neural conversation agent powered by the visual world experiences which are retrieved from a large-scale image index. Maria consists of three flexible components,i.e., text-to-image retriever, visual concept detector and visual-knowledge-grounded response generator. The retriever aims to retrieve a correlated image to the dialog from an image index, while the visual concept detector extracts rich visual knowledge from the image. Then, the response generator is grounded on the extracted visual knowledge and dialog context to generate the target response. Extensive experiments demonstrate Maria outperforms previous state-of-the-art methods on automatic metrics and human evaluation, and can generate informative responses that have some visual commonsense of the physical world.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_436", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_436.flac", "type": "audio", "duration": 709.056}, {"from": "Assistant", "value": "Conversational dialogue systems (CDSs) are hard to evaluate due to the complexity of natural language. Automatic evaluation of dialogues often shows insufficient correlation with human judgements. Human evaluation is reliable but labor-intensive. We introduce a human-machine collaborative framework, HMCEval, that can guarantee reliability of the evaluation outcomes with reduced human effort. HMCEval casts dialogue evaluation as a sample assignment problem, where we need to decide to assign a sample to a human or a machine for evaluation. HMCEval includes a model confidence estimation module to estimate the confidence of the predicted sample assignment, and a human effort estimation module to estimate the human effort should the sample be assigned to human evaluation, as well as a sample assignment execution module that finds the optimum assignment solution based on the estimated confidence and effort. We assess the performance of HMCEval on the task of evaluating malevolence in dialogues. The experimental results show that HMCEval achieves around 99% evaluation accuracy with half of the human effort spared, showing that HMCEval provides reliable evaluation outcomes while reducing human effort by a large amount.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_437", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_437.flac", "type": "audio", "duration": 717.055}, {"from": "Assistant", "value": "Conditional Variational AutoEncoder (CVAE) effectively increases the diversity and informativeness of responses in open-ended dialogue generation tasks through enriching the context vector with sampled latent variables. However, due to the inherent one-to-many and many-to-one phenomena in human dialogues, the sampled latent variables may not correctly reflect the contexts’ semantics, leading to irrelevant and incoherent generated responses. To resolve this problem, we propose Self-separated Conditional Variational AutoEncoder (abbreviated as SepaCVAE) that introduces group information to regularize the latent variables, which enhances CVAE by improving the responses’ relevance and coherence while maintaining their diversity and informativeness. SepaCVAE actively divides the input data into groups, and then widens the absolute difference between data pairs from distinct groups, while narrowing the relative distance between data pairs in the same group. Empirical results from automatic evaluation and detailed analysis demonstrate that SepaCVAE can significantly boost responses in well-established open-domain dialogue datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_438", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_438.flac", "type": "audio", "duration": 691.165}, {"from": "Assistant", "value": "Conversational Question Simplification (CQS) aims to simplify self-contained questions into conversational ones by incorporating some conversational characteristics, e.g., anaphora and ellipsis. Existing maximum likelihood estimation based methods often get trapped in easily learned tokens as all tokens are treated equally during training. In this work, we introduce a Reinforcement Iterative Sequence Editing (RISE) framework that optimizes the minimum Levenshtein distance through explicit editing actions. RISE is able to pay attention to tokens that are related to conversational characteristics. To train RISE, we devise an Iterative Reinforce Training (IRT) algorithm with a Dynamic Programming based Sampling (DPS) process to improve exploration. Experimental results on two benchmark datasets show that RISE significantly outperforms state-of-the-art methods and generalizes well on unseen data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_439", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_439.flac", "type": "audio", "duration": 654.059}, {"from": "Assistant", "value": "A video-grounded dialogue system is required to understand both dialogue, which contains semantic dependencies from turn to turn, and video, which contains visual cues of spatial and temporal scene variations. Building such dialogue systems is a challenging problem, involving various reasoning types on both visual and language inputs. Existing benchmarks do not have enough annotations to thoroughly analyze dialogue systems and understand their capabilities and limitations in isolation. These benchmarks are also not explicitly designed to minimise biases that models can exploit without actual reasoning. To address these limitations, in this paper, we present DVD, a Diagnostic Dataset for Video-grounded Dialogue. The dataset is designed to contain minimal biases and has detailed annotations for the different types of reasoning over the spatio-temporal space of video. Dialogues are synthesized over multiple question turns, each of which is injected with a set of cross-turn semantic relationships. We use DVD to analyze existing approaches, providing interesting insights into their abilities and limitations. In total, DVD is built from 11k CATER synthetic videos and contains 10 instances of 10-round dialogues for each video, resulting in more than 100k dialogues and 1M question-answer pairs. Our code and dataset are publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_440", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_440.flac", "type": "audio", "duration": 704.052}, {"from": "Assistant", "value": "Emotion recognition in conversation (ERC) is a crucial component in affective dialogue systems, which helps the system understand users’ emotions and generate empathetic responses. However, most works focus on modeling speaker and contextual information primarily on the textual modality or simply leveraging multimodal information through feature concatenation. In order to explore a more effective way of utilizing both multimodal and long-distance contextual information, we propose a new model based on multimodal fused graph convolutional network, MMGCN, in this work. MMGCN can not only make use of multimodal dependencies effectively, but also leverage speaker information to model inter-speaker and intra-speaker dependency. We evaluate our proposed model on two public benchmark datasets, IEMOCAP and MELD, and the results prove the effectiveness of MMGCN, which outperforms other SOTA methods by a significant margin under the multimodal conversation setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_441", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_441.flac", "type": "audio", "duration": 723.093}, {"from": "Assistant", "value": "A dialogue is essentially a multi-turn interaction among interlocutors. Effective evaluation metrics should reflect the dynamics of such interaction. Existing automatic metrics are focused very much on the turn-level quality, while ignoring such dynamics. To this end, we propose DynaEval, a unified automatic evaluation framework which is not only capable of performing turn-level evaluation, but also holistically considers the quality of the entire dialogue. In DynaEval, the graph convolutional network (GCN) is adopted to model a dialogue in totality, where the graph nodes denote each individual utterance and the edges represent the dependency between pairs of utterances. A contrastive loss is then applied to distinguish well-formed dialogues from carefully constructed negative samples. Experiments show that DynaEval significantly outperforms the state-of-the-art dialogue coherence model, and correlates strongly with human judgements across multiple dialogue evaluation aspects at both turn and dialogue level.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_442", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_442.flac", "type": "audio", "duration": 536.567}, {"from": "Assistant", "value": "Finding codes given natural language query is beneficial to the productivity of software developers. Future progress towards better semantic matching between query and code requires richer supervised training resources. To remedy this, we introduce CoSQA dataset. It includes 20,604 labels for pairs of natural language queries and codes, each annotated by at least 3 human annotators. We further introduce a contrastive learning method dubbed CoCLR to enhance text-code matching, which works as a data augmenter to bring more artificially generated training instances. We show that, evaluated on CodeXGLUE with the same CodeBERT model, training on CoSQA improves the accuracy of code question answering by 5.1% and incorporating CoCLR brings a further improvement of 10.5%.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_444", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_444.flac", "type": "audio", "duration": 548.061}, {"from": "Assistant", "value": "Neural chat translation aims to translate bilingual conversational text, which has a broad application in international exchanges and cooperation. Despite the impressive performance of sentence-level and context-aware Neural Machine Translation (NMT), there still remain challenges to translate bilingual conversational text due to its inherent characteristics such as role preference, dialogue coherence, and translation consistency. In this paper, we aim to promote the translation quality of conversational text by modeling the above properties. Specifically, we design three latent variational modules to learn the distributions of bilingual conversational characteristics. Through sampling from these learned distributions, the latent variables, tailored for role preference, dialogue coherence, and translation consistency, are incorporated into the NMT model for better translation. We evaluate our approach on the benchmark dataset BConTrasT (English<->German) and a self-collected bilingual dialogue corpus, named BMELD (English<->Chinese). Extensive experiments show that our approach notably boosts the performance over strong baselines by a large margin and significantly surpasses some state-of-the-art context-aware NMT models in terms of BLEU and TER. Additionally, we make the BMELD dataset publicly available for the research community.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_445", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_445.flac", "type": "audio", "duration": 649.044}, {"from": "Assistant", "value": "Multilingual neural machine translation with a single model has drawn much attention due to its capability to deal with multiple languages. However, the current multilingual translation paradigm often makes the model tend to preserve the general knowledge, but ignore the language-specific knowledge. Some previous works try to solve this problem by adding various kinds of language-specific modules to the model, but they suffer from the parameter explosion problem and require specialized manual design. To solve these problems, we propose to divide the model neurons into general and language-specific parts based on their importance across languages. The general part is responsible for preserving the general knowledge and participating in the translation of all the languages, while the language-specific part is responsible for preserving the language-specific knowledge and participating in the translation of some specific languages. Experimental results on several language pairs, covering IWSLT and Europarl corpus datasets, demonstrate the effectiveness and universality of the proposed method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_446", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_446.flac", "type": "audio", "duration": 714.641}, {"from": "Assistant", "value": "Multi-source sequence generation (MSG) is an important kind of sequence generation tasks that takes multiple sources, including automatic post-editing, multi-source translation, multi-document summarization, etc. As MSG tasks suffer from the data scarcity problem and recent pretrained models have been proven to be effective for low-resource downstream tasks, transferring pretrained sequence-to-sequence models to MSG tasks is essential. Although directly finetuning pretrained models on MSG tasks and concatenating multiple sources into a single long sequence is regarded as a simple method to transfer pretrained models to MSG tasks, we conjecture that the direct finetuning method leads to catastrophic forgetting and solely relying on pretrained self-attention layers to capture cross-source information is not sufficient. Therefore, we propose a two-stage finetuning method to alleviate the pretrain-finetune discrepancy and introduce a novel MSG model with a fine encoder to learn better representations in MSG tasks. Experiments show that our approach achieves new state-of-the-art results on the WMT17 APE task and multi-source translation task using the WMT14 test set. When adapted to document-level translation, our framework outperforms strong baselines significantly.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_447", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_447.flac", "type": "audio", "duration": 897.057}, {"from": "Assistant", "value": "Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT. Despite its growing popularity, little to no attention has been paid to standardizing and analyzing the design of few-shot experiments. In this work, we highlight a fundamental risk posed by this shortcoming, illustrating that the model exhibits a high degree of sensitivity to the selection of few shots. We conduct a large-scale experimental study on 40 sets of sampled few shots for six diverse NLP tasks across up to 40 languages. We provide an analysis of success and failure cases of few-shot transfer, which highlights the role of lexical features. Additionally, we show that a straightforward full model finetuning approach is quite effective for few-shot transfer, outperforming several state-of-the-art few-shot approaches. As a step towards standardizing few-shot crosslingual experimental designs, we make our sampled few shots publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_448", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_448.flac", "type": "audio", "duration": 457.062}, {"from": "Assistant", "value": "Coreference resolution is essential for natural language understanding and has been long studied in NLP. In recent years, as the format of Question Answering (QA) became a standard for machine reading comprehension (MRC), there have been data collection efforts, e.g., Dasigi et al. (2019), that attempt to evaluate the ability of MRC models to reason about coreference. However, as we show, coreference reasoning in MRC is a greater challenge than earlier thought; MRC datasets do not reflect the natural distribution and, consequently, the challenges of coreference reasoning. Specifically, success on these datasets does not reflect a model’s proficiency in coreference reasoning. We propose a methodology for creating MRC datasets that better reflect the challenges of coreference reasoning and use it to create a sample evaluation set. The results on our dataset show that state-of-the-art models still struggle with these phenomena. Furthermore, we develop an effective way to use naturally occurring coreference phenomena from existing coreference resolution datasets when training MRC models. This allows us to show an improvement in the coreference reasoning abilities of state-of-the-art models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_449", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_449.flac", "type": "audio", "duration": 618.092}, {"from": "Assistant", "value": "One of the main bottlenecks in developing discourse dependency parsers is the lack of annotated training data. A potential solution is to utilize abundant unlabeled data by using unsupervised techniques, but there is so far little research in unsupervised discourse dependency parsing. Fortunately, unsupervised syntactic dependency parsing has been studied by decades, which could potentially be adapted for discourse parsing. In this paper, we propose a simple yet effective method to adapt unsupervised syntactic dependency parsing methodology for unsupervised discourse dependency parsing. We apply the method to adapt two state-of-the-art unsupervised syntactic dependency parsing methods. Experimental results demonstrate that our adaptation is effective. Moreover, we extend the adapted methods to the semi-supervised and supervised setting and surprisingly, we find that they outperform previous methods specially designed for supervised discourse parsing. Further analysis shows our adaptations result in superiority not only in parsing accuracy but also in time and space efficiency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_450", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_450.flac", "type": "audio", "duration": 497.046}, {"from": "Assistant", "value": "We introduce a generic seq2seq parsing framework that casts constituency parsing problems (syntactic and discourse parsing) into a series of conditional splitting decisions. Our parsing model estimates the conditional probability distribution of possible splitting points in a given text span and supports efficient top-down decoding, which is linear in number of nodes. The conditional splitting formulation together with efficient beam search inference facilitate structural consistency without relying on expensive structured inference. Crucially, for discourse analysis we show that in our formulation, discourse segmentation can be framed as a special case of parsing which allows us to perform discourse parsing without requiring segmentation as a pre-requisite. Experiments show that our model achieves good results on the standard syntactic parsing tasks under settings with/without pre-trained representations and rivals state-of-the-art (SoTA) methods that are more computationally expensive than ours. In discourse parsing, our method outperforms SoTA by a good margin.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_451", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_451.flac", "type": "audio", "duration": 717.055}, {"from": "Assistant", "value": "Named Entity Recognition (NER) is the task of identifying spans that represent entities in sentences. Whether the entity spans are nested or discontinuous, the NER task can be categorized into the flat NER, nested NER, and discontinuous NER subtasks. These subtasks have been mainly solved by the token-level sequence labelling or span-level classification. However, these solutions can hardly tackle the three kinds of NER subtasks concurrently. To that end, we propose to formulate the NER subtasks as an entity span sequence generation task, which can be solved by a unified sequence-to-sequence (Seq2Seq) framework. Based on our unified framework, we can leverage the pre-trained Seq2Seq model to solve all three kinds of NER subtasks without the special design of the tagging schema or ways to enumerate spans. We exploit three types of entity representations to linearize entities into a sequence. Our proposed framework is easy-to-implement and achieves state-of-the-art (SoTA) or near SoTA performance on eight English NER datasets, including two flat NER datasets, three nested NER datasets, and three discontinuous NER datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_452", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_452.flac", "type": "audio", "duration": 612.055}, {"from": "Assistant", "value": "Unlike English letters, Chinese characters have rich and specific meanings. Usually, the meaning of a word can be derived from its constituent characters in some way. Several previous works on syntactic parsing propose to annotate shallow word-internal structures for better utilizing character-level information. This work proposes to model the deep internal structures of Chinese words as dependency trees with 11 labels for distinguishing syntactic relationships. First, based on newly compiled annotation guidelines, we manually annotate a word-internal structure treebank (WIST) consisting of over 30K multi-char words from Chinese Penn Treebank. To guarantee quality, each word is independently annotated by two annotators and inconsistencies are handled by a third senior annotator. Second, we present detailed and interesting analysis on WIST to reveal insights on Chinese word formation. Third, we propose word-internal structure parsing as a new task, and conduct benchmark experiments using a competitive dependency parser. Finally, we present two simple ways to encode word-internal structures, leading to promising gains on the sentence-level syntactic parsing task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_453", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_453.flac", "type": "audio", "duration": 719.54}, {"from": "Assistant", "value": "Named Entity Recognition (NER) for low-resource languages is a both practical and challenging research problem. This paper addresses zero-shot transfer for cross-lingual NER, especially when the amount of source-language training data is also limited. The paper first proposes a simple but effective labeled sequence translation method to translate source-language training data to target languages and avoids problems such as word order change and entity span determination. With the source-language data as well as the translated data, a generation-based multilingual data augmentation method is introduced to further increase diversity by generating synthetic labeled data in multiple languages. These augmented data enable the language model based NER models to generalize better with both the language-specific features from the target-language synthetic data and the language-independent features from multilingual synthetic data. An extensive set of experiments were conducted to demonstrate encouraging cross-lingual transfer performance of the new research on a wide variety of target languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_454", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_454.flac", "type": "audio", "duration": 640.522}, {"from": "Assistant", "value": "Lexicon information and pre-trained models, such as BERT, have been combined to explore Chinese sequence labeling tasks due to their respective strengths. However, existing methods solely fuse lexicon features via a shallow and random initialized sequence layer and do not integrate them into the bottom layers of BERT. In this paper, we propose Lexicon Enhanced BERT (LEBERT) for Chinese sequence labeling, which integrates external lexicon knowledge into BERT layers directly by a Lexicon Adapter layer. Compared with existing methods, our model facilitates deep lexicon knowledge fusion at the lower layers of BERT. Experiments on ten Chinese datasets of three tasks including Named Entity Recognition, Word Segmentation, and Part-of-Speech Tagging, show that LEBERT achieves state-of-the-art results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_455", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_455.flac", "type": "audio", "duration": 662.047}, {"from": "Assistant", "value": "In recent years, math word problem solving has received considerable attention and achieved promising results, but previous methods rarely take numerical values into consideration. Most methods treat the numerical values in the problems as number symbols, and ignore the prominent role of the numerical values in solving the problem. In this paper, we propose a novel approach called NumS2T, which enhances math word problem solving performance by explicitly incorporating numerical values into a sequence-to-tree network. In addition, a numerical properties prediction mechanism is used to capture the category and comparison information of numerals and measure their importance in global expressions. Experimental results on the Math23K and APE datasets demonstrate that our model achieves better performance than existing state-of-the-art models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_456", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_456.flac", "type": "audio", "duration": 578.525}, {"from": "Assistant", "value": "Previous math word problem solvers following the encoder-decoder paradigm fail to explicitly incorporate essential math symbolic constraints, leading to unexplainable and unreasonable predictions. Herein, we propose Neural-Symbolic Solver (NS-Solver) to explicitly and seamlessly incorporate different levels of symbolic constraints by auxiliary tasks. Our NS-Solver consists of a problem reader to encode problems, a programmer to generate symbolic equations, and a symbolic executor to obtain answers. Along with target expression supervision, our solver is also optimized via 4 new auxiliary objectives to enforce different symbolic reasoning: a) self-supervised number prediction task predicting both number quantity and number locations; b) commonsense constant prediction task predicting what prior knowledge (e.g. how many legs a chicken has) is required; c) program consistency checker computing the semantic loss between predicted equation and target equation to ensure reasonable equation mapping; d) duality exploiting task exploiting the quasi-duality between symbolic equation generation and problem’s part-of-speech generation to enhance the understanding ability of a solver. Besides, to provide a more realistic and challenging benchmark for developing a universal and scalable solver, we also construct a new largescale MWP benchmark CM17K consisting of 4 kinds of MWPs (arithmetic, one-unknown linear, one-unknown non-linear, equation set) with more than 17K samples. Extensive experiments on Math23K and our CM17k demonstrate the superiority of our NS-Solver compared to state-of-the-art methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_457", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_457.flac", "type": "audio", "duration": 465.045}, {"from": "Assistant", "value": "Recently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding. For medical domains, the background knowledge sources are especially useful, due to the massive medical terms and their complicated relations are difficult to understand in text. In this work, we introduce SMedBERT, a medical PLM trained on large-scale medical corpora, incorporating deep structured semantic knowledge from neighbours of linked-entity. In SMedBERT, the mention-neighbour hybrid attention is proposed to learn heterogeneous-entity information, which infuses the semantic representations of entity types into the homogeneous neighbouring entity structure. Apart from knowledge integration as external features, we propose to employ the neighbors of linked-entities in the knowledge graph as additional global contexts of text mentions, allowing them to communicate via shared neighbors, thus enrich their semantic representations. Experiments demonstrate that SMedBERT significantly outperforms strong baselines in various knowledge-intensive Chinese medical tasks. It also improves the performance of other tasks such as question answering, question matching and natural language inference.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_458", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_458.flac", "type": "audio", "duration": 846.042}, {"from": "Assistant", "value": "When evaluating an article and the claims it makes, a critical reader must be able to assess where the information presented comes from, and whether the various claims are mutually consistent and support the conclusion. This motivates the study ofclaim provenance, which seeks to trace and explain the origins of claims. In this paper, we introduce new techniques to model and reason about the provenance ofmultipleinteracting claims, including how to capturefine-grainedinformation about the context. Our solution hinges on first identifying the sentences that potentially contain important external information. We then develop a query generator with our novelrank-aware cross attentionmechanism, which aims at generating metadata for the source article, based on the context and the signals collected from a search engine. This establishes relevant search queries, and it allows us to obtain source article candidates for each identified sentence and propose an ILP based algorithm to infer the best sources. We experiment with a newly created evaluation dataset, Politi-Prov, based on fact-checking articles fromwww.politifact.com; our experimental results show that our solution leads to a significant improvement over baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_459", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_459.flac", "type": "audio", "duration": 482.046}, {"from": "Assistant", "value": "Medical imaging plays a significant role in clinical practice of medical diagnosis, where the text reports of the images are essential in understanding them and facilitating later treatments. By generating the reports automatically, it is beneficial to help lighten the burden of radiologists and significantly promote clinical automation, which already attracts much attention in applying artificial intelligence to medical domain. Previous studies mainly follow the encoder-decoder paradigm and focus on the aspect of text generation, with few studies considering the importance of cross-modal mappings and explicitly exploit such mappings to facilitate radiology report generation. In this paper, we propose a cross-modal memory networks (CMN) to enhance the encoder-decoder framework for radiology report generation, where a shared memory is designed to record the alignment between images and texts so as to facilitate the interaction and generation across modalities. Experimental results illustrate the effectiveness of our proposed model, where state-of-the-art performance is achieved on two widely used benchmark datasets, i.e., IU X-Ray and MIMIC-CXR. Further analyses also prove that our model is able to better align information from radiology images and texts so as to help generating more accurate reports in terms of clinical indicators.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_460", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_460.flac", "type": "audio", "duration": 712.853}, {"from": "Assistant", "value": "There is content such as hate speech, offensive, toxic or aggressive documents, which are perceived differently by their consumers. They are commonly identified using classifiers solely based on textual content that generalize pre-agreed meanings of difficult problems. Such models provide the same results for each user, which leads to high misclassification rate observable especially for contentious, aggressive documents. Both document controversy and user nonconformity require new solutions. Therefore, we propose novel personalized approaches that respect individual beliefs expressed by either user conformity-based measures or various embeddings of their previous text annotations. We found that only a few annotations of most controversial documents are enough for all our personalization methods to significantly outperform classic, generalized solutions. The more controversial the content, the greater the gain. The personalized solutions may be used to efficiently filter unwanted aggressive content in the way adjusted to a given person.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_461", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_461.flac", "type": "audio", "duration": 659.563}, {"from": "Assistant", "value": "As more and more product reviews are posted in both text and images, Multimodal Review Analysis (MRA) becomes an attractive research topic. Among the existing review analysis tasks, helpfulness prediction on review text has become predominant due to its importance for e-commerce platforms and online shops, i.e. helping customers quickly acquire useful product information. This paper proposes a new task Multimodal Review Helpfulness Prediction (MRHP) aiming to analyze the review helpfulness from text and visual modalities. Meanwhile, a novel Multi-perspective Coherent Reasoning method (MCR) is proposed to solve the MRHP task, which conducts joint reasoning over texts and images from both the product and the review, and aggregates the signals to predict the review helpfulness. Concretely, we first propose a product-review coherent reasoning module to measure the intra- and inter-modal coherence between the target product and the review. In addition, we also devise an intra-review coherent reasoning module to identify the coherence between the text content and images of the review, which is a piece of strong evidence for review helpfulness prediction. To evaluate the effectiveness of MCR, we present two newly collected multimodal review datasets as benchmark evaluation resources for the MRHP task. Experimental results show that our MCR method can lead to a performance increase of up to 8.5% as compared to the best performing text-only model. The source code and datasets can be obtained fromhttps://github.com/jhliu17/MCR.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_462", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_462.flac", "type": "audio", "duration": 566.172}, {"from": "Assistant", "value": "In this paper, we propose Shallow Aggressive Decoding (SAD) to improve the online inference efficiency of the Transformer for instantaneous Grammatical Error Correction (GEC). SAD optimizes the online inference efficiency for GEC by two innovations: 1) it aggressively decodes as many tokens as possible in parallel instead of always decoding only one token in each step to improve computational parallelism; 2) it uses a shallow decoder instead of the conventional Transformer architecture with balanced encoder-decoder depth to reduce the computational cost during inference. Experiments in both English and Chinese GEC benchmarks show that aggressive decoding could yield identical predictions to greedy decoding but with significant speedup for online inference. Its combination with the shallow decoder could offer an even higher online inference speedup over the powerful Transformer baseline without quality loss. Not only does our approach allow a single model to achieve the state-of-the-art results in English GEC benchmarks: 66.4 F0.5 in the CoNLL-14 and 72.9 F0.5 in the BEA-19 test set with an almost 10x online inference speedup over the Transformer-big model, but also it is easily adapted to other languages. Our code is available athttps://github.com/AutoTemp/Shallow-Aggressive-Decoding.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_464", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_464.flac", "type": "audio", "duration": 663.04}, {"from": "Assistant", "value": "Chinese Spelling Check (CSC) is a challenging task due to the complex characteristics of Chinese characters. Statistics reveal that most Chinese spelling errors belong to phonological or visual errors. However, previous methods rarely utilize phonological and morphological knowledge of Chinese characters or heavily rely on external resources to model their similarities. To address the above issues, we propose a novel end-to-end trainable model called PHMOSpell, which promotes the performance of CSC with multi-modal information. Specifically, we derive pinyin and glyph representations for Chinese characters from audio and visual modalities respectively, which are integrated into a pre-trained language model by a well-designed adaptive gating mechanism. To verify its effectiveness, we conduct comprehensive experiments and ablation tests. Experimental results on three shared benchmarks demonstrate that our model consistently outperforms previous state-of-the-art models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_465", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_465.flac", "type": "audio", "duration": 732.102}, {"from": "Assistant", "value": "This paper explores the task of Difficulty-Controllable Question Generation (DCQG), which aims at generating questions with required difficulty levels. Previous research on this task mainly defines the difficulty of a question as whether it can be correctly answered by a Question Answering (QA) system, lacking interpretability and controllability. In our work, we redefine question difficulty as the number of inference steps required to answer it and argue that Question Generation (QG) systems should have stronger control over the logic of generated questions. To this end, we propose a novel framework that progressively increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain. A dataset is automatically constructed to facilitate the research, on which extensive experiments are conducted to test the performance of our method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_466", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_466.flac", "type": "audio", "duration": 695.851}, {"from": "Assistant", "value": "Table-to-text generation aims at automatically generating natural text to help people conveniently obtain salient information in tables. Although neural models for table-to-text have achieved remarkable progress, some problems are still overlooked. Previous methods cannot deduce the factual results from the entity’s (player or team) performance and the relations between entities. To solve this issue, we first build an entity graph from the input tables and introduce a reasoning module to perform reasoning on the graph. Moreover, there are different relations (e.g., the numeric size relation and the importance relation) between records in different dimensions. And these relations may contribute to the data-to-text generation. However, it is hard for a vanilla encoder to capture these. Consequently, we propose to utilize two auxiliary tasks, Number Ranking (NR) and Importance Ranking (IR), to supervise the encoder to capture the different relations. Experimental results on ROTOWIRE and RW-FG show that our method not only has a good generalization but also outperforms previous methods on several metrics: BLEU, Content Selection, Content Ordering.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_467", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_467.flac", "type": "audio", "duration": 621.056}, {"from": "Assistant", "value": "The multimodality problem has become a major challenge of existing non-autoregressive generation (NAG) systems. A common solution often resorts to sequence-level knowledge distillation by rebuilding the training dataset through autoregressive generation (hereinafter known as “teacher AG”). The success of such methods may largely depend on a latent assumption, i.e., the teacher AG is superior to the NAG model. However, in this work, we experimentally reveal that this assumption does not always hold for the text generation tasks like text summarization and story ending generation. To provide a feasible solution to the multimodality problem of NAG, we propose incorporating linguistic structure (Part-of-Speech sequence in particular) into NAG inference instead of relying on teacher AG. More specifically, the proposed POS-constrained Parallel Decoding (POSPD) method aims at providing a specific POS sequence to constrain the NAG model during decoding. Our experiments demonstrate that POSPD consistently improves NAG models on four text generation tasks to a greater extent compared to knowledge distillation. This observation validates the necessity of exploring the alternatives for sequence-level knowledge distillation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_468", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_468.flac", "type": "audio", "duration": 677.056}, {"from": "Assistant", "value": "A well-known limitation in pretrain-finetune paradigm lies in its inflexibility caused by the one-size-fits-all vocabulary. This potentially weakens the effect when applying pretrained models into natural language generation (NLG) tasks, especially for the subword distributions between upstream and downstream tasks with significant discrepancy. Towards approaching this problem, we extend the vanilla pretrain-finetune pipeline with an extra embedding transfer step. Specifically, a plug-and-play embedding generator is introduced to produce the representation of any input token, according to pre-trained embeddings of its morphologically similar ones. Thus, embeddings of mismatch tokens in downstream tasks can also be efficiently initialized. We conduct experiments on a variety of NLG tasks under the pretrain-finetune fashion. Experimental results and extensive analyses show that the proposed strategy offers us opportunities to feel free to transfer the vocabulary, leading to more efficient and better performed downstream NLG models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_469", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_469.flac", "type": "audio", "duration": 666.041}, {"from": "Assistant", "value": "In order to deeply understand the capability of pretrained language models in text generation and conduct a diagnostic evaluation, we propose TGEA, an error-annotated dataset with multiple benchmark tasks for text generation from pretrained language models (PLMs). We use carefully selected prompt words to guide GPT-2 to generate candidate sentences, from which we select 47K for error annotation. Crowdsourced workers manually check each of these sentences and detect 12k erroneous sentences. We create an error taxonomy to cover 24 types of errors occurring in these erroneous sentences according to the nature of errors with respect to linguistics and knowledge (e.g., common sense). For each erroneous span in PLM-generated sentences, we also detect another span that is closely associated with it. Each error is hence manually labeled with comprehensive annotations, including the span of the error, the associated span, minimal correction to the error, the type of the error, and rationale behind the error. Apart from the fully annotated dataset, we also present a detailed description of the data collection procedure, statistics and analysis of the dataset. This is the first dataset with comprehensive annotations for PLM-generated texts, which facilitates the diagnostic evaluation of PLM-based text generation. Furthermore, we use TGEA as a benchmark dataset and propose a series of automatic diagnosis tasks, including error detection, error type classification, associated span detection, error rationale generation, to further promote future study on the automatic error detection and correction on texts generated by pretrained language models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_470", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_470.flac", "type": "audio", "duration": 595.081}, {"from": "Assistant", "value": "Transformer-based models have achieved state-of-the-art results in a wide range of natural language processing (NLP) tasks including document summarization. Typically these systems are trained by fine-tuning a large pre-trained model to the target task. One issue with these transformer-based models is that they do not scale well in terms of memory and compute requirements as the input length grows. Thus, for long document summarization, it can be challenging to train or fine-tune these models. In this work, we exploit large pre-trained transformer-based models and address long-span dependencies in abstractive summarization using two methods: local self-attention; and explicit content selection. These approaches are compared on a range of network configurations. Experiments are carried out on standard long-span summarization tasks, including Spotify Podcast, arXiv, and PubMed datasets. We demonstrate that by combining these methods, we can achieve state-of-the-art results on all three tasks in the ROUGE scores. Moreover, without a large-scale GPU card, our approach can achieve comparable or better results than existing approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_471", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_471.flac", "type": "audio", "duration": 692.326}, {"from": "Assistant", "value": "In the field of dialogue summarization, due to the lack of training data, it is often difficult for supervised summary generation methods to learn vital information from dialogue context with limited data. Several attempts on unsupervised summarization for text by leveraging semantic information solely or auto-encoder strategy (i.e., sentence compression), it however cannot be adapted to the dialogue scene due to the limited words in utterances and huge gap between the dialogue and its summary. In this study, we propose a novel unsupervised strategy to address this challenge, which roots from the hypothetical foundation that a superior summary approximates a replacement of the original dialogue, and they are roughly equivalent for auxiliary (self-supervised) tasks, e.g., dialogue generation. The proposed strategy RepSum is applied to generate both extractive and abstractive summary with the guidance of the followed nˆth utterance generation and classification tasks. Extensive experiments on various datasets demonstrate the superiority of the proposed model compared with the state-of-the-art methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_472", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_472.flac", "type": "audio", "duration": 747.009}, {"from": "Assistant", "value": "Abstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text. In this paper, we present BASS, a novel framework for Boosting Abstractive Summarization based on a unified Semantic graph, which aggregates co-referent phrases distributing across a long range of context and conveys rich relations between phrases. Further, a graph-based encoder-decoder model is proposed to improve both the document representation and summary generation process by leveraging the graph structure. Specifically, several graph augmentation methods are designed to encode both the explicit and implicit relations in the text while the graph-propagation attention mechanism is developed in the decoder to select salient content into the summary. Empirical results show that the proposed architecture brings substantial improvements for both long-document and multi-document summarization tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_473", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_473.flac", "type": "audio", "duration": 678.528}, {"from": "Assistant", "value": "Given a set of related publications, related work section generation aims to provide researchers with an overview of the specific research area by summarizing these works and introducing them in a logical order. Most of existing related work generation models follow the inflexible extractive style, which directly extract sentences from multiple original papers to form a related work discussion. Hence, in this paper, we propose a Relation-aware Related work Generator (RRG), which generates an abstractive related work from the given multiple scientific papers in the same research area. Concretely, we propose a relation-aware multi-document encoder that relates one document to another according to their content dependency in a relation graph. The relation graph and the document representation are interacted and polished iteratively, complementing each other in the training process. We also contribute two public datasets composed of related work sections and their corresponding papers. Extensive experiments on the two datasets show that the proposed model brings substantial improvements over several strong baselines. We hope that this work will promote advances in related work generation task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_474", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_474.flac", "type": "audio", "duration": 714.06}, {"from": "Assistant", "value": "Professional summaries are written with document-level information, such as the theme of the document, in mind. This is in contrast with most seq2seq decoders which simultaneously learn to focus on salient content, while deciding what to generate, at each decoding step. With the motivation to narrow this gap, we introduce Focus Attention Mechanism, a simple yet effective method to encourage decoders to proactively generate tokens that are similar or topical to the input document. Further, we propose a Focus Sampling method to enable generation of diverse summaries, an area currently understudied in summarization. When evaluated on the BBC extreme summarization task, two state-of-the-art models augmented with Focus Attention generate summaries that are closer to the target and more faithful to their input documents, outperforming their vanilla counterparts on ROUGE and multiple faithfulness measures. We also empirically demonstrate that Focus Sampling is more effective in generating diverse and faithful summaries than top-k or nucleus sampling-based decoding methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_475", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_475.flac", "type": "audio", "duration": 618.046}, {"from": "Assistant", "value": "The availability of large-scale datasets has driven the development of neural models that create generic summaries from single or multiple documents. In this work we consider query focused summarization (QFS), a task for which training data in the form of queries, documents, and summaries is not readily available. We propose to decompose QFS into (1) query modeling (i.e., finding supportive evidence within a set of documents for a query) and (2) conditional language modeling (i.e., summary generation). We introduce MaRGE, a Masked ROUGE Regression framework for evidence estimation and ranking which relies on a unified representation for summaries and queries, so that summaries in generic data can be converted into proxy queries for learning a query model. Experiments across QFS benchmarks and query types show that our model achieves state-of-the-art performance despite learning from weak supervision.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_476", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_476.flac", "type": "audio", "duration": 636.864}, {"from": "Assistant", "value": "This paper studies the bias problem of multi-hop question answering models, of answering correctly without correct reasoning. One way to robustify these models is by supervising to not only answer right, but also with right reasoning chains. An existing direction is to annotate reasoning chains to train models, requiring expensive additional annotations. In contrast, we propose a new approach to learn evidentiality, deciding whether the answer prediction is supported by correct evidences, without such annotations. Instead, we compare counterfactual changes in answer confidence with and without evidence sentences, to generate “pseudo-evidentiality” annotations. We validate our proposed model on an original set and challenge set in HotpotQA, showing that our method is accurate and robust in multi-hop reasoning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_477", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_477.flac", "type": "audio", "duration": 705.051}, {"from": "Assistant", "value": "Dense passage retrieval has been shown to be an effective approach for information retrieval tasks such as open domain question answering. Under this paradigm, a dual-encoder model is learned to encode questions and passages separately into vector representations, and all the passage vectors are then pre-computed and indexed, which can be efficiently retrieved by vector space search during inference time. In this paper, we propose a new contrastive learning method called Cross Momentum Contrastive learning (xMoCo), for learning a dual-encoder model for question-passage matching. Our method efficiently maintains a large pool of negative samples like the original MoCo, and by jointly optimizing question-to-passage and passage-to-question matching tasks, enables using separate encoders for questions and passages. We evaluate our method on various open-domain question answering dataset, and the experimental results show the effectiveness of the proposed method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_478", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_478.flac", "type": "audio", "duration": 719.052}, {"from": "Assistant", "value": "One of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis. However, existing approaches do not explicitly train QA models on how to resolve the dependency, and thus these models are limited in understanding human dialogues. In this paper, we propose a novel framework, ExCorD (Explicit guidance on how to resolve Conversational Dependency) to enhance the abilities of QA models in comprehending conversational context. ExCorD first generates self-contained questions that can be understood without the conversation history, then trains a QA model with the pairs of original and self-contained questions using a consistency-based regularizer. In our experiments, we demonstrate that ExCorD significantly improves the QA models’ performance by up to 1.2 F1 on QuAC, and 5.2 F1 on CANARD, while addressing the limitations of the existing approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_479", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_479.flac", "type": "audio", "duration": 687.102}, {"from": "Assistant", "value": "We present a new human-human dialogue dataset - PhotoChat, the first dataset that casts light on the photo sharing behavior in online messaging. PhotoChat contains 12k dialogues, each of which is paired with a user photo that is shared during the conversation. Based on this dataset, we propose two tasks to facilitate research on image-text modeling: a photo-sharing intent prediction task that predicts whether one intends to share a photo in the next conversation turn, and a photo retrieval task that retrieves the most relevant photo according to the dialogue context. In addition, for both tasks, we provide baseline models using the state-of-the-art models and report their benchmark performances. The best image retrieval model achieves 10.4% recall@1 (out of 1000 candidates) and the best photo intent prediction model achieves 58.1% F1 score, indicating that the dataset presents interesting yet challenging real-world problems. We are releasing PhotoChat to facilitate future research work among the community.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_480", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_480.flac", "type": "audio", "duration": 633.045}, {"from": "Assistant", "value": "A neural multimodal machine translation (MMT) system is one that aims to perform better translation by extending conventional text-only translation models with multimodal information. Many recent studies report improvements when equipping their models with the multimodal module, despite the controversy of whether such improvements indeed come from the multimodal part. We revisit the contribution of multimodal information in MMT by devising two interpretable MMT models. To our surprise, although our models replicate similar gains as recently developed multimodal-integrated systems achieved, our models learn to ignore the multimodal information. Upon further investigation, we discover that the improvements achieved by the multimodal models over text-only counterparts are in fact results of the regularization effect. We report empirical findings that highlight the importance of MMT models’ interpretability, and discuss how our findings will benefit future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_481", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_481.flac", "type": "audio", "duration": 704.099}, {"from": "Assistant", "value": "Video Question Answering is a task which requires an AI agent to answer questions grounded in video. This task entails three key challenges: (1) understand the intention of various questions, (2) capturing various elements of the input video (e.g., object, action, causality), and (3) cross-modal grounding between language and vision information. We propose Motion-Appearance Synergistic Networks (MASN), which embed two cross-modal features grounded on motion and appearance information and selectively utilize them depending on the question’s intentions. MASN consists of a motion module, an appearance module, and a motion-appearance fusion module. The motion module computes the action-oriented cross-modal joint representations, while the appearance module focuses on the appearance aspect of the input video. Finally, the motion-appearance fusion module takes each output of the motion module and the appearance module as input, and performs question-guided fusion. As a result, MASN achieves new state-of-the-art performance on the TGIF-QA and MSVD-QA datasets. We also conduct qualitative analysis by visualizing the inference results of MASN.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_482", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_482.flac", "type": "audio", "duration": 675.654}, {"from": "Assistant", "value": "We study the problem of learning a named entity recognition (NER) tagger using noisy labels from multiple weak supervision sources. Though cheap to obtain, the labels from weak supervision sources are often incomplete, inaccurate, and contradictory, making it difficult to learn an accurate NER model. To address this challenge, we propose a conditional hidden Markov model (CHMM), which can effectively infer true labels from multi-source noisy labels in an unsupervised way. CHMM enhances the classic hidden Markov model with the contextual representation power of pre-trained language models. Specifically, CHMM learns token-wise transition and emission probabilities from the BERT embeddings of the input tokens to infer the latent true labels from noisy observations. We further refine CHMM with an alternate-training approach (CHMM-ALT). It fine-tunes a BERT-NER model with the labels inferred by CHMM, and this BERT-NER’s output is regarded as an additional weak source to train the CHMM in return. Experiments on four NER benchmarks from various domains show that our method outperforms state-of-the-art weakly supervised NER models by wide margins.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_483", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_483.flac", "type": "audio", "duration": 642.176}, {"from": "Assistant", "value": "The journey of reducing noise from distant supervision (DS) generated training data has been started since the DS was first introduced into the relation extraction (RE) task. For the past decade, researchers apply the multi-instance learning (MIL) framework to find the most reliable feature from a bag of sentences. Although the pattern of MIL bags can greatly reduce DS noise, it fails to represent many other useful sentence features in the datasets. In many cases, these sentence features can only be acquired by extra sentence-level human annotation with heavy costs. Therefore, the performance of distantly supervised RE models is bounded. In this paper, we go beyond typical MIL framework and propose a novel contrastive instance learning (CIL) framework. Specifically, we regard the initial MIL as the relational triple encoder and constraint positive pairs against negative pairs for each instance. Experiments demonstrate the effectiveness of our proposed framework, with significant improvements over the previous methods on NYT10, GDS and KBP.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_484", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_484.flac", "type": "audio", "duration": 601.536}, {"from": "Assistant", "value": "Distant supervision for relation extraction provides uniform bag labels for each sentence inside the bag, while accurate sentence labels are important for downstream applications that need the exact relation type. Directly using bag labels for sentence-level training will introduce much noise, thus severely degrading performance. In this work, we propose the use of negative training (NT), in which a model is trained using complementary labels regarding that “the instance does not belong to these complementary labels”. Since the probability of selecting a true label as a complementary label is low, NT provides less noisy information. Furthermore, the model trained with NT is able to separate the noisy data from the training data. Based on NT, we propose a sentence-level framework, SENT, for distant relation extraction. SENT not only filters the noisy data to construct a cleaner dataset, but also performs a re-labeling process to transform the noisy data into useful training data, thus further benefiting the model’s performance. Experimental results show the significant improvement of the proposed method over previous methods on sentence-level evaluation and de-noise effect.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_485", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_485.flac", "type": "audio", "duration": 719.765}, {"from": "Assistant", "value": "Medical named entity recognition (NER) and normalization (NEN) are fundamental for constructing knowledge graphs and building QA systems. Existing implementations for medical NER and NEN are suffered from the error propagation between the two tasks. The mispredicted mentions from NER will directly influence the results of NEN. Therefore, the NER module is the bottleneck of the whole system. Besides, the learnable features for both tasks are beneficial to improving the model performance. To avoid the disadvantages of existing models and exploit the generalized representation across the two tasks, we design an end-to-end progressive multi-task learning model for jointly modeling medical NER and NEN in an effective way. There are three level tasks with progressive difficulty in the framework. The progressive tasks can reduce the error propagation with the incremental task settings which implies the lower level tasks gain the supervised signals other than errors from the higher level tasks to improve their performances. Besides, the context features are exploited to enrich the semantic information of entity mentions extracted by NER. The performance of NEN profits from the enhanced entity mention features. The standard entities from knowledge bases are introduced into the NER module for extracting corresponding entity mentions correctly. The empirical results on two publicly available medical literature datasets demonstrate the superiority of our method over nine typical methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_486", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_486.flac", "type": "audio", "duration": 474.048}, {"from": "Assistant", "value": "Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction. Recent methods achieve considerable performance but still suffer from some inherent limitations, such as redundancy of relation prediction, poor generalization of span-based extraction and inefficiency. In this paper, we decompose this task into three subtasks, Relation Judgement, Entity Extraction and Subject-object Alignment from a novel perspective and then propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC). Specifically, we design a component to predict potential relations, which constrains the following entity extraction to the predicted relation subset rather than all relations; then a relation-specific sequence tagging component is applied to handle the overlapping problem between subjects and objects; finally, a global correspondence component is designed to align the subject and object into a triple with low-complexity. Extensive experiments show that PRGC achieves state-of-the-art performance on public benchmarks with higher efficiency and delivers consistent performance gain on complex scenarios of overlapping triples. The source code has been submitted as the supplementary material and will be made publicly available after the blind review.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_487", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_487.flac", "type": "audio", "duration": 500.042}, {"from": "Assistant", "value": "Few-shot Named Entity Recognition (NER) exploits only a handful of annotations to iden- tify and classify named entity mentions. Pro- totypical network shows superior performance on few-shot NER. However, existing prototyp- ical methods fail to differentiate rich seman- tics in other-class words, which will aggravate overfitting under few shot scenario. To address the issue, we propose a novel model, Mining Undefined Classes from Other-class (MUCO), that can automatically induce different unde- fined classes from the other class to improve few-shot NER. With these extra-labeled unde- fined classes, our method will improve the dis- criminative ability of NER classifier and en- hance the understanding of predefined classes with stand-by semantic knowledge. Experi- mental results demonstrate that our model out- performs five state-of-the-art models in both 1- shot and 5-shots settings on four NER bench- marks. We will release the code upon accep- tance. The source code is released on https: //github.com/shuaiwa16/OtherClassNER.git.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_488", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_488.flac", "type": "audio", "duration": 718.165}, {"from": "Assistant", "value": "Compared to the general news domain, information extraction (IE) from biomedical text requires much broader domain knowledge. However, many previous IE methods do not utilize any external knowledge during inference. Due to the exponential growth of biomedical publications, models that do not go beyond their fixed set of parameters will likely fall behind. Inspired by how humans look up relevant information to comprehend a scientific text, we present a novel framework that utilizes external knowledge for joint entity and relation extraction named KECI (Knowledge-Enhanced Collective Inference). Given an input text, KECI first constructs an initial span graph representing its initial understanding of the text. It then uses an entity linker to form a knowledge graph containing relevant background knowledge for the the entity mentions in the text. To make the final predictions, KECI fuses the initial span graph and the knowledge graph into a more refined graph using an attention mechanism. KECI takes a collective approach to link mention spans to entities by integrating global relational information into local representations using graph convolutional networks. Our experimental results show that the framework is highly effective, achieving new state-of-the-art results in two different benchmark datasets: BioRelEx (binding interaction detection) and ADE (adverse drug event extraction). For example, KECI achieves absolute improvements of 4.59% and 4.91% in F1 scores over the state-of-the-art on the BioRelEx entity and relation extraction tasks", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_489", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_489.flac", "type": "audio", "duration": 616.861}, {"from": "Assistant", "value": "Biomedical Information Extraction from scientific literature presents two unique and non-trivial challenges. First, compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements. Moreover, comprehending the fine-grained scientific entities and events urgently requires domain-specific background knowledge. In this paper, we propose a novel biomedical Information Extraction (IE) model to tackle these two challenges and extract scientific entities and events from English research papers. We perform Abstract Meaning Representation (AMR) to compress the wide context to uncover a clear semantic structure for each complex sentence. Besides, we construct the sentence-level knowledge graph from an external knowledge base and use it to enrich the AMR graph to improve the model’s understanding of complex scientific concepts. We use an edge-conditioned graph attention network to encode the knowledge-enriched AMR graph for biomedical IE tasks. Experiments on the GENIA 2011 dataset show that the AMR and external knowledge have contributed 1.8% and 3.0% absolute F-score gains respectively. In order to evaluate the impact of our approach on real-world problems that involve topic-specific fine-grained knowledge elements, we have also created a new ontology and annotated corpus for entity and event extraction for the COVID-19 scientific literature, which can serve as a new benchmark for the biomedical IE community.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_490", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_490.flac", "type": "audio", "duration": 644.841}, {"from": "Assistant", "value": "Event Detection (ED) aims to recognize mentions of events (i.e., event triggers) and their types in text. Recently, several ED datasets in various domains have been proposed. However, the major limitation of these resources is the lack of enough training data for individual event types which hinders the efficient training of data-hungry deep learning models. To overcome this issue, we propose to exploit the powerful pre-trained language model GPT-2 to generate training samples for ED. To prevent the noises inevitable in automatically generated data from hampering training process, we propose to exploit a teacher-student architecture in which the teacher is supposed to learn anchor knowledge from the original data. The student is then trained on combination of the original and GPT-generated data while being led by the anchor knowledge from the teacher. Optimal transport is introduced to facilitate the anchor knowledge-based guidance between the two networks. We evaluate the proposed model on multiple ED benchmark datasets, gaining consistent improvement and establishing state-of-the-art results for ED.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_491", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_491.flac", "type": "audio", "duration": 702.102}, {"from": "Assistant", "value": "Event extraction (EE) has considerably benefited from pre-trained language models (PLMs) by fine-tuning. However, existing pre-training methods have not involved modeling event characteristics, resulting in the developed EE models cannot take full advantage of large-scale unsupervised data. To this end, we propose CLEVE, a contrastive pre-training framework for EE to better learn event knowledge from large unsupervised data and their semantic structures (e.g. AMR) obtained with automatic parsers. CLEVE contains a text encoder to learn event semantics and a graph encoder to learn event structures respectively. Specifically, the text encoder learns event semantic representations by self-supervised contrastive learning to represent the words of the same events closer than those unrelated words; the graph encoder learns event structure representations by graph contrastive pre-training on parsed event-related semantic structures. The two complementary representations then work together to improve both the conventional supervised EE and the unsupervised “liberal” EE, which requires jointly extracting events and discovering event schemata without any annotated data. Experiments on ACE 2005 and MAVEN datasets show that CLEVE achieves significant improvements, especially in the challenging unsupervised setting. The source code and pre-trained checkpoints can be obtained fromhttps://github.com/THU-KEG/CLEVE.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_492", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_492.flac", "type": "audio", "duration": 502.062}, {"from": "Assistant", "value": "Document-level event extraction (DEE) is indispensable when events are described throughout a document. We argue that sentence-level extractors are ill-suited to the DEE task where event arguments always scatter across sentences and multiple events may co-exist in a document. It is a challenging task because it requires a holistic understanding of the document and an aggregated ability to assemble arguments across multiple sentences. In this paper, we propose an end-to-end model, which can extract structured events from a document in a parallel manner. Specifically, we first introduce a document-level encoder to obtain the document-aware representations. Then, a multi-granularity non-autoregressive decoder is used to generate events in parallel. Finally, to train the entire model, a matching loss function is proposed, which can bootstrap a global optimization. The empirical results on the widely used DEE dataset show that our approach significantly outperforms current state-of-the-art methods in the challenging DEE task. Code will be available athttps://github.com/HangYang-NLP/DE-PPN.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_493", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_493.flac", "type": "audio", "duration": 636.575}, {"from": "Assistant", "value": "Large pre-trained language models achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, they almost exclusively focus on text-only representation, while neglecting cell-level layout information that is important for form image understanding. In this paper, we propose a new pre-training approach, StructuralLM, to jointly leverage cell and layout information from scanned documents. Specifically, we pre-train StructuralLM with two new designs to make the most of the interactions of cell and layout information: 1) each cell as a semantic unit; 2) classification of cell positions. The pre-trained StructuralLM achieves new state-of-the-art results in different types of downstream tasks, including form understanding (from 78.95 to 85.14), document visual question answering (from 72.59 to 83.94) and document image classification (from 94.43 to 96.08).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_494", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_494.flac", "type": "audio", "duration": 587.929}, {"from": "Assistant", "value": "Aspect-based sentiment analysis is a fine-grained sentiment classification task. Recently, graph neural networks over dependency trees have been explored to explicitly model connections between aspects and opinion words. However, the improvement is limited due to the inaccuracy of the dependency parsing results and the informal expressions and complexity of online reviews. To overcome these challenges, in this paper, we propose a dual graph convolutional networks (DualGCN) model that considers the complementarity of syntax structures and semantic correlations simultaneously. Particularly, to alleviate dependency parsing errors, we design a SynGCN module with rich syntactic knowledge. To capture semantic correlations, we design a SemGCN module with self-attention mechanism. Furthermore, we propose orthogonal and differential regularizers to capture semantic correlations between words precisely by constraining attention scores in the SemGCN module. The orthogonal regularizer encourages the SemGCN to learn semantically correlated words with less overlap for each word. The differential regularizer encourages the SemGCN to learn semantic features that the SynGCN fails to capture. Experimental results on three public datasets show that our DualGCN model outperforms state-of-the-art methods and verify the effectiveness of our model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_495", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_495.flac", "type": "audio", "duration": 661.258}, {"from": "Assistant", "value": "Aspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence. In this paper, we formulate ACD in the few-shot learning scenario. However, existing few-shot learning approaches mainly focus on single-label predictions. These methods can not work well for the ACD task since a sentence may contain multiple aspect categories. Therefore, we propose a multi-label few-shot learning method based on the prototypical network. To alleviate the noise, we design two effective attention mechanisms. The support-set attention aims to extract better prototypes by removing irrelevant aspects. The query-set attention computes multiple prototype-specific representations for each query instance, which are then used to compute accurate distances with the corresponding prototypes. To achieve multi-label inference, we further learn a dynamic threshold per instance by a policy network. Extensive experimental results on three datasets demonstrate that the proposed method significantly outperforms strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_496", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_496.flac", "type": "audio", "duration": 586.606}, {"from": "Assistant", "value": "Argument pair extraction (APE) is a research task for extracting arguments from two passages and identifying potential argument pairs. Prior research work treats this task as a sequence labeling problem and a binary classification problem on two passages that are directly concatenated together, which has a limitation of not fully utilizing the unique characteristics and inherent relations of two different passages. This paper proposes a novel attention-guided multi-layer multi-cross encoding scheme to address the challenges. The new model processes two passages with two individual sequence encoders and updates their representations using each other’s representations through attention. In addition, the pair prediction part is formulated as a table-filling problem by updating the representations of two sequences’ Cartesian product. Furthermore, an auxiliary attention loss is introduced to guide each argument to align to its paired argument. An extensive set of experiments show that the new model significantly improves the APE performance over several alternatives.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_497", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_497.flac", "type": "audio", "duration": 652.053}, {"from": "Assistant", "value": "The goal of argumentation mining is to automatically extract argumentation structures from argumentative texts. Most existing methods determine argumentative relations by exhaustively enumerating all possible pairs of argument components, which suffer from low efficiency and class imbalance. Moreover, due to the complex nature of argumentation, there is, so far, no universal method that can address both tree and non-tree structured argumentation. Towards these issues, we propose a neural transition-based model for argumentation mining, which incrementally builds an argumentation graph by generating a sequence of actions, avoiding inefficient enumeration operations. Furthermore, our model can handle both tree and non-tree structured argumentation without introducing any structural constraints. Experimental results show that our model achieves the best performance on two public datasets of different structures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_498", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_498.flac", "type": "audio", "duration": 716.245}, {"from": "Assistant", "value": "This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplicity. We train the model with a novel algorithm to optimize the reward (k-SCST), in which the model proposes several candidate simplifications, computes each candidate’s reward, and encourages candidates that outperform the mean reward. Finally, we propose a realistic text comprehension task as an evaluation method for text simplification. When tested on the English news domain, the KiS model outperforms strong supervised baselines by more than 4 SARI points, and can help people complete a comprehension task an average of 18% faster while retaining accuracy, when compared to the original text.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_499", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_499.flac", "type": "audio", "duration": 616.049}, {"from": "Assistant", "value": "Generating long and coherent text is an important but challenging task, particularly for open-ended language generation tasks such as story generation. Despite the success in modeling intra-sentence coherence, existing generation models (e.g., BART) still struggle to maintain a coherent event sequence throughout the generated text. We conjecture that this is because of the difficulty for the decoder to capture the high-level semantics and discourse structures in the context beyond token-level co-occurrence. In this paper, we propose a long text generation model, which can represent the prefix sentences at sentence level and discourse level in the decoding process. To this end, we propose two pretraining objectives to learn the representations by predicting inter-sentence semantic similarity and distinguishing between normal and shuffled sentence orders. Extensive experiments show that our model can generate more coherent texts than state-of-the-art baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_500", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_500.flac", "type": "audio", "duration": 691.049}, {"from": "Assistant", "value": "Automatic metrics are essential for developing natural language generation (NLG) models, particularly for open-ended language generation tasks such as story generation. However, existing automatic metrics are observed to correlate poorly with human evaluation. The lack of standardized benchmark datasets makes it difficult to fully evaluate the capabilities of a metric and fairly compare different metrics. Therefore, we propose OpenMEVA, a benchmark for evaluating open-ended story generation metrics. OpenMEVA provides a comprehensive test suite to assess the capabilities of metrics, including (a) the correlation with human judgments, (b) the generalization to different model outputs and datasets, (c) the ability to judge story coherence, and (d) the robustness to perturbations. To this end, OpenMEVA includes both manually annotated stories and auto-constructed test examples. We evaluate existing metrics on OpenMEVA and observe that they have poor correlation with human judgments, fail to recognize discourse-level incoherence, and lack inferential knowledge (e.g., causal order between events), the generalization ability and robustness. Our study presents insights for developing NLG models and metrics in further research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_501", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_501.flac", "type": "audio", "duration": 713.572}, {"from": "Assistant", "value": "We study the task of long-form opinion text generation, which faces at least two distinct challenges. First, existing neural generation models fall short of coherence, thus requiring efficient content planning. Second, diverse types of information are needed to guide the generator to cover both subjective and objective content. To this end, we propose DYPLOC, a generation framework that conducts dynamic planning of content while generating the output based on a novel design of mixed language models. To enrich the generation with diverse content, we further propose to use large pre-trained models to predict relevant concepts and to generate claims. We experiment with two challenging tasks on newly collected datasets: (1) argument generation with Reddit ChangeMyView, and (2) writing articles using New York Times’ Opinion section. Automatic evaluation shows that our model significantly outperforms competitive comparisons. Human judges further confirm that our generations are more coherent with richer content.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_502", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_502.flac", "type": "audio", "duration": 706.049}, {"from": "Assistant", "value": "We investigate the less-explored task of generating open-ended questions that are typically answered by multiple sentences. We first define a new question type ontology which differentiates the nuanced nature of questions better than widely used question words. A new dataset with 4,959 questions is labeled based on the new ontology. We then propose a novel question type-aware question generation framework, augmented by a semantic graph representation, to jointly predict question focuses and produce the question. Based on this framework, we further use both exemplars and automatically generated templates to improve controllability and diversity. Experiments on two newly collected large-scale datasets show that our model improves question quality over competitive comparisons based on automatic metrics. Human judges also rate our model outputs highly in answerability, coverage of scope, and overall quality. Finally, our model variants with templates can produce questions with enhanced controllability and diversity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_503", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_503.flac", "type": "audio", "duration": 658.053}, {"from": "Assistant", "value": "We present BERTGen, a novel, generative, decoder-only model which extends BERT by fusing multimodal and multilingual pre-trained models VL-BERT and M-BERT, respectively. BERTGen is auto-regressively trained for language generation tasks, namely image captioning, machine translation and multimodal machine translation, under a multi-task setting. With a comprehensive set of evaluations, we show that BERTGen outperforms many strong baselines across the tasks explored. We also show BERTGen’s ability for zero-shot language generation, where it exhibits competitive performance to supervised counterparts. Finally, we conduct ablation studies which demonstrate that BERTGen substantially benefits from multi-tasking and effectively transfers relevant inductive biases from the pre-trained models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_504", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_504.flac", "type": "audio", "duration": 553.053}, {"from": "Assistant", "value": "Neural Machine Translation (NMT) models achieve state-of-the-art performance on many translation benchmarks. As an active research field in NMT, knowledge distillation is widely applied to enhance the model’s performance by transferring teacher model’s knowledge on each training sample. However, previous work rarely discusses the different impacts and connections among these samples, which serve as the medium for transferring teacher knowledge. In this paper, we design a novel protocol that can effectively analyze the different impacts of samples by comparing various samples’ partitions. Based on above protocol, we conduct extensive experiments and find that the teacher’s knowledge is not the more, the better. Knowledge over specific samples may even hurt the whole performance of knowledge distillation. Finally, to address these issues, we propose two simple yet effective strategies, i.e., batch-level and global-level selections, to pick suitable samples for distillation. We evaluate our approaches on two large-scale machine translation tasks, WMT’14 English-German and WMT’19 Chinese-English. Experimental results show that our approaches yield up to +1.28 and +0.89 BLEU points improvements over the Transformer baseline, respectively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_acl-long_505", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_acl-long_505.flac", "type": "audio", "duration": 607.403}, {"from": "Assistant", "value": "Recent work in neural machine translation has demonstrated both the necessity and feasibility of using inter-sentential context, context from sentences other than those currently being translated. However, while many current methods present model architectures that theoretically can use this extra context, it is often not clear how much they do actually utilize it at translation time. In this paper, we introduce a new metric, conditional cross-mutual information, to quantify usage of context by these models. Using this metric, we measure how much document-level machine translation systems use particular varieties of context. We find that target context is referenced more than source context, and that including more context has a diminishing affect on results. We then introduce a new, simple training method, context-aware word dropout, to increase the usage of context by context-aware models. Experiments show that our method not only increases context usage, but also improves the translation quality according to metrics such as BLEU and COMET, as well as performance on anaphoric pronoun resolution and lexical cohesion contrastive datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1001", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1001.flac", "type": "audio", "duration": 1296.0}, {"from": "Assistant", "value": "We propose an efficient method to conduct phrase alignment on parse forests for paraphrase detection. Unlike previous studies, our method identifies syntactic paraphrases under linguistically motivated grammar. In addition, it allows phrases to non-compositionally align to handle paraphrases with non-homographic phrase correspondences. A dataset that provides gold parse trees and their phrase alignments is created. The experimental results confirm that the proposed method conducts highly accurate phrase alignment compared to human performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1002", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1002.flac", "type": "audio", "duration": 1443.008}, {"from": "Assistant", "value": "We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our minimal feature set into the dynamic-programming framework of Huang and Sagae (2010) and Kuhlmann et al. (2011) to produce the first implementation of worst-caseO(n3)exact decoders for arc-hybrid and arc-eager transition systems. With our minimal features, we also presentO(n3)global training methods. Finally, using ensembles including our new parsers, we achieve the best unlabeled attachment score reported (to our knowledge) on the Chinese Treebank and the “second-best-in-class” result on the English Penn Treebank.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1003", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1003.flac", "type": "audio", "duration": 1197.013}, {"from": "Assistant", "value": "We propose a new Maximum Subgraph algorithm for first-order parsing to 1-endpoint-crossing, pagenumber-2 graphs. Our algorithm has two characteristics: (1) it separates the construction for noncrossing edges and crossing edges; (2) in a single construction step, whether to create a new arc is deterministic. These two characteristics make our algorithm relatively easy to be extended to incorporiate crossing-sensitive second-order features. We then introduce a new algorithm for quasi-second-order parsing. Experiments demonstrate that second-order features are helpful for Maximum Subgraph parsing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1004", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1004.flac", "type": "audio", "duration": 1459.819}, {"from": "Assistant", "value": "Organized relational knowledge in the form of “knowledge graphs” is important for many applications. However, the ability to populate knowledge bases with facts automatically extracted from documents has improved frustratingly slowly. This paper simultaneously addresses two issues that have held back prior work. We first propose an effective new model, which combines an LSTM sequence model with a form of entity position-aware attention that is better suited to relation extraction. Then we build TACRED, a large (119,474 examples) supervised relation extraction dataset obtained via crowdsourcing and targeted towards TAC KBP relations. The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance. When the model trained on this new dataset replaces the previous relation extraction component of the best TAC KBP 2015 slot filling system, its F1 score increases markedly from 22.2% to 26.7%.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1005", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1005.flac", "type": "audio", "duration": 1108.117}, {"from": "Assistant", "value": "Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-consuming. To overcome this drawback, we propose a novel framework, REHession, to conduct relation extractor learning using annotations from heterogeneous information source, e.g., knowledge base and domain heuristics. These annotations, referred as heterogeneous supervision, often conflict with each other, which brings a new challenge to the original relation extraction task: how to infer the true label from noisy labels for a given instance. Identifying context information as the backbone of both relation extraction and true label discovery, we adopt embedding techniques to learn the distributed representations of context, which bridges all components with mutual enhancement in an iterative fashion. Extensive experimental results demonstrate the superiority of REHession over the state-of-the-art.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1006", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1006.flac", "type": "audio", "duration": 1213.589}, {"from": "Assistant", "value": "There has been a recent line of work automatically learning scripts from unstructured texts, by modeling narrative event chains. While the dominant approach group events using event pair relations, LSTMs have been used to encode full chains of narrative events. The latter has the advantage of learning long-range temporal orders, yet the former is more adaptive to partial orders. We propose a neural model that leverages the advantages of both methods, by using LSTM hidden states as features for event pair modelling. A dynamic memory network is utilized to automatically induce weights on existing events for inferring a subsequent event. Standard evaluation shows that our method significantly outperforms both methods above, giving the best results reported so far.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1007", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1007.flac", "type": "audio", "duration": 1075.819}, {"from": "Assistant", "value": "We present a simple yet effective approach for linking entities in queries. The key idea is to search sentences similar to a query from Wikipedia articles and directly use the human-annotated entities in the similar sentences as candidate entities for the query. Then, we employ a rich set of features, such as link-probability, context-matching, word embeddings, and relatedness among candidate entities as well as their related entities, to rank the candidates under a regression based framework. The advantages of our approach lie in two aspects, which contribute to the ranking process and final linking result. First, it can greatly reduce the number of candidate entities by filtering out irrelevant entities with the words in the query. Second, we can obtain the query sensitive prior probability in addition to the static link-probability derived from all Wikipedia articles. We conduct experiments on two benchmark datasets on entity linking for queries, namely the ERD14 dataset and the GERDAQ dataset. Experimental results show that our method outperforms state-of-the-art systems and yields 75.0% in F1 on the ERD14 dataset and 56.9% on the GERDAQ dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1008", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1008.flac", "type": "audio", "duration": 1385.984}, {"from": "Assistant", "value": "Annotating large numbers of sentences with senses is the heaviest requirement of current Word Sense Disambiguation. We present Train-O-Matic, a language-independent method for generating millions of sense-annotated training instances for virtually all meanings of words in a language’s vocabulary. The approach is fully automatic: no human intervention is required and the only type of human knowledge used is a WordNet-like resource. Train-O-Matic achieves consistently state-of-the-art performance across gold standard datasets and languages, while at the same time removing the burden of manual annotation. All the training data is available for research purposes athttp://trainomatic.org.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1009", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1009.flac", "type": "audio", "duration": 1437.013}, {"from": "Assistant", "value": "Universal Dependencies (UD) offer a uniform cross-lingual syntactic representation, with the aim of advancing multilingual applications. Recent work shows that semantic parsing can be accomplished by transforming syntactic dependencies to logical forms. However, this work is limited to English, and cannot process dependency graphs, which allow handling complex phenomena such as control. In this work, we introduce UDepLambda, a semantic interface for UD, which maps natural language to logical forms in an almost language-independent fashion and can process dependency graphs. We perform experiments on question answering against Freebase and provide German and Spanish translations of the WebQuestions and GraphQuestions datasets to facilitate multilingual evaluation. Results show that UDepLambda outperforms strong baselines across languages and datasets. For English, it achieves a 4.9 F1 point improvement over the state-of-the-art on GraphQuestions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1010", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1010.flac", "type": "audio", "duration": 1413.013}, {"from": "Assistant", "value": "Word embeddings improve generalization over lexical features by placing each word in a lower-dimensional space, using distributional information obtained from unlabeled data. However, the effectiveness of word embeddings for downstream NLP tasks is limited by out-of-vocabulary (OOV) words, for which embeddings do not exist. In this paper, we present MIMICK, an approach to generating OOV word embeddings compositionally, by learning a function from spellings to distributional embeddings. Unlike prior work, MIMICK does not require re-training on the original word embedding corpus; instead, learning is performed at the type level. Intrinsic and extrinsic evaluations demonstrate the power of this simple approach. On 23 languages, MIMICK improves performance over a word-based baseline for tagging part-of-speech and morphosyntactic attributes. It is competitive with (and complementary to) a supervised character-based model in low resource settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1011", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1011.flac", "type": "audio", "duration": 1338.005}, {"from": "Assistant", "value": "We present SuperPivot, an analysis method for low-resource languages that occur in a superparallel corpus, i.e., in a corpus that contains an order of magnitude more languages than parallel corpora currently in use. We show that SuperPivot performs well for the crosslingual analysis of the linguistic phenomenon of tense. We produce analysis results for more than 1000 languages, conducting – to the best of our knowledge – the largest crosslingual computational study performed to date. We extend existing methodology for leveraging parallel corpora for typological analysis by overcoming a limiting assumption of earlier work: We only require that a linguistic feature is overtly marked in a few of thousands of languages as opposed to requiring that it be marked in all languages under investigation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1012", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1012.flac", "type": "audio", "duration": 1449.003}, {"from": "Assistant", "value": "This paper presents a novel neural machine translation model which jointly learns translation and source-side latent graph representations of sentences. Unlike existing pipelined approaches using syntactic parsers, our end-to-end model learns a latent graph parser as part of the encoder of an attention-based neural machine translation model, and thus the parser is optimized according to the translation objective. In experiments, we first show that our model compares favorably with state-of-the-art sequential and pipelined syntax-based NMT models. We also show that the performance of our model can be further improved by pre-training it with a small amount of treebank annotations. Our final ensemble model significantly outperforms the previous best models on the standard English-to-Japanese translation dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1014", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1014.flac", "type": "audio", "duration": 1269.013}, {"from": "Assistant", "value": "We propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation. We reformulate decoding, a discrete optimization problem, into a continuous problem, such that optimization can make use of efficient gradient-based techniques. Our powerful decoding framework allows for more accurate decoding for standard neural machine translation models, as well as enabling decoding in intractable models such as intersection of several different NMT models. Our empirical results show that our decoding framework is effective, and can leads to substantial improvements in translations, especially in situations where greedy search and beam search are not feasible. Finally, we show how the technique is highly competitive with, and complementary to, reranking.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1015", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1015.flac", "type": "audio", "duration": 1239.424}, {"from": "Assistant", "value": "We present a model for locating regions in space based on natural language descriptions. Starting with a 3D scene and a sentence, our model is able to associate words in the sentence with regions in the scene, interpret relations such as ‘on top of’ or ‘next to,’ and finally locate the region described in the sentence. All components form a single neural network that is trained end-to-end without prior knowledge of object segmentation. To evaluate our model, we construct and release a new dataset consisting of Minecraft scenes with crowdsourced natural language descriptions. We achieve a 32% relative error reduction compared to a strong neural baseline.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1016", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1016.flac", "type": "audio", "duration": 1293.248}, {"from": "Assistant", "value": "We propose a method for embedding two-dimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation and lexical dialectology. Evaluated over Twitter data, the proposed model outperforms conventional regression-based geolocation and provides a better estimate of uncertainty. We also show the effectiveness of the representation for predicting words from location in lexical dialectology, and evaluate it using the DARE dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1017", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1017.flac", "type": "audio", "duration": 1597.227}, {"from": "Assistant", "value": "Generating captions for images is a task that has recently received considerable attention. Another type of visual inputs are abstract scenes or object layouts where the only information provided is a set of objects and their locations. This type of imagery is commonly found in many applications in computer graphics, virtual reality, and storyboarding. We explore in this paper OBJ2TEXT, a sequence-to-sequence model that encodes a set of objects and their locations as an input sequence using an LSTM network, and decodes this representation using an LSTM language model. We show in our paper that this model despite using a sequence encoder can effectively represent complex spatial object-object relationships and produce descriptions that are globally coherent and semantically relevant. We test our approach for the task of describing object layouts in the MS-COCO dataset by producing sentences given only object annotations. We additionally show that our model combined with a state-of-the-art object detector can improve the accuracy of an image captioning model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1018", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1018.flac", "type": "audio", "duration": 1326.016}, {"from": "Assistant", "value": "We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector. The key idea is to directly consider all spans in a document as potential mentions and learn distributions over possible antecedents for each. The model computes span embeddings that combine context-dependent boundary representations with a head-finding attention mechanism. It is trained to maximize the marginal likelihood of gold antecedent spans from coreference clusters and is factored to enable aggressive pruning of potential mentions. Experiments demonstrate state-of-the-art performance, with a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model ensemble, despite the fact that this is the first approach to be successfully trained with no external resources.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1019", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1019.flac", "type": "audio", "duration": 1653.013}, {"from": "Assistant", "value": "Discourse coherence is strongly associated with text quality, making it important to natural language generation and understanding. Yet existing models of coherence focus on measuring individual aspects of coherence (lexical overlap, rhetorical structure, entity centering) in narrow domains. In this paper, we describe domain-independent neural models of discourse coherence that are capable of measuring multiple aspects of coherence in existing sentences and can maintain coherence while generating new sentences. We study both discriminative models that learn to distinguish coherent from incoherent discourse, and generative models that produce coherent text, including a novel neural latent-variable Markovian generative model that captures the latent discourse dependencies between sentences in a text. Our work achieves state-of-the-art performance on multiple coherence evaluations, and marks an initial step in generating coherent texts given discourse contexts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1020", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1020.flac", "type": "audio", "duration": 1239.019}, {"from": "Assistant", "value": "Multi-document summarization provides users with a short text that summarizes the information in a set of related documents. This paper introduces affinity-preserving random walk to the summarization task, which preserves the affinity relations of sentences by an absorbing random walk model. Meanwhile, we put forward adjustable affinity-preserving random walk to enforce the diversity constraint of summarization in the random walk process. The ROUGE evaluations on DUC 2003 topic-focused summarization task and DUC 2004 generic summarization task show the good performance of our method, which has the best ROUGE-2 recall among the graph-based ranking methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1060", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1060.flac", "type": "audio", "duration": 1323.008}, {"from": "Assistant", "value": "We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector-space by sampling the most promising relation to extend its path. In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration. Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1061", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1061.flac", "type": "audio", "duration": 1368.0}, {"from": "Assistant", "value": "Search engines play an important role in our everyday lives by assisting us in finding the information we need. When we input a complex query, however, results are often far from satisfactory. In this work, we introduce a query reformulation system based on a neural network that rewrites a query to maximize the number of relevant documents returned. We train this neural network with reinforcement learning. The actions correspond to selecting terms to build a reformulated query, and the reward is the document recall. We evaluate our approach on three datasets against strong baselines and show a relative improvement of 5-20% in terms of recall. Furthermore, we present a simple method to estimate a conservative upper-bound performance of a model in a particular environment and verify that there is still large room for improvements.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1062", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1062.flac", "type": "audio", "duration": 1572.011}, {"from": "Assistant", "value": "Sentence simplification aims to make sentences easier to read and understand. Most recent approaches draw on insights from machine translation to learn simplification rewrites from monolingual corpora of complex and simple sentences. We address the simplification problem with an encoder-decoder model coupled with a deep reinforcement learning framework. Our model, which we call DRESS (as shorthand forDeepREinforcementSentenceSimplification), explores the space of possible simplifications while learning to optimize a reward function that encourages outputs which are simple, fluent, and preserve the meaning of the input. Experiments on three datasets demonstrate that our model outperforms competitive simplification systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1063", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1063.flac", "type": "audio", "duration": 1699.2}, {"from": "Assistant", "value": "Active learning aims to select a small subset of data for annotation such that a classifier learned on the data is highly accurate. This is usually done using heuristic selection methods, however the effectiveness of such methods is limited and moreover, the performance of heuristics varies between datasets. To address these shortcomings, we introduce a novel formulation by reframing the active learning as a reinforcement learning problem and explicitly learning a data selection policy, where the policy takes the role of the active learning heuristic. Importantly, our method allows the selection policy learned using simulation to one language to be transferred to other languages. We demonstrate our method using cross-lingual named entity recognition, observing uniform improvements over traditional active learning algorithms.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1064", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1064.flac", "type": "audio", "duration": 1344.619}, {"from": "Assistant", "value": "We propose a new sentence simplification task (Split-and-Rephrase) where the aim is to split a complex sentence into a meaning preserving sequence of shorter sentences. Like sentence simplification, splitting-and-rephrasing has the potential of benefiting both natural language processing and societal applications. Because shorter sentences are generally better processed by NLP systems, it could be used as a preprocessing step which facilitates and improves the performance of parsers, semantic role labellers and machine translation systems. It should also be of use for people with reading disabilities because it allows the conversion of longer sentences into shorter ones. This paper makes two contributions towards this new task. First, we create and make available a benchmark consisting of 1,066,115 tuples mapping a single complex sentence to a sequence of sentences expressing the same meaning. Second, we propose five models (vanilla sequence-to-sequence to semantically-motivated models) to understand the difficulty of the proposed task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1065", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1065.flac", "type": "audio", "duration": 1544.853}, {"from": "Assistant", "value": "This paper presents a Generative Adversarial Network (GAN) to model single-turn short-text conversations, which trains a sequence-to-sequence (Seq2Seq) network for response generation simultaneously with a discriminative classifier that measures the differences between human-produced responses and machine-generated ones. In addition, the proposed method introduces an approximate embedding layer to solve the non-differentiable problem caused by the sampling-based output decoding procedure in the Seq2Seq generative model. The GAN setup provides an effective way to avoid noninformative responses (a.k.a “safe responses”), which are frequently observed in traditional neural response generators. The experimental results show that the proposed approach significantly outperforms existing neural response generation models in diversity metrics, with slight increases in relevance scores as well, when evaluated on both a Mandarin corpus and an English corpus.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1066", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1066.flac", "type": "audio", "duration": 1381.355}, {"from": "Assistant", "value": "In this paper we explore the effect of architectural choices on learning a variational autoencoder (VAE) for text generation. In contrast to the previously introduced VAE model for text where both the encoder and decoder are RNNs, we propose a novel hybrid architecture that blends fully feed-forward convolutional and deconvolutional components with a recurrent language model. Our architecture exhibits several attractive properties such as faster run time and convergence, ability to better handle long sequences and, more importantly, it helps to avoid the issue of the VAE collapsing to a deterministic model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1067", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1067.flac", "type": "audio", "duration": 1386.688}, {"from": "Assistant", "value": "Computerized generation of humor is a notoriously difficult AI problem. We develop an algorithm called Libitum that helps humans generate humor in a Mad Lib, which is a popular fill-in-the-blank game. The algorithm is based on a machine learned classifier that determines whether a potential fill-in word is funny in the context of the Mad Lib story. We use Amazon Mechanical Turk to create ground truth data and to judge humor for our classifier to mimic, and we make this data freely available. Our testing shows that Libitum successfully aids humans in filling in Mad Libs that are usually judged funnier than those filled in by humans with no computerized help. We go on to analyze why some words are better than others at making a Mad Lib funny.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1068", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1068.flac", "type": "audio", "duration": 1371.008}, {"from": "Assistant", "value": "In this paper, we introduce a new distributional method for modeling predicate-argument thematic fit judgments. We use a syntax-based DSM to build a prototypical representation of verb-specific roles: for every verb, we extract the most salient second order contexts for each of its roles (i.e. the most salient dimensions of typical role fillers), and then we compute thematic fit as a weighted overlap between the top features of candidate fillers and role prototypes. Our experiments show that our method consistently outperforms a baseline re-implementing a state-of-the-art system, and achieves better or comparable results to those reported in the literature for the other unsupervised systems. Moreover, it provides an explicit representation of the features characterizing verb-specific semantic roles.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1069", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1069.flac", "type": "audio", "duration": 1059.008}, {"from": "Assistant", "value": "We present a feature vector formation technique for documents - Sparse Composite Document Vector (SCDV) - which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation. In SCDV, word embeddings are clustered to capture multiple semantic contexts in which words occur. They are then chained together to form document topic-vectors that can express complex, multi-topic documents. Through extensive experiments on multi-class and multi-label classification tasks, we outperform the previous state-of-the-art method, NTSG. We also show that SCDV embeddings perform well on heterogeneous tasks like Topic Coherence, context-sensitive Learning and Information Retrieval. Moreover, we achieve a significant reduction in training and prediction times compared to other representation methods. SCDV achieves best of both worlds - better performance with lower time and space complexity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1070", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1070.flac", "type": "audio", "duration": 1506.005}, {"from": "Assistant", "value": "Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of text, such as sentences, have however not been so successful. Several attempts at learning unsupervised representations of sentences have not reached satisfactory enough performance to be widely adopted. In this paper, we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors on a wide range of transfer tasks. Much like how computer vision uses ImageNet to obtain features, which can then be transferred to other tasks, our work tends to indicate the suitability of natural language inference for transfer learning to other NLP tasks. Our encoder is publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1071", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1071.flac", "type": "audio", "duration": 1419.008}, {"from": "Assistant", "value": "Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, logical semantic representations capture deeper levels of sentence semantics, but their symbolic nature does not offer graded notions of textual similarity. We propose a method for determining semantic textual similarity by combining shallow features with features extracted from natural deduction proofs of bidirectional entailment relations between sentence pairs. For the natural deduction proofs, we use ccg2lambda, a higher-order automatic inference system, which converts Combinatory Categorial Grammar (CCG) derivation trees into semantic representations and conducts natural deduction proofs. Experiments show that our system was able to outperform other logic-based systems and that features derived from the proofs are effective for learning textual similarity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1108", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1108.flac", "type": "audio", "duration": 1409.984}, {"from": "Assistant", "value": "Identifying temporal relations between events is an essential step towards natural language understanding. However, the temporal relation between two events in a story depends on, and is often dictated by, relations among other events. Consequently, effectively identifying temporal relations between events is a challenging problem even for human annotators. This paper suggests that it is important to take these dependencies into account while learning to identify these relations and proposes a structured learning approach to address this challenge. As a byproduct, this provides a new perspective on handling missing relations, a known issue that hurts existing methods. As we show, the proposed approach results in significant improvements on the two commonly used data sets for this problem.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1109", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1109.flac", "type": "audio", "duration": 1493.973}, {"from": "Assistant", "value": "Knowledge base population (KBP) systems take in a large document corpus and extract entities and their relations. Thus far, KBP evaluation has relied on judgements on the pooled predictions of existing systems. We show that this evaluation is problematic: when a new system predicts a previously unseen relation, it is penalized even if it is correct. This leads to significant bias against new systems, which counterproductively discourages innovation in the field. Our first contribution is a new importance-sampling based evaluation which corrects for this bias by annotating a new system’s predictions on-demand via crowdsourcing. We show this eliminates bias and reduces variance using data from the 2015 TAC KBP task. Our second contribution is an implementation of our method made publicly available as an online KBP evaluation service. We pilot the service by testing diverse state-of-the-art systems on the TAC KBP 2016 corpus and obtain accurate scores in a cost effective manner.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1110", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1110.flac", "type": "audio", "duration": 926.976}, {"from": "Assistant", "value": "In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query. While previous works have successfully captured unigram term matches, how to fully employ position-dependent information such as proximity and term dependencies has been insufficiently explored. In this work, we propose a novel neural IR model named PACRR aiming at better modeling position-dependent interactions between a query and a document. Extensive experiments on six years’ TREC Web Track data confirm that the proposed model yields better results under multiple benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1111", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1111.flac", "type": "audio", "duration": 1472.981}, {"from": "Assistant", "value": "Rapid progress has been made towards question answering (QA) systems that can extract answers from text. Existing neural approaches make use of expensive bi-directional attention mechanisms or score all possible answer spans, limiting scalability. We propose instead to cast extractive QA as an iterative search problem: select the answer’s sentence, start word, and end word. This representation reduces the space of each search step and allows computation to be conditionally allocated to promising search paths. We show that globally normalizing the decision process and back-propagating through beam search makes this representation viable and learning efficient. We empirically demonstrate the benefits of this approach using our model, Globally Normalized Reader (GNR), which achieves the second highest single model performance on the Stanford Question Answering Dataset (68.4 EM, 76.21 F1 dev) and is 24.7x faster than bi-attention-flow. We also introduce a data-augmentation method to produce semantically valid examples by aligning named entities to a knowledge base and swapping them with new entities of the same type. This method improves the performance of all models considered in this work and is of independent interest for a variety of NLP tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1112", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1112.flac", "type": "audio", "duration": 1464.0}, {"from": "Assistant", "value": "We present the first unsupervised LSTM speech segmenter as a cognitive model of the acquisition of words from unsegmented input. Cognitive biases toward phonological and syntactic predictability in speech are rooted in the limitations of human memory (Baddeley et al., 1998); compressed representations are easier to acquire and retain in memory. To model the biases introduced by these memory limitations, our system uses an LSTM-based encoder-decoder with a small number of hidden units, then searches for a segmentation that minimizes autoencoding loss. Linguistically meaningful segments (e.g. words) should share regular patterns of features that facilitate decoder performance in comparison to random segmentations, and we show that our learner discovers these patterns when trained on either phoneme sequences or raw acoustics. To our knowledge, ours is the first fully unsupervised system to be able to segment both symbolic and acoustic representations of speech.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1113", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1113.flac", "type": "audio", "duration": 1515.008}, {"from": "Assistant", "value": "Research in computational semantics is increasingly guided by our understanding of human semantic processing. However, semantic models are typically studied in the context of natural language processing system performance. In this paper, we present a systematic evaluation and comparison of a range of widely-used, state-of-the-art semantic models in their ability to predict patterns of conceptual representation in the human brain. Our results provide new insights both for the design of computational semantic models and for further research in cognitive neuroscience.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1114", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1114.flac", "type": "audio", "duration": 1482.005}, {"from": "Assistant", "value": "The rapid increase of the multimedia data over the Internet necessitates multi-modal summarization from collections of text, image, audio and video. In this work, we propose an extractive Multi-modal Summarization (MMS) method which can automatically generate a textual summary given a set of documents, images, audios and videos related to a specific topic. The key idea is to bridge the semantic gaps between multi-modal contents. For audio information, we design an approach to selectively use its transcription. For vision information, we learn joint representations of texts and images using a neural network. Finally, all the multi-modal aspects are considered to generate the textural summary by maximizing the salience, non-redundancy, readability and coverage through budgeted optimization of submodular functions. We further introduce an MMS corpus in English and Chinese. The experimental results on this dataset demonstrate that our method outperforms other competitive baseline methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1115", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1115.flac", "type": "audio", "duration": 1539.008}, {"from": "Assistant", "value": "Multimodal sentiment analysis is an increasingly popular research area, which extends the conventional language-based definition of sentiment analysis to a multimodal setup where other relevant modalities accompany language. In this paper, we pose the problem of multimodal sentiment analysis as modeling intra-modality and inter-modality dynamics. We introduce a novel model, termed Tensor Fusion Networks, which learns both such dynamics end-to-end. The proposed approach is tailored for the volatile nature of spoken language in online videos as well as accompanying gestures and voice. In the experiments, our model outperforms state-of-the-art approaches for both multimodal and unimodal sentiment analysis.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1116", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1116.flac", "type": "audio", "duration": 1452.011}, {"from": "Assistant", "value": "Manual annotations are a prerequisite for many applications of machine learning. However, weaknesses in the annotation process itself are easy to overlook. In particular, scholars often choose what information to give to annotators without examining these decisions empirically. For subjective tasks such as sentiment analysis, sarcasm, and stance detection, such choices can impact results. Here, for the task of political stance detection on Twitter, we show that providing too little context can result in noisy and uncertain annotations, whereas providing too strong a context may cause it to outweigh other signals. To characterize and reduce these biases, we develop ConStance, a general model for reasoning about annotations across information conditions. Given conflicting labels produced by multiple annotators seeing the same instances with different contexts, ConStance simultaneously estimates gold standard labels and also learns a classifier for new instances. We show that the classifier learned by ConStance outperforms a variety of baselines at predicting political stance, while the model’s interpretable parameters shed light on the effects of each context.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1117", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1117.flac", "type": "audio", "duration": 1464.0}, {"from": "Assistant", "value": "Experimenting with a new dataset of 1.6M user comments from a news portal and an existing dataset of 115K Wikipedia talk page comments, we show that an RNN operating on word embeddings outpeforms the previous state of the art in moderation, which used logistic regression or an MLP classifier with character or word n-grams. We also compare against a CNN operating on word embeddings, and a word-list baseline. A novel, deep, classificationspecific attention mechanism improves the performance of the RNN further, and can also highlight suspicious words for free, without including highlighted words in the training data. We consider both fully automatic and semi-automatic moderation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1118", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1118.flac", "type": "audio", "duration": 1374.016}, {"from": "Assistant", "value": "This article evaluates three proposed laws of semantic change. Our claim is that in order to validate a putative law of semantic change, the effect should be observed in the genuine condition but absent or reduced in a suitably matched control condition, in which no change can possibly have taken place. Our analysis shows that the effects reported in recent literature must be substantially revised: (i) the proposed negative correlation between meaning change and word frequency is shown to be largely an artefact of the models of word representation used; (ii) the proposed negative correlation between meaning change and prototypicality is shown to be much weaker than what has been claimed in prior art; and (iii) the proposed positive correlation between meaning change and polysemy is largely an artefact of word frequency. These empirical observations are corroborated by analytical proofs that show that count representations introduce an inherent dependence on word frequency, and thus word frequency cannot be evaluated as an independent factor with these representations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1119", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1119.flac", "type": "audio", "duration": 1413.013}, {"from": "Assistant", "value": "We pose the general task of user-factor adaptation – adapting supervised learning models to real-valued user factors inferred from a background of their language, reflecting the idea that a piece of text should be understood within the context of the user that wrote it. We introduce a continuous adaptation technique, suited for real-valued user factors that are common in social science and bringing us closer to personalized NLP, adapting to each user uniquely. We apply this technique with known user factors including age, gender, and personality traits, as well as latent factors, evaluating over five tasks: POS tagging, PP-attachment, sentiment analysis, sarcasm detection, and stance detection. Adaptation provides statistically significant benefits for 3 of the 5 tasks: up to +1.2 points for PP-attachment, +3.4 points for sarcasm, and +3.0 points for stance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1159", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1159.flac", "type": "audio", "duration": 1448.939}, {"from": "Assistant", "value": "Semantic role labeling (SRL) is the task of identifying the predicate-argument structure of a sentence. It is typically regarded as an important step in the standard NLP pipeline. As the semantic representations are closely related to syntactic ones, we exploit syntactic information in our model. We propose a version of graph convolutional networks (GCNs), a recent class of neural networks operating on graphs, suited to model syntactic dependency graphs. GCNs over syntactic dependency trees are used as sentence encoders, producing latent feature representations of words in a sentence. We observe that GCN layers are complementary to LSTM ones: when we stack both GCN and LSTM layers, we obtain a substantial improvement over an already state-of-the-art LSTM SRL model, resulting in the best reported scores on the standard benchmark (CoNLL-2009) both for Chinese and English.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1160", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1160.flac", "type": "audio", "duration": 1391.979}, {"from": "Assistant", "value": "We present a new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables. Our parser is an encoder-decoder neural network with two key technical innovations: (1) a grammar for the decoder that only generates well-typed logical forms; and (2) an entity embedding and linking module that identifies entity mentions while generalizing across tables. We also introduce a novel method for training our neural model with question-answer supervision. On the WikiTableQuestions data set, our parser achieves a state-of-the-art accuracy of 43.3% for a single model and 45.9% for a 5-model ensemble, improving on the best prior score of 38.7% set by a 15-model ensemble. These results suggest that type constraints and entity linking are valuable components to incorporate in neural semantic parsers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1161", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1161.flac", "type": "audio", "duration": 1463.979}, {"from": "Assistant", "value": "Natural language constitutes a predominant medium for much of human learning and pedagogy. We consider the problem of concept learning from natural language explanations, and a small number of labeled examples of the concept. For example, in learning the concept of a phishing email, one might say ‘this is a phishing email because it asks for your bank account number’. Solving this problem involves both learning to interpret open ended natural language statements, and learning the concept itself. We present a joint model for (1) language interpretation (semantic parsing) and (2) concept learning (classification) that does not require labeling statements with logical forms. Instead, the model prefers discriminative interpretations of statements in context of observable features of the data as a weak signal for parsing. On a dataset of email-related concepts, our approach yields across-the-board improvements in classification performance, with a 30% relative improvement in F1 score over competitive methods in the low data regime.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1162", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1162.flac", "type": "audio", "duration": 1211.968}, {"from": "Assistant", "value": "The ubiquity of metaphor in our everyday communication makes it an important problem for natural language understanding. Yet, the majority of metaphor processing systems to date rely on hand-engineered features and there is still no consensus in the field as to which features are optimal for this task. In this paper, we present the first deep learning architecture designed to capture metaphorical composition. Our results demonstrate that it outperforms the existing approaches in the metaphor identification task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1163", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1163.flac", "type": "audio", "duration": 1443.008}, {"from": "Assistant", "value": "We propose a new, socially-impactful task for natural language processing: from a news corpus, extract names of persons who have been killed by police. We present a newly collected police fatality corpus, which we release publicly, and present a model to solve this problem that uses EM-based distant supervision with logistic regression and convolutional neural network classifiers. Our model outperforms two off-the-shelf event extractor systems, and it can suggest candidate victim names in some cases faster than one of the major manually-collected police fatality databases.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1165", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1165.flac", "type": "audio", "duration": 1332.011}, {"from": "Assistant", "value": "We explore how to detect people’s perspectives that occupy a certain proposition. We propose a Bayesian modelling approach where topics (or propositions) and their associated perspectives (or viewpoints) are modeled as latent variables. Words associated with topics or perspectives follow different generative routes. Based on the extracted perspectives, we can extract the top associated sentences from text to generate a succinct summary which allows a quick glimpse of the main viewpoints in a document. The model is evaluated on debates from the House of Commons of the UK Parliament, revealing perspectives from the debates without the use of labelled data and obtaining better results than previous related solutions under a variety of evaluations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1166", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1166.flac", "type": "audio", "duration": 1716.011}, {"from": "Assistant", "value": "Social media users often make explicit predictions about upcoming events. Such statements vary in the degree of certainty the author expresses toward the outcome: “Leonardo DiCaprio will win Best Actor” vs. “Leonardo DiCaprio may win” or “No way Leonardo wins!”. Can popular beliefs on social media predict who will win? To answer this question, we build a corpus of tweets annotated for veridicality on which we train a log-linear classifier that detects positive veridicality with high precision. We then forecast uncertain outcomes using the wisdom of crowds, by aggregating users’ explicit predictions. Our method for forecasting winners is fully automated, relying only on a set of contenders as input. It requires no training data of past outcomes and outperforms sentiment and tweet volume baselines on a broad range of contest prediction tasks. We further demonstrate how our approach can be used to measure the reliability of individual accounts’ predictions and retrospectively identify surprise outcomes.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1167", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1167.flac", "type": "audio", "duration": 1130.987}, {"from": "Assistant", "value": "Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identification as a reading comprehension task in QA. Inspired by convolutional neural networks, we propose a new mechanism to store relevant context in different memory slots to model context information. Our proposed approach can extract both word level sequence features and lexical features. Performance evaluation shows that our method achieves the state-of-the-art performance on a recently released emotion cause dataset, outperforming a number of competitive baselines by at least 3.01% in F-measure.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1168", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1168.flac", "type": "audio", "duration": 1518.016}, {"from": "Assistant", "value": "Automatic story comprehension is a fundamental challenge in Natural Language Understanding, and can enable computers to learn about social norms, human behavior and commonsense. In this paper, we present a story comprehension model that explores three distinct semantic aspects: (i) the sequence of events described in the story, (ii) its emotional trajectory, and (iii) its plot consistency. We judge the model’s understanding of real-world stories by inquiring if, like humans, it can develop an expectation of what will happen next in a given story. Specifically, we use it to predict the correct ending of a given short story from possible alternatives. The model uses a hidden variable to weigh the semantic aspects in the context of the story. Our experiments demonstrate the potential of our approach to characterize these semantic aspects, and the strength of the hidden variable based approach. The model outperforms the state-of-the-art approaches and achieves best results on a publicly available dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1169", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1169.flac", "type": "audio", "duration": 1440.0}, {"from": "Assistant", "value": "NLP tasks are often limited by scarcity of manually annotated data. In social media sentiment analysis and related tasks, researchers have therefore used binarized emoticons and specific hashtags as forms of distant supervision. Our paper shows that by extending the distant supervision to a more diverse set of noisy labels, the models can learn richer representations. Through emoji prediction on a dataset of 1246 million tweets containing one of 64 common emojis we obtain state-of-the-art performance on 8 benchmark datasets within emotion, sentiment and sarcasm detection using a single pretrained model. Our analyses confirm that the diversity of our emotional labels yield a performance improvement over previous distant supervision approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1170", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1170.flac", "type": "audio", "duration": 1497.003}, {"from": "Assistant", "value": "We present opinion recommendation, a novel task of jointly generating a review with a rating score that a certain user would give to a certain product which is unreviewed by the user, given existing reviews to the product by other users, and the reviews that the user has given to other products. A characteristic of opinion recommendation is the reliance of multiple data sources for multi-task joint learning. We use a single neural network to model users and products, generating customised product representations using a deep memory network, from which customised ratings and reviews are constructed jointly. Results show that our opinion recommendation system gives ratings that are closer to real user ratings on Yelp.com data compared with Yelp’s own ratings. our methods give better results compared to several pipelines baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1207", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1207.flac", "type": "audio", "duration": 1472.981}, {"from": "Assistant", "value": "Cross-lingual natural language processing hinges on the premise that there exists invariance across languages. At the word level, researchers have identified such invariance in the word embedding semantic spaces of different languages. However, in order to connect the separate spaces, cross-lingual supervision encoded in parallel data is typically required. In this paper, we attempt to establish the cross-lingual connection without relying on any cross-lingual supervision. By viewing word embedding spaces as distributions, we propose to minimize their earth mover’s distance, a measure of divergence between distributions. We demonstrate the success on the unsupervised bilingual lexicon induction task. In addition, we reveal an interesting finding that the earth mover’s distance shows potential as a measure of language difference.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1208", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1208.flac", "type": "audio", "duration": 1406.976}, {"from": "Assistant", "value": "Ensembling is a well-known technique in neural machine translation (NMT) to improve system performance. Instead of a single neural net, multiple neural nets with the same topology are trained separately, and the decoder generates predictions by averaging over the individual models. Ensembling often improves the quality of the generated translations drastically. However, it is not suitable for production systems because it is cumbersome and slow. This work aims to reduce the runtime to be on par with a single system without compromising the translation quality. First, we show that the ensemble can be unfolded into a single large neural network which imitates the output of the ensemble system. We show that unfolding can already improve the runtime in practice since more work can be done on the GPU. We proceed by describing a set of techniques to shrink the unfolded network by reducing the dimensionality of layers. On Japanese-English we report that the resulting network has the size and decoding speed of a single NMT network but performs on the level of a 3-ensemble system.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1210", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1210.flac", "type": "audio", "duration": 1406.933}, {"from": "Assistant", "value": "Recent research in neural machine translation has largely focused on two aspects; neural network architectures and end-to-end learning algorithms. The problem of decoding, however, has received relatively little attention from the research community. In this paper, we solely focus on the problem of decoding given a trained neural machine translation model. Instead of trying to build a new decoding algorithm for any specific decoding objective, we propose the idea of trainable decoding algorithm in which we train a decoding algorithm to find a translation that maximizes an arbitrary decoding objective. More specifically, we design an actor that observes and manipulates the hidden state of the neural machine translation decoder and propose to train it using a variant of deterministic policy gradient. We extensively evaluate the proposed algorithm using four language pairs and two decoding objectives and show that we can indeed train a trainable greedy decoder that generates a better translation (in terms of a target decoding objective) with minimal computational overhead.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1211", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1211.flac", "type": "audio", "duration": 1362.005}, {"from": "Assistant", "value": "Satirical news is considered to be entertainment, but it is potentially deceptive and harmful. Despite the embedded genre in the article, not everyone can recognize the satirical cues and therefore believe the news as true news. We observe that satirical cues are often reflected in certain paragraphs rather than the whole document. Existing works only consider document-level features to detect the satire, which could be limited. We consider paragraph-level linguistic features to unveil the satire by incorporating neural network and attention mechanism. We investigate the difference between paragraph-level features and document-level features, and analyze them on a large satirical news dataset. The evaluation shows that the proposed model detects satirical news effectively and reveals what features are important at which level.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1212", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1212.flac", "type": "audio", "duration": 1557.013}, {"from": "Assistant", "value": "Verifiability is one of the core editing principles in Wikipedia, where editors are encouraged to provide citations for the added content. For a Wikipedia article determining what content is covered by a citation or the citation span is not trivial, an important aspect for automated citation finding for uncovered content, or fact assessments. We address the problem of determining the citation span in Wikipedia articles. We approach this problem by classifying which textual fragments in an article are covered or hold true given a citation. We propose a sequence classification approach where for a paragraph and a citation, we determine the citation span at a fine-grained level. We provide a thorough experimental evaluation and compare our approach against baselines adopted from the scientific domain, where we show improvement for all evaluation metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1213", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1213.flac", "type": "audio", "duration": 1574.549}, {"from": "Assistant", "value": "Most studies on human editing focus merely on syntactic revision operations, failing to capture the intentions behind revision changes, which are essential for facilitating the single and collaborative writing process. In this work, we develop in collaboration with Wikipedia editors a 13-category taxonomy of the semantic intention behind edits in Wikipedia articles. Using labeled article edits, we build a computational classifier of intentions that achieved a micro-averaged F1 score of 0.621. We use this model to investigate edit intention effectiveness: how different types of edits predict the retention of newcomers and changes in the quality of articles, two key concerns for Wikipedia today. Our analysis shows that the types of edits that users make in their first session predict their subsequent survival as Wikipedia editors, and articles in different stages need different types of edits.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1214", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1214.flac", "type": "audio", "duration": 1329.003}, {"from": "Assistant", "value": "We introduce a hierarchical architecture for machine reading capable of extracting precise information from long documents. The model divides the document into small, overlapping windows and encodes all windows in parallel with an RNN. It then attends over these window encodings, reducing them to a single encoding, which is decoded into an answer using a sequence decoder. This hierarchical approach allows the model to scale to longer documents without increasing the number of sequential steps. In a supervised setting, our model achieves state of the art accuracy of 76.8 on the WikiReading dataset. We also evaluate the model in a semi-supervised setting by downsampling the WikiReading training set to create increasingly smaller amounts of supervision, while leaving the full unlabeled document corpus to train a sequence autoencoder on document windows. We evaluate models that can reuse autoencoder states and outputs without fine-tuning their weights, allowing for more efficient training and inference.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1215", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1215.flac", "type": "audio", "duration": 1512.0}, {"from": "Assistant", "value": "Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. In this adversarial setting, the accuracy of sixteen published models drops from an average of 75% F1 score to 36%; when the adversary is allowed to add ungrammatical sequences of words, average accuracy on four models decreases further to 7%. We hope our insights will motivate the development of new models that understand language more precisely.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1216", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1216.flac", "type": "audio", "duration": 1368.0}, {"from": "Assistant", "value": "Reasoning with commonsense knowledge is critical for natural language understanding. Traditional methods for commonsense machine comprehension mostly only focus on one specific kind of knowledge, neglecting the fact that commonsense reasoning requires simultaneously considering different kinds of commonsense knowledge. In this paper, we propose a multi-knowledge reasoning method, which can exploit heterogeneous knowledge for commonsense machine comprehension. Specifically, we first mine different kinds of knowledge (including event narrative knowledge, entity semantic knowledge and sentiment coherent knowledge) and encode them as inference rules with costs. Then we propose a multi-knowledge reasoning model, which selects inference rules for a specific reasoning context using attention mechanism, and reasons by summarizing all valid inference rules. Experiments on RocStories show that our method outperforms traditional models significantly.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1217", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1217.flac", "type": "audio", "duration": 1293.013}, {"from": "Assistant", "value": "Document-level multi-aspect sentiment classification is an important task for customer relation management. In this paper, we model the task as a machine comprehension problem where pseudo question-answer pairs are constructed by a small number of aspect-related keywords and aspect ratings. A hierarchical iterative attention model is introduced to build aspectspecific representations by frequent and repeated interactions between documents and aspect questions. We adopt a hierarchical architecture to represent both word level and sentence level information, and use the attention operations for aspect questions and documents alternatively with the multiple hop mechanism. Experimental results on the TripAdvisor and BeerAdvocate datasets show that our model outperforms classical baselines. We will release our code and data for the method replicability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1252", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1252.flac", "type": "audio", "duration": 1434.005}, {"from": "Assistant", "value": "Neural networks have achieved state-of-the-art performance on several structured-output prediction tasks, trained in a fully supervised fashion. However, annotated examples in structured domains are often costly to obtain, which thus limits the applications of neural networks. In this work, we propose Maximum Margin Reward Networks, a neural network-based framework that aims to learn from both explicit (full structures) and implicit supervision signals (delayed feedback on the correctness of the predicted structure). On named entity recognition and semantic parsing, our model outperforms previous systems on the benchmark datasets, CoNLL-2003 and WebQuestionsSP.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1253", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1253.flac", "type": "audio", "duration": 1560.0}, {"from": "Assistant", "value": "Several approaches have been proposed to model either the explicit sequential structure of an argumentative text or its implicit hierarchical structure. So far, the adequacy of these models of overall argumentation remains unclear. This paper asks what type of structure is actually important to tackle downstream tasks in computational argumentation. We analyze patterns in the overall argumentation of texts from three corpora. Then, we adapt the idea of positional tree kernels in order to capture sequential and hierarchical argumentative structure together for the first time. In systematic experiments for three text classification tasks, we find strong evidence for the impact of both types of structure. Our results suggest that either of them is necessary while their combination may be beneficial.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1254", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1254.flac", "type": "audio", "duration": 1452.011}, {"from": "Assistant", "value": "We propose a new encoder-decoder approach to learn distributed sentence representations that are applicable to multiple purposes. The model is learned by using a convolutional neural network as an encoder to map an input sentence into a continuous vector, and using a long short-term memory recurrent neural network as a decoder. Several tasks are considered, including sentence reconstruction and future sentence prediction. Further, a hierarchical encoder-decoder model is proposed to encode a sentence to predict multiple future sentences. By training our models on a large collection of novels, we obtain a highly generic convolutional sentence encoder that performs well in practice. Experimental results on several benchmark datasets, and across a broad range of applications, demonstrate the superiority of the proposed model over competing methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1255", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1255.flac", "type": "audio", "duration": 1131.008}, {"from": "Assistant", "value": "We present a novel approach for training artificial neural networks. Our approach is inspired by broad evidence in psychology that shows human learners can learn efficiently and effectively by increasing intervals of time between subsequent reviews of previously learned materials (spaced repetition). We investigate the analogy between training neural models and findings in psychology about human memory model and develop an efficient and effective algorithm to train neural models. The core part of our algorithm is a cognitively-motivated scheduler according to which training instances and their “reviews” are spaced over time. Our algorithm uses only 34-50% of data per epoch, is 2.9-4.8 times faster than standard training, and outperforms competing state-of-the-art baselines. Our code is available atscholar.harvard.edu/hadi/RbF/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1256", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1256.flac", "type": "audio", "duration": 1433.941}, {"from": "Assistant", "value": "In this work, we study the problem of part-of-speech tagging for Tweets. In contrast to newswire articles, Tweets are usually informal and contain numerous out-of-vocabulary words. Moreover, there is a lack of large scale labeled datasets for this domain. To tackle these challenges, we propose a novel neural network to make use of out-of-domain labeled data, unlabeled in-domain data, and labeled in-domain data. Inspired by adversarial neural networks, the proposed method tries to learn common features through adversarial discriminator. In addition, we hypothesize that domain-specific features of target domain should be preserved in some degree. Hence, the proposed method adopts a sequence-to-sequence autoencoder to perform this task. Experimental results on three different datasets show that our method achieves better performance than state-of-the-art methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1258", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1258.flac", "type": "audio", "duration": 1493.952}, {"from": "Assistant", "value": "Discourse segmentation is the first step in building discourse parsers. Most work on discourse segmentation does not scale to real-world discourse parsing across languages, for two reasons: (i) models rely on constituent trees, and (ii) experiments have relied on gold standard identification of sentence and token boundaries. We therefore investigate to what extent constituents can be replaced with universal dependencies, or left out completely, as well as how state-of-the-art segmenters fare in the absence of sentence boundaries. Our results show that dependency information is less useful than expected, but we provide a fully scalable, robust model that only relies on part-of-speech information, and show that it performs well across languages in the absence of any gold-standard annotation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1259", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1259.flac", "type": "audio", "duration": 1496.939}, {"from": "Assistant", "value": "Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other’s reward functions must reach an agreement (or a deal) via natural language dialogue. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states. We also introduce dialogue rollouts, in which the model plans ahead by simulating possible complete continuations of the conversation, and find that this technique dramatically improves performance. Our code and dataset are publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1260", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1260.flac", "type": "audio", "duration": 1064.981}, {"from": "Assistant", "value": "Hand-crafted rules and reinforcement learning (RL) are two popular choices to obtain dialogue policy. The rule-based policy is often reliable within predefined scope but not self-adaptable, whereas RL is evolvable with data but often suffers from a bad initial performance. We employ acompanion learningframework to integrate the two approaches foron-linedialogue policy learning, in which a pre-defined rule-based policy acts as a “teacher” and guides a data-driven RL system by giving example actions as well as additional rewards. A novelagent-aware dropoutDeep Q-Network (AAD-DQN) is proposed to address the problem of when to consult the teacher and how to learn from the teacher’s experiences. AAD-DQN, as a data-driven student policy, provides (1) two separate experience memories for student and teacher, (2) an uncertainty estimated by dropout to control the timing of consultation and learning. Simulation experiments showed that the proposed approach can significantly improve bothsafetyandefficiencyof on-line policy optimization compared to other companion learning approaches as well as supervised pre-training using static dialogue corpus.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1261", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1261.flac", "type": "audio", "duration": 1583.979}, {"from": "Assistant", "value": "In this paper we introduce a practical first step towards the creation of an automated debate agent: a state-of-the-art recurrent predictive model for predicting debate winners. By having an accurate predictive model, we are able to objectively rate the quality of a statement made at a specific turn in a debate. The model is based on a recurrent neural network architecture with attention, which allows the model to effectively account for the entire debate when making its prediction. Our model achieves state-of-the-art accuracy on a dataset of debate transcripts annotated with audience favorability of the debate teams. Finally, we discuss how future work can leverage our proposed model for the creation of an automated debate agent. We accomplish this by determining the model input that will maximize audience favorability toward a given side of a debate at an arbitrary turn.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1299", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1299.flac", "type": "audio", "duration": 726.016}, {"from": "Assistant", "value": "Stylistic variations of language, such as formality, carry speakers’ intention beyond literal meaning and should be conveyed adequately in translation. We propose to use lexical formality models to control the formality level of machine translation output. We demonstrate the effectiveness of our approach in empirical evaluations, as measured by automatic metrics and human assessments.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1300", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1300.flac", "type": "audio", "duration": 858.005}, {"from": "Assistant", "value": "Attentional sequence-to-sequence models have become the new standard for machine translation, but one challenge of such models is a significant increase in training and decoding cost compared to phrase-based systems. In this work we focus on efficient decoding, with a goal of achieving accuracy close the state-of-the-art in neural machine translation (NMT), while achieving CPU decoding speed/throughput close to that of a phrasal decoder. We approach this problem from two angles: First, we describe several techniques for speeding up an NMT beam search decoder, which obtain a 4.4x speedup over a very efficient baseline decoder without changing the decoder output. Second, we propose a simple but powerful network architecture which uses an RNN (GRU/LSTM) layer at bottom, followed by a series of stacked fully-connected layers applied at every timestep. This architecture achieves similar accuracy to a deep recurrent model, at a small fraction of the training and decoding cost. By combining these techniques, our best system achieves a very competitive accuracy of 38.3 BLEU on WMT English-French NewsTest2014, while decoding at 100 words/sec on single-threaded CPU. We believe this is the best published accuracy/speed trade-off of an NMT system.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1301", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1301.flac", "type": "audio", "duration": 777.003}, {"from": "Assistant", "value": "In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a cross-sentence context-aware approach and investigate the influence of historical contextual information on the performance of neural machine translation (NMT). First, this history is summarized in a hierarchical way. We then integrate the historical representation into NMT in two strategies: 1) a warm-start of encoder and decoder states, and 2) an auxiliary context source for updating decoder states. Experimental results on a large Chinese-English translation task show that our approach significantly improves upon a strong attention-based NMT system by up to +2.1 BLEU points.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1302", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1302.flac", "type": "audio", "duration": 735.019}, {"from": "Assistant", "value": "Training a POS tagging model with crosslingual transfer learning usually requires linguistic knowledge and resources about the relation between the source language and the target language. In this paper, we introduce a cross-lingual transfer learning model for POS tagging without ancillary resources such as parallel corpora. The proposed cross-lingual model utilizes a common BLSTM that enables knowledge transfer from other languages, and private BLSTMs for language-specific representations. The cross-lingual model is trained with language-adversarial training and bidirectional language modeling as auxiliary objectives to better represent language-general information while not losing the information about a specific target language. Evaluating on POS datasets from 14 languages in the Universal Dependencies corpus, we show that the proposed transfer learning model improves the POS tagging performance of the target languages without exploiting any linguistic knowledge between the source language and the target language.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1303", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1303.flac", "type": "audio", "duration": 747.008}, {"from": "Assistant", "value": "In this paper we propose a model to learn multimodal multilingual representations for matching images and sentences in different languages, with the aim of advancing multilingual versions of image search and image understanding. Our model learns a common representation for images and their descriptions in two different languages (which need not be parallel) by considering the image as a pivot between two languages. We introduce a new pairwise ranking loss function which can handle both symmetric and asymmetric similarity between the two modalities. We evaluate our models on image-description ranking for German and English, and on semantic textual similarity of image descriptions in English. In both cases we achieve state-of-the-art performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1304", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1304.flac", "type": "audio", "duration": 765.013}, {"from": "Assistant", "value": "Source dependency information has been successfully introduced into statistical machine translation. However, there are only a few preliminary attempts for Neural Machine Translation (NMT), such as concatenating representations of source word and its dependency label together. In this paper, we propose a novel NMT with source dependency representation to improve translation performance of NMT, especially long sentences. Empirical results on NIST Chinese-to-English translation task show that our method achieves 1.6 BLEU improvements on average over a strong NMT system.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1305", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1305.flac", "type": "audio", "duration": 560.981}, {"from": "Assistant", "value": "In the logic approach to Recognizing Textual Entailment, identifying phrase-to-phrase semantic relations is still an unsolved problem. Resources such as the Paraphrase Database offer limited coverage despite their large size whereas unsupervised distributional models of meaning often fail to recognize phrasal entailments. We propose to map phrases to their visual denotations and compare their meaning in terms of their images. We show that our approach is effective in the task of Recognizing Textual Entailment when combined with specific linguistic and logic features.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1306", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1306.flac", "type": "audio", "duration": 818.987}, {"from": "Assistant", "value": "Manual data annotation is a vital component of NLP research. When designing annotation tasks, properties of the annotation interface can unintentionally lead to artefacts in the resulting dataset, biasing the evaluation. In this paper, we explore sequence effects where annotations of an item are affected by the preceding items. Having assigned one label to an instance, the annotator may be less (or more) likely to assign the same label to the next. During rating tasks, seeing a low quality item may affect the score given to the next item either positively or negatively. We see clear evidence of both types of effects using auto-correlation studies over three different crowdsourced datasets. We then recommend a simple way to minimise sequence effects.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1307", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1307.flac", "type": "audio", "duration": 731.947}, {"from": "Assistant", "value": "First-order factoid question answering assumes that the question can be answered by a single fact in a knowledge base (KB). While this does not seem like a challenging task, many recent attempts that apply either complex linguistic reasoning or deep neural networks achieve 65%–76% accuracy on benchmark sets. Our approach formulates the task as two machine learning problems: detecting the entities in the question, and classifying the question as one of the relation types in the KB. We train a recurrent neural network to solve each problem. On the SimpleQuestions dataset, our approach yields substantial improvements over previously published results — even neural networks based on much more complex architectures. The simplicity of our approach also has practical advantages, such as efficiency and modularity, that are valuable especially in an industry setting. In fact, we present a preliminary analysis of the performance of our model on real queries from Comcast’s X1 entertainment platform with millions of users every day.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1308", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1308.flac", "type": "audio", "duration": 833.984}, {"from": "Assistant", "value": "Despite their ubiquity, word embeddings trained with skip-gram negative sampling (SGNS) remain poorly understood. We find that vector positions are not simply determined by semantic similarity, but rather occupy a narrow cone, diametrically opposed to the context vectors. We show that this geometric concentration depends on the ratio of positive to negative examples, and that it is neither theoretically nor empirically inherent in related embedding algorithms.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1309", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1309.flac", "type": "audio", "duration": 875.968}, {"from": "Assistant", "value": "We show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while being considerably cheaper in memory and computational requirements than deep recurrent models. Motivated by resource-constrained environments like mobile phones, we showcase simple techniques for obtaining such small neural network models, and investigate different tradeoffs when deciding how to allocate a small memory budget.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1310", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1310.flac", "type": "audio", "duration": 827.947}, {"from": "Assistant", "value": "We propose a novel LSTM-based deep multi-task learning framework for aspect term extraction from user review sentences. Two LSTMs equipped with extended memories and neural memory operations are designed for jointly handling the extraction tasks of aspects and opinions via memory interactions. Sentimental sentence constraint is also added for more accurate prediction via another LSTM. Experiment results over two benchmark datasets demonstrate the effectiveness of our framework.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1311", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1311.flac", "type": "audio", "duration": 896.981}, {"from": "Assistant", "value": "We investigate the compositional structure of message vectors computed by a deep network trained on a communication game. By comparing truth-conditional representations of encoder-produced message vectors to human-produced referring expressions, we are able to identify aligned (vector, utterance) pairs with the same meaning. We then search for structured relationships among these aligned pairs to discover simple vector space transformations corresponding to negation, conjunction, and disjunction. Our results suggest that neural representations are capable of spontaneously developing a “syntax” with functional analogues to qualitative properties of natural language.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1312", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1312.flac", "type": "audio", "duration": 699.2}, {"from": "Assistant", "value": "Learning word embeddings has received a significant amount of attention recently. Often, word embeddings are learned in an unsupervised manner from a large collection of text. The genre of the text typically plays an important role in the effectiveness of the resulting embeddings. How to effectively train word embedding models using data from different domains remains a problem that is less explored. In this paper, we present a simple yet effective method for learning word embeddings based on text from different domains. We demonstrate the effectiveness of our approach through extensive experiments on various down-stream NLP tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1314", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1314.flac", "type": "audio", "duration": 839.979}, {"from": "Assistant", "value": "Traditional supervised learning makes the closed-world assumption that the classes appeared in the test data must have appeared in training. This also applies to text learning or text classification. As learning is used increasingly in dynamic open environments where some new/test documents may not belong to any of the training classes, identifying these novel documents during classification presents an important problem. This problem is called open-world classification or open classification. This paper proposes a novel deep learning based approach. It outperforms existing state-of-the-art techniques dramatically.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1315", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1315.flac", "type": "audio", "duration": 794.987}, {"from": "Assistant", "value": "Portmanteaus are a word formation phenomenon where two words combine into a new word. We propose character-level neural sequence-to-sequence (S2S) methods for the task of portmanteau generation that are end-to-end-trainable, language independent, and do not explicitly use additional phonetic information. We propose a noisy-channel-style model, which allows for the incorporation of unsupervised word lists, improving performance over a standard source-to-target model. This model is made possible by an exhaustive candidate generation strategy specifically enabled by the features of the portmanteau task. Experiments find our approach superior to a state-of-the-art FST-based baseline with respect to ground truth accuracy and human evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1316", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1316.flac", "type": "audio", "duration": 870.016}, {"from": "Assistant", "value": "The diagnosis of serious mental health conditions such as schizophrenia is based on the judgment of clinicians whose training takes several years, and cannot be easily formalized into objective measures. However, previous research suggests there are disturbances in aspects of the language use of patients with schizophrenia. Using metaphor-identification and sentiment-analysis algorithms to automatically generate features, we create a classifier, that, with high accuracy, can predict which patients will develop (or currently suffer from) schizophrenia. To our knowledge, this study is the first to demonstrate the utility of automated metaphor identification algorithms for detection or prediction of disease.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1317", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1317.flac", "type": "audio", "duration": 852.011}, {"from": "Assistant", "value": "We present an analytic study on the language of news media in the context of political fact-checking and fake news detection. We compare the language of real news with that of satire, hoaxes, and propaganda to find linguistic characteristics of untrustworthy text. To probe the feasibility of automatic political fact-checking, we also present a case study based on PolitiFact.com using their factuality judgments on a 6-point scale. Experiments show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1318", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1318.flac", "type": "audio", "duration": 764.971}, {"from": "Assistant", "value": "We present a topic-based analysis of agreement and disagreement in political manifestos, which relies on a new method for topic detection based on key concept clustering. Our approach outperforms both standard techniques like LDA and a state-of-the-art graph-based method, and provides promising initial results for this new task in computational social science.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D17-1319", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D17-1319.flac", "type": "audio", "duration": 849.28}, {"from": "Assistant", "value": "We introduce Zipporah, a fast and scalable data cleaning system. We propose a novel type of bag-of-words translation feature, and train logistic regression models to classify good data and synthetic noisy data in the proposed feature space. The trained model is used to score parallel sentences in the data pool for selection. As shown in experiments, Zipporah selects a high-quality parallel corpus from a large, mixed quality data pool. In particular, for one noisy dataset, Zipporah achieves a 2.1 BLEU score improvement with using 1/5 of the data over using the entire corpus.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "W18-5441", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/W18-5441.flac", "type": "audio", "duration": 1069.035}, {"from": "Assistant", "value": "We present a large scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation encoded by a neural network captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. Our collection of diverse datasets is available athttp://www.decomp.net/, and will grow over time as additional resources are recast and added from novel sources.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1002", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1002.flac", "type": "audio", "duration": 1045.397}, {"from": "Assistant", "value": "Recent advances in Representation Learning and Adversarial Training seem to succeed in removing unwanted features from the learned representation. We show that demographic information of authors is encoded in—and can be recovered from—the intermediate representations learned by text-based neural classifiers. The implication is that decisions of classifiers trained on textual data are not agnostic to—and likely condition on—demographic attributes. When attempting to remove such demographic information using adversarial training, we find that while the adversarial component achieves chance-level development-set accuracy during training, a post-hoc classifier, trained on the encoded sentences from the first part, still manages to reach substantially higher classification accuracies on the same data. This behavior is consistent across several tasks, demographic properties and datasets. We explore several techniques to improve the effectiveness of the adversarial component. Our main conclusion is a cautionary one: do not rely on the adversarial training to achieve invariant representation to sensitive features.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1003", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1003.flac", "type": "audio", "duration": 1076.288}, {"from": "Assistant", "value": "Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed methods based on supervised learning, but these approaches do not consider external evidence apart from labeled training instances. Recent approaches counter this deficit by considering external sources related to a claim. However, these methods require substantial feature modeling and rich lexicons. This paper overcomes these limitations of prior work with an end-to-end model for evidence-aware credibility assessment of arbitrary textual claims, without any human intervention. It presents a neural network model that judiciously aggregates signals from external evidence articles, the language of these articles and the trustworthiness of their sources. It also derives informative features for generating user-comprehensible explanations that makes the neural network predictions transparent to the end-user. Experiments with four datasets and ablation studies show the strength of our method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1004", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1004.flac", "type": "audio", "duration": 1047.765}, {"from": "Assistant", "value": "People use online platforms to seek out support for their informational and emotional needs. Here, we ask what effect does revealing one’s gender have on receiving support. To answer this, we create (i) a new dataset and method for identifying supportive replies and (ii) new methods for inferring gender from text and name. We apply these methods to create a new massive corpus of 102M online interactions with gender-labeled users, each rated by degree of supportiveness. Our analysis shows wide-spread and consistent disparity in support: identifying as a woman is associated with higher rates of support - but also higher rates of disparagement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1005", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1005.flac", "type": "audio", "duration": 1353.024}, {"from": "Assistant", "value": "Gang-involved youth in cities such as Chicago have increasingly turned to social media to post about their experiences and intents online. In some situations, when they experience the loss of a loved one, their online expression of emotion may evolve into aggression towards rival gangs and ultimately into real-world violence. In this paper, we present a novel system for detecting Aggression and Loss in social media. Our system features the use of domain-specific resources automatically derived from a large unlabeled corpus, and contextual representations of the emotional and semantic content of the user’s recent tweets as well as their interactions with other users. Incorporating context in our Convolutional Neural Network (CNN) leads to a significant improvement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1006", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1006.flac", "type": "audio", "duration": 1038.891}, {"from": "Assistant", "value": "Comprehending procedural text, e.g., a paragraph describing photosynthesis, requires modeling actions and the state changes they produce, so that questions about entities at different timepoints can be answered. Although several recent systems have shown impressive progress in this task, their predictions can be globally inconsistent or highly improbable. In this paper, we show how the predicted effects of actions in the context of a paragraph can be improved in two ways: (1) by incorporating global, commonsense constraints (e.g., a non-existent entity cannot be destroyed), and (2) by biasing reading with preferences from large-scale corpora (e.g., trees rarely move). Unlike earlier methods, we treat the problem as a neural structured prediction task, allowing hard and soft constraints to steer the model away from unlikely predictions. We show that the new model significantly outperforms earlier systems on a benchmark dataset for procedural text comprehension (+8% relative gain), and that it also avoids some of the nonsensical predictions that earlier systems make.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1007", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1007.flac", "type": "audio", "duration": 1069.035}, {"from": "Assistant", "value": "We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online athttps://www.decomp.net, and will grow over time as additional resources are recast and added from novel sources.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1008", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1008.flac", "type": "audio", "duration": 1070.656}, {"from": "Assistant", "value": "To understand a sentence like “whereas only 10% of White Americans live at or below the poverty line, 28% of African Americans do” it is important not only to identify individual facts, e.g., poverty rates of distinct demographic groups, but also the higher-order relations between them, e.g., the disparity between them. In this paper, we propose the task of Textual Analogy Parsing (TAP) to model this higher-order meaning. Given a sentence such as the one above, TAP outputs a frame-style meaning representation which explicitly specifies what is shared (e.g., poverty rates) and what is compared (e.g., White Americans vs. African Americans, 10% vs. 28%) between its component facts. Such a meaning representation can enable new applications that rely on discourse understanding such as automated chart generation from quantitative text. We present a new dataset for TAP, baselines, and a model that successfully uses an ILP to enforce the structural constraints of the problem.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1009", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1009.flac", "type": "audio", "duration": 1052.352}, {"from": "Assistant", "value": "Given a partial description like “she opened the hood of the car,” humans can reason about the situation and anticipate what might come next (”then, she examined the engine”). In this paper, we introduce the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning. We present SWAG, a new dataset with 113k multiple choice questions about a rich spectrum of grounded situations. To address the recurring challenges of the annotation artifacts and human biases found in many existing datasets, we propose Adversarial Filtering (AF), a novel procedure that constructs a de-biased dataset by iteratively training an ensemble of stylistic classifiers, and using them to filter the data. To account for the aggressive adversarial filtering, we use state-of-the-art language models to massively oversample a diverse set of potential counterfactuals. Empirical results demonstrate that while humans can solve the resulting inference problems with high accuracy (88%), various competitive models struggle on our task. We provide comprehensive analysis that indicates significant opportunities for future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1010", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1010.flac", "type": "audio", "duration": 1003.819}, {"from": "Assistant", "value": "Determining whether a given claim is supported by evidence is a fundamental NLP problem that is best modeled as Textual Entailment. However, given a large collection of text, finding evidence that could support or refute a given claim is a challenge in itself, amplified by the fact that different evidence might be needed to support or refute a claim. Nevertheless, most prior work decouples evidence finding from determining the truth value of the claim given the evidence. We propose to consider these two aspects jointly. We develop TwoWingOS (two-wing optimization strategy), a system that, while identifying appropriate evidence for a claim, also determines whether or not the claim is supported by the evidence. Given the claim, TwoWingOS attempts to identify a subset of the evidence candidates; given the predicted evidence, it then attempts to determine the truth value of the corresponding claim entailment problem. We treat this problem as coupled optimization problems, training a joint model for it. TwoWingOS offers two advantages: (i) Unlike pipeline systems it facilitates flexible-size evidence set, and (ii) Joint training improves both the claim entailment and the evidence identification. Experiments on a benchmark dataset show state-of-the-art performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1011", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1011.flac", "type": "audio", "duration": 1069.269}, {"from": "Assistant", "value": "In this paper we address the problem of learning multimodal word representations by integrating textual, visual and auditory inputs. Inspired by the re-constructive and associative nature of human memory, we propose a novel associative multichannel autoencoder (AMA). Our model first learns the associations between textual and perceptual modalities, so as to predict the missing perceptual information of concepts. Then the textual and predicted perceptual representations are fused through reconstructing their original and associated embeddings. Using a gating mechanism our model assigns different weights to each modality according to the different concepts. Results on six benchmark concept similarity tests show that the proposed method significantly outperforms strong unimodal baselines and state-of-the-art multimodal models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1012", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1012.flac", "type": "audio", "duration": 1085.696}, {"from": "Assistant", "value": "Current dialogue systems focus more on textual and speech context knowledge and are usually based on two speakers. Some recent work has investigated static image-based dialogue. However, several real-world human interactions also involve dynamic visual context (similar to videos) as well as dialogue exchanges among multiple speakers. To move closer towards such multimodal conversational skills and visually-situated applications, we introduce a new video-context, many-speaker dialogue dataset based on live-broadcast soccer game videos and chats from Twitch.tv. This challenging testbed allows us to develop visually-grounded dialogue models that should generate relevant temporal and spatial event language from the live video, while also being relevant to the chat history. For strong baselines, we also present several discriminative and generative models, e.g., based on tridirectional attention flow (TriDAF). We evaluate these models via retrieval ranking-recall, automatic phrase-matching metrics, as well as human evaluation studies. We also present dataset analyses, model ablations, and visualizations to understand the contribution of different modalities and model components.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1013", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1013.flac", "type": "audio", "duration": 804.053}, {"from": "Assistant", "value": "The encode-decoder framework has shown recent success in image captioning. Visual attention, which is good at detailedness, and semantic attention, which is good at comprehensiveness, have been separately proposed to ground the caption on the image. In this paper, we propose the Stepwise Image-Topic Merging Network (simNet) that makes use of the two kinds of attention at the same time. At each time step when generating the caption, the decoder adaptively merges the attentive information in the extracted topics and the image according to the generated context, so that the visual information and the semantic information can be effectively combined. The proposed approach is evaluated on two benchmark datasets and reaches the state-of-the-art performances.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1014", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1014.flac", "type": "audio", "duration": 1088.128}, {"from": "Assistant", "value": "Computational modeling of human multimodal language is an emerging research area in natural language processing spanning the language, visual and acoustic modalities. Comprehending multimodal language requires modeling not only the interactions within each modality (intra-modal interactions) but more importantly the interactions between modalities (cross-modal interactions). In this paper, we propose the Recurrent Multistage Fusion Network (RMFN) which decomposes the fusion problem into multiple stages, each of them focused on a subset of multimodal signals for specialized, effective fusion. Cross-modal interactions are modeled using this multistage fusion approach which builds upon intermediate representations of previous stages. Temporal and intra-modal interactions are modeled by integrating our proposed fusion approach with a system of recurrent neural networks. The RMFN displays state-of-the-art performance in modeling human multimodal language across three public datasets relating to multimodal sentiment analysis, emotion recognition, and speaker traits recognition. We provide visualizations to show that each stage of fusion focuses on a different subset of multimodal signals, learning increasingly discriminative multimodal representations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1015", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1015.flac", "type": "audio", "duration": 843.115}, {"from": "Assistant", "value": "We introduce an effective and efficient method that grounds (i.e., localizes) natural sentences in long, untrimmed video sequences. Specifically, a novel Temporal GroundNet (TGN) is proposed to temporally capture the evolving fine-grained frame-by-word interactions between video and sentence. TGN sequentially scores a set of temporal candidates ended at each frame based on the exploited frame-by-word interactions, and finally grounds the segment corresponding to the sentence. Unlike traditional methods treating the overlapping segments separately in a sliding window fashion, TGN aggregates the historical information and generates the final grounding result in one single pass. We extensively evaluate our proposed TGN on three public datasets with significant improvements over the state-of-the-arts. We further show the consistent effectiveness and efficiency of TGN through an ablation study and a runtime test.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1016", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1016.flac", "type": "audio", "duration": 1194.475}, {"from": "Assistant", "value": "We introduce PreCo, a large-scale English dataset for coreference resolution. The dataset is designed to embody the core challenges in coreference, such as entity representation, by alleviating the challenge of low overlap between training and test sets and enabling separated analysis of mention detection and mention clustering. To strengthen the training-test overlap, we collect a large corpus of 38K documents and 12.5M words which are mostly from the vocabulary of English-speaking preschoolers. Experiments show that with higher training-test overlap, error analysis on PreCo is more efficient than the one on OntoNotes, a popular existing dataset. Furthermore, we annotate singleton mentions making it possible for the first time to quantify the influence that a mention detector makes on coreference resolution performance. The dataset is freely available athttps://preschool-lab.github.io/PreCo/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1017", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1017.flac", "type": "audio", "duration": 997.013}, {"from": "Assistant", "value": "Named entity recognition (NER) is an important task in natural language processing area, which needs to determine entities boundaries and classify them into pre-defined categories. For Chinese NER task, there is only a very small amount of annotated data available. Chinese NER task and Chinese word segmentation (CWS) task have many similar word boundaries. There are also specificities in each task. However, existing methods for Chinese NER either do not exploit word boundary information from CWS or cannot filter the specific information of CWS. In this paper, we propose a novel adversarial transfer learning framework to make full use of task-shared boundaries information and prevent the task-specific features of CWS. Besides, since arbitrary character can provide important cues when predicting entity type, we exploit self-attention to explicitly capture long range dependencies between two tokens. Experimental results on two different widely used datasets show that our proposed model significantly and consistently outperforms other state-of-the-art methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1018", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1018.flac", "type": "audio", "duration": 886.464}, {"from": "Assistant", "value": "Coreference resolution is an intermediate step for text understanding. It is used in tasks and domains for which we do not necessarily have coreference annotated corpora. Therefore, generalization is of special importance for coreference resolution. However, while recent coreference resolvers have notable improvements on the CoNLL dataset, they struggle to generalize properly to new domains or datasets. In this paper, we investigate the role of linguistic features in building more generalizable coreference resolvers. We show that generalization improves only slightly by merely using a set of additional linguistic features. However, employing features and subsets of their values that are informative for coreference resolution, considerably improves generalization. Thanks to better generalization, our system achieves state-of-the-art results in out-of-domain evaluations, e.g., on WikiCoref, our system, which is trained on CoNLL, achieves on-par performance with a system designed for this dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1019", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1019.flac", "type": "audio", "duration": 916.629}, {"from": "Assistant", "value": "In this work, we propose a novel segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets. We show that our model built on top of such a new representation is able to capture features and interactions that cannot be captured by previous models while maintaining a low time complexity for inference. We also present a theoretical analysis to formally assess how our representation is better than alternative representations reported in the literature in terms of representational power. Coupled with neural networks for feature learning, our model achieves the state-of-the-art performance in three benchmark datasets annotated with overlapping mentions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1020", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1020.flac", "type": "audio", "duration": 1264.832}, {"from": "Assistant", "value": "We introduce a family of multitask variational methods for semi-supervised sequence labeling. Our model family consists of a latent-variable generative model and a discriminative labeler. The generative models use latent variables to define the conditional probability of a word given its context, drawing inspiration from word prediction objectives commonly used in learning word embeddings. The labeler helps inject discriminative information into the latent space. We explore several latent variable configurations, including ones with hierarchical structure, which enables the model to account for both label-specific and word-specific information. Our models consistently outperform standard sequential baselines on 8 sequence labeling datasets, and improve further with unlabeled data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1051", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1051.flac", "type": "audio", "duration": 714.283}, {"from": "Assistant", "value": "The SimpleQuestions dataset is one of the most commonly used benchmarks for studying single-relation factoid questions. In this paper, we present new evidence that this benchmark can be nearly solved by standard methods. First, we show that ambiguity in the data bounds performance at 83.4%; many questions have more than one equally plausible interpretation. Second, we introduce a baseline that sets a new state-of-the-art performance level at 78.1% accuracy, despite using standard methods. Finally, we report an empirical analysis showing that the upperbound is loose; roughly a quarter of the remaining errors are also not resolvable from the linguistic signal. Together, these results suggest that the SimpleQuestions dataset is nearly solved.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1052", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1052.flac", "type": "audio", "duration": 659.2}, {"from": "Assistant", "value": "We formalize a new modular variant of current question answering tasks by enforcing complete independence of the document encoder from the question encoder. This formulation addresses a key challenge in machine comprehension by building a standalone representation of the document discourse. It additionally leads to a significant scalability advantage since the encoding of the answer candidate phrases in the document can be pre-computed and indexed offline for efficient retrieval. We experiment with baseline models for the new task, which achieve a reasonable accuracy but significantly underperform unconstrained QA models. We invite the QA research community to engage in Phrase-Indexed Question Answering (PIQA, pika) for closing the gap. The leaderboard is at:nlp.cs.washington.edu/piqa", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1053", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1053.flac", "type": "audio", "duration": 624.811}, {"from": "Assistant", "value": "Recently, open-domain question answering (QA) has been combined with machine comprehension models to find answers in a large knowledge source. As open-domain QA requires retrieving relevant documents from text corpora to answer questions, its performance largely depends on the performance of document retrievers. However, since traditional information retrieval systems are not effective in obtaining documents with a high probability of containing answers, they lower the performance of QA systems. Simply extracting more documents increases the number of irrelevant documents, which also degrades the performance of QA systems. In this paper, we introduce Paragraph Ranker which ranks paragraphs of retrieved documents for a higher answer recall with less noise. We show that ranking paragraphs and aggregating answers using Paragraph Ranker improves performance of open-domain QA pipeline on the four open-domain QA datasets by 7.8% on average.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1054", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1054.flac", "type": "audio", "duration": 723.904}, {"from": "Assistant", "value": "In recent years many deep neural networks have been proposed to solve Reading Comprehension (RC) tasks. Most of these models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span in a given document. We present a novel neural-based architecture that is capable of extracting relevant regions based on a given question-document pair and generating a well-formed answer. To show the effectiveness of our architecture, we conducted several experiments on the recently proposed and challenging RC dataset ‘NarrativeQA’. The proposed architecture outperforms state-of-the-art results by 12.62% (ROUGE-L) relative improvement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1055", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1055.flac", "type": "audio", "duration": 724.864}, {"from": "Assistant", "value": "State-of-the-art systems in deep question answering proceed as follows: (1)an initial document retrieval selects relevant documents, which (2) are then processed by a neural network in order to extract the final answer. Yet the exact interplay between both components is poorly understood, especially concerning the number of candidate documents that should be retrieved. We show that choosing a static number of documents - as used in prior research - suffers from a noise-information trade-off and yields suboptimal results. As a remedy, we propose an adaptive document retrieval model. This learns the optimal candidate number for document retrieval, conditional on the size of the corpus and the query. We report extensive experimental results showing that our adaptive approach outperforms state-of-the-art methods on multiple benchmark datasets, as well as in the context of corpora with variable sizes.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1056", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1056.flac", "type": "audio", "duration": 735.147}, {"from": "Assistant", "value": "This paper presents a challenge to the community: Generative adversarial networks (GANs) can perfectly align independent English word embeddings induced using the same algorithm, based on distributional information alone; but fails to do so, for two different embeddings algorithms. Why is that? We believe understanding why, is key to understand both modern word embedding algorithms and the limitations and instability dynamics of GANs. This paper shows that (a) in all these cases, where alignment fails, there exists a linear transform between the two embeddings (so algorithm biases do not lead to non-linear differences), and (b) similar effects can not easily be obtained by varying hyper-parameters. One plausible suggestion based on our initial experiments is that the differences in the inductive biases of the embedding algorithms lead to an optimization landscape that is riddled with local optima, leading to a very small basin of convergence, but we present this more as a challenge paper than a technical contribution.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1058", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1058.flac", "type": "audio", "duration": 674.453}, {"from": "Assistant", "value": "Capturing the semantic relations of words in a vector space contributes to many natural language processing tasks. One promising approach exploits lexico-syntactic patterns as features of word pairs. In this paper, we propose a novel model of this pattern-based approach, neural latent relational analysis (NLRA). NLRA can generalize co-occurrences of word pairs and lexico-syntactic patterns, and obtain embeddings of the word pairs that do not co-occur. This overcomes the critical data sparseness problem encountered in previous pattern-based models. Our experimental results on measuring relational similarity demonstrate that NLRA outperforms the previous pattern-based models. In addition, when combined with a vector offset model, NLRA achieves a performance comparable to that of the state-of-the-art model that exploits additional semantic relational data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1059", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1059.flac", "type": "audio", "duration": 731.072}, {"from": "Assistant", "value": "We approach the problem of generalizing pre-trained word embeddings beyond fixed-size vocabularies without using additional contextual information. We propose a subword-level word vector generation model that views words as bags of charactern-grams. The model is simple, fast to train and provides good vectors for rare or unseen words. Experiments show that our model achieves state-of-the-art performances in English word similarity task and in joint prediction of part-of-speech tag and morphosyntactic attributes in 23 languages, suggesting our model’s ability in capturing the relationship between words’ textual representations and their embeddings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1060", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1060.flac", "type": "audio", "duration": 766.677}, {"from": "Assistant", "value": "We present end-to-end neural models for detecting metaphorical word use in context. We show that relatively standard BiLSTM models which operate on complete sentences work well in this setting, in comparison to previous work that used more restricted forms of linguistic context. These models establish a new state-of-the-art on existing verb metaphor detection benchmarks, and show strong performance on jointly predicting the metaphoricity of all words in a running text.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1061", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1061.flac", "type": "audio", "duration": 649.899}, {"from": "Assistant", "value": "a cross-lingual neural part-of-speech tagger that learns from disparate sources of distant supervision, and realistically scales to hundreds of low-resource languages. The model exploits annotation projection, instance selection, tag dictionaries, morphological lexicons, and distributed representations, all in a uniform framework. The approach is simple, yet surprisingly effective, resulting in a new state of the art without access to any gold annotated data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1062", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1062.flac", "type": "audio", "duration": 477.888}, {"from": "Assistant", "value": "Bilingual lexicon extraction has been studied for decades and most previous methods have relied on parallel corpora or bilingual dictionaries. Recent studies have shown that it is possible to build a bilingual dictionary by aligning monolingual word embedding spaces in an unsupervised way. With the recent advances in generative models, we propose a novel approach which builds cross-lingual dictionaries via latent variable models and adversarial training with no parallel corpora. To demonstrate the effectiveness of our approach, we evaluate our approach on several language pairs and the experimental results show that our model could achieve competitive and even superior performance compared with several state-of-the-art models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1063", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1063.flac", "type": "audio", "duration": 577.387}, {"from": "Assistant", "value": "Word translation, or bilingual dictionary induction, is an important capability that impacts many multilingual language processing tasks. Recent research has shown that word translation can be achieved in an unsupervised manner, without parallel seed dictionaries or aligned corpora. However, state of the art methods unsupervised bilingual dictionary induction are based on generative adversarial models, and as such suffer from their well known problems of instability and hyper-parameter sensitivity. We present a statistical dependency-based approach to bilingual dictionary induction that is unsupervised – no seed dictionary or parallel corpora required; and introduces no adversary – therefore being much easier to train. Our method performs comparably to adversarial alternatives and outperforms prior non-adversarial methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1064", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1064.flac", "type": "audio", "duration": 764.864}, {"from": "Assistant", "value": "This paper proposes an adversarial training method for the multi-task and multi-lingual joint modeling needed for utterance intent classification. In joint modeling, common knowledge can be efficiently utilized among multiple tasks or multiple languages. This is achieved by introducing both language-specific networks shared among different tasks and task-specific networks shared among different languages. However, the shared networks are often specialized in majority tasks or languages, so performance degradation must be expected for some minor data sets. In order to improve the invariance of shared networks, the proposed method introduces both language-specific task adversarial networks and task-specific language adversarial networks; both are leveraged for purging the task or language dependencies of the shared networks. The effectiveness of the adversarial training proposal is demonstrated using Japanese and English data sets for three different utterance intent classification tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1065", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1065.flac", "type": "audio", "duration": 573.76}, {"from": "Assistant", "value": "In this paper we show that a simple beam approximation of the joint distribution between attention and output is an easy, accurate, and efficient attention mechanism for sequence to sequence learning. The method combines the advantage of sharp focus in hard attention and the implementation ease of soft attention. On five translation tasks we show effortless and consistent gains in BLEU compared to existing attention mechanisms.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1066", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1066.flac", "type": "audio", "duration": 526.827}, {"from": "Assistant", "value": "We present a neural network-based joint approach for emotion classification and emotion cause detection, which attempts to capture mutual benefits across the two sub-tasks of emotion analysis. Considering that emotion classification and emotion cause detection need different kinds of features (affective and event-based separately), we propose a joint encoder which uses a unified framework to extract features for both sub-tasks and a joint model trainer which simultaneously learns two models for the two sub-tasks separately. Our experiments on Chinese microblogs show that the joint approach is very promising.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1067", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1067.flac", "type": "audio", "duration": 762.901}, {"from": "Assistant", "value": "Identifying optimistic and pessimistic viewpoints and users from Twitter is useful for providing better social support to those who need such support, and for minimizing the negative influence among users and maximizing the spread of positive attitudes and ideas. In this paper, we explore a range of deep learning models to predict optimism and pessimism in Twitter at both tweet and user level and show that these models substantially outperform traditional machine learning classifiers used in prior work. In addition, we show evidence that a sentiment classifier would not be sufficient for accurately predicting optimism and pessimism in Twitter. Last, we study the verb tense usage as well as the presence of polarity words in optimistic and pessimistic tweets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1068", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1068.flac", "type": "audio", "duration": 708.224}, {"from": "Assistant", "value": "Newspapers need to attract readers with headlines, anticipating their readers’ preferences. These preferences rely on topical, structural, and lexical factors. We model each of these factors in a multi-task GRU network to predict headline popularity. We find that pre-trained word embeddings provide significant improvements over untrained embeddings, as do the combination of two auxiliary tasks, news-section prediction and part-of-speech tagging. However, we also find that performance is very similar to that of a simple Logistic Regression model over character n-grams. Feature analysis reveals structural patterns of headline popularity, including the use of forward-looking deictic expressions and second person pronouns.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1069", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1069.flac", "type": "audio", "duration": 605.376}, {"from": "Assistant", "value": "Inferring the agreement/disagreement relation in debates, especially in online debates, is one of the fundamental tasks in argumentation mining. The expressions of agreement/disagreement usually rely on argumentative expressions in text as well as interactions between participants in debates. Previous works usually lack the capability of jointly modeling these two factors. To alleviate this problem, this paper proposes a hybrid neural attention model which combines self and cross attention mechanism to locate salient part from textual context and interaction between users. Experimental results on three (dis)agreement inference datasets show that our model outperforms the state-of-the-art models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1092", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1092.flac", "type": "audio", "duration": 649.6}, {"from": "Assistant", "value": "Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applications. Yet, one of the biggest challenges is running these complex networks on devices such as mobile phones or smart watches with tiny memory footprint and low computational capacity. We propose on-device Self-Governing Neural Networks (SGNNs), which learn compact projection vectors with local sensitive hashing. The key advantage of SGNNs over existing work is that they surmount the need for pre-trained word embeddings and complex networks with huge parameters. We conduct extensive evaluation on dialog act classification and show significant improvement over state-of-the-art results. Our findings show that SGNNs are effective at capturing low-dimensional semantic text representations, while maintaining high accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1100", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1100.flac", "type": "audio", "duration": 729.429}, {"from": "Assistant", "value": "In this work, we examine methods for data augmentation for text-based tasks such as neural machine translation (NMT). We formulate the design of a data augmentation policy with desirable properties as an optimization problem, and derive a generic analytic solution. This solution not only subsumes some existing augmentation schemes, but also leads to an extremely simple data augmentation strategy for NMT: randomly replacing words in both the source sentence and the target sentence with other random words from their corresponding vocabularies. We name this method SwitchOut. Experiments on three translation datasets of different scales show that SwitchOut yields consistent improvements of about 0.5 BLEU, achieving better or comparable performances to strong alternatives such as word dropout (Sennrich et al., 2016a). Code to implement this method is included in the appendix.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1101", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1101.flac", "type": "audio", "duration": 660.395}, {"from": "Assistant", "value": "Unsupervised learning of cross-lingual word embedding offers elegant matching of words across languages, but has fundamental limitations in translating sentences. In this paper, we propose simple yet effective methods to improve word-by-word translation of cross-lingual embeddings, using only monolingual corpora but without any back-translation. We integrate a language model for context-aware search, and use a novel denoising autoencoder to handle reordering. Our system surpasses state-of-the-art unsupervised translation systems without costly iterative training. We also analyze the effect of vocabulary size and denoising type on the translation performance, which provides better understanding of learning the cross-lingual word embedding and its usage in translation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1102", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1102.flac", "type": "audio", "duration": 760.277}, {"from": "Assistant", "value": "Decipherment of homophonic substitution ciphers using language models is a well-studied task in NLP. Previous work in this topic scores short local spans of possible plaintext decipherments using n-gram language models. The most widely used technique is the use of beam search with n-gram language models proposed by Nuhn et al.(2013). We propose a beam search algorithm that scores the entire candidate plaintext at each step of the decipherment using a neural language model. We augment beam search with a novel rest cost estimation that exploits the prediction power of a neural language model. We compare against the state of the art n-gram based methods on many different decipherment tasks. On challenging ciphers such as the Beale cipher we provide significantly better error rates with much smaller beam sizes.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1103", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1103.flac", "type": "audio", "duration": 760.277}, {"from": "Assistant", "value": "This paper examines the problem of adapting neural machine translation systems to new, low-resourced languages (LRLs) as effectively and rapidly as possible. We propose methods based on starting with massively multilingual “seed models”, which can be trained ahead-of-time, and then continuing training on data related to the LRL. We contrast a number of strategies, leading to a novel, simple, yet effective method of “similar-language regularization”, where we jointly train on both a LRL of interest and a similar high-resourced language to prevent over-fitting to small LRL data. Experiments demonstrate that massively multilingual models, even without any explicit adaptation, are surprisingly effective, achieving BLEU scores of up to 15.5 with no data from the LRL, and that the proposed similar-language regularization method improves over other adaptation methods by 1.7 BLEU points average over 4 LRL settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1104", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1104.flac", "type": "audio", "duration": 724.971}, {"from": "Assistant", "value": "We propose and compare methods for gradient-based domain adaptation of self-attentive neural machine translation models. We demonstrate that a large proportion of model parameters can be frozen during adaptation with minimal or no reduction in translation quality by encouraging structured sparsity in the set of offset tensors during learning via group lasso regularization. We evaluate this technique for both batch and incremental adaptation across multiple data sets and language pairs. Our system architecture–combining a state-of-the-art self-attentive model with compact domain adaptation–provides high quality personalized machine translation that is both space and time efficient.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1105", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1105.flac", "type": "audio", "duration": 649.6}, {"from": "Assistant", "value": "Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applications. Yet, one of the biggest challenges is running these complex networks on devices such as mobile phones or smart watches with tiny memory footprint and low computational capacity. We propose on-device Self-Governing Neural Networks (SGNNs), which learn compact projection vectors with local sensitive hashing. The key advantage of SGNNs over existing work is that they surmount the need for pre-trained word embeddings and complex networks with huge parameters. We conduct extensive evaluation on dialog act classification and show significant improvement over state-of-the-art results. Our findings show that SGNNs are effective at capturing low-dimensional semantic text representations, while maintaining high accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1106", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1106.flac", "type": "audio", "duration": 640.96}, {"from": "Assistant", "value": "In large-scale domain classification for natural language understanding, leveraging each user’s domain enablement information, which refers to the preferred or authenticated domains by the user, with attention mechanism has been shown to improve the overall domain classification performance. In this paper, we propose a supervised enablement attention mechanism, which utilizes sigmoid activation for the attention weighting so that the attention can be computed with more expressive power without the weight sum constraint of softmax attention. The attention weights are explicitly encouraged to be similar to the corresponding elements of the output one-hot vector, and self-distillation is used to leverage the attention information of the other enabled domains. By evaluating on the actual utterances from a large-scale IPDA, we show that our approach significantly improves domain classification performance", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1107", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1107.flac", "type": "audio", "duration": 580.416}, {"from": "Assistant", "value": "In the sentence classification task, context formed from sentences adjacent to the sentence being classified can provide important information for classification. This context is, however, often ignored. Where methods do make use of context, only small amounts are considered, making it difficult to scale. We present a new method for sentence classification, Context-LSTM-CNN, that makes use of potentially large contexts. The method also utilizes long-range dependencies within the sentence being classified, using an LSTM, and short-span features, using a stacked CNN. Our experiments demonstrate that this approach consistently improves over previous methods on two different datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1108", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1108.flac", "type": "audio", "duration": 715.051}, {"from": "Assistant", "value": "Deep NLP models benefit from underlying structures in the data—e.g., parse trees—typically extracted using off-the-shelf parsers. Recent attempts to jointly learn the latent structure encounter a tradeoff: either make factorization assumptions that limit expressiveness, or sacrifice end-to-end differentiability. Using the recently proposed SparseMAP inference, which retrieves a sparse distribution over latent structures, we propose a novel approach for end-to-end learning of latent structure predictors jointly with a downstream predictor. To the best of our knowledge, our method is the first to enable unrestricted dynamic computation graph construction from the global latent structure, while maintaining differentiability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1109", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1109.flac", "type": "audio", "duration": 693.333}, {"from": "Assistant", "value": "We introduce a class of convolutional neural networks (CNNs) that utilize recurrent neural networks (RNNs) as convolution filters. A convolution filter is typically implemented as a linear affine transformation followed by a non-linear function, which fails to account for language compositionality. As a result, it limits the use of high-order filters that are often warranted for natural language processing tasks. In this work, we model convolution filters with RNNs that naturally capture compositionality and long-term dependencies in language. We show that simple CNN architectures equipped with recurrent neural filters (RNFs) achieve results that are on par with the best published ones on the Stanford Sentiment Treebank and two answer sentence selection datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1110", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1110.flac", "type": "audio", "duration": 661.035}, {"from": "Assistant", "value": "Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency or constituent trees. In this paper, we first propose to use the syntactic graph to represent three types of syntactic information, i.e., word order, dependency and constituency features; then employ a graph-to-sequence model to encode the syntactic graph and decode a logical form. Experimental results on benchmark datasets show that our model is comparable to the state-of-the-art on Jobs640, ATIS, and Geo880. Experimental results on adversarial examples demonstrate the robustness of the model is also improved by encoding more syntactic information.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1111", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1111.flac", "type": "audio", "duration": 681.749}, {"from": "Assistant", "value": "In models to generate program source code from natural language, representing this code in a tree structure has been a common approach. However, existing methods often fail to generate complex code correctly due to a lack of ability to memorize large and complex structures. We introduce RECODE, a method based on subtree retrieval that makes it possible to explicitly reference existing code examples within a neural code generation model. First, we retrieve sentences that are similar to input sentences using a dynamic-programming-based sentence similarity scoring method. Next, we extract n-grams of action sequences that build the associated abstract syntax tree. Finally, we increase the probability of actions that cause the retrieved n-gram action subtree to be in the predicted code. We show that our approach improves the performance on two code generation tasks by up to +2.6 BLEU.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1112", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1112.flac", "type": "audio", "duration": 709.461}, {"from": "Assistant", "value": "Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query. In this paper, we propose a graph-to-sequence model to encode the global structure information into node embeddings. This model can effectively learn the correlation between the SQL query pattern and its interpretation. Experimental results on the WikiSQL dataset and Stackoverflow dataset show that our model outperforms the Seq2Seq and Tree2Seq baselines, achieving the state-of-the-art performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1113", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1113.flac", "type": "audio", "duration": 574.592}, {"from": "Assistant", "value": "We study the automatic generation of syntactic paraphrases using four different models for generation: data-to-text generation, text-to-text generation, text reduction and text expansion, We derive training data for each of these tasks from the WebNLG dataset and we show (i) that conditioning generation on syntactic constraints effectively permits the generation of syntactically distinct paraphrases for the same input and (ii) that exploiting different types of input (data, text or data+text) further increases the number of distinct paraphrases that can be generated for a given input.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1114", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1114.flac", "type": "audio", "duration": 648.725}, {"from": "Assistant", "value": "We present a model for semantic proto-role labeling (SPRL) using an adapted bidirectional LSTM encoding strategy that we call NeuralDavidsonian: predicate-argument structure is represented as pairs of hidden states corresponding to predicate and argument head tokens of the input sequence. We demonstrate: (1) state-of-the-art results in SPRL, and (2) that our network naturally shares parameters between attributes, allowing for learning new attribute types with limited added supervision.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1116", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1116.flac", "type": "audio", "duration": 552.597}, {"from": "Assistant", "value": "Discourse segmentation, which segments texts into Elementary Discourse Units, is a fundamental step in discourse analysis. Previous discourse segmenters rely on complicated hand-crafted features and are not practical in actual use. In this paper, we propose an end-to-end neural segmenter based on BiLSTM-CRF framework. To improve its accuracy, we address the problem of data insufficiency by transferring a word representation model that is trained on a large corpus. We also propose a restricted self-attention mechanism in order to capture useful information within a neighborhood. Experiments on the RST-DT corpus show that our model is significantly faster than previous methods, while achieving new state-of-the-art performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1117", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1117.flac", "type": "audio", "duration": 449.429}, {"from": "Assistant", "value": "Video content on social media platforms constitutes a major part of the communication between people, as it allows everyone to share their stories. However, if someone is unable to consume video, either due to a disability or network bandwidth, this severely limits their participation and communication. Automatically telling the stories using multi-sentence descriptions of videos would allow bridging this gap. To learn and evaluate such models, we introduce VideoStory a new large-scale dataset for video description as a new challenge for multi-sentence video description. Our VideoStory captions dataset is complementary to prior work and contains 20k videos posted publicly on a social media platform amounting to 396 hours of video with 123k sentences, temporally aligned to the video.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1149", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1149.flac", "type": "audio", "duration": 1109.312}, {"from": "Assistant", "value": "We propose a conditional non-autoregressive neural sequence model based on iterative refinement. The proposed model is designed based on the principles of latent variable models and denoising autoencoders, and is generally applicable to any sequence generation task. We extensively evaluate the proposed model on machine translation (En-De and En-Ro) and image caption generation, and observe that it significantly speeds up decoding while maintaining the generation quality comparable to the autoregressive counterpart.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1150", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1150.flac", "type": "audio", "duration": 952.725}, {"from": "Assistant", "value": "We propose a large margin criterion for training neural language models. Conventionally, neural language models are trained by minimizing perplexity (PPL) on grammatical sentences. However, we demonstrate that PPL may not be the best metric to optimize in some tasks, and further propose a large margin formulation. The proposed method aims to enlarge the margin between the “good” and “bad” sentences in a task-specific sense. It is trained end-to-end and can be widely applied to tasks that involve re-scoring of generated text. Compared with minimum-PPL training, our method gains up to 1.1 WER reduction for speech recognition and 1.0 BLEU increase for machine translation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1151", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1151.flac", "type": "audio", "duration": 998.336}, {"from": "Assistant", "value": "We present a data set for evaluating the grammaticality of the predictions of a language model. We automatically construct a large number of minimally different pairs of English sentences, each consisting of a grammatical and an ungrammatical sentence. The sentence pairs represent different variations of structure-sensitive phenomena: subject-verb agreement, reflexive anaphora and negative polarity items. We expect a language model to assign a higher probability to the grammatical sentence than the ungrammatical one. In an experiment using this data set, an LSTM language model performed poorly on many of the constructions. Multi-task training with a syntactic objective (CCG supertagging) improved the LSTM’s accuracy, but a large gap remained between its performance and the accuracy of human participants recruited online. This suggests that there is considerable room for improvement over LSTMs in capturing syntax in a language model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1152", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1152.flac", "type": "audio", "duration": 1096.107}, {"from": "Assistant", "value": "Despite the tremendous empirical success of neural models in natural language processing, many of them lack the strong intuitions that accompany classical machine learning approaches. Recently, connections have been shown between convolutional neural networks (CNNs) and weighted finite state automata (WFSAs), leading to new interpretations and insights. In this work, we show that some recurrent neural networks also share this connection to WFSAs. We characterize this connection formally, defining rational recurrences to be recurrent hidden state update functions that can be written as the Forward calculation of a finite set of WFSAs. We show that several recent neural models use rational recurrences. Our analysis provides a fresh view of these models and facilitates devising new neural architectures that draw inspiration from WFSAs. We present one such model, which performs better than two recent baselines on language modeling and text classification. Our results demonstrate that transferring intuitions from classical models like WFSAs can be an effective approach to designing and understanding neural models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1153", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1153.flac", "type": "audio", "duration": 1056.363}, {"from": "Assistant", "value": "Many efforts have been made to facilitate natural language processing tasks with pre-trained language models (LMs), and brought significant improvements to various applications. To fully leverage the nearly unlimited corpora and capture linguistic information of multifarious levels, large-size LMs are required; but for a specific task, only parts of these information are useful. Such large-sized LMs, even in the inference stage, may cause heavy computation workloads, making them too time-consuming for large-scale applications. Here we propose to compress bulky LMs while preserving useful information with regard to a specific task. As different layers of the model keep different information, we develop a layer selection method for model pruning using sparsity-inducing regularization. By introducing the dense connectivity, we can detach any layer without affecting others, and stretch shallow and wide LMs to be deep and narrow. In model training, LMs are learned with layer-wise dropouts for better robustness. Experiments on two benchmark datasets demonstrate the effectiveness of our method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1154", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1154.flac", "type": "audio", "duration": 1111.211}, {"from": "Assistant", "value": "Identifying the salience (i.e. importance) of discourse units is an important task in language understanding. While events play important roles in text documents, little research exists on analyzing their saliency status. This paper empirically studies Event Salience and proposes two salience detection models based on discourse relations. The first is a feature based salience model that incorporates cohesion among discourse units. The second is a neural model that captures more complex interactions between discourse units. In our new large-scale event salience corpus, both methods significantly outperform the strong frequency baseline, while our neural model further improves the feature based one by a large margin. Our analyses demonstrate that our neural model captures interesting connections between salience and discourse unit relations (e.g., scripts and frame structures).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1155", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1155.flac", "type": "audio", "duration": 910.784}, {"from": "Assistant", "value": "The current leading paradigm for temporal information extraction from text consists of three phases: (1) recognition of events and temporal expressions, (2) recognition of temporal relations among them, and (3) time-line construction from the temporal relations. In contrast to the first two phases, the last phase, time-line construction, received little attention and is the focus of this work. In this paper, we propose a new method to construct a linear time-line from a set of (extracted) temporal relations. But more importantly, we propose a novel paradigm in which we directly predict start and end-points for events from the text, constituting a time-line without going through the intermediate step of prediction of temporal relations as in earlier work. Within this paradigm, we propose two models that predict in linear complexity, and a new training loss using TimeML-style annotations, yielding promising results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1156", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1156.flac", "type": "audio", "duration": 1129.237}, {"from": "Assistant", "value": "Event extraction is of practical utility in natural language processing. In the real world, it is a common phenomenon that multiple events existing in the same sentence, where extracting them are more difficult than extracting a single event. Previous works on modeling the associations between events by sequential modeling methods suffer a lot from the low efficiency in capturing very long-range dependencies. In this paper, we propose a novel Jointly Multiple Events Extraction (JMEE) framework to jointly extract multiple event triggers and arguments by introducing syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information. The experiment results demonstrate that our proposed framework achieves competitive results compared with state-of-the-art methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1157", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1157.flac", "type": "audio", "duration": 972.288}, {"from": "Assistant", "value": "Distantly-supervised Relation Extraction (RE) methods train an extractor by automatically aligning relation instances in a Knowledge Base (KB) with unstructured text. In addition to relation instances, KBs often contain other relevant side information, such as aliases of relations (e.g., founded and co-founded are aliases for the relation founderOfCompany). RE models usually ignore such readily available side information. In this paper, we propose RESIDE, a distantly-supervised neural relation extraction method which utilizes additional side information from KBs for improved relation extraction. It uses entity type and relation alias information for imposing soft constraints while predicting relations. RESIDE employs Graph Convolution Networks (GCN) to encode syntactic information from text and improves performance even when limited side information is available. Through extensive experiments on benchmark datasets, we demonstrate RESIDE’s effectiveness. We have made RESIDE’s source code available to encourage reproducible research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1158", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1158.flac", "type": "audio", "duration": 1094.165}, {"from": "Assistant", "value": "Traditional approaches to the task of ACE event detection primarily regard multiple events in one sentence as independent ones and recognize them separately by using sentence-level information. However, events in one sentence are usually interdependent and sentence-level information is often insufficient to resolve ambiguities for some types of events. This paper proposes a novel framework dubbed as Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms (HBTNGMA) to solve the two problems simultaneously. Firstly, we propose a hierachical and bias tagging networks to detect multiple events in one sentence collectively. Then, we devise a gated multi-level attention to automatically extract and dynamically fuse the sentence-level and document-level information. The experimental results on the widely used ACE 2005 dataset show that our approach significantly outperforms other state-of-the-art methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1159", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1159.flac", "type": "audio", "duration": 971.691}, {"from": "Assistant", "value": "We present a complete, automated, and efficient approach for utilizing valency analysis in making dependency parsing decisions. It includes extraction of valency patterns, a probabilistic model for tagging these patterns, and a joint decoding process that explicitly considers the number and types of each token’s syntactic dependents. On 53 treebanks representing 41 languages in the Universal Dependencies data, we find that incorporating valency information yields higher precision and F1 scores on the core arguments (subjects and complements) and functional relations (e.g., auxiliaries) that we employ for valency analysis. Precision on core arguments improves from 80.87 to 85.43. We further show that our approach can be applied to an ostensibly different formalism and dataset, Tree Adjoining Grammar as extracted from the Penn Treebank; there, we outperform the previous state-of-the-art labeled attachment score by 0.7. Finally, we explore the potential of extending valency patterns beyond their traditional domain by confirming their helpfulness in improving PP attachment decisions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1160", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1160.flac", "type": "audio", "duration": 1007.552}, {"from": "Assistant", "value": "Unsupervised learning of syntactic structure is typically performed using generative models with discrete latent variables and multinomial parameters. In most cases, these models have not leveraged continuous word representations. In this work, we propose a novel generative model that jointly learns discrete syntactic structure and continuous word representations in an unsupervised fashion by cascading an invertible neural network with a structured generative prior. We show that the invertibility condition allows for efficient exact inference and marginal likelihood computation in our model so long as the prior is well-behaved. In experiments we instantiate our approach with both Markov and tree-structured priors, evaluating on two tasks: part-of-speech (POS) induction, and unsupervised dependency parsing without gold POS annotation. On the Penn Treebank, our Markov-structured model surpasses state-of-the-art results on POS induction. Similarly, we find that our tree-structured model achieves state-of-the-art performance on unsupervised dependency parsing for the difficult training condition where neither gold POS annotation nor punctuation-based constraints are available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1161", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1161.flac", "type": "audio", "duration": 997.269}, {"from": "Assistant", "value": "We introduce novel dynamic oracles for training two of the most accurate known shift-reduce algorithms for constituent parsing: the top-down and in-order transition-based parsers. In both cases, the dynamic oracles manage to notably increase their accuracy, in comparison to that obtained by performing classic static training. In addition, by improving the performance of the state-of-the-art in-order shift-reduce parser, we achieve the best accuracy to date (92.0 F1) obtained by a fully-supervised single-model greedy shift-reduce constituent parser on the WSJ benchmark.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1162", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1162.flac", "type": "audio", "duration": 1144.555}, {"from": "Assistant", "value": "We introduce a method to reduce constituent parsing to sequence labeling. For each wordwt, it generates a label that encodes: (1) the number of ancestors in the tree that the wordswtandwt+1have in common, and (2) the nonterminal symbol at the lowest common ancestor. We first prove that the proposed encoding function is injective for any tree without unary branches. In practice, the approach is made extensible to all constituency trees by collapsing unary branches. We then use the PTB and CTB treebanks as testbeds and propose a set of fast baselines. We achieve 90% F-score on the PTB test set, outperforming the Vinyals et al. (2015) sequence-to-sequence parser. In addition, sacrificing some accuracy, our approach achieves the fastest constituent parsing speeds reported to date on PTB by a wide margin.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1163", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1163.flac", "type": "audio", "duration": 1141.056}, {"from": "Assistant", "value": "To approximately parse an unfamiliar language, it helps to have a treebank of a similar language. But what if the closest available treebank still has the wrong word order? We show how to (stochastically) permute the constituents of an existing dependency treebank so that its surface part-of-speech statistics approximately match those of the target language. The parameters of the permutation model can be evaluated for quality by dynamic programming and tuned by gradient descent (up to a local optimum). This optimization procedure yields trees for a new artificial language that resembles the target language. We show that delexicalized parsers for the target language can be successfully trained using such “made to order” artificial languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1165", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1165.flac", "type": "audio", "duration": 1095.509}, {"from": "Assistant", "value": "Active learning identifies data points to label that are expected to be the most useful in improving a supervised model. Opportunistic active learning incorporates active learning into interactive tasks that constrain possible queries during interactions. Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in an interactive object retrieval task. In this work, we use reinforcement learning for such an object retrieval task, to learn a policy that effectively trades off task completion with model improvement that would benefit future tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1166", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1166.flac", "type": "audio", "duration": 891.669}, {"from": "Assistant", "value": "Understanding and reasoning about cooking recipes is a fruitful research direction towards enabling machines to interpret procedural text. In this work, we introduce RecipeQA, a dataset for multimodal comprehension of cooking recipes. It comprises of approximately 20K instructional recipes with multiple modalities such as titles, descriptions and aligned set of images. With over 36K automatically generated question-answer pairs, we design a set of comprehension and reasoning tasks that require joint understanding of images and text, capturing the temporal flow of events and making sense of procedural knowledge. Our preliminary results indicate that RecipeQA will serve as a challenging test bed and an ideal benchmark for evaluating machine comprehension systems. The data and leaderboard are available athttp://hucvl.github.io/recipeqa.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1167", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1167.flac", "type": "audio", "duration": 1085.632}, {"from": "Assistant", "value": "Recent years have witnessed an increasing interest in image-based question-answering (QA) tasks. However, due to data limitations, there has been much less work on video-based QA. In this paper, we present TVQA, a large-scale video QA dataset based on 6 popular TV shows. TVQA consists of 152,545 QA pairs from 21,793 clips, spanning over 460 hours of video. Questions are designed to be compositional in nature, requiring systems to jointly localize relevant moments within a clip, comprehend subtitle-based dialogue, and recognize relevant visual concepts. We provide analyses of this new dataset as well as several baselines and a multi-stream end-to-end trainable neural network framework for the TVQA task. The dataset is publicly available athttp://tvqa.cs.unc.edu.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1168", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1168.flac", "type": "audio", "duration": 1128.128}, {"from": "Assistant", "value": "Localizing moments in a longer video via natural language queries is a new, challenging task at the intersection of language and video understanding. Though moment localization with natural language is similar to other language and vision tasks like natural language object retrieval in images, moment localization offers an interesting opportunity to model temporal dependencies and reasoning in text. We propose a new model that explicitly reasons about different temporal segments in a video, and shows that temporal context is important for localizing phrases which include temporal language. To benchmark whether our model, and other recent video localization models, can effectively reason about temporal language, we collect the novel TEMPOral reasoning in video and language (TEMPO) dataset. Our dataset consists of two parts: a dataset with real videos and template sentences (TEMPO - Template Language) which allows for controlled studies on temporal language, and a human language dataset which consists of temporal sentences annotated by humans (TEMPO - Human Language).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1199", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1199.flac", "type": "audio", "duration": 972.309}, {"from": "Assistant", "value": "Many idiomatic expressions can be interpreted figuratively or literally depending on their contexts. This paper proposes an unsupervised learning method for recognizing the intended usages of idioms. We treat the usages as a latent variable in probabilistic models and train them in a linguistically motivated feature space. Crucially, we show that distributional semantics is a helpful heuristic for distinguishing the literal usage of idioms, giving us a way to formulate a literal usage metric to estimate the likelihood that the idiom is intended literally. This information then serves as a form of distant supervision to guide the unsupervised training process for the probabilistic models. Experiments show that our overall model performs competitively against supervised methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1200", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1200.flac", "type": "audio", "duration": 1155.456}, {"from": "Assistant", "value": "The point of departure of this article is the claim that sense-specific vectors provide an advantage over normal vectors due to the polysemy that they presumably represent. This claim is based on performance gains observed in gold standard evaluation tests such as word similarity tasks. We demonstrate that this claim, at least as it is instantiated in prior art, is unfounded in two ways. Furthermore, we provide empirical data and an analytic discussion that may account for the previously reported improved performance. First, we show that ground-truth polysemy degrades performance in word similarity tasks. Therefore word similarity tasks are not suitable as an evaluation test for polysemy representation. Second, random assignment of words to senses is shown to improve performance in the same task. This and additional results point to the conclusion that performance gains as reported in previous work may be an artifact of random sense assignment, which is equivalent to sub-sampling and multiple estimation of word vector representations. Theoretical analysis shows that this may on its own be beneficial for the estimation of word similarity, by reducing the bias in the estimation of the cosine distance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1201", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1201.flac", "type": "audio", "duration": 1046.891}, {"from": "Assistant", "value": "Semantic graphs, such as WordNet, are resources which curate natural language on two distinguishable layers. On the local level, individual relations between synsets (semantic building blocks) such as hypernymy and meronymy enhance our understanding of the words used to express their meanings. Globally, analysis of graph-theoretic properties of the entire net sheds light on the structure of human language as a whole. In this paper, we combine global and local properties of semantic graphs through the framework of Max-Margin Markov Graph Models (M3GM), a novel extension of Exponential Random Graph Model (ERGM) that scales to large multi-relational graphs. We demonstrate how such global modeling improves performance on the local task of predicting semantic relations between synsets, yielding new state-of-the-art results on the WN18RR dataset, a challenging version of WordNet link prediction in which “easy” reciprocal cases are removed. In addition, the M3GM model identifies multirelational motifs that are characteristic of well-formed lexical semantic ontologies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1202", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1202.flac", "type": "audio", "duration": 987.456}, {"from": "Assistant", "value": "Adjectives like “warm”, “hot”, and “scalding” all describe temperature but differ in intensity. Understanding these differences between adjectives is a necessary part of reasoning about natural language. We propose a new paraphrase-based method to automatically learn the relative intensity relation that holds between a pair of scalar adjectives. Our approach analyzes over 36k adjectival pairs from the Paraphrase Database under the assumption that, for example, paraphrase pair “really hot” <–> “scalding” suggests that “hot” < “scalding”. We show that combining this paraphrase evidence with existing, complementary pattern- and lexicon-based approaches improves the quality of systems for automatically ordering sets of scalar adjectives and inferring the polarity of indirect answers to “yes/no” questions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1203", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1203.flac", "type": "audio", "duration": 877.291}, {"from": "Assistant", "value": "In this paper, we propose a new kernel-based co-occurrence measure that can be applied to sparse linguistic expressions (e.g., sentences) with a very short learning time, as an alternative to pointwise mutual information (PMI). As well as deriving PMI from mutual information, we derive this new measure from the Hilbert–Schmidt independence criterion (HSIC); thus, we call the new measure the pointwise HSIC (PHSIC). PHSIC can be interpreted as a smoothed variant of PMI that allows various similarity metrics (e.g., sentence embeddings) to be plugged in as kernels. Moreover, PHSIC can be estimated by simple and fast (linear in the size of the data) matrix calculations regardless of whether we use linear or nonlinear kernels. Empirically, in a dialogue response selection task, PHSIC is learned thousands of times faster than an RNN-based PMI while outperforming PMI in accuracy. In addition, we also demonstrate that PHSIC is beneficial as a criterion of a data selection task for machine translation owing to its ability to give high (low) scores to a consistent (inconsistent) pair with other pairs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1204", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1204.flac", "type": "audio", "duration": 1143.915}, {"from": "Assistant", "value": "Conventional solutions to automatic related work summarization rely heavily on human-engineered features. In this paper, we develop a neural data-driven summarizer by leveraging the seq2seq paradigm, in which a joint context-driven attention mechanism is proposed to measure the contextual relevance within full texts and a heterogeneous bibliography graph simultaneously. Our motivation is to maintain the topic coherency between a related work section and its target document, where both the textual and graphic contexts play a big role in characterizing the relationship among scientific publications accurately. Experimental results on a large dataset show that our approach achieves a considerable improvement over a typical seq2seq summarizer and five classical summarization baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1205", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1205.flac", "type": "audio", "duration": 1059.797}, {"from": "Assistant", "value": "Information selection is the most important component in document summarization task. In this paper, we propose to extend the basic neural encoding-decoding framework with an information selection layer to explicitly model and optimize the information selection process in abstractive document summarization. Specifically, our information selection layer consists of two parts: gated global information filtering and local sentence selection. Unnecessary information in the original document is first globally filtered, then salient sentences are selected locally while generating each summary sentence sequentially. To optimize the information selection process directly, distantly-supervised training guided by the golden summary is also imported. Experimental results demonstrate that the explicit modeling and optimizing of the information selection process improves document summarization performance significantly, which enables our model to generate more informative and concise summaries, and thus significantly outperform state-of-the-art neural abstractive methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1206", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1206.flac", "type": "audio", "duration": 1111.552}, {"from": "Assistant", "value": "We introduce “extreme summarization”, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach. The idea is to create a short, one-sentence news summary answering the question “What is the article about?”. We collect a real-world, large-scale dataset for this task by harvesting online articles from the British Broadcasting Corporation (BBC). We propose a novel abstractive model which is conditioned on the article’s topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1207", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1207.flac", "type": "audio", "duration": 1106.347}, {"from": "Assistant", "value": "Abstractive text summarization aims to shorten long text documents into a human readable form that contains the most important facts from the original document. However, the level of actual abstraction as measured by novel phrases that do not appear in the source document remains low in existing approaches. We propose two techniques to improve the level of abstraction of generated summaries. First, we decompose the decoder into a contextual network that retrieves relevant parts of the source document, and a pretrained language model that incorporates prior knowledge about language generation. Second, we propose a novelty metric that is optimized directly through policy learning to encourage the generation of novel phrases. Our model achieves results comparable to state-of-the-art models, as determined by ROUGE scores and human evaluations, while achieving a significantly higher level of abstraction as measured by n-gram overlap with the source document.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1208", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1208.flac", "type": "audio", "duration": 1100.373}, {"from": "Assistant", "value": "We carry out experiments with deep learning models of summarization across the domains of news, personal stories, meetings, and medical articles in order to understand how content selection is performed. We find that many sophisticated features of state of the art extractive summarizers do not improve performance over simpler models. These results suggest that it is easier to create a summarizer for a new domain than previous work suggests and bring into question the benefit of deep learning models for summarization for those domains that do have massive datasets (i.e., news). At the same time, they suggest important questions for new research in summarization; namely, new forms of sentence representations or external knowledge sources are needed that are better suited to the sumarization task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1209", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1209.flac", "type": "audio", "duration": 1086.059}, {"from": "Assistant", "value": "Network embeddings, which learns low-dimensional representations for each vertex in a large-scale network, have received considerable attention in recent years. For a wide range of applications, vertices in a network are typically accompanied by rich textual information such as user profiles, paper abstracts, etc. In this paper, we propose to incorporate semantic features into network embeddings by matching important words between text sequences for all pairs of vertices. We introduce an word-by-word alignment framework that measures the compatibility of embeddings between word pairs, and then adaptively accumulates these alignment features with a simple yet effective aggregation function. In experiments, we evaluate the proposed framework on three real-world benchmarks for downstream tasks, including link prediction and multi-label vertex classification. The experimental results demonstrate that our model outperforms state-of-the-art network embedding methods by a large margin.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1210", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1210.flac", "type": "audio", "duration": 1163.008}, {"from": "Assistant", "value": "Convolutional neural networks (CNNs) have recently emerged as a popular building block for natural language processing (NLP). Despite their success, most existing CNN models employed in NLP share the same learned (and static) set of filters for all input sentences. In this paper, we consider an approach of using a small meta network to learn context-sensitive convolutional filters for text processing. The role of meta network is to abstract the contextual information of a sentence or document into a set of input-sensitive filters. We further generalize this framework to model sentence pairs, where a bidirectional filter generation mechanism is introduced to encapsulate co-dependent sentence representations. In our benchmarks on four different tasks, including ontology classification, sentiment analysis, answer sentence selection, and paraphrase identification, our proposed model, a modified CNN with context-sensitive filters, consistently outperforms the standard CNN and attention-based CNN baselines. By visualizing the learned context-sensitive filters, we further validate and rationalize the effectiveness of proposed framework.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1211", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1211.flac", "type": "audio", "duration": 1109.227}, {"from": "Assistant", "value": "We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016). Unlike DRMM, which uses context-insensitive encodings of terms and query-document term interactions, we inject rich context-sensitive encodings throughout our models, inspired by PACRR’s (Hui et al., 2017) convolutional n-gram matching features, but extended in several ways including multiple views of query and document inputs. We test our models on datasets from the BIOASQ question answering challenge (Tsatsaronis et al., 2015) and TREC ROBUST 2004 (Voorhees, 2005), showing they outperform BM25-based baselines, DRMM, and PACRR.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1212", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1212.flac", "type": "audio", "duration": 775.509}, {"from": "Assistant", "value": "The existing studies in cross-language information retrieval (CLIR) mostly rely on general text representation models (e.g., vector space model or latent semantic analysis). These models are not optimized for the target retrieval task. In this paper, we follow the success of neural representation in natural language processing (NLP) and develop a novel text representation model based on adversarial learning, which seeks a task-specific embedding space for CLIR. Adversarial learning is implemented as an interplay between the generator process and the discriminator process. In order to adapt adversarial learning to CLIR, we design three constraints to direct representation learning, which are (1) a matching constraint capturing essential characteristics of cross-language ranking, (2) a translation constraint bridging language gaps, and (3) an adversarial constraint forcing both language and media invariant to be reached more efficiently and effectively. Through the joint exploitation of these constraints in an adversarial manner, the underlying cross-language semantics relevant to retrieval tasks are better preserved in the embedding space. Standard CLIR experiments show that our model significantly outperforms state-of-the-art continuous space models and is better than the strong machine translation baseline.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1213", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1213.flac", "type": "audio", "duration": 917.803}, {"from": "Assistant", "value": "Knowledge of the creation date of documents facilitates several tasks such as summarization, event extraction, temporally focused information extraction etc. Unfortunately, for most of the documents on the Web, the time-stamp metadata is either missing or can’t be trusted. Thus, predicting creation time from document content itself is an important task. In this paper, we propose Attentive Deep Document Dater (AD3), an attention-based neural document dating system which utilizes both context and temporal information in documents in a flexible and principled manner. We perform extensive experimentation on multiple real-world datasets to demonstrate the effectiveness of AD3 over neural and non-neural baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1214", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1214.flac", "type": "audio", "duration": 1136.512}, {"from": "Assistant", "value": "Cross-lingual or cross-domain correspondences play key roles in tasks ranging from machine translation to transfer learning. Recently, purely unsupervised methods operating on monolingual embeddings have become effective alignment tools. Current state-of-the-art methods, however, involve multiple steps, including heuristic post-hoc refinement strategies. In this paper, we cast the correspondence problem directly as an optimal transport (OT) problem, building on the idea that word embeddings arise from metric recovery algorithms. Indeed, we exploit the Gromov-Wasserstein distance that measures how similarities between pairs of words relate across languages. We show that our OT objective can be estimated efficiently, requires little or no tuning, and results in performance comparable with the state-of-the-art in various unsupervised word translation tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1215", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1215.flac", "type": "audio", "duration": 1147.221}, {"from": "Assistant", "value": "Deep learning has emerged as a versatile tool for a wide range of NLP tasks, due to its superior capacity in representation learning. But its applicability is limited by the reliance on annotated examples, which are difficult to produce at scale. Indirect supervision has emerged as a promising direction to address this bottleneck, either by introducing labeling functions to automatically generate noisy examples from unlabeled text, or by imposing constraints over interdependent label decisions. A plethora of methods have been proposed, each with respective strengths and limitations. Probabilistic logic offers a unifying language to represent indirect supervision, but end-to-end modeling with probabilistic logic is often infeasible due to intractable inference and learning. In this paper, we propose deep probabilistic logic (DPL) as a general framework for indirect supervision, by composing probabilistic logic with deep learning. DPL models label decisions as latent variables, represents prior knowledge on their relations using weighted first-order logical formulas, and alternates between learning a deep neural network for the end task and refining uncertain formula weights for indirect supervision, using variational EM. This framework subsumes prior indirect supervision methods as special cases, and enables novel combination via infusion of rich domain and linguistic knowledge. Experiments on biomedical machine reading demonstrate the promise of this approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1216", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1216.flac", "type": "audio", "duration": 941.76}, {"from": "Assistant", "value": "Attention-based models are successful when trained on large amounts of data. In this paper, we demonstrate that even in the low-resource scenario, attention can be learned effectively. To this end, we start with discrete human-annotated rationales and map them into continuous attention. Our central hypothesis is that this mapping is general across domains, and thus can be transferred from resource-rich domains to low-resource ones. Our model jointly learns a domain-invariant representation and induces the desired mapping between rationales and attention. Our empirical results validate this hypothesis and show that our approach delivers significant gains over state-of-the-art baselines, yielding over 15% average error reduction on benchmark datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1217", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1217.flac", "type": "audio", "duration": 1079.531}, {"from": "Assistant", "value": "Unsupervised representation learning algorithms such as word2vec and ELMo improve the accuracy of many supervised NLP models, mainly because they can take advantage of large amounts of unlabeled text. However, the supervised models only learn from task-specific labeled data during the main training phase. We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data. On labeled examples, standard supervised learning is used. On unlabeled examples, CVT teaches auxiliary prediction modules that see restricted views of the input (e.g., only part of a sentence) to match the predictions of the full model seeing the whole input. Since the auxiliary modules and the full model share intermediate representations, this in turn improves the full model. Moreover, we show that CVT is particularly effective when combined with multi-task learning. We evaluate CVT on five sequence tagging tasks, machine translation, and dependency parsing, achieving state-of-the-art results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1252", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1252.flac", "type": "audio", "duration": 1001.472}, {"from": "Assistant", "value": "Referring to entities in situated dialog is a collaborative process, whereby interlocutors often expand, repair and/or replace referring expressions in an iterative process, converging on conceptual pacts of referring language use in doing so. Nevertheless, much work on exophoric reference resolution (i.e. resolution of references to entities outside of a given text) follows a literary model, whereby individual referring expressions are interpreted as unique identifiers of their referents given the state of the dialog the referring expression is initiated. In this paper, we address this collaborative nature to improve dialogic reference resolution in two ways: First, we trained a words-as-classifiers logistic regression model of word semantics and incrementally adapt the model to idiosyncratic language between dyad partners during evaluation of the dialog. We then used these semantic models to learn the general referring ability of each word, which is independent of referent features. These methods facilitate accurate automatic reference resolution in situated dialog without annotation of referring expressions, even with little background data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1253", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1253.flac", "type": "audio", "duration": 1055.488}, {"from": "Assistant", "value": "Developing agents to engage in complex goal-oriented dialogues is challenging partly because the main learning signals are very sparse in long conversations. In this paper, we propose a divide-and-conquer approach that discovers and exploits the hidden structure of the task to enable efficient policy learning. First, given successful example dialogues, we propose the Subgoal Discovery Network (SDN) to divide a complex goal-oriented task into a set of simpler subgoals in an unsupervised fashion. We then use these subgoals to learn a multi-level policy by hierarchical reinforcement learning. We demonstrate our method by building a dialogue agent for the composite task of travel planning. Experiments with simulated and real users show that our approach performs competitively against a state-of-the-art method that requires human-defined subgoals. Moreover, we show that the learned subgoals are often human comprehensible.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1254", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1254.flac", "type": "audio", "duration": 1117.184}, {"from": "Assistant", "value": "Modern automated dialog systems require complex dialog managers able to deal with user intent triggered by high-level semantic questions. In this paper, we propose a model for automatically clustering questions into user intents to help the design tasks. Since questions are short texts, uncovering their semantics to group them together can be very challenging. We approach the problem by using powerful semantic classifiers from question duplicate/matching research along with a novel idea of supervised clustering methods based on structured output. We test our approach on two intent clustering corpora, showing an impressive improvement over previous methods for two languages/domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1255", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1255.flac", "type": "audio", "duration": 987.029}, {"from": "Assistant", "value": "Existing dialog datasets contain a sequence of utterances and responses without any explicit background knowledge associated with them. This has resulted in the development of models which treat conversation as a sequence-to-sequence generation task (i.e., given a sequence of utterances generate the response sequence). This is not only an overly simplistic view of conversation but it is also emphatically different from the way humans converse by heavily relying on their background knowledge about the topic (as opposed to simply relying on the previous sequence of utterances). For example, it is common for humans to (involuntarily) produce utterances which are copied or suitably modified from background articles they have read about the topic. To facilitate the development of such natural conversation models which mimic the human process of conversing, we create a new dataset containing movie chats wherein each response is explicitly generated by copying and/or modifying sentences from unstructured background knowledge such as plots, comments and reviews about the movie. We establish baseline results on this dataset (90K utterances from 9K conversations) using three different models: (i) pure generation based models which ignore the background knowledge (ii) generation based models which learn to copy information from the background knowledge when required and (iii) span prediction based models which predict the appropriate response span in the background knowledge.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1256", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1256.flac", "type": "audio", "duration": 1103.851}, {"from": "Assistant", "value": "We consider negotiation settings in which two agents use natural language to bargain on goods. Agents need to decide on both high-level strategy (e.g., proposing $50) and the execution of that strategy (e.g., generating “The bike is brand new. Selling for just $50!”). Recent work on negotiation trains neural models, but their end-to-end nature makes it hard to control their strategy, and reinforcement learning tends to lead to degenerate solutions. In this paper, we propose a modular approach based on coarse dialogue acts (e.g., propose(price=50)) that decouples strategy and generation. We show that we can flexibly set the strategy using supervised learning, reinforcement learning, or domain-specific knowledge without degeneracy, while our retrieval-based generation can maintain context-awareness and produce diverse utterances. We test our approach on the recently proposed DEALORNODEAL game, and we also collect a richer dataset based on real items on Craigslist. Human evaluation shows that our systems achieve higher task success rate and more human-like negotiation behavior than previous approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1257", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1257.flac", "type": "audio", "duration": 1128.043}, {"from": "Assistant", "value": "Cloze tests are widely adopted in language exams to evaluate students’ language proficiency. In this paper, we propose the first large-scale human-created cloze test dataset CLOTH, containing questions used in middle-school and high-school language exams. With missing blanks carefully created by teachers and candidate choices purposely designed to be nuanced, CLOTH requires a deeper language understanding and a wider attention span than previously automatically-generated cloze datasets. We test the performance of dedicatedly designed baseline models including a language model trained on the One Billion Word Corpus and show humans outperform them by a significant margin. We investigate the source of the performance gap, trace model deficiencies to some distinct properties of CLOTH, and identify the limited ability of comprehending the long-term context to be the key bottleneck.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1258", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1258.flac", "type": "audio", "duration": 1042.283}, {"from": "Assistant", "value": "We propose a novel methodology to generate domain-specific large-scale question answering (QA) datasets by re-purposing existing annotations for other NLP tasks. We demonstrate an instance of this methodology in generating a large-scale QA dataset for electronic medical records by leveraging existing expert annotations on clinical notes for various NLP tasks from the community shared i2b2 datasets. The resulting corpus (emrQA) has 1 million questions-logical form and 400,000+ question-answer evidence pairs. We characterize the dataset and explore its learning potential by training baseline models for question to logical form and question to answer mapping.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1259", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1259.flac", "type": "audio", "duration": 960.576}, {"from": "Assistant", "value": "Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems’ ability to extract relevant facts and perform necessary comparison. We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1260", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1260.flac", "type": "audio", "duration": 1016.533}, {"from": "Assistant", "value": "We present a new kind of question answering dataset, OpenBookQA, modeled after open book exams for assessing human understanding of a subject. The open book that comes with our questions is a set of 1326 elementary level science facts. Roughly 6000 questions probe an understanding of these facts and their application to novel situations. This requires combining an open book fact (e.g., metals conduct electricity) with broad common knowledge (e.g., a suit of armor is made of metal) obtained from other sources. While existing QA datasets over documents or knowledge bases, being generally self-contained, focus on linguistic understanding, OpenBookQA probes a deeper understanding of both the topic—in the context of common knowledge—and the language it is expressed in. Human performance on OpenBookQA is close to 92%, but many state-of-the-art pre-trained QA methods perform surprisingly poorly, worse than several simple neural baselines we develop. Our oracle experiments designed to circumvent the knowledge retrieval bottleneck demonstrate the value of both the open book and additional facts. We leave it as a challenge to solve the retrieval problem in this multi-hop setting and to close the large gap to human performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1261", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1261.flac", "type": "audio", "duration": 1118.293}, {"from": "Assistant", "value": "We propose a new dataset for evaluating question answering models with respect to their capacity to reason about beliefs. Our tasks are inspired by theory-of-mind experiments that examine whether children are able to reason about the beliefs of others, in particular when those beliefs differ from reality. We evaluate a number of recent neural models with memory augmentation. We find that all fail on our tasks, which require keeping track of inconsistent states of the world; moreover, the models’ accuracy decreases notably when random sentences are introduced to the tasks at test.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1262", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1262.flac", "type": "audio", "duration": 965.952}, {"from": "Assistant", "value": "Semantic role labeling (SRL) aims to recognize the predicate-argument structure of a sentence. Syntactic information has been paid a great attention over the role of enhancing SRL. However, the latest advance shows that syntax would not be so important for SRL with the emerging much smaller gap between syntax-aware and syntax-agnostic SRL. To comprehensively explore the role of syntax for SRL task, we extend existing models and propose a unified framework to investigate more effective and more diverse ways of incorporating syntax into sequential neural networks. Exploring the effect of syntactic input quality on SRL performance, we confirm that high-quality syntactic parse could still effectively enhance syntactically-driven SRL. Using empirically optimized integration strategy, we even enlarge the gap between syntax-aware and syntax-agnostic SRL. Our framework achieves state-of-the-art results on CoNLL-2009 benchmarks both for English and Chinese, substantially outperforming all previous models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1263", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1263.flac", "type": "audio", "duration": 1099.371}, {"from": "Assistant", "value": "We propose a novel approach to semantic dependency parsing (SDP) by casting the task as an instance of multi-lingual machine translation, where each semantic representation is a different foreign dialect. To that end, we first generalize syntactic linearization techniques to account for the richer semantic dependency graph structure. Following, we design a neural sequence-to-sequence framework which can effectively recover our graph linearizations, performing almost on-par with previous SDP state-of-the-art while requiring less parallel training annotations. Beyond SDP, our linearization technique opens the door to integration of graph-based semantic representations as features in neural models for downstream applications.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1264", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1264.flac", "type": "audio", "duration": 986.368}, {"from": "Assistant", "value": "In this paper, we propose a new rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser. Our aligner is further tuned by our oracle parser via picking the alignment that leads to the highest-scored achievable AMR graph. Experimental results show that our aligner outperforms the rule-based aligner in previous work by achieving higher alignment F1 score and consistently improving two open-sourced AMR parsers. Based on our aligner and transition system, we develop a transition-based AMR parser that parses a sentence into its AMR graph directly. An ensemble of our parsers with only words and POS tags as input leads to 68.4 Smatch F1 score, which outperforms the current state-of-the-art parser.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1265", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1265.flac", "type": "audio", "duration": 1045.845}, {"from": "Assistant", "value": "We propose a novel dependency-based hybrid tree model for semantic parsing, which converts natural language utterance into machine interpretable meaning representations. Unlike previous state-of-the-art models, the semantic information is interpreted as the latent dependency between the natural language words in our joint representation. Such dependency information can capture the interactions between the semantics and natural language words. We integrate a neural component into our model and propose an efficient dynamic-programming algorithm to perform tractable inference. Through extensive experiments on the standard multilingual GeoQuery dataset with eight languages, we demonstrate that our proposed approach is able to achieve state-of-the-art performance across several languages. Analysis also justifies the effectiveness of using our new dependency-based representation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1266", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1266.flac", "type": "audio", "duration": 1107.221}, {"from": "Assistant", "value": "Semantic parsing from denotations faces two key challenges in model training: (1) given only the denotations (e.g., answers), search for good candidate semantic parses, and (2) choose the best model update algorithm. We propose effective and general solutions to each of them. Using policy shaping, we bias the search procedure towards semantic parses that are more compatible to the text, which provide better supervision signals for training. In addition, we propose an update equation that generalizes three different families of learning algorithms, which enables fast model exploration. When experimented on a recently proposed sequential question answering dataset, our framework leads to a new state-of-the-art model that outperforms previous work by 5.0% absolute on exact match accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1267", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1267.flac", "type": "audio", "duration": 1017.088}, {"from": "Assistant", "value": "In this paper we advocate the use of bilingual corpora which are abundantly available for training sentence compression models. Our approach borrows much of its machinery from neural machine translation and leverages bilingual pivoting: compressions are obtained by translating a source string into a foreign language and then back-translating it into the source while controlling the translation length. Our model can be trained for any language as long as a bilingual corpus is available and performs arbitrary rewrites without access to compression specific data. We release. Moss, a new parallel Multilingual Compression dataset for English, German, and French which can be used to evaluate compression models across languages and genres.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1268", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1268.flac", "type": "audio", "duration": 1157.995}, {"from": "Assistant", "value": "Cross-lingual transfer of word embeddings aims to establish the semantic mappings among words in different languages by learning the transformation functions over the corresponding word embedding spaces. Successfully solving this problem would benefit many downstream tasks such as to translate text classification models from resource-rich languages (e.g. English) to low-resource languages. Supervised methods for this problem rely on the availability of cross-lingual supervision, either using parallel corpora or bilingual lexicons as the labeled data for training, which may not be available for many low resource languages. This paper proposes an unsupervised learning approach that does not require any cross-lingual labeled data. Given two monolingual word embedding spaces for any language pair, our algorithm optimizes the transformation functions in both directions simultaneously based on distributional matching as well as minimizing the back-translation losses. We use a neural network implementation to calculate the Sinkhorn distance, a well-defined distributional similarity measure, and optimize our objective through back-propagation. Our evaluation on benchmark datasets for bilingual lexicon induction and cross-lingual word similarity prediction shows stronger or competitive performance of the proposed method compared to other state-of-the-art supervised and unsupervised baseline methods over many language pairs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1269", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1269.flac", "type": "audio", "duration": 1036.352}, {"from": "Assistant", "value": "State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic, there has been a growing interest in cross-lingual language understanding (XLU) and low-resource cross-language transfer. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 14 languages, including low-resource languages such as Swahili and Urdu. We hope that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task. In addition, we provide several baselines for multilingual sentence understanding, including two based on machine translation systems, and two that use parallel data to train aligned multilingual bag-of-words and LSTM encoders. We find that XNLI represents a practical and challenging evaluation suite, and that directly translating the test data yields the best performance among available baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1270", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1270.flac", "type": "audio", "duration": 986.965}, {"from": "Assistant", "value": "Cross-lingual Entity Linking (XEL) aims to ground entity mentions written in any language to an English Knowledge Base (KB), such as Wikipedia. XEL for most languages is challenging, owing to limited availability of resources as supervision. We address this challenge by developing the first XEL approach that combines supervision from multiple languages jointly. This enables our approach to: (a) augment the limited supervision in the target language with additional supervision from a high-resource language (like English), and (b) train a single entity linking model for multiple languages, improving upon individually trained models for each language. Extensive evaluation on three benchmark datasets across 8 languages shows that our approach significantly improves over the current state-of-the-art. We also provide analyses in two limited resource settings: (a) zero-shot setting, when no supervision in the target language is available, and in (b) low-resource setting, when some supervision in the target language is available. Our analysis provides insights into the limitations of zero-shot XEL approaches in realistic scenarios, and shows the value of joint supervision in low-resource settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1271", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1271.flac", "type": "audio", "duration": 925.312}, {"from": "Assistant", "value": "This paper proposes to study fine-grained coordinated cross-lingual text stream alignment through a novel information network decipherment paradigm. We use Burst Information Networks as media to represent text streams and present a simple yet effective network decipherment algorithm with diverse clues to decipher the networks for accurate text stream alignment. Experiments on Chinese-English news streams show our approach not only outperforms previous approaches on bilingual lexicon extraction from coordinated text streams but also can harvest high-quality alignments from large amounts of streaming data for endless language knowledge mining, which makes it promising to be a new paradigm for automatic language knowledge acquisition.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1296", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1296.flac", "type": "audio", "duration": 724.779}, {"from": "Assistant", "value": "We propose to generalize language models for conversational speech recognition to allow them to operate across utterance boundaries and speaker changes, thereby capturing conversation-level phenomena such as adjacency pairs, lexical entrainment, and topical coherence. The model consists of a long-short-term memory (LSTM) recurrent network that reads the entire word-level history of a conversation, as well as information about turn taking and speaker overlap, in order to predict each next word. The model is applied in a rescoring framework, where the word history prior to the current utterance is approximated with preliminary recognition results. In experiments in the conversational telephone speech domain (Switchboard) we find that such a model gives substantial perplexity reductions over a standard LSTM-LM with utterance scope, as well as improvements in word error rate.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1297", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1297.flac", "type": "audio", "duration": 638.357}, {"from": "Assistant", "value": "Sequence-to-sequence neural generation models have achieved promising performance on short text conversation tasks. However, they tend to generate generic/dull responses, leading to unsatisfying dialogue experience. We observe that in the conversation tasks, each query could have multiple responses, which forms a 1-to-n or m-to-n relationship in the view of the total corpus. The objective function used in standard sequence-to-sequence models will be dominated by loss terms with generic patterns. Inspired by this observation, we introduce a statistical re-weighting method that assigns different weights for the multiple responses of the same query, and trains the common neural generation model with the weights. Experimental results on a large Chinese dialogue corpus show that our method improves the acceptance rate of generated responses compared with several baseline models and significantly reduces the number of generated generic responses.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1298", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1298.flac", "type": "audio", "duration": 664.768}, {"from": "Assistant", "value": "Current dialogue systems fail at being engaging for users, especially when trained end-to-end without relying on proactive reengaging scripted strategies. Zhang et al. (2018) showed that the engagement level of end-to-end dialogue models increases when conditioning them on text personas providing some personalized back-story to the model. However, the dataset used in Zhang et al. (2018) is synthetic and only contains around 1k different personas. In this paper we introduce a new dataset providing 5 million personas and 700 million persona-based dialogues. Our experiments show that, at this scale, training using personas still improves the performance of end-to-end systems. In addition, we show that other tasks benefit from the wide coverage of our dataset by fine-tuning our model on the data from Zhang et al. (2018) and achieving state-of-the-art results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1299", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1299.flac", "type": "audio", "duration": 578.88}, {"from": "Assistant", "value": "Dialogue state tracker is the core part of a spoken dialogue system. It estimates the beliefs of possible user’s goals at every dialogue turn. However, for most current approaches, it’s difficult to scale to large dialogue domains. They have one or more of following limitations: (a) Some models don’t work in the situation where slot values in ontology changes dynamically; (b) The number of model parameters is proportional to the number of slots; (c) Some models extract features based on hand-crafted lexicons. To tackle these challenges, we propose StateNet, a universal dialogue state tracker. It is independent of the number of values, shares parameters across all slots, and uses pre-trained word vectors instead of explicit semantic dictionaries. Our experiments on two datasets show that our approach not only overcomes the limitations, but also significantly outperforms the performance of state-of-the-art approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1300", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1300.flac", "type": "audio", "duration": 894.229}, {"from": "Assistant", "value": "Task oriented dialog systems typically first parse user utterances to semantic frames comprised of intents and slots. Previous work on task oriented intent and slot-filling work has been restricted to one intent per query and one slot label per token, and thus cannot model complex compositional requests. Alternative semantic parsing systems have represented queries as logical forms, but these are challenging to annotate and parse. We propose a hierarchical annotation scheme for semantic parsing that allows the representation of compositional queries, and can be efficiently and accurately parsed by standard constituency parsing models. We release a dataset of 44k annotated queries (http://fb.me/semanticparsingdialog), and show that parsing models outperform sequence-to-sequence approaches on this dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1301", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1301.flac", "type": "audio", "duration": 897.28}, {"from": "Assistant", "value": "In this paper, we provide empirical evidence based on a rigourously studied mathematical model for bi-populated networks, that a glass ceiling within the field of NLP has developed since the mid 2000s.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1302", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1302.flac", "type": "audio", "duration": 696.064}, {"from": "Assistant", "value": "Abusive language detection models tend to have a problem of being biased toward identity words of a certain group of people because of imbalanced training datasets. For example, “You are a good woman” was considered “sexist” when trained on an existing dataset. Such model bias is an obstacle for models to be robust enough for practical use. In this work, we measure them on models trained with different datasets, while analyzing the effect of different pre-trained word embeddings and model architectures. We also experiment with three mitigation methods: (1) debiased word embeddings, (2) gender swap data augmentation, and (3) fine-tuning with a larger corpus. These methods can effectively reduce model bias by 90-98% and can be extended to correct model bias in other scenarios.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1303", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1303.flac", "type": "audio", "duration": 678.357}, {"from": "Assistant", "value": "With the recent rise of #MeToo, an increasing number of personal stories about sexual harassment and sexual abuse have been shared online. In order to push forward the fight against such harassment and abuse, we present the task of automatically categorizing and analyzing various forms of sexual harassment, based on stories shared on the online forum SafeCity. For the labels of groping, ogling, and commenting, our single-label CNN-RNN model achieves an accuracy of 86.5%, and our multi-label model achieves a Hamming score of 82.5%. Furthermore, we present analysis using LIME, first-derivative saliency heatmaps, activation clustering, and embedding visualization to interpret neural model predictions and demonstrate how this helps extract features that can help automatically fill out incident reports, identify unsafe areas, avoid unsafe practices, and ‘pin the creeps’.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1304", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1304.flac", "type": "audio", "duration": 633.109}, {"from": "Assistant", "value": "As the incidence of Alzheimer’s Disease (AD) increases, early detection becomes crucial. Unfortunately, datasets for AD assessment are often sparse and incomplete. In this work, we leverage the multiview nature of a small AD dataset, DementiaBank, to learn an embedding that captures different modes of cognitive impairment. We apply generalized canonical correlation analysis (GCCA) to our dataset and demonstrate the added benefit of using multiview embeddings in two downstream tasks: identifying AD and predicting clinical scores. By including multiview embeddings, we obtain an F1 score of 0.82 in the classification task and a mean absolute error of 3.42 in the regression task. Furthermore, we show that multiview embeddings can be obtained from other datasets as well.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1305", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1305.flac", "type": "audio", "duration": 583.552}, {"from": "Assistant", "value": "We present a corpus that encompasses the complete history of conversations between contributors to Wikipedia, one of the largest online collaborative communities. By recording the intermediate states of conversations - including not only comments and replies, but also their modifications, deletions and restorations - this data offers an unprecedented view of online conversation. Our framework is designed to be language agnostic, and we show that it extracts high quality data in both Chinese and English. This level of detail supports new research questions pertaining to the process (and challenges) of large-scale online collaboration. We illustrate the corpus’ potential with two case studies on English Wikipedia that highlight new perspectives on earlier work. First, we explore how a person’s conversational behavior depends on how they relate to the discussion’s venue. Second, we show that community moderation of toxic behavior happens at a higher rate than previously estimated.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1306", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1306.flac", "type": "audio", "duration": 848.683}, {"from": "Assistant", "value": "Extracting typed entity mentions from text is a fundamental component to language understanding and reasoning. While there exist substantial labeled text datasets for multiple subsets of biomedical entity types—such as genes and proteins, or chemicals and diseases—it is rare to find large labeled datasets containing labels for all desired entity types together. This paper presents a method for training a single CRF extractor from multiple datasets with disjoint or partially overlapping sets of entity types. Our approach employs marginal likelihood training to insist on labels that are present in the data, while filling in “missing labels”. This allows us to leverage all the available data within a single model. In experimental results on the Biocreative V CDR (chemicals/diseases), Biocreative VI ChemProt (chemicals/proteins) and MedMentions (19 entity types) datasets, we show that joint training on multiple datasets improves NER F1 over training in isolation, and our methods achieve state-of-the-art results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1307", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1307.flac", "type": "audio", "duration": 621.269}, {"from": "Assistant", "value": "Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data. We show how to use AT for the tasks of entity recognition and relation extraction. In particular, we demonstrate that applying AT to a general purpose baseline model for jointly extracting entities and relations, allows improving the state-of-the-art effectiveness on several datasets in different contexts (i.e., news, biomedical, and real estate data) and for different languages (English and Dutch).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1308", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1308.flac", "type": "audio", "duration": 665.451}, {"from": "Assistant", "value": "We propose a model for tagging unstructured texts with an arbitrary number of terms drawn from a tree-structured vocabulary (i.e., an ontology). We treat this as a special case of sequence-to-sequence learning in which the decoder begins at the root node of an ontological tree and recursively elects to expand child nodes as a function of the input text, the current node, and the latent decoder state. We demonstrate that this method yields state-of-the-art results on the important task of assigning MeSH terms to biomedical abstracts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1309", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1309.flac", "type": "audio", "duration": 652.8}, {"from": "Assistant", "value": "We propose a simple deep neural model for nested named entity recognition (NER). Most NER models focused on flat entities and ignored nested entities, which failed to fully capture underlying semantic information in texts. The key idea of our model is to enumerate all possible regions or spans as potential entity mentions and classify them with deep neural networks. To reduce the computational costs and capture the information of the contexts around the regions, the model represents the regions using the outputs of shared underlying bidirectional long short-term memory. We evaluate our exhaustive model on the GENIA and JNLPBA corpora in biomedical domain, and the results show that our model outperforms state-of-the-art models on nested and flat NER, achieving 77.1% and 78.4% respectively in terms of F-score, without any external knowledge resources.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1310", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1310.flac", "type": "audio", "duration": 681.259}, {"from": "Assistant", "value": "Conventional wisdom is that hand-crafted features are redundant for deep learning models, as they already learn adequate representations of text automatically from corpora. In this work, we test this claim by proposing a new method for exploiting handcrafted features as part of a novel hybrid learning approach, incorporating a feature auto-encoder loss component. We evaluate on the task of named entity recognition (NER), where we show that including manual features for part-of-speech, word shapes and gazetteers can improve the performance of a neural CRF model. We obtain a F 1 of 91.89 for the CoNLL-2003 English shared task, which significantly outperforms a collection of highly competitive baseline models. We also present an ablation study showing the importance of auto-encoding, over using features as either inputs or outputs alone, and moreover, show including the autoencoder components reduces training requirements to 60%, while retaining the same predictive accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1311", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1311.flac", "type": "audio", "duration": 695.381}, {"from": "Assistant", "value": "Pre-trained word embeddings and language model have been shown useful in a lot of tasks. However, both of them cannot directly capture word connections in a sentence, which is important for dependency parsing given its goal is to establish dependency relations between words. In this paper, we propose to implicitly capture word connections from unlabeled data by a word ordering model with self-attention mechanism. Experiments show that these implicit word connections do improve our parsing model. Furthermore, by combining with a pre-trained language model, our model gets state-of-the-art performance on the English PTB dataset, achieving 96.35% UAS and 95.25% LAS.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1312", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1312.flac", "type": "audio", "duration": 789.205}, {"from": "Assistant", "value": "This paper presents a simple framework for characterizing morphological complexity and how it encodes syntactic information. In particular, we propose a new measure of morpho-syntactic complexity in terms of governor-dependent preferential attachment that explains parsing performance. Through experiments on dependency parsing with data from Universal Dependencies (UD), we show that representations derived from morphological attributes deliver important parsing performance improvements over standard word form embeddings when trained on the same datasets. We also show that the new morpho-syntactic complexity measure is predictive of the gains provided by using morphological attributes over plain forms on parsing scores, making it a tool to distinguish languages using morphology as a syntactic marker from others.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1313", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1313.flac", "type": "audio", "duration": 496.576}, {"from": "Assistant", "value": "Neural sequence-to-sequence models have proven very effective for machine translation, but at the expense of model interpretability. To shed more light into the role played by linguistic structure in the process of neural machine translation, we perform a fine-grained analysis of how various source-side morphological features are captured at different levels of the NMT encoder while varying the target language. Differently from previous work, we find no correlation between the accuracy of source morphology encoding and translation quality. We do find that morphological features are only captured in context and only to the extent that they are directly transferable to the target words.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1314", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1314.flac", "type": "audio", "duration": 626.432}, {"from": "Assistant", "value": "We employ imitation learning to train a neural transition-based string transducer for morphological tasks such as inflection generation and lemmatization. Previous approaches to training this type of model either rely on an external character aligner for the production of gold action sequences, which results in a suboptimal model due to the unwarranted dependence on a single gold action sequence despite spurious ambiguity, or require warm starting with an MLE model. Our approach only requires a simple expert policy, eliminating the need for a character aligner or warm start. It also addresses familiar MLE training biases and leads to strong and state-of-the-art performance on several benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1315", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1315.flac", "type": "audio", "duration": 723.392}, {"from": "Assistant", "value": "The Paradigm Cell Filling Problem in morphology asks to complete word inflection tables from partial ones. We implement novel neural models for this task, evaluating them on 18 data sets in 8 languages, showing performance that is comparable with previous work with far less training data. We also publish a new dataset for this task and code implementing the system described in this paper.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1348", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1348.flac", "type": "audio", "duration": 1120.235}, {"from": "Assistant", "value": "User intent detection plays a critical role in question-answering and dialog systems. Most previous works treat intent detection as a classification problem where utterances are labeled with predefined intents. However, it is labor-intensive and time-consuming to label users’ utterances as intents are diversely expressed and novel intents will continually be involved. Instead, we study the zero-shot intent detection problem, which aims to detect emerging user intents where no labeled utterances are currently available. We propose two capsule-based architectures: IntentCapsNet that extracts semantic features from utterances and aggregates them to discriminate existing intents, and IntentCapsNet-ZSL which gives IntentCapsNet the zero-shot learning ability to discriminate emerging intents via knowledge transfer from existing intents. Experiments on two real-world datasets show that our model not only can better discriminate diversely expressed existing intents, but is also able to discriminate emerging intents when no labeled utterances are available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1349", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1349.flac", "type": "audio", "duration": 1140.181}, {"from": "Assistant", "value": "Prevalent models based on artificial neural network (ANN) for sentence classification often classify sentences in isolation without considering the context in which sentences appear. This hampers the traditional sentence classification approaches to the problem of sequential sentence classification, where structured prediction is needed for better overall classification performance. In this work, we present a hierarchical sequential labeling network to make use of the contextual information within surrounding sentences to help classify the current sentence. Our model outperforms the state-of-the-art results by 2%-3% on two benchmarking datasets for sequential sentence classification in medical scientific abstracts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1350", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1350.flac", "type": "audio", "duration": 727.829}, {"from": "Assistant", "value": "In this study, we explore capsule networks with dynamic routing for text classification. We propose three strategies to stabilize the dynamic routing process to alleviate the disturbance of some noise capsules which may contain “background” information or have not been successfully trained. A series of experiments are conducted with capsule networks on six text classification benchmarks. Capsule networks achieve state of the art on 4 out of 6 datasets, which shows the effectiveness of capsule networks for text classification. We additionally show that capsule networks exhibit significant improvement when transfer single-label to multi-label text classification over strong baseline methods. To the best of our knowledge, this is the first work that capsule networks have been empirically investigated for text modeling.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1351", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1351.flac", "type": "audio", "duration": 1067.904}, {"from": "Assistant", "value": "Many classification models work poorly on short texts due to data sparsity. To address this issue, we propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels. Different from most prior work that focuses on extending features with external knowledge or pre-trained topics, our model jointly explores topic inference and text classification with memory networks in an end-to-end manner. Experimental results on four benchmark datasets show that our model outperforms state-of-the-art models on short text classification, meanwhile generates coherent topics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1352", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1352.flac", "type": "audio", "duration": 1203.221}, {"from": "Assistant", "value": "Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group), and labels that never appear in the training dataset (zero-shot group). Multi-label few- and zero-shot label prediction is mostly unexplored on datasets with large label spaces, especially for text classification. In this paper, we perform a fine-grained evaluation to understand how state-of-the-art methods perform on infrequent labels. Furthermore, we develop few- and zero-shot methods for multi-label text classification when there is a known structure over the label space, and evaluate them on two publicly available medical text datasets: MIMIC II and MIMIC III. For few-shot labels we achieve improvements of 6.2% and 4.8% in R@10 for MIMIC II and MIMIC III, respectively, over prior efforts; the corresponding R@10 improvements for zero-shot labels are 17.3% and 19%.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1353", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1353.flac", "type": "audio", "duration": 1144.96}, {"from": "Assistant", "value": "Poetry is one of the most beautiful forms of human language art. As a crucial step towards computer creativity, automatic poetry generation has drawn researchers’ attention for decades. In recent years, some neural models have made remarkable progress in this task. However, they are all based on maximum likelihood estimation, which only learns common patterns of the corpus and results in loss-evaluation mismatch. Human experts evaluate poetry in terms of some specific criteria, instead of word-level likelihood. To handle this problem, we directly model the criteria and use them as explicit rewards to guide gradient update by reinforcement learning, so as to motivate the model to pursue higher scores. Besides, inspired by writing theories, we propose a novel mutual reinforcement learning schema. We simultaneously train two learners (generators) which learn not only from the teacher (rewarder) but also from each other to further improve performance. We experiment on Chinese poetry. Based on a strong basic model, our method achieves better results and outperforms the current state-of-the-art method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1354", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1354.flac", "type": "audio", "duration": 956.16}, {"from": "Assistant", "value": "Combining the virtues of probability graphic models and neural networks, Conditional Variational Auto-encoder (CVAE) has shown promising performance in applications such as response generation. However, existing CVAE-based models often generate responses from a single latent variable which may not be sufficient to model high variability in responses. To solve this problem, we propose a novel model that sequentially introduces a series of latent variables to condition the generation of each word in the response sequence. In addition, the approximate posteriors of these latent variables are augmented with a backward Recurrent Neural Network (RNN), which allows the latent variables to capture long-term dependencies of future tokens in generation. To facilitate training, we supplement our model with an auxiliary objective that predicts the subsequent bag of words. Empirical experiments conducted on Opensubtitle and Reddit datasets show that the proposed model leads to significant improvement on both relevance and diversity over state-of-the-art baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1355", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1355.flac", "type": "audio", "duration": 1174.443}, {"from": "Assistant", "value": "Sentence simplification aims to reduce the complexity of a sentence while retaining its original meaning. Current models for sentence simplification adopted ideas from machine translation studies and implicitly learned simplification mapping rules from normal-simple sentence pairs. In this paper, we explore a novel model based on a multi-layer and multi-head attention architecture and we propose two innovative approaches to integrate the Simple PPDB (A Paraphrase Database for Simplification), an external paraphrase knowledge base for simplification that covers a wide range of real-world simplification rules. The experiments show that the integration provides two major benefits: (1) the integrated model outperforms multiple state-of-the-art baseline models for sentence simplification in the literature (2) through analysis of the rule utilization, the model seeks to select more accurate simplification rules. The code and models used in the paper are available athttps://github.com/Sanqiang/text_simplification.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1356", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1356.flac", "type": "audio", "duration": 1129.109}, {"from": "Assistant", "value": "While neural, encoder-decoder models have had significant empirical success in text generation, there remain several unaddressed problems with this style of generation. Encoder-decoder models are largely (a) uninterpretable, and (b) difficult to control in terms of their phrasing or content. This work proposes a neural generation system using a hidden semi-markov model (HSMM) decoder, which learns latent, discrete templates jointly with learning to generate. We show that this model learns useful templates, and that these templates make generation both more interpretable and controllable. Furthermore, we show that this approach scales to real data sets and achieves strong performance nearing that of encoder-decoder text generation models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1357", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1357.flac", "type": "audio", "duration": 1214.464}, {"from": "Assistant", "value": "Neural text generation, including neural machine translation, image captioning, and summarization, has been quite successful recently. However, during training time, typically only one reference is considered for each example, even though there are often multiple references available, e.g., 4 references in NIST MT evaluations, and 5 references in image captioning data. We first investigate several different ways of utilizing multiple human references during training. But more importantly, we then propose an algorithm to generate exponentially many pseudo-references by first compressing existing human references into lattices and then traversing them to generate new pseudo-references. These approaches lead to substantial improvements over strong baselines in both machine translation (+1.5 BLEU) and image captioning (+3.1 BLEU / +11.7 CIDEr).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1358", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1358.flac", "type": "audio", "duration": 833.707}, {"from": "Assistant", "value": "The rapid development of knowledge graphs (KGs), such as Freebase and WordNet, has changed the paradigm for AI-related applications. However, even though these KGs are impressively large, most of them are suffering from incompleteness, which leads to performance degradation of AI applications. Most existing researches are focusing on knowledge graph embedding (KGE) models. Nevertheless, those models simply embed entities and relations into latent vectors without leveraging the rich information from the relation structure. Indeed, relations in KGs conform to a three-layer hierarchical relation structure (HRS), i.e., semantically similar relations can make up relation clusters and some relations can be further split into several fine-grained sub-relations. Relation clusters, relations and sub-relations can fit in the top, the middle and the bottom layer of three-layer HRS respectively. To this end, in this paper, we extend existing KGE models TransE, TransH and DistMult, to learn knowledge representations by leveraging the information from the HRS. Particularly, our approach is capable to extend other KGE models. Finally, the experiment results clearly validate the effectiveness of the proposed approach against baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1359", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1359.flac", "type": "audio", "duration": 1052.459}, {"from": "Assistant", "value": "Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however, primarily focus on simple link structure between a finite set of entities, ignoring the variety of data types that are often used in knowledge bases, such as text, images, and numerical values. In this paper, we propose multimodal knowledge base embeddings (MKBE) that use different neural encoders for this variety of observed data, and combine them with existing relational models to learn embeddings of the entities and multimodal data. Further, using these learned embedings and different neural decoders, we introduce a novel multimodal imputation model to generate missing multimodal values, like text and images, from information in the knowledge base. We enrich existing relational datasets to create two novel benchmarks that contain additional information such as textual descriptions and images of the original entities. We demonstrate that our models utilize this additional information effectively to provide more accurate link prediction, achieving state-of-the-art results with a considerable gap of 5-7% over existing methods. Further, we evaluate the quality of our generated multimodal values via a user study.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1360", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1360.flac", "type": "audio", "duration": 1308.651}, {"from": "Assistant", "value": "We introduce a multi-task setup of identifying entities, relations, and coreference clusters in scientific articles. We create SciERC, a dataset that includes annotations for all three tasks and develop a unified framework called SciIE with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1361", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1361.flac", "type": "audio", "duration": 909.419}, {"from": "Assistant", "value": "The 20 Questions (Q20) game is a well known game which encourages deductive reasoning and creativity. In the game, the answerer first thinks of an object such as a famous person or a kind of animal. Then the questioner tries to guess the object by asking 20 questions. In a Q20 game system, the user is considered as the answerer while the system itself acts as the questioner which requires a good strategy of question selection to figure out the correct object and win the game. However, the optimal policy of question selection is hard to be derived due to the complexity and volatility of the game environment. In this paper, we propose a novel policy-based Reinforcement Learning (RL) method, which enables the questioner agent to learn the optimal policy of question selection through continuous interactions with users. To facilitate training, we also propose to use a reward network to estimate the more informative reward. Compared to previous methods, our RL method is robust to noisy answers and does not rely on the Knowledge Base of objects. Experimental results show that our RL method clearly outperforms an entropy-based engineering system and has competitive performance in a noisy-free simulation environment.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1362", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1362.flac", "type": "audio", "duration": 1063.275}, {"from": "Assistant", "value": "Multi-hop reasoning is an effective approach for query answering (QA) over incomplete knowledge graphs (KGs). The problem can be formulated in a reinforcement learning (RL) setup, where a policy-based agent sequentially extends its inference path until it reaches a target. However, in an incomplete KG environment, the agent receives low-quality rewards corrupted by false negatives in the training data, which harms generalization at test time. Furthermore, since no golden action sequence is used for training, the agent can be misled by spurious search trajectories that incidentally lead to the correct answer. We propose two modeling advances to address both issues: (1) we reduce the impact of false negative supervision by adopting a pretrained one-hop embedding model to estimate the reward of unobserved facts; (2) we counter the sensitivity to spurious paths of on-policy RL by forcing the agent to explore a diverse set of paths using randomly generated edge masks. Our approach significantly improves over existing path-based KGQA models on several benchmark datasets and is comparable or better than embedding-based models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1363", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1363.flac", "type": "audio", "duration": 1050.368}, {"from": "Assistant", "value": "Neural state-of-the-art sequence-to-sequence (seq2seq) models often do not perform well for small training sets. We address paradigm completion, the morphological task of, given a partial paradigm, generating all missing forms. We propose two new methods for the minimal-resource setting: (i) Paradigm transduction: Since we assume only few paradigms available for training, neural seq2seq models are able to capture relationships between paradigm cells, but are tied to the idiosyncracies of the training set. Paradigm transduction mitigates this problem by exploiting the input subset of inflected forms at test time. (ii) Source selection with high precision (SHIP): Multi-source models which learn to automatically select one or multiple sources to predict a target inflection do not perform well in the minimal-resource setting. SHIP is an alternative to identify a reliable source if training data is limited. On a 52-language benchmark dataset, we outperform the previous state of the art by up to 9.71% absolute accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1364", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1364.flac", "type": "audio", "duration": 1089.792}, {"from": "Assistant", "value": "This paper focuses on the most basic implicational universals in phonological theory, called T-orders after Anttila and Andrus (2006). It shows that the T-orders predicted by stochastic (and partial order) Optimality Theory coincide with those predicted by categorical OT. Analogously, the T-orders predicted by stochastic Harmonic Grammar coincide with those predicted by categorical HG. In other words, these stochastic constraint-based frameworks do not tamper with the typological structure induced by the original categorical frameworks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1365", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1365.flac", "type": "audio", "duration": 1063.765}, {"from": "Assistant", "value": "Character-level features are currently used in different neural network-based natural language processing algorithms. However, little is known about the character-level patterns those models learn. Moreover, models are often compared only quantitatively while a qualitative analysis is missing. In this paper, we investigate which character-level patterns neural networks learn and if those patterns coincide with manually-defined word segmentations and annotations. To that end, we extend the contextual decomposition technique (Murdoch et al. 2018) to convolutional neural networks which allows us to compare convolutional neural networks and bidirectional long short-term memory networks. We evaluate and compare these models for the task of morphological tagging on three morphologically different languages and show that these models implicitly discover understandable linguistic rules.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1366", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1366.flac", "type": "audio", "duration": 1012.885}, {"from": "Assistant", "value": "Much work in Natural Language Processing (NLP) has been for resource-rich languages, making generalization to new, less-resourced languages challenging. We present two approaches for improving generalization to low-resourced languages by adapting continuous word representations using linguistically motivated subword units: phonemes, morphemes and graphemes. Our method requires neither parallel corpora nor bilingual dictionaries and provides a significant gain in performance over previous methods relying on these resources. We demonstrate the effectiveness of our approaches on Named Entity Recognition for four languages, namely Uyghur, Turkish, Bengali and Hindi, of which Uyghur and Bengali are low resource languages, and also perform experiments on Machine Translation. Exploiting subwords with transfer learning gives us a boost of +15.2 NER F1 for Uyghur and +9.7 F1 for Bengali. We also show improvements in the monolingual setting where we achieve (avg.) +3 F1 and (avg.) +1.35 BLEU.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1396", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1396.flac", "type": "audio", "duration": 1025.899}, {"from": "Assistant", "value": "Neural machine translation usually adopts autoregressive models and suffers from exposure bias as well as the consequent error propagation problem. Many previous works have discussed the relationship between error propagation and theaccuracy drop(i.e., the left part of the translated sentence is often better than its right part in left-to-right decoding models) problem. In this paper, we conduct a series of analyses to deeply understand this problem and get several interesting findings. (1) The role of error propagation on accuracy drop is overstated in the literature, although it indeed contributes to the accuracy drop problem. (2) Characteristics of a language play a more important role in causing the accuracy drop: the left part of the translation result in a right-branching language (e.g., English) is more likely to be more accurate than its right part, while the right part is more accurate for a left-branching language (e.g., Japanese). Our discoveries are confirmed on different model structures including Transformer and RNN, and in other sequence generation tasks such as text summarization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1397", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1397.flac", "type": "audio", "duration": 963.669}, {"from": "Assistant", "value": "Recent studies have shown that reinforcement learning (RL) is an effective approach for improving the performance of neural machine translation (NMT) system. However, due to its instability, successfully RL training is challenging, especially in real-world systems where deep models and large datasets are leveraged. In this paper, taking several large-scale translation tasks as testbeds, we conduct a systematic study on how to train better NMT models using reinforcement learning. We provide a comprehensive comparison of several important factors (e.g., baseline reward, reward shaping) in RL training. Furthermore, to fill in the gap that it remains unclear whether RL is still beneficial when monolingual data is used, we propose a new method to leverage RL to further boost the performance of NMT systems trained with source/target monolingual data. By integrating all our findings, we obtain competitive results on WMT14 English-German, WMT17 English-Chinese, and WMT17 Chinese-English translation tasks, especially setting a state-of-the-art performance on WMT17 Chinese-English translation task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1398", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1398.flac", "type": "audio", "duration": 1146.048}, {"from": "Assistant", "value": "In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm (MAML, Finn, et al., 2017) for low-resource neural machine translation (NMT). We frame low-resource translation as a meta-learning problem where we learn to adapt to low-resource languages based on multilingual high-resource language tasks. We use the universal lexical representation (Gu et al., 2018b) to overcome the input-output mismatch across different languages. We evaluate the proposed meta-learning strategy using eighteen European languages (Bg, Cs, Da, De, El, Es, Et, Fr, Hu, It, Lt, Nl, Pl, Pt, Sk, Sl, Sv and Ru) as source tasks and five diverse languages (Ro,Lv, Fi, Tr and Ko) as target tasks. We show that the proposed approach significantly outperforms the multilingual, transfer learning based approach (Zoph et al., 2016) and enables us to train a competitive NMT system with only a fraction of training examples. For instance, the proposed approach can achieve as high as 22.04 BLEU on Romanian-English WMT’16 by seeing only 16,000 translated words (~600 parallel sentences)", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1399", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1399.flac", "type": "audio", "duration": 1146.517}, {"from": "Assistant", "value": "While modern machine translation has relied on large parallel corpora, a recent line of work has managed to train Neural Machine Translation (NMT) systems from monolingual corpora only (Artetxe et al., 2018c; Lample et al., 2018). Despite the potential of this approach for low-resource settings, existing systems are far behind their supervised counterparts, limiting their practical interest. In this paper, we propose an alternative approach based on phrase-based Statistical Machine Translation (SMT) that significantly closes the gap with supervised systems. Our method profits from the modular architecture of SMT: we first induce a phrase table from monolingual corpora through cross-lingual embedding mappings, combine it with an n-gram language model, and fine-tune hyperparameters through an unsupervised MERT variant. In addition, iterative backtranslation improves results further, yielding, for instance, 14.08 and 26.22 BLEU points in WMT 2014 English-German and English-French, respectively, an improvement of more than 7-10 BLEU points over previous unsupervised systems, and closing the gap with supervised SMT (Moses trained on Europarl) down to 2-5 BLEU points. Our implementation is available athttps://github.com/artetxem/monoses.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1400", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1400.flac", "type": "audio", "duration": 1104.875}, {"from": "Assistant", "value": "We introduce a novel multimodal machine translation model that utilizes parallel visual and textual information. Our model jointly optimizes the learning of a shared visual-language embedding and a translator. The model leverages a visual attention grounding mechanism that links the visual semantics with the corresponding textual semantics. Our approach achieves competitive state-of-the-art results on the Multi30K and the Ambiguous COCO datasets. We also collected a new multilingual multimodal product description dataset to simulate a real-world international online shopping scenario. On this dataset, our visual attention grounding model outperforms other methods by a large margin.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1401", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1401.flac", "type": "audio", "duration": 949.632}, {"from": "Assistant", "value": "In an e-commerce environment, user-oriented question-answering (QA) text pair could carry rich sentiment information. In this study, we propose a novel task/method to address QA sentiment analysis. In particular, we create a high-quality annotated corpus with specially-designed annotation guidelines for QA-style sentiment classification. On the basis, we propose a three-stage hierarchical matching network to explore deep sentiment information in a QA text pair. First, we segment both the question and answer text into sentences and construct a number of [Q-sentence, A-sentence] units in each QA text pair. Then, by leveraging a QA bidirectional matching layer, the proposed approach can learn the matching vectors of each [Q-sentence, A-sentence] unit. Finally, we characterize the importance of the generated matching vectors via a self-matching attention layer. Experimental results, comparing with a number of state-of-the-art baselines, demonstrate the impressive effectiveness of the proposed approach for QA-style sentiment classification.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1402", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1402.flac", "type": "audio", "duration": 955.499}, {"from": "Assistant", "value": "Argument mining is a core technology for automating argument search in large document collections. Despite its usefulness for this task, most current approaches are designed for use only with specific text types and fall short when applied to heterogeneous texts. In this paper, we propose a new sentential annotation scheme that is reliably applicable by crowd workers to arbitrary Web texts. We source annotations for over 25,000 instances covering eight controversial topics. We show that integrating topic information into bidirectional long short-term memory networks outperforms vanilla BiLSTMs by more than 3 percentage points in F1 in two- and three-label cross-topic settings. We also show that these results can be further improved by leveraging additional data for topic relevance using multi-task learning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1403", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1403.flac", "type": "audio", "duration": 1227.605}, {"from": "Assistant", "value": "We present a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision (e.g., in the form of product domain labels and user-provided ratings). Our method combines two weakly supervised components to identify salient opinions and form extractive summaries from multiple reviews: an aspect extractor trained under a multi-task objective, and a sentiment predictor based on multiple instance learning. We introduce an opinion summarization dataset that includes a training set of product reviews from six diverse domains and human-annotated development and test sets with gold standard aspect annotations, salience labels, and opinion summaries. Automatic evaluation shows significant improvements over baselines, and a large-scale study indicates that our opinion summaries are preferred by human judges according to multiple criteria.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1404", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1404.flac", "type": "audio", "duration": 1115.157}, {"from": "Assistant", "value": "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1405", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1405.flac", "type": "audio", "duration": 1139.008}, {"from": "Assistant", "value": "Noise Contrastive Estimation (NCE) is a powerful parameter estimation method for log-linear models, which avoids calculation of the partition function or its derivatives at each training step, a computationally demanding step in many cases. It is closely related to negative sampling methods, now widely used in NLP. This paper considers NCE-based estimation of conditional models. Conditional models are frequently encountered in practice; however there has not been a rigorous theoretical analysis of NCE in this setting, and we will argue there are subtle but important questions when generalizing NCE to the conditional case. In particular, we analyze two variants of NCE for conditional models: one based on a classification objective, the other based on a ranking objective. We show that the ranking-based variant of NCE gives consistent parameter estimates under weaker assumptions than the classification-based method; we analyze the statistical efficiency of the ranking-based and classification-based variants of NCE; finally we describe experiments on synthetic data and language modeling showing the effectiveness and tradeoffs of both methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1406", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1406.flac", "type": "audio", "duration": 1151.296}, {"from": "Assistant", "value": "Maximum-likelihood estimation (MLE) is one of the most widely used approaches for training structured prediction models for text-generation based natural language processing applications. However, besides exposure bias, models trained with MLE suffer from wrong objective problem where they are trained to maximize the word-level correct next step prediction, but are evaluated with respect to sequence-level discrete metrics such as ROUGE and BLEU. Several variants of policy-gradient methods address some of these problems by optimizing for final discrete evaluation metrics and showing improvements over MLE training for downstream tasks like text summarization and machine translation. However, policy-gradient methods suffers from high sample variance, making the training process very difficult and unstable. In this paper, we present an alternative direction towards mitigating this problem by introducing a new objective (CaLcs) based on a differentiable surrogate of longest common subsequence (LCS) measure that captures sequence-level structure similarity. Experimental results on abstractive summarization and machine translation validate the effectiveness of the proposed approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1407", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1407.flac", "type": "audio", "duration": 1040.043}, {"from": "Assistant", "value": "One way to interpret neural model predictions is to highlight the most important input features—for example, a heatmap visualization over the words in an input sentence. In existing interpretation methods for NLP, a word’s importance is determined by either input perturbation—measuring the decrease in model confidence when that word is removed—or by the gradient with respect to that word. To understand the limitations of these methods, we use input reduction, which iteratively removes the least important word from the input. This exposes pathological behaviors of neural models: the remaining words appear nonsensical to humans and are not the ones determined as important by interpretation methods. As we confirm with human experiments, the reduced examples lack information to support the prediction of any label, but models still make the same predictions with high confidence. To explain these counterintuitive results, we draw connections to adversarial examples and confidence calibration: pathological behaviors reveal difficulties in interpreting neural models trained with maximum likelihood. To mitigate their deficiencies, we fine-tune the models by encouraging high entropy outputs on reduced examples. Fine-tuned models become more interpretable under input reduction, without accuracy loss on regular examples.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1408", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1408.flac", "type": "audio", "duration": 797.376}, {"from": "Assistant", "value": "Universal sentence encoding is a hot topic in recent NLP research. Attention mechanism has been an integral part in many sentence encoding models, allowing the models to capture context dependencies regardless of the distance between the elements in the sequence. Fully attention-based models have recently attracted enormous interest due to their highly parallelizable computation and significantly less training time. However, the memory consumption of their models grows quadratically with the sentence length, and the syntactic information is neglected. To this end, we propose Phrase-level Self-Attention Networks (PSAN) that perform self-attention across words inside a phrase to capture context dependencies at the phrase level, and use the gated memory updating mechanism to refine each word’s representation hierarchically with longer-term context dependencies captured in a larger phrase. As a result, the memory consumption can be reduced because the self-attention is performed at the phrase level instead of the sentence level. At the same time, syntactic information can be easily integrated in the model. Experiment results show that PSAN can achieve the state-of-the-art performance across a plethora of NLP tasks including binary and multi-class classification, natural language inference and sentence similarity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1409", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1409.flac", "type": "audio", "duration": 983.168}, {"from": "Assistant", "value": "In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels. We call our approach BanditSum as it treats extractive summarization as a contextual bandit (CB) problem, where the model receives a document to summarize (the context), and chooses a sequence of sentences to include in the summary (the action). A policy gradient reinforcement learning algorithm is used to train the model to select sequences of sentences that maximize ROUGE score. We perform a series of experiments demonstrating that BanditSum is able to achieve ROUGE scores that are better than or comparable to the state-of-the-art for extractive summarization, and converges using significantly fewer update steps than competing approaches. In addition, we show empirically that BanditSum performs significantly better than competing approaches when good summary sentences appear late in the source document.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1410", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1410.flac", "type": "audio", "duration": 1252.565}, {"from": "Assistant", "value": "Current lexical simplification approaches rely heavily on heuristics and corpus level features that do not always align with human judgment. We create a human-rated word-complexity lexicon of 15,000 English words and propose a novel neural readability ranking model with a Gaussian-based feature vectorization layer that utilizes these human ratings to measure the complexity of any given word or phrase. Our model performs better than the state-of-the-art systems for different lexical simplification tasks and evaluation datasets. Additionally, we also produce SimplePPDB++, a lexical resource of over 10 million simplifying paraphrase rules, by applying our model to the Paraphrase Database (PPDB).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1411", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1411.flac", "type": "audio", "duration": 875.115}, {"from": "Assistant", "value": "Previous work on grounded language learning did not fully capture the semantics underlying the correspondences between structured world state representations and texts, especially those between numerical values and lexical terms. In this paper, we attempt at learning explicit latent semantic annotations from paired structured tables and texts, establishing correspondences between various types of values and texts. We model the joint probability of data fields, texts, phrasal spans, and latent annotations with an adapted semi-hidden Markov model, and impose a soft statistical constraint to further improve the performance. As a by-product, we leverage the induced annotations to extract templates for language generation. Experimental results suggest the feasibility of the setting in this study, as well as the effectiveness of our proposed framework.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1412", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1412.flac", "type": "audio", "duration": 961.109}, {"from": "Assistant", "value": "We introduce the syntactic scaffold, an approach to incorporating syntactic information into semantic tasks. Syntactic scaffolds avoid expensive syntactic processing at runtime, only making use of a treebank during training, through a multitask objective. We improve over strong baselines on PropBank semantics, frame semantics, and coreference resolution, achieving competitive performance on all three tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1413", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1413.flac", "type": "audio", "duration": 1288.064}, {"from": "Assistant", "value": "Scripts define knowledge about how everyday scenarios (such as going to a restaurant) are expected to unfold. One of the challenges to learning scripts is the hierarchical nature of the knowledge. For example, a suspect arrested might plead innocent or guilty, and a very different track of events is then expected to happen. To capture this type of information, we propose an autoencoder model with a latent space defined by a hierarchy of categorical variables. We utilize a recently proposed vector quantization based approach, which allows continuous embeddings to be associated with each latent variable value. This permits the decoder to softly decide what portions of the latent hierarchy to condition on by attending over the value embeddings for a given setting. Our model effectively encodes and generates scripts, outperforming a recent language modeling-based method on several standard tasks, and allowing the autoencoder model to achieve substantially lower perplexity scores compared to the previous language modeling-based method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1414", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1414.flac", "type": "audio", "duration": 832.299}, {"from": "Assistant", "value": "This paper studies semantic parsing for interlanguage (L2), taking semantic role labeling (SRL) as a case task and learner Chinese as a case language. We first manually annotate the semantic roles for a set of learner texts to derive a gold standard for automatic SRL. Based on the new data, we then evaluate three off-the-shelf SRL systems, i.e., the PCFGLA-parser-based, neural-parser-based and neural-syntax-agnostic systems, to gauge how successful SRL for learner Chinese can be. We find two non-obvious facts: 1) the L1-sentence-trained systems performs rather badly on the L2 data; 2) the performance drop from the L1 data to the L2 data of the two parser-based systems is much smaller, indicating the importance of syntactic parsing in SRL for interlanguages. Finally, the paper introduces a new agreement-based model to explore the semantic coherency information in the large-scale L2-L1 parallel data. We then show such information is very effective to enhance SRL for learner texts. Our model achieves an F-score of 72.06, which is a 2.02 point improvement over the best baseline.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1452", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1452.flac", "type": "audio", "duration": 1095.744}, {"from": "Assistant", "value": "We address jointly two important tasks for Question Answering in community forums: given a new question, (i) find related existing questions, and (ii) find relevant answers to this new question. We further use an auxiliary task to complement the previous two, i.e., (iii) find good answers with respect to the thread question in a question-comment thread. We use deep neural networks (DNNs) to learn meaningful task-specific embeddings, which we then incorporate into a conditional random field (CRF) model for the multitask setting, performing joint learning over a complex graph structure. While DNNs alone achieve competitive results when trained to produce the embeddings, the CRF, which makes use of the embeddings and the dependencies between the tasks, improves the results significantly and consistently across a variety of evaluation metrics, thus showing the complementarity of DNNs and structured learning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1453", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1453.flac", "type": "audio", "duration": 1176.661}, {"from": "Assistant", "value": "A challenge in creating a dataset for machine reading comprehension (MRC) is to collect questions that require a sophisticated understanding of language to answer beyond using superficial cues. In this work, we investigate what makes questions easier across recent 12 MRC datasets with three question styles (answer extraction, description, and multiple choice). We propose to employ simple heuristics to split each dataset into easy and hard subsets and examine the performance of two baseline models for each of the subsets. We then manually annotate questions sampled from each subset with both validity and requisite reasoning skills to investigate which skills explain the difference between easy and hard questions. From this study, we observed that (i) the baseline performances for the hard subsets remarkably degrade compared to those of entire datasets, (ii) hard questions require knowledge inference and multiple-sentence reasoning in comparison with easy questions, and (iii) multiple-choice questions tend to require a broader range of reasoning skills than answer extraction and description questions. These results suggest that one might overestimate recent advances in MRC.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1454", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1454.flac", "type": "audio", "duration": 1167.979}, {"from": "Assistant", "value": "Reading comprehension QA tasks have seen a recent surge in popularity, yet most works have focused on fact-finding extractive QA. We instead focus on a more challenging multi-hop generative task (NarrativeQA), which requires the model to reason, gather, and synthesize disjoint pieces of information within the context to generate an answer. This type of multi-step reasoning also often requires understanding implicit relations, which humans resolve via external, background commonsense knowledge. We first present a strong generative baseline that uses a multi-attention mechanism to perform multiple hops of reasoning and a pointer-generator decoder to synthesize the answer. This model performs substantially better than previous generative models, and is competitive with current state-of-the-art span prediction models. We next introduce a novel system for selecting grounded multi-hop relational commonsense information from ConceptNet via a pointwise mutual information and term-frequency based scoring function. Finally, we effectively use this extracted commonsense information to fill in gaps of reasoning between context hops, using a selectively-gated attention mechanism. This boosts the model’s performance significantly (also verified via human evaluation), establishing a new state-of-the-art for the task. We also show that our background knowledge enhancements are generalizable and improve performance on QAngaroo-WikiHop, another multi-hop reasoning dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1455", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1455.flac", "type": "audio", "duration": 946.453}, {"from": "Assistant", "value": "Open Domain Question Answering (QA) is evolving from complex pipelined systems to end-to-end deep neural networks. Specialized neural models have been developed for extracting answers from either text alone or Knowledge Bases (KBs) alone. In this paper we look at a more practical setting, namely QA over the combination of a KB and entity-linked text, which is appropriate when an incomplete KB is available with a large text corpus. Building on recent advances in graph representation learning we propose a novel model, GRAFT-Net, for extracting answers from a question-specific subgraph containing text and KB entities and relations. We construct a suite of benchmark tasks for this problem, varying the difficulty of questions, the amount of training data, and KB completeness. We show that GRAFT-Net is competitive with the state-of-the-art when tested using either KBs or text alone, and vastly outperforms existing methods in the combined setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1456", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1456.flac", "type": "audio", "duration": 1110.827}, {"from": "Assistant", "value": "Recently, there has been a surge of interest in reading comprehension-based (RC) question answering (QA). However, current approaches suffer from an impractical assumption that every question has a valid answer in the associated passage. A practical QA system must possess the ability to determine whether a valid answer exists in a given text passage. In this paper, we focus on developing QA systems that can extract an answer for a question if and only if the associated passage contains an answer. If the associated passage does not contain any valid answer, the QA system will correctly return Nil. We propose a novel nil-aware answer span extraction framework that is capable of returning Nil or a text span from the associated passage as an answer in a single step. We show that our proposed framework can be easily integrated with several recently proposed QA models developed for reading comprehension and can be trained in an end-to-end fashion. Our proposed nil-aware answer extraction neural network decomposes pieces of evidence into relevant and irrelevant parts and then combines them to infer the existence of any answer. Experiments on the NewsQA dataset show that the integration of our proposed framework significantly outperforms several strong baseline systems that use pipeline or threshold-based approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1457", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1457.flac", "type": "audio", "duration": 1064.811}, {"from": "Assistant", "value": "Advanced neural machine translation (NMT) models generally implement encoder and decoder as multiple layers, which allows systems to model complex functions and capture complicated linguistic structures. However, only the top layers of encoder and decoder are leveraged in the subsequent process, which misses the opportunity to exploit the useful information embedded in other layers. In this work, we propose to simultaneously expose all of these signals with layer aggregation and multi-layer attention mechanisms. In addition, we introduce an auxiliary regularization term to encourage different layers to capture diverse information. Experimental results on widely-used WMT14 English-German and WMT17 Chinese-English translation data demonstrate the effectiveness and universality of the proposed approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1458", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1458.flac", "type": "audio", "duration": 1122.005}, {"from": "Assistant", "value": "Recently, non-recurrent architectures (convolutional, self-attentional) have outperformed RNNs in neural machine translation. CNNs and self-attentional networks can connect distant words via shorter network paths than RNNs, and it has been speculated that this improves their ability to model long-range dependencies. However, this theoretical argument has not been tested empirically, nor have alternative explanations for their strong performance been explored in-depth. We hypothesize that the strong performance of CNNs and self-attentional networks could also be due to their ability to extract semantic features from the source text, and we evaluate RNNs, CNNs and self-attention networks on two tasks: subject-verb agreement (where capturing long-range dependencies is required) and word sense disambiguation (where semantic feature extraction is required). Our experimental results show that: 1) self-attentional networks and CNNs do not outperform RNNs in modeling subject-verb agreement over long distances; 2) self-attentional networks perform distinctly better than RNNs and CNNs on word sense disambiguation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1459", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1459.flac", "type": "audio", "duration": 1064.064}, {"from": "Assistant", "value": "In this paper, we propose an additionsubtraction twin-gated recurrent network (ATR) to simplify neural machine translation. The recurrent units of ATR are heavily simplified to have the smallest number of weight matrices among units of all existing gated RNNs. With the simple addition and subtraction operation, we introduce a twin-gated mechanism to build input and forget gates which are highly correlated. Despite this simplification, the essential non-linearities and capability of modeling long-distance dependencies are preserved. Additionally, the proposed ATR is more transparent than LSTM/GRU due to the simplification. Forward self-attention can be easily established in ATR, which makes the proposed network interpretable. Experiments on WMT14 translation tasks demonstrate that ATR-based neural machine translation can yield competitive performance on English-German and English-French language pairs in terms of both translation quality and speed. Further experiments on NIST Chinese-English translation, natural language inference and Chinese word segmentation verify the generality and applicability of ATR on different natural language processing tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1460", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1460.flac", "type": "audio", "duration": 846.528}, {"from": "Assistant", "value": "Although neural machine translation has achieved promising results, it suffers from slow translation speed. The direct consequence is that a trade-off has to be made between translation quality and speed, thus its performance can not come into full play. We apply cube pruning, a popular technique to speed up dynamic programming, into neural machine translation to speed up the translation. To construct the equivalence class, similar target hidden states are combined, leading to less RNN expansion operations on the target side and less softmax operations over the large target vocabulary. The experiments show that, at the same or even better translation quality, our method can translate faster compared with naive beam search by 3.3x on GPUs and 3.5x on CPUs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1461", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1461.flac", "type": "audio", "duration": 1156.203}, {"from": "Assistant", "value": "Translating characters instead of words or word-fragments has the potential to simplify the processing pipeline for neural machine translation (NMT), and improve results by eliminating hyper-parameters and manual feature engineering. However, it results in longer sequences in which each symbol contains less information, creating both modeling and computational challenges. In this paper, we show that the modeling problem can be solved by standard sequence-to-sequence architectures of sufficient depth, and that deep models operating at the character level outperform identical models operating over word fragments. This result implies that alternative architectures for handling character input are better viewed as methods for reducing computation time than as improved ways of modeling longer sequences. From this perspective, we evaluate several techniques for character-level NMT, verify that they do not match the performance of our deep character baseline model, and evaluate the performance versus computation time tradeoffs they offer. Within this framework, we also perform the first evaluation for NMT of conditional computation over time, in which the model learns which timesteps can be skipped, rather than having them be dictated by a fixed schedule specified before training begins.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1462", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1462.flac", "type": "audio", "duration": 1148.352}, {"from": "Assistant", "value": "Narrative story generation is a challenging problem because it demands the generated sentences with tight semantic connections, which has not been well studied by most existing generative models. To address this problem, we propose a skeleton-based model to promote the coherence of generated stories. Different from traditional models that generate a complete sentence at a stroke, the proposed model first generates the most critical phrases, called skeleton, and then expands the skeleton to a complete and fluent sentence. The skeleton is not manually defined, but learned by a reinforcement learning method. Compared to the state-of-the-art models, our skeleton-based model can generate significantly more coherent text according to human evaluation and automatic evaluation. The G-score is improved by 20.1% in human evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1463", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1463.flac", "type": "audio", "duration": 1219.563}, {"from": "Assistant", "value": "Sequence-to-Sequence (seq2seq) models have become overwhelmingly popular in building end-to-end trainable dialogue systems. Though highly efficient in learning the backbone of human-computer communications, they suffer from the problem of strongly favoring short generic responses. In this paper, we argue that a good response should smoothly connect both the preceding dialogue history and the following conversations. We strengthen this connection by mutual information maximization. To sidestep the non-differentiability of discrete natural language tokens, we introduce an auxiliary continuous code space and map such code space to a learnable prior distribution for generation purpose. Experiments on two dialogue datasets validate the effectiveness of our model, where the generated responses are closely related to the dialogue context and lead to more interactive conversations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1464", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1464.flac", "type": "audio", "duration": 932.032}, {"from": "Assistant", "value": "We propose a local coherence model that captures the flow of what semantically connects adjacent sentences in a text. We represent the semantics of a sentence by a vector and capture its state at each word of the sentence. We model what relates two adjacent sentences based on the two most similar semantic states, each of which is in one of the sentences. We encode the perceived coherence of a text by a vector, which represents patterns of changes in salient information that relates adjacent sentences. Our experiments demonstrate that our approach is beneficial for two downstream tasks: Readability assessment, in which our model achieves new state-of-the-art results; and essay scoring, in which the combination of our coherence vectors and other task-dependent features significantly improves the performance of a strong essay scorer.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1465", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1465.flac", "type": "audio", "duration": 1048.597}, {"from": "Assistant", "value": "In this paper, we propose a novel deep attentive sentence ordering network (referred as ATTOrderNet) which integrates self-attention mechanism with LSTMs in the encoding of input sentences. It enables us to capture global dependencies among sentences regardless of their input order and obtains a reliable representation of the sentence set. With this representation, a pointer network is exploited to generate an ordered sequence. The proposed model is evaluated on Sentence Ordering and Order Discrimination tasks. The extensive experimental results demonstrate its effectiveness and superiority to the state-of-the-art methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1466", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1466.flac", "type": "audio", "duration": 956.501}, {"from": "Assistant", "value": "When a reader is first introduced to an entity, its referring expression must describe the entity. For entities that are widely known, a single word or phrase often suffices. This paper presents the first study of how expressions that refer to the same entity develop over time. We track thousands of person and organization entities over 20 years of New York Times (NYT). As entities move from hearer-new (first introduction to the NYT audience) to hearer-old (common knowledge) status, we show empirically that the referring expressions along this trajectory depend on the type of the entity, and exhibit linguistic properties related to becoming common knowledge (e.g., shorter length, less use of appositives, more definiteness). These properties can also be used to build a model to predict how long it will take for an entity to reach hearer-old status. Our results reach 10-30% absolute improvement over a majority-class baseline.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1467", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1467.flac", "type": "audio", "duration": 1096.448}, {"from": "Assistant", "value": "In an online community, new words come and go: today’s “haha” may be replaced by tomorrow’s “lol.” Changes in online writing are usually studied as a social process, with innovations diffusing through a network of individuals in a speech community. But unlike other types of innovation, language change is shaped and constrained by the grammatical system in which it takes part. To investigate the role of social and structural factors in language change, we undertake a large-scale analysis of the frequencies of non-standard words in Reddit. Dissemination across many linguistic contexts is a predictor of success: words that appear in more linguistic contexts grow faster and survive longer. Furthermore, social dissemination plays a less important role in explaining word growth and decline than previously hypothesized.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1468", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1468.flac", "type": "audio", "duration": 1010.645}, {"from": "Assistant", "value": "Statistical phylogenetic models have allowed the quantitative analysis of the evolution of a single categorical feature and a pair of binary features, but correlated evolution involving multiple discrete features is yet to be explored. Here we propose latent representation-based analysis in which (1) a sequence of discrete surface features is projected to a sequence of independent binary variables and (2) phylogenetic inference is performed on the latent space. In the experiments, we analyze the features of linguistic typology, with a special focus on the order of subject, object and verb. Our analysis suggests that languages sharing the same word order are not necessarily a coherent group but exhibit varying degrees of diachronic stability depending on other features.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1469", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1469.flac", "type": "audio", "duration": 1096.277}, {"from": "Assistant", "value": "Dialects are one of the main drivers of language variation, a major challenge for natural language processing tools. In most languages, dialects exist along a continuum, and are commonly discretized by combining the extent of several preselected linguistic variables. However, the selection of these variables is theory-driven and itself insensitive to change. We use Doc2Vec on a corpus of 16.8M anonymous online posts in the German-speaking area to learn continuous document representations of cities. These representations capture continuous regional linguistic distinctions, and can serve as input to downstream NLP tasks sensitive to regional variation. By incorporating geographic information via retrofitting and agglomerative clustering with structure, we recover dialect areas at various levels of granularity. Evaluating these clusters against an existing dialect map, we achieve a match of up to 0.77 V-score (harmonic mean of cluster completeness and homogeneity). Our results show that representation learning with retrofitting offers a robust general method to automatically expose dialectal differences and regional variation at a finer granularity than was previously possible.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1470", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1470.flac", "type": "audio", "duration": 1018.304}, {"from": "Assistant", "value": "This paper presents a set of dimensions to characterize the association between two people. We distinguish between interactions (when somebody refers to somebody in a conversation) and relationships (a sequence of interactions). We work with dialogue scripts from the TV show Friends, and do not impose any restrictions on the interactions and relationships. We introduce and analyze a new corpus, and present experimental results showing that the task can be automated.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1471", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1471.flac", "type": "audio", "duration": 1032.981}, {"from": "Assistant", "value": "Vulgar words are employed in language use for several different functions, ranging from expressing aggression to signaling group identity or the informality of the communication. This versatility of usage of a restricted set of words is challenging for downstream applications and has yet to be studied quantitatively or using natural language processing techniques. We introduce a novel data set of 7,800 tweets from users with known demographic traits where all instances of vulgar words are annotated with one of the six categories of vulgar word use. Using this data set, we present the first analysis of the pragmatic aspects of vulgarity and how they relate to social factors. We build a model able to predict the category of a vulgar word based on the immediate context it appears in with 67.4 macro F1 across six classes. Finally, we demonstrate the utility of modeling the type of vulgar word use in context by using this information to achieve state-of-the-art performance in hate speech detection on a benchmark data set.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1499", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1499.flac", "type": "audio", "duration": 752.363}, {"from": "Assistant", "value": "It has been argued that humans rapidly adapt their lexical and syntactic expectations to match the statistics of the current linguistic context. We provide further support to this claim by showing that the addition of a simple adaptation mechanism to a neural language model improves our predictions of human reading times compared to a non-adaptive model. We analyze the performance of the model on controlled materials from psycholinguistic experiments and show that it adapts not only to lexical items but also to abstract syntactic structures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1500", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1500.flac", "type": "audio", "duration": 542.208}, {"from": "Assistant", "value": "Interpreting the performance of deep learning models beyond test set accuracy is challenging. Characteristics of individual data points are often not considered during evaluation, and each data point is treated equally. In this work we examine the impact of a test set question’s difficulty to determine if there is a relationship between difficulty and performance. We model difficulty using well-studied psychometric methods on human response patterns. Experiments on Natural Language Inference (NLI) and Sentiment Analysis (SA) show that the likelihood of answering a question correctly is impacted by the question’s difficulty. In addition, as DNNs are trained on larger datasets easy questions start to have a higher probability of being answered correctly than harder questions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1501", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1501.flac", "type": "audio", "duration": 737.088}, {"from": "Assistant", "value": "We investigate neural models’ ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts. We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1502", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1502.flac", "type": "audio", "duration": 767.488}, {"from": "Assistant", "value": "In this paper, we propose a new approach to employ the fixed-size ordinally-forgetting encoding (FOFE) (Zhang et al., 2015b) in neural languages modelling, called dual-FOFE. The main idea of dual-FOFE is that it allows to use two different forgetting factors so that it can avoid the trade-off in choosing either a small or large values for the single forgetting factor. In our experiments, we have compared the dual-FOFE based neural network language models (NNLM) against the original FOFE counterparts and various traditional NNLMs. Our results on the challenging Google Billion word corpus show that both FOFE and dual FOFE yield very strong performance while significantly reducing the computational complexity over other NNLMs. Furthermore, the proposed dual-FOFE method further gives over 10% improvement in perplexity over the original FOFE model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1503", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1503.flac", "type": "audio", "duration": 796.736}, {"from": "Assistant", "value": "Recent work has shown that recurrent neural networks (RNNs) can implicitly capture and exploit hierarchical information when trained to solve common natural language processing tasks (Blevins et al., 2018) such as language modeling (Linzen et al., 2016; Gulordava et al., 2018) and neural machine translation (Shi et al., 2016). In contrast, the ability to model structured data with non-recurrent neural networks has received little attention despite their success in many NLP tasks (Gehring et al., 2017; Vaswani et al., 2017). In this work, we compare the two architectures—recurrent versus non-recurrent—with respect to their ability to model hierarchical structure and find that recurrency is indeed important for this purpose. The code and data used in our experiments is available athttps://github.com/ketranm/fan_vs_rnn", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1504", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1504.flac", "type": "audio", "duration": 640.149}, {"from": "Assistant", "value": "Targeted sentiment analysis (TSA) aims at extracting targets and classifying their sentiment classes. Previous works only exploit word embeddings as features and do not explore more potentials of neural networks when jointly learning the two tasks. In this paper, we carefully design the hierarchical stack bidirectional gated recurrent units (HSBi-GRU) model to learn abstract features for both tasks, and we propose a HSBi-GRU based joint model which allows the target label to have influence on their sentiment label. Experimental results on two datasets show that our joint learning model can outperform other baselines and demonstrate the effectiveness of HSBi-GRU in learning abstract features.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1505", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1505.flac", "type": "audio", "duration": 607.253}, {"from": "Assistant", "value": "We analyze the performance of different sentiment classification models on syntactically complex inputs like A-but-B sentences. The first contribution of this analysis addresses reproducible research: to meaningfully compare different models, their accuracies must be averaged over far more random seeds than what has traditionally been reported. With proper averaging in place, we notice that the distillation model described in Hu et al. (2016), which incorporates explicit logic rules for sentiment classification, is ineffective. In contrast, using contextualized ELMo embeddings (Peters et al., 2018a) instead of logic rules yields significantly better performance. Additionally, we provide analysis and visualizations that demonstrate ELMo’s ability to implicitly learn logic rules. Finally, a crowdsourced analysis reveals how ELMo outperforms baseline models even on sentences with ambiguous sentiment labels.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1506", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1506.flac", "type": "audio", "duration": 743.189}, {"from": "Assistant", "value": "Emotion cause analysis has been a key topic in natural language processing. Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue. Meanwhile, the clauses in a document play different roles on stimulating a certain emotion, depending on their content relevance. Therefore, we propose a co-attention neural network model for emotion cause analysis with emotional context awareness. The method encodes the clauses with a co-attention based bi-directional long short-term memory into high-level input representations, which are further fed into a convolutional layer for emotion cause analysis. Experimental results show that our approach outperforms the state-of-the-art baseline methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1507", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1507.flac", "type": "audio", "duration": 682.091}, {"from": "Assistant", "value": "Computational detection and understanding of empathy is an important factor in advancing human-computer interaction. Yet to date, text-based empathy prediction has the following major limitations: It underestimates the psychological complexity of the phenomenon, adheres to a weak notion of ground truth where empathic states are ascribed by third parties, and lacks a shared corpus. In contrast, this contribution presents the first publicly available gold standard for empathy prediction. It is constructed using a novel annotation methodology which reliably captures empathy assessments by the writer of a statement using multi-item scales. This is also the first computational work distinguishing between multiple forms of empathy, empathic concern, and personal distress, as recognized throughout psychology. Finally, we present experimental results for three different predictive models, of which a CNN performs the best.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1508", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1508.flac", "type": "audio", "duration": 758.528}, {"from": "Assistant", "value": "Human language has evolved towards newer forms of communication such as social media, where emojis (i.e., ideograms bearing a visual meaning) play a key role. While there is an increasing body of work aimed at the computational modeling of emoji semantics, there is currently little understanding about what makes a computational model represent or predict a given emoji in a certain way. In this paper we propose a label-wise attention mechanism with which we attempt to better understand the nuances underlying emoji prediction. In addition to advantages in terms of interpretability, we show that our proposed architecture improves over standard baselines in emoji prediction, and does particularly well when predicting infrequent emojis.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1509", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1509.flac", "type": "audio", "duration": 757.504}, {"from": "Assistant", "value": "Recent advances in Neural Machine Translation (NMT) show that adding syntactic information to NMT systems can improve the quality of their translations. Most existing work utilizes some specific types of linguistically-inspired tree structures, like constituency and dependency parse trees. This is often done via a standard RNN decoder that operates on a linearized target tree structure. However, it is an open question of what specific linguistic formalism, if any, is the best structural representation for NMT. In this paper, we (1) propose an NMT model that can naturally generate the topology of an arbitrary tree structure on the target side, and (2) experiment with various target tree structures. Our experiments show the surprising result that our model delivers the best improvements with balanced binary trees constructed without any linguistic knowledge; this model outperforms standard seq2seq models by up to 2.1 BLEU points, and other methods for incorporating target-side syntax by up to 0.7 BLEU.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1510", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1510.flac", "type": "audio", "duration": 682.795}, {"from": "Assistant", "value": "Neural machine translation (NMT) models are usually trained with the word-level loss using the teacher forcing algorithm, which not only evaluates the translation improperly but also suffers from exposure bias. Sequence-level training under the reinforcement framework can mitigate the problems of the word-level loss, but its performance is unstable due to the high variance of the gradient estimation. On these grounds, we present a method with a differentiable sequence-level training objective based on probabilistic n-gram matching which can avoid the reinforcement framework. In addition, this method performs greedy search in the training which uses the predicted words as context just as at inference to alleviate the problem of exposure bias. Experiment results on the NIST Chinese-to-English translation tasks show that our method significantly outperforms the reinforcement-based algorithms and achieves an improvement of 1.5 BLEU points on average over a strong baseline system.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1511", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1511.flac", "type": "audio", "duration": 691.072}, {"from": "Assistant", "value": "In Neural Machine Translation (NMT), the decoder can capture the features of the entire prediction history with neural connections and representations. This means that partial hypotheses with different prefixes will be regarded differently no matter how similar they are. However, this might be inefficient since some partial hypotheses can contain only local differences that will not influence future predictions. In this work, we introduce recombination in NMT decoding based on the concept of the “equivalence” of partial hypotheses. Heuristically, we use a simple n-gram suffix based equivalence function and adapt it into beam search decoding. Through experiments on large-scale Chinese-to-English and English-to-Germen translation tasks, we show that the proposed method can obtain similar translation quality with a smaller beam size, making NMT decoding more efficient.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1512", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1512.flac", "type": "audio", "duration": 806.955}, {"from": "Assistant", "value": "Recent research suggests that neural machine translation achieves parity with professional human translation on the WMT Chinese–English news translation task. We empirically test this claim with alternative evaluation protocols, contrasting the evaluation of single sentences and entire documents. In a pairwise ranking experiment, human raters assessing adequacy and fluency show a stronger preference for human over machine translation when evaluating documents as compared to isolated sentences. Our findings emphasise the need to shift towards document-level evaluation as machine translation improves to the degree that errors which are hard or impossible to spot at the sentence-level become decisive in discriminating quality of different translation outputs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1513", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1513.flac", "type": "audio", "duration": 825.963}, {"from": "Assistant", "value": "We compare the performance of the APT and AutoPRF metrics for pronoun translation against a manually annotated dataset comprising human judgements as to the correctness of translations of the PROTEST test suite. Although there is some correlation with the human judgements, a range of issues limit the performance of the automated metrics. Instead, we recommend the use of semi-automatic metrics and test suites in place of fully automatic metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1514", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1514.flac", "type": "audio", "duration": 764.501}, {"from": "Assistant", "value": "We present a Few-Shot Relation Classification Dataset (dataset), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers. We adapt the most recent state-of-the-art few-shot learning methods for relation classification and conduct thorough evaluation of these methods. Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. We also show that a range of different reasoning skills are needed to solve our task. These results indicate that few-shot relation classification remains an open problem and still requires further research. Our detailed analysis points multiple directions for future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1515", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1515.flac", "type": "audio", "duration": 682.795}, {"from": "Assistant", "value": "The best systems at the SemEval-16 and SemEval-17 community question answering shared tasks – a task that amounts to question relevancy ranking – involve complex pipelines and manual feature engineering. Despite this, many of these still fail at beating the IR baseline, i.e., the rankings provided by Google’s search engine. We present a strong baseline for question relevancy ranking by training a simple multi-task feed forward network on a bag of 14 distance measures for the input question pair. This baseline model, which is fast to train and uses only language-independent features, outperforms the best shared task systems on the task of retrieving relevant previously asked questions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1516", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1516.flac", "type": "audio", "duration": 771.349}, {"from": "Assistant", "value": "Research on link prediction in knowledge graphs has mainly focused on static multi-relational data. In this work we consider temporal knowledge graphs where relations between entities may only hold for a time interval or a specific point in time. In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations. To incorporate temporal information, we utilize recurrent neural networks to learn time-aware representations of relation types which can be used in conjunction with existing latent factorization methods. The proposed approach is shown to be robust to common challenges in real-world KGs: the sparsity and heterogeneity of temporal expressions. Experiments show the benefits of our approach on four temporal KGs. The data sets are available under a permissive BSD-3 license.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1517", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1517.flac", "type": "audio", "duration": 711.445}, {"from": "Assistant", "value": "Event detection (ED) and word sense disambiguation (WSD) are two similar tasks in that they both involve identifying the classes (i.e. event types or word senses) of some word in a given sentence. It is thus possible to extract the knowledge hidden in the data for WSD, and utilize it to improve the performance on ED. In this work, we propose a method to transfer the knowledge learned on WSD to ED by matching the neural representations learned for the two tasks. Our experiments on two widely used datasets for ED demonstrate the effectiveness of the proposed method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1518", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1518.flac", "type": "audio", "duration": 653.867}, {"from": "Assistant", "value": "In this work, we present a word embedding model that learns cross-sentence dependency for improving end-to-end co-reference resolution (E2E-CR). While the traditional E2E-CR model generates word representations by running long short-term memory (LSTM) recurrent neural networks on each sentence of an input article or conversation separately, we propose linear sentence linking and attentional sentence linking models to learn cross-sentence dependency. Both sentence linking strategies enable the LSTMs to make use of valuable information from context sentences while calculating the representation of the current input word. With this approach, the LSTMs learn word embeddings considering knowledge not only from the current sentence but also from the entire input document. Experiments show that learning cross-sentence dependency enriches information contained by the word representations, and improves the performance of the co-reference resolution model compared with our baseline.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1546", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1546.flac", "type": "audio", "duration": 747.029}, {"from": "Assistant", "value": "Many recent papers address reading comprehension, where examples consist of (question, passage, answer) tuples. Presumably, a model must combine information from both questions and passages to predict corresponding answers. However, despite intense interest in the topic, with hundreds of published papers vying for leaderboard dominance, basic questions about the difficulty of many popular benchmarks remain unanswered. In this paper, we establish sensible baselines for the bAbI, SQuAD, CBT, CNN, and Who-did-What datasets, finding that question- and passage-only models often perform surprisingly well. On 14 out of 20 bAbI tasks, passage-only models achieve greater than 50% accuracy, sometimes matching the full model. Interestingly, while CBT provides 20-sentence passages, only the last is needed for accurate prediction. By comparison, SQuAD and CNN appear better-constructed.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1547", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1547.flac", "type": "audio", "duration": 904.405}, {"from": "Assistant", "value": "Even though machine learning has become the major scene in dialogue research community, the real breakthrough has been blocked by the scale of data available. To address this fundamental obstacle, we introduce the Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics. At a size of 10k dialogues, it is at least one order of magnitude larger than all previous annotated task-oriented corpora. The contribution of this work apart from the open-sourced dataset is two-fold:firstly, a detailed description of the data collection procedure along with a summary of data structure and analysis is provided. The proposed data-collection pipeline is entirely based on crowd-sourcing without the need of hiring professional annotators;secondly, a set of benchmark results of belief tracking, dialogue act and response generation is reported, which shows the usability of the data and sets a baseline for future studies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1548", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1548.flac", "type": "audio", "duration": 1223.723}, {"from": "Assistant", "value": "Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features. However, prior work has shown that gold syntax trees can dramatically improve SRL decoding, suggesting the possibility of increased accuracy from explicit modeling of syntax. In this work, we present linguistically-informed self-attention (LISA): a neural network model that combines multi-head self-attention with multi-task learning across dependency parsing, part-of-speech tagging, predicate detection and SRL. Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates. Syntax is incorporated by training one attention head to attend to syntactic parents for each token. Moreover, if a high-quality syntactic parse is already available, it can be beneficially injected at test time without re-training our SRL model. In experiments on CoNLL-2005 SRL, LISA achieves new state-of-the-art performance for a model using predicted predicates and standard word embeddings, attaining 2.5 F1 absolute higher than the previous state-of-the-art on newswire and more than 3.5 F1 on out-of-domain data, nearly 10% reduction in error. On ConLL-2012 English SRL we also show an improvement of more than 2.5 F1. LISA also out-performs the state-of-the-art with contextually-encoded (ELMo) word representations, by nearly 1.0 F1 on news and more than 2.0 F1 on out-of-domain text.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "D18-1549", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/D18-1549.flac", "type": "audio", "duration": 1122.389}, {"from": "Assistant", "value": "Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability to the majority of language pairs. This work investigates how to learn to translate when having access to only large monolingual corpora in each language. We propose two model variants, a neural and a phrase-based model. Both versions leverage a careful initialization of the parameters, the denoising effect of language models and automatic generation of parallel data by iterative back-translation. These models are significantly better than methods from the literature, while being simpler and having fewer hyper-parameters. On the widely used WMT’14 English-French and WMT’16 German-English benchmarks, our models respectively obtain 28.1 and 25.2 BLEU points without using a single parallel sentence, outperforming the state of the art by more than 11 BLEU points. On low-resource languages like English-Urdu and English-Romanian, our methods achieve even better results than semi-supervised and supervised approaches leveraging the paucity of available bitexts. Our code for NMT and PBSMT is publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_1", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_1.flac", "type": "audio", "duration": 585.05}, {"from": "Assistant", "value": "Non-autoregressive neural machine translation (NART) models suffer from the multi-modality problem which causes translation inconsistency such as token repetition. Most recent approaches have attempted to solve this problem by implicitly modeling dependencies between outputs. In this paper, we introduce AligNART, which leverages full alignment information to explicitly reduce the modality of the target distribution. AligNART divides the machine translation task into (i) alignment estimation and (ii) translation with aligned decoder inputs, guiding the decoder to focus on simplified one-to-one translation. To alleviate the alignment estimation problem, we further propose a novel alignment decomposition method. Our experiments show that AligNART outperforms previous non-iterative NART models that focus on explicit modality reduction on WMT14 En↔De and WMT16 Ro→En. Furthermore, AligNART achieves BLEU scores comparable to those of the state-of-the-art connectionist temporal classification based models on WMT14 En↔De. We also observe that AligNART effectively addresses the token repetition problem even without sequence-level knowledge distillation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_2", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_2.flac", "type": "audio", "duration": 688.007}, {"from": "Assistant", "value": "Previous work mainly focuses on improving cross-lingual transfer for NLU tasks with a multilingual pretrained encoder (MPE), or improving the performance on supervised machine translation with BERT. However, it is under-explored that whether the MPE can help to facilitate the cross-lingual transferability of NMT model. In this paper, we focus on a zero-shot cross-lingual transfer task in NMT. In this task, the NMT model is trained with parallel dataset of only one language pair and an off-the-shelf MPE, then it is directly tested on zero-shot language pairs. We propose SixT, a simple yet effective model for this task. SixT leverages the MPE with a two-stage training schedule and gets further improvement with a position disentangled encoder and a capacity-enhanced decoder. Using this method, SixT significantly outperforms mBART, a pretrained multilingual encoder-decoder model explicitly designed for NMT, with an average improvement of 7.1 BLEU on zero-shot any-to-English test sets across 14 source languages. Furthermore, with much less training computation cost and training data, our model achieves better performance on 15 any-to-English test sets than CRISS and m2m-100, two strong multilingual NMT baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_3", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_3.flac", "type": "audio", "duration": 704.888}, {"from": "Assistant", "value": "Recent studies have demonstrated that pre-trained cross-lingual models achieve impressive performance in downstream cross-lingual tasks. This improvement benefits from learning a large amount of monolingual and parallel corpora. Although it is generally acknowledged that parallel corpora are critical for improving the model performance, existing methods are often constrained by the size of parallel corpora, especially for low-resource languages. In this paper, we propose Ernie-M, a new training method that encourages the model to align the representation of multiple languages with monolingual corpora, to overcome the constraint that the parallel corpus size places on the model performance. Our key insight is to integrate back-translation into the pre-training process. We generate pseudo-parallel sentence pairs on a monolingual corpus to enable the learning of semantic alignments between different languages, thereby enhancing the semantic modeling of cross-lingual models. Experimental results show that Ernie-M outperforms existing cross-lingual models and delivers new state-of-the-art results in various cross-lingual downstream tasks. The codes and pre-trained models will be made publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_4", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_4.flac", "type": "audio", "duration": 705.004}, {"from": "Assistant", "value": "This paper proposes a novel architecture, Cross Attention Augmented Transducer (CAAT), for simultaneous translation. The framework aims to jointly optimize the policy and translation models. To effectively consider all possible READ-WRITE simultaneous translation action paths, we adapt the online automatic speech recognition (ASR) model, RNN-T, but remove the strong monotonic constraint, which is critical for the translation task to consider reordering. To make CAAT work, we introduce a novel latency loss whose expectation can be optimized by a forward-backward algorithm. We implement CAAT with Transformer while the general CAAT architecture can also be implemented with other attention-based encoder-decoder frameworks. Experiments on both speech-to-text (S2T) and text-to-text (T2T) simultaneous translation tasks show that CAAT achieves significantly better latency-quality trade-offs compared to the state-of-the-art simultaneous translation approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_5", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_5.flac", "type": "audio", "duration": 698.603}, {"from": "Assistant", "value": "Schema translation is the task of automatically translating headers of tabular data from one language to another. High-quality schema translation plays an important role in cross-lingual table searching, understanding and analysis. Despite its importance, schema translation is not well studied in the community, and state-of-the-art neural machine translation models cannot work well on this task because of two intrinsic differences between plain text and tabular data: morphological difference and context difference. To facilitate the research study, we construct the first parallel dataset for schema translation, which consists of 3,158 tables with 11,979 headers written in 6 different languages, including English, Chinese, French, German, Spanish, and Japanese. Also, we propose the first schema translation model called CAST, which is a header-to-header neural machine translation model augmented with schema context. Specifically, we model a target header and its context as a directed graph to represent their entity types and relations. Then CAST encodes the graph with a relational-aware transformer and uses another transformer to decode the header in the target language. Experiments on our dataset demonstrate that CAST significantly outperforms state-of-the-art neural machine translation models. Our dataset will be released athttps://github.com/microsoft/ContextualSP.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_6", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_6.flac", "type": "audio", "duration": 665.045}, {"from": "Assistant", "value": "Neural Chat Translation (NCT) aims to translate conversational text between speakers of different languages. Despite the promising performance of sentence-level and context-aware neural machine translation models, there still remain limitations in current NCT models because the inherent dialogue characteristics of chat, such as dialogue coherence and speaker personality, are neglected. In this paper, we propose to promote the chat translation by introducing the modeling of dialogue characteristics into the NCT model. To this end, we design four auxiliary tasks including monolingual response generation, cross-lingual response generation, next utterance discrimination, and speaker identification. Together with the main chat translation task, we optimize the enhanced NCT model through the training objectives of all these tasks. By this means, the NCT model can be enhanced by capturing the inherent dialogue characteristics, thus generating more coherent and speaker-relevant translations. Comprehensive experiments on four language directions (English<->German and English<->Chinese) verify the effectiveness and superiority of the proposed approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_7", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_7.flac", "type": "audio", "duration": 693.023}, {"from": "Assistant", "value": "With the rapid increase in the volume of dialogue data from daily life, there is a growing demand for dialogue summarization. Unfortunately, training a large summarization model is generally infeasible due to the inadequacy of dialogue data with annotated summaries. Most existing works for low-resource dialogue summarization directly pretrain models in other domains, e.g., the news domain, but they generally neglect the huge difference between dialogues and conventional articles. To bridge the gap between out-of-domain pretraining and in-domain fine-tuning, in this work, we propose a multi-source pretraining paradigm to better leverage the external summary data. Specifically, we exploit large-scale in-domain non-summary data to separately pretrain the dialogue encoder and the summary decoder. The combined encoder-decoder model is then pretrained on the out-of-domain summary data using adversarial critics, aiming to facilitate domain-agnostic summarization. The experimental results on two public datasets show that with only limited training data, our approach achieves competitive performance and generalizes well in different dialogue scenarios.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_8", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_8.flac", "type": "audio", "duration": 589.44}, {"from": "Assistant", "value": "In this paper, we propose a controllable neural generation framework that can flexibly guide dialogue summarization with personal named entity planning. The conditional sequences are modulated to decide what types of information or what perspective to focus on when forming summaries to tackle the under-constrained problem in summarization tasks. This framework supports two types of use cases: (1) Comprehensive Perspective, which is a general-purpose case with no user-preference specified, considering summary points from all conversational interlocutors and all mentioned persons; (2) Focus Perspective, positioning the summary based on a user-specified personal named entity, which could be one of the interlocutors or one of the persons mentioned in the conversation. During training, we exploit occurrence planning of personal named entities and coreference information to improve temporal coherence and to minimize hallucination in neural generation. Experimental results show that our proposed framework generates fluent and factually consistent summaries under various planning controls using both objective metrics and human evaluations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_9", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_9.flac", "type": "audio", "duration": 642.055}, {"from": "Assistant", "value": "Factual inconsistencies existed in the output of abstractive summarization models with original documents are frequently presented. Fact consistency assessment requires the reasoning capability to find subtle clues to identify whether a model-generated summary is consistent with the original document. This paper proposes a fine-grained two-stage Fact Consistency assessment framework for Summarization models (SumFC). Given a document and a summary sentence, in the first stage, SumFC selects the top-K most relevant sentences with the summary sentence from the document. In the second stage, the model performs fine-grained consistency reasoning at the sentence level, and then aggregates all sentences’ consistency scores to obtain the final assessment result. We get the training data pairs by data synthesis and adopt contrastive loss of data pairs to help the model identify subtle cues. Experiment results show that SumFC has made a significant improvement over the previous state-of-the-art methods. Our experiments also indicate that SumFC distinguishes detailed differences better.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_10", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_10.flac", "type": "audio", "duration": 616.049}, {"from": "Assistant", "value": "Relevance in summarization is typically de- fined based on textual information alone, without incorporating insights about a particular decision. As a result, to support risk analysis of pancreatic cancer, summaries of medical notes may include irrelevant information such as a knee injury. We propose a novel problem, decision-focused summarization, where the goal is to summarize relevant information for a decision. We leverage a predictive model that makes the decision based on the full text to provide valuable insights on how a decision can be inferred from text. To build a summary, we then select representative sentences that lead to similar model decisions as using the full text while accounting for textual non-redundancy. To evaluate our method (DecSum), we build a testbed where the task is to summarize the first ten reviews of a restaurant in support of predicting its future rating on Yelp. DecSum substantially outperforms text-only summarization methods and model-based explanation methods in decision faithfulness and representativeness. We further demonstrate that DecSum is the only method that enables humans to outperform random chance in predicting which restaurant will be better rated in the future.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_11", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_11.flac", "type": "audio", "duration": 405.769}, {"from": "Assistant", "value": "Extractive text summarization aims at extracting the most representative sentences from a given document as its summary. To extract a good summary from a long text document, sentence embedding plays an important role. Recent studies have leveraged graph neural networks to capture the inter-sentential relationship (e.g., the discourse graph) within the documents to learn contextual sentence embedding. However, those approaches neither consider multiple types of inter-sentential relationships (e.g., semantic similarity and natural connection relationships), nor model intra-sentential relationships (e.g, semantic similarity and syntactic relationship among words). To address these problems, we propose a novel Multiplex Graph Convolutional Network (Multi-GCN) to jointly model different types of relationships among sentences and words. Based on Multi-GCN, we propose a Multiplex Graph Summarization (Multi-GraS) model for extractive text summarization. Finally, we evaluate the proposed models on the CNN/DailyMail benchmark dataset to demonstrate effectiveness of our method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_12", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_12.flac", "type": "audio", "duration": 483.691}, {"from": "Assistant", "value": "Task-agnostic pretraining objectives like masked language models or corrupted span prediction are applicable to a wide range of NLP downstream tasks (Raffel et al.,2019), but are outperformed by task-specific pretraining objectives like predicting extracted gap sentences on summarization (Zhang et al.,2020). We compare three summarization specific pretraining objectives with the task agnostic corrupted span prediction pretraining in controlled study. We also extend our study to a low resource and zero shot setup, to understand how many training examples are needed in order to ablate the task-specific pretraining without quality loss. Our results show that task-agnostic pretraining is sufficient for most cases which hopefully reduces the need for costly task-specific pretraining. We also report new state-of-the-art number for two summarization task using a T5 model with 11 billion parameters and an optimal beam search length penalty.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_13", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_13.flac", "type": "audio", "duration": 457.062}, {"from": "Assistant", "value": "To capture the semantic graph structure from raw text, most existing summarization approaches are built on GNNs with a pre-trained model. However, these methods suffer from cumbersome procedures and inefficient computations for long-text documents. To mitigate these issues, this paper proposes HetFormer, a Transformer-based pre-trained model with multi-granularity sparse attentions for long-text extractive summarization. Specifically, we model different types of semantic nodes in raw text as a potential heterogeneous graph and directly learn heterogeneous relationships (edges) among nodes by Transformer. Extensive experiments on both single- and multi-document summarization tasks show that HetFormer achieves state-of-the-art performance in Rouge F1 while using less memory and fewer parameters.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_14", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_14.flac", "type": "audio", "duration": 649.3}, {"from": "Assistant", "value": "Embedding based methods are widely used for unsupervised keyphrase extraction (UKE) tasks. Generally, these methods simply calculate similarities between phrase embeddings and document embedding, which is insufficient to capture different context for a more effective UKE model. In this paper, we propose a novel method for UKE, where local and global contexts are jointly modeled. From a global view, we calculate the similarity between a certain phrase and the whole document in the vector space as transitional embedding based models do. In terms of the local view, we first build a graph structure based on the document where phrases are regarded as vertices and the edges are similarities between vertices. Then, we proposed a new centrality computation method to capture local salient information based on the graph structure. Finally, we further combine the modeling of global and local context for ranking. We evaluate our models on three public benchmarks (Inspec, DUC 2001, SemEval 2010) and compare with existing state-of-the-art models. The results show that our model outperforms most models while generalizing better on input documents with different domains and length. Additional ablation study shows that both the local and global information is crucial for unsupervised keyphrase extraction tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_15", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_15.flac", "type": "audio", "duration": 625.964}, {"from": "Assistant", "value": "Distantly supervised relation extraction is widely used in the construction of knowledge bases due to its high efficiency. However, the automatically obtained instances are of low quality with numerous irrelevant words. In addition, the strong assumption of distant supervision leads to the existence of noisy sentences in the sentence bags. In this paper, we propose a novel Multi-Layer Revision Network (MLRN) which alleviates the effects of word-level noise by emphasizing inner-sentence correlations before extracting relevant information within sentences. Then, we devise a balanced and noise-resistant Confidence-based Multi-Instance Learning (CMIL) method to filter out noisy sentences as well as assign proper weights to relevant ones. Extensive experiments on two New York Times (NYT) datasets demonstrate that our approach achieves significant improvements over the baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_16", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_16.flac", "type": "audio", "duration": 692.93}, {"from": "Assistant", "value": "Table-based fact verification task aims to verify whether the given statement is supported by the given semi-structured table. Symbolic reasoning with logical operations plays a crucial role in this task. Existing methods leverage programs that contain rich logical information to enhance the verification process. However, due to the lack of fully supervised signals in the program generation process, spurious programs can be derived and employed, which leads to the inability of the model to catch helpful logical operations. To address the aforementioned problems, in this work, we formulate the table-based fact verification task as an evidence retrieval and reasoning framework, proposing the Logic-level Evidence Retrieval and Graph-based Verification network (LERGV). Specifically, we first retrieve logic-level program-like evidence from the given table and statement as supplementary evidence for the table. After that, we construct a logic-level graph to capture the logical relations between entities and functions in the retrieved evidence, and design a graph-based verification network to perform logic-level graph-based reasoning based on the constructed graph to classify the final entailment relation. Experimental results on the large-scale benchmark TABFACT show the effectiveness of the proposed approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_17", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_17.flac", "type": "audio", "duration": 708.928}, {"from": "Assistant", "value": "In joint entity and relation extraction, existing work either sequentially encode task-specific features, leading to an imbalance in inter-task feature interaction where features extracted later have no direct contact with those that come first. Or they encode entity features and relation features in a parallel manner, meaning that feature representation learning for each task is largely independent of each other except for input sharing. We propose a partition filter network to model two-way interaction between tasks properly, where feature encoding is decomposed into two steps: partition and filter. In our encoder, we leverage two gates: entity and relation gate, to segment neurons into two task partitions and one shared partition. The shared partition represents inter-task information valuable to both tasks and is evenly shared across two tasks to ensure proper two-way interaction. The task partitions represent intra-task information and are formed through concerted efforts of both gates, making sure that encoding of task-specific features is dependent upon each other. Experiment results on six public datasets show that our model performs significantly better than previous approaches. In addition, contrary to what previous work has claimed, our auxiliary experiments suggest that relation prediction is contributory to named entity prediction in a non-negligible way. The source code can be found athttps://github.com/Coopercoppers/PFN.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_18", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_18.flac", "type": "audio", "duration": 677.303}, {"from": "Assistant", "value": "To alleviate label scarcity in Named Entity Recognition (NER) task, distantly supervised NER methods are widely applied to automatically label data and identify entities. Although the human effort is reduced, the generated incomplete and noisy annotations pose new challenges for learning effective neural models. In this paper, we propose a novel dictionary extension method which extracts new entities through the type expanded model. Moreover, we design a multi-granularity boundary-aware network which detects entity boundaries from both local and global perspectives. We conduct experiments on different types of datasets, the results show that our model outperforms previous state-of-the-art distantly supervised systems and even surpasses the supervised models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_19", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_19.flac", "type": "audio", "duration": 883.101}, {"from": "Assistant", "value": "In this paper, we investigate the Aspect Category Sentiment Analysis (ACSA) task from a novel perspective by exploring a Beta Distribution guided aspect-aware graph construction based on external knowledge. That is, we are no longer entangled about how to laboriously search the sentiment clues for coarse-grained aspects from the context, but how to preferably find the words highly related to the aspects in the context and determine their importance based on the public knowledge base. In this way, the contextual sentiment clues can be explicitly tracked in ACSA for the aspects in the light of these aspect-related words. To be specific, we first regard each aspect as a pivot to derive aspect-aware words that are highly related to the aspect from external affective commonsense knowledge. Then, we employ Beta Distribution to educe the aspect-aware weight, which reflects the importance to the aspect, for each aspect-aware word. Afterward, the aspect-aware words are served as the substitutes of the coarse-grained aspect to construct graphs for leveraging the aspect-related contextual sentiment dependencies in ACSA. Experiments on 6 benchmark datasets show that our approach significantly outperforms the state-of-the-art baseline methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_20", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_20.flac", "type": "audio", "duration": 696.575}, {"from": "Assistant", "value": "The rise of pre-trained language models has yielded substantial progress in the vast majority of Natural Language Processing (NLP) tasks. However, a generic approach towards the pre-training procedure can naturally be sub-optimal in some cases. Particularly, fine-tuning a pre-trained language model on a source domain and then applying it to a different target domain, results in a sharp performance decline of the eventual classifier for many source-target domain pairs. Moreover, in some NLP tasks, the output categories substantially differ between domains, making adaptation even more challenging. This, for example, happens in the task of aspect extraction, where the aspects of interest of reviews of, e.g., restaurants or electronic devices may be very different. This paper presents a new fine-tuning scheme for BERT, which aims to address the above challenges. We name this scheme DILBERT: Domain Invariant Learning with BERT, and customize it for aspect extraction in the unsupervised domain adaptation setting. DILBERT harnesses the categorical information of both the source and the target domains to guide the pre-training process towards a more domain and category invariant representation, thus closing the gap between the domains. We show that DILBERT yields substantial improvements over state-of-the-art baselines while using a fraction of the unlabeled data, particularly in more challenging domain adaptation setups.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_21", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_21.flac", "type": "audio", "duration": 703.04}, {"from": "Assistant", "value": "Multimodal sentiment analysis is a trending area of research, and multimodal fusion is one of its most active topic. Acknowledging humans communicate through a variety of channels (i.e visual, acoustic, linguistic), multimodal systems aim at integrating different unimodal representations into a synthetic one. So far, a consequent effort has been made on developing complex architectures allowing the fusion of these modalities. However, such systems are mainly trained by minimising simple losses such asL1or cross-entropy. In this work, we investigate unexplored penalties and propose a set of new objectives that measure the dependency between modalities. We demonstrate that our new penalties lead to a consistent improvement (up to 4.3 on accuracy) across a large variety of state-of-the-art models on two well-known sentiment analysis datasets: CMU-MOSI and CMU-MOSEI. Our method not only achieves a new SOTA on both datasets but also produces representations that are more robust to modality drops. Finally, a by-product of our methods includes a statistical network which can be used to interpret the high dimensional representations learnt by the model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_22", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_22.flac", "type": "audio", "duration": 663.808}, {"from": "Assistant", "value": "Aspect-based sentiment analysis aims to identify the sentiment polarity of a specific aspect in product reviews. We notice that about 30% of reviews do not contain obvious opinion words, but still convey clear human-aware sentiment orientation, which is known as implicit sentiment. However, recent neural network-based approaches paid little attention to implicit sentiment entailed in the reviews. To overcome this issue, we adopt Supervised Contrastive Pre-training on large-scale sentiment-annotated corpora retrieved from in-domain language resources. By aligning the representation of implicit sentiment expressions to those with the same sentiment label, the pre-training process leads to better capture of both implicit and explicit sentiment orientation towards aspects in reviews. Experimental results show that our method achieves state-of-the-art performance on SemEval2014 benchmarks, and comprehensive analysis validates its effectiveness on learning implicit sentiment.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_23", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_23.flac", "type": "audio", "duration": 730.128}, {"from": "Assistant", "value": "Aspect term extraction aims to extract aspect terms from a review sentence that users have expressed opinions on. One of the remaining challenges for aspect term extraction resides in the lack of sufficient annotated data. While self-training is potentially an effective method to address this issue, the pseudo-labels it yields on unlabeled data could induce noise. In this paper, we use two means to alleviate the noise in the pseudo-labels. One is that inspired by the curriculum learning, we refine the conventional self-training to progressive self-training. Specifically, the base model infers pseudo-labels on a progressive subset at each iteration, where samples in the subset become harder and more numerous as the iteration proceeds. The other is that we use a discriminator to filter the noisy pseudo-labels. Experimental results on four SemEval datasets show that our model significantly outperforms the previous baselines and achieves state-of-the-art performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_24", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_24.flac", "type": "audio", "duration": 675.051}, {"from": "Assistant", "value": "Data augmentation and adversarial perturbation approaches have recently achieved promising results in solving the over-fitting problem in many natural language processing (NLP) tasks including sentiment classification. However, existing studies aimed to improve the generalization ability by augmenting the training data with synonymous examples or adding random noises to word embeddings, which cannot address the spurious association problem. In this work, we propose an end-to-end reinforcement learning framework, which jointly performs counterfactual data generation and dual sentiment classification. Our approach has three characteristics:1) the generator automatically generates massive and diverse antonymous sentences; 2) the discriminator contains a original-side sentiment predictor and an antonymous-side sentiment predictor, which jointly evaluate the quality of the generated sample and help the generator iteratively generate higher-quality antonymous samples; 3) the discriminator is directly used as the final sentiment classifier without the need to build an extra one. Extensive experiments show that our approach outperforms strong data augmentation baselines on several benchmark sentiment classification datasets. Further analysis confirms our approach’s advantages in generating more diverse training samples and solving the spurious association problem in sentiment classification.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_25", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_25.flac", "type": "audio", "duration": 732.961}, {"from": "Assistant", "value": "An individual’s variation in writing style is often a function of both social and personal attributes. While structured social variation has been extensively studied, e.g., gender based variation, far less is known about how to characterize individual styles due to their idiosyncratic nature. We introduce a new approach to studying idiolects through a massive cross-author comparison to identify and encode stylistic features. The neural model achieves strong performance at authorship identification on short texts and through an analogy-based probing task, showing that the learned representations exhibit surprising regularities that encode qualitative and quantitative shifts of idiolectal styles. Through text perturbation, we quantify the relative contributions of different linguistic elements to idiolectal variation. Furthermore, we provide a description of idiolects through measuring inter- and intra-author variation, showing that variation in idiolects is often distinctive yet consistent.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_26", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_26.flac", "type": "audio", "duration": 739.179}, {"from": "Assistant", "value": "Over the past decade, the field of natural language processing has developed a wide array of computational methods for reasoning about narrative, including summarization, commonsense inference, and event detection. While this work has brought an important empirical lens for examining narrative, it is by and large divorced from the large body of theoretical work on narrative within the humanities, social and cognitive sciences. In this position paper, we introduce the dominant theoretical frameworks to the NLP community, situate current research in NLP within distinct narratological traditions, and argue that linking computational work in NLP to theory opens up a range of new empirical questions that would both help advance our understanding of narrative and open up new practical applications.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_27", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_27.flac", "type": "audio", "duration": 750.933}, {"from": "Assistant", "value": "Stance detection, which aims to determine whether an individual is for or against a target concept, promises to uncover public opinion from large streams of social media data. Yet even human annotation of social media content does not always capture “stance” as measured by public opinion polls. We demonstrate this by directly comparing an individual’s self-reported stance to the stance inferred from their social media data. Leveraging a longitudinal public opinion survey with respondent Twitter handles, we conducted this comparison for 1,129 individuals across four salient targets. We find that recall is high for both “Pro’’ and “Anti’’ stance classifications but precision is variable in a number of cases. We identify three factors leading to the disconnect between text and author stance: temporal inconsistencies, differences in constructs, and measurement errors from both survey respondents and annotators. By presenting a framework for assessing the limitations of stance detection models, this work provides important insight into what stance detection truly measures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_28", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_28.flac", "type": "audio", "duration": 717.056}, {"from": "Assistant", "value": "As NLP models are increasingly deployed in socially situated settings such as online abusive content detection, it is crucial to ensure that these models are robust. One way of improving model robustness is to generate counterfactually augmented data (CAD) for training models that can better learn to distinguish between core features and data artifacts. While models trained on this type of data have shown promising out-of-domain generalizability, it is still unclear what the sources of such improvements are. We investigate the benefits of CAD for social NLP models by focusing on three social computing constructs — sentiment, sexism, and hate speech. Assessing the performance of models trained with and without CAD across different types of datasets, we find that while models trained on CAD show lower in-domain performance, they generalize better out-of-domain. We unpack this apparent discrepancy using machine explanations and find that CAD reduces model reliance on spurious features. Leveraging a novel typology of CAD to analyze their relationship with model performance, we find that CAD which acts on the construct directly or a diverse set of CAD leads to higher performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_29", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_29.flac", "type": "audio", "duration": 726.976}, {"from": "Assistant", "value": "Hate speech has grown significantly on social media, causing serious consequences for victims of all demographics. Despite much attention being paid to characterize and detect discriminatory speech, most work has focused on explicit or overt hate speech, failing to address a more pervasive form based on coded or indirect language. To fill this gap, this work introduces a theoretically-justified taxonomy of implicit hate speech and a benchmark corpus with fine-grained labels for each message and its implication. We present systematic analyses of our dataset using contemporary baselines to detect and explain implicit hate speech, and we discuss key features that challenge existing models. This dataset will continue to serve as a useful benchmark for understanding this multifaceted issue.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_30", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_30.flac", "type": "audio", "duration": 692.048}, {"from": "Assistant", "value": "A computationally expensive and memory intensive neural network lies behind the recent success of language representation learning. Knowledge distillation, a major technique for deploying such a vast language model in resource-scarce environments, transfers the knowledge on individual word representations learned without restrictions. In this paper, inspired by the recent observations that language representations are relatively positioned and have more semantic knowledge as a whole, we present a new knowledge distillation objective for language representation learning that transfers the contextual knowledge via two types of relationships across representations: Word Relation and Layer Transforming Relation. Unlike other recent distillation techniques for the language models, our contextual distillation does not have any restrictions on architectural changes between teacher and student. We validate the effectiveness of our method on challenging benchmarks of language understanding tasks, not only in architectures of various sizes but also in combination with DynaBERT, the recently proposed adaptive size pruning method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_31", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_31.flac", "type": "audio", "duration": 678.023}, {"from": "Assistant", "value": "Knowledge distillation (KD) has been proved effective for compressing large-scale pre-trained language models. However, existing methods conduct KD statically, e.g., the student model aligns its output distribution to that of a selected teacher model on the pre-defined training dataset. In this paper, we explore whether a dynamic knowledge distillation that empowers the student to adjust the learning procedure according to its competency, regarding the student performance and learning efficiency. We explore the dynamical adjustments on three aspects: teacher model adoption, data selection, and KD objective adaptation. Experimental results show that (1) proper selection of teacher model can boost the performance of student model; (2) conducting KD with 10% informative instances achieves comparable performance while greatly accelerates the training; (3) the student performance can be boosted by adjusting the supervision contribution of different alignment objective. We find dynamic knowledge distillation is promising and provide discussions on potential future directions towards more efficient KD methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_32", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_32.flac", "type": "audio", "duration": 654.059}, {"from": "Assistant", "value": "Providing pretrained language models with simple task descriptions in natural language enables them to solve some tasks in a fully unsupervised fashion. Moreover, when combined with regular learning from examples, this idea yields impressive few-shot results for a wide range of text classification tasks. It is also a promising direction to improve data efficiency in generative settings, but there are several challenges to using a combination of task descriptions and example-based learning for text generation. In particular, it is crucial to find task descriptions that are easy to understand for the pretrained model and to ensure that it actually makes good use of them; furthermore, effective measures against overfitting have to be implemented. In this paper, we show how these challenges can be tackled: We introduce GenPET, a method for text generation that is based on pattern-exploiting training, a recent approach for combining textual instructions with supervised learning that only works for classification tasks. On several summarization and headline generation datasets, GenPET gives consistent improvements over strong baselines in few-shot settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_33", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_33.flac", "type": "audio", "duration": 691.003}, {"from": "Assistant", "value": "Sentence Compression (SC), which aims to shorten sentences while retaining important words that express the essential meanings, has been studied for many years in many languages, especially in English. However, improvements on Chinese SC task are still quite few due to several difficulties: scarce of parallel corpora, different segmentation granularity of Chinese sentences, and imperfect performance of syntactic analyses. Furthermore, entire neural Chinese SC models have been under-investigated so far. In this work, we construct an SC dataset of Chinese colloquial sentences from a real-life question answering system in the telecommunication domain, and then, we propose a neural Chinese SC model enhanced with a Self-Organizing Map (SOM-NCSCM), to gain a valuable insight from the data and improve the performance of the whole neural Chinese SC model in a valid manner. Experimental results show that our SOM-NCSCM can significantly benefit from the deep investigation of similarity among data, and achieve a promising F1 score of 89.655 and BLEU4 score of 70.116, which also provides a baseline for further research on Chinese SC task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_34", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_34.flac", "type": "audio", "duration": 678.059}, {"from": "Assistant", "value": "Multi-task auxiliary learning utilizes a set of relevant auxiliary tasks to improve the performance of a primary task. A common usage is to manually select multiple auxiliary tasks for multi-task learning on all data, which raises two issues: (1) selecting beneficial auxiliary tasks for a primary task is nontrivial; (2) when the auxiliary datasets are large, training on all data becomes time-expensive and impractical. Therefore, this paper focuses on addressing these problems and proposes a time-efficient sampling method to select the data that is most relevant to the primary task. The proposed method allows us to only train on the most beneficial sub-datasets from the auxiliary tasks, achieving efficient multi-task auxiliary learning. The experiments on three benchmark datasets (RTE, MRPC, STS-B) show that our method significantly outperforms random sampling and ST-DNN. Also, by applying our method, the model can surpass fully-trained MT-DNN on RTE, MRPC, STS-B, using only 50%, 66%, and 1% of data, respectively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_35", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_35.flac", "type": "audio", "duration": 722.768}, {"from": "Assistant", "value": "Practical dialogue systems require robust methods of detecting out-of-scope (OOS) utterances to avoid conversational breakdowns and related failure modes. Directly training a model with labeled OOS examples yields reasonable performance, but obtaining such data is a resource-intensive process. To tackle this limited-data problem, previous methods focus on better modeling the distribution of in-scope (INS) examples. We introduce GOLD as an orthogonal technique that augments existing data to train better OOS detectors operating in low-data regimes. GOLD generates pseudo-labeled candidates using samples from an auxiliary dataset and keeps only the most beneficial candidates for training through a novel filtering mechanism. In experiments across three target benchmarks, the top GOLD model outperforms all existing methods on all key metrics, achieving relative gains of 52.4%, 48.9% and 50.3% against median baseline performance. We also analyze the unique properties of OOS data to identify key factors for optimally applying our proposed method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_36", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_36.flac", "type": "audio", "duration": 670.059}, {"from": "Assistant", "value": "Dialogue-based relation extraction (RE) aims to extract relation(s) between two arguments that appear in a dialogue. Because dialogues have the characteristics of high personal pronoun occurrences and low information density, and since most relational facts in dialogues are not supported by any single sentence, dialogue-based relation extraction requires a comprehensive understanding of dialogue. In this paper, we propose the TUrn COntext awaRE Graph Convolutional Network (TUCORE-GCN) modeled by paying attention to the way people understand dialogues. In addition, we propose a novel approach which treats the task of emotion recognition in conversations (ERC) as a dialogue-based RE. Experiments on a dialogue-based RE dataset and three ERC datasets demonstrate that our model is very effective in various dialogue-based natural language understanding tasks. In these experiments, TUCORE-GCN outperforms the state-of-the-art models on most of the benchmark datasets. Our code is available athttps://github.com/BlackNoodle/TUCORE-GCN.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_37", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_37.flac", "type": "audio", "duration": 685.056}, {"from": "Assistant", "value": "Neural dialog models are known to suffer from problems such as generating unsafe and inconsistent responses. Even though these problems are crucial and prevalent, they are mostly manually identified by model designers through interactions. Recently, some research instructs crowdworkers to goad the bots into triggering such problems. However, humans leverage superficial clues such as hate speech, while leaving systematic problems undercover. In this paper, we propose two methods including reinforcement learning to automatically trigger a dialog model into generating problematic responses. We show the effect of our methods in exposing safety and contradiction issues with state-of-the-art dialog models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_38", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_38.flac", "type": "audio", "duration": 720.167}, {"from": "Assistant", "value": "Cross-document event coreference resolution (CDCR) is the task of identifying which event mentions refer to the same events throughout a collection of documents. Annotating CDCR data is an arduous and expensive process, explaining why existing corpora are small and lack domain coverage. To overcome this bottleneck, we automatically extract event coreference data from hyperlinks in online news: When referring to a significant real-world event, writers often add a hyperlink to another article covering this event. We demonstrate that collecting hyperlinks which point to the same article(s) produces extensive and high-quality CDCR data and create a corpus of 2M documents and 2.7M silver-standard event mentions called HyperCoref. We evaluate a state-of-the-art system on three CDCR corpora and find that models trained on small subsets of HyperCoref are highly competitive, with performance similar to models trained on gold-standard data. With our work, we free CDCR research from depending on costly human-annotated training data and open up possibilities for research beyond English CDCR, as our data extraction approach can be easily adapted to other languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_39", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_39.flac", "type": "audio", "duration": 436.053}, {"from": "Assistant", "value": "Stereotypical character roles-also known as archetypes or dramatis personae-play an important function in narratives: they facilitate efficient communication with bundles of default characteristics and associations and ease understanding of those characters’ roles in the overall narrative. We present a fully unsupervised k-means clustering approach for learning stereotypical roles given only structural plot information. We demonstrate the technique on Vladimir Propp’s structural theory of Russian folktales (captured in the extended ProppLearner corpus, with 46 tales), showing that our approach can induce six out of seven of Propp’s dramatis personae with F1 measures of up to 0.70 (0.58 average), with an additional category for minor characters. We have explored various feature sets and variations of a cluster evaluation method. The best-performing feature set comprises plot functions, unigrams, tf-idf weights, and embeddings over coreference chain heads. Roles that are mentioned more often (Hero, Villain), or have clearly distinct plot patterns (Princess) are more strongly differentiated than less frequent or distinct roles (Dispatcher, Helper, Donor). Detailed error analysis suggests that the quality of the coreference chain and plot functions annotations are critical for this task. We provide all our data and code for reproducibility.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_40", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_40.flac", "type": "audio", "duration": 720.654}, {"from": "Assistant", "value": "As labeling schemas evolve over time, small differences can render datasets following older schemas unusable. This prevents researchers from building on top of previous annotation work and results in the existence, in discourse learning in particular, of many small class-imbalanced datasets. In this work, we show that a multitask learning approach can combine discourse datasets from similar and diverse domains to improve discourse classification. We show an improvement of 4.9% Micro F1-score over current state-of-the-art benchmarks on theNewsDiscoursedataset, one of the largest discourse datasets recently published, due in part to label correlations across tasks, which improve performance for underrepresented classes. We also offer an extensive review of additional techniques proposed to address resource-poor problems in NLP, and show that none of these approaches can improve classification accuracy in our setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_41", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_41.flac", "type": "audio", "duration": 600.043}, {"from": "Assistant", "value": "We use a dataset of U.S. first names with labels based on predominant gender and racial group to examine the effect of training corpus frequency on tokenization, contextualization, similarity to initial representation, and bias in BERT, GPT-2, T5, and XLNet. We show that predominantly female and non-white names are less frequent in the training corpora of these four language models. We find that infrequent names are more self-similar across contexts, with Spearman’s rho between frequency and self-similarity as low as -.763. Infrequent names are also less similar to initial representation, with Spearman’s rho between frequency and linear centered kernel alignment (CKA) similarity to initial representation as high as .702. Moreover, we find Spearman’s rho between racial bias and name frequency in BERT of .492, indicating that lower-frequency minority group names are more associated with unpleasantness. Representations of infrequent names undergo more processing, but are more self-similar, indicating that models rely on less context-informed representations of uncommon and minority names which are overfit to a lower number of observed contexts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_42", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_42.flac", "type": "audio", "duration": 696.808}, {"from": "Assistant", "value": "In this paper, we study ethnic bias and how it varies across languages by analyzing and mitigating ethnic bias in monolingual BERT for English, German, Spanish, Korean, Turkish, and Chinese. To observe and quantify ethnic bias, we develop a novel metric called Categorical Bias score. Then we propose two methods for mitigation; first using a multilingual model, and second using contextual word alignment of two monolingual models. We compare our proposed methods with monolingual BERT and show that these methods effectively alleviate the ethnic bias. Which of the two methods works better depends on the amount of NLP resources available for that language. We additionally experiment with Arabic and Greek to verify that our proposed methods work for a wider variety of languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_43", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_43.flac", "type": "audio", "duration": 720.043}, {"from": "Assistant", "value": "Contextual representations learned by language models can often encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated target task. We aim to scrub such undesirable attributes and learn fair representations while maintaining performance on the target task. In this paper, we present an adversarial learning framework “Adversarial Scrubber” (AdS), to debias contextual representations. We perform theoretical analysis to show that our framework converges without leaking demographic information under certain conditions. We extend previous evaluation techniques by evaluating debiasing performance using Minimum Description Length (MDL) probing. Experimental evaluations on 8 datasets show that AdS generates representations with minimal information about demographic attributes while being maximally informative about the target task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_44", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_44.flac", "type": "audio", "duration": 407.04}, {"from": "Assistant", "value": "An overarching goal of natural language processing is to enable machines to communicate seamlessly with humans. However, natural language can be ambiguous or unclear. In cases of uncertainty, humans engage in an interactive process known as repair: asking questions and seeking clarification until their uncertainty is resolved. We propose a framework for building a visually grounded question-asking model capable of producing polar (yes-no) clarification questions to resolve misunderstandings in dialogue. Our model uses an expected information gain objective to derive informative questions from an off-the-shelf image captioner without requiring any supervised question-answer data. We demonstrate our model’s ability to pose questions that improve communicative success in a goal-oriented 20 questions game with synthetic and human answerers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_45", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_45.flac", "type": "audio", "duration": 593.045}, {"from": "Assistant", "value": "In this paper, we proposeSequenceSpanRewriting (SSR), a self-supervised task for sequence-to-sequence (Seq2Seq) pre-training. SSR learns to refine the machine-generated imperfect text spans into ground truth text. SSR provides more fine-grained and informative supervision in addition to the original text-infilling objective. Compared to the prevalent text infilling objectives for Seq2Seq pre-training, SSR is naturally more consistent with many downstream generation tasks that require sentence rewriting (e.g., text summarization, question generation, grammatical error correction, and paraphrase generation). We conduct extensive experiments by using SSR to improve the typical Seq2Seq pre-trained model T5 in a continual pre-training setting and show substantial improvements over T5 on various natural language generation tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_46", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_46.flac", "type": "audio", "duration": 607.04}, {"from": "Assistant", "value": "Existing text classification methods mainly focus on a fixed label set, whereas many real-world applications require extending to new fine-grained classes as the number of samples per label increases. To accommodate such requirements, we introduce a new problem called coarse-to-fine grained classification, which aims to perform fine-grained classification on coarsely annotated data. Instead of asking for new fine-grained human annotations, we opt to leverage label surface names as the only human guidance and weave in rich pre-trained generative language models into the iterative weak supervision strategy. Specifically, we first propose a label-conditioned fine-tuning formulation to attune these generators for our task. Furthermore, we devise a regularization objective based on the coarse-fine label constraints derived from our problem setting, giving us even further improvements over the prior formulation. Our framework uses the fine-tuned generative models to sample pseudo-training data for training the classifier, and bootstraps on real unlabeled data for model refinement. Extensive experiments and case studies on two real-world datasets demonstrate superior performance over SOTA zero-shot classification baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_47", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_47.flac", "type": "audio", "duration": 720.051}, {"from": "Assistant", "value": "We propose a new task, Text2Mol, to retrieve molecules using natural language descriptions as queries. Natural language and molecules encode information in very different ways, which leads to the exciting but challenging problem of integrating these two very different modalities. Although some work has been done on text-based retrieval and structure-based retrieval, this new task requires integrating molecules and natural language more directly. Moreover, this can be viewed as an especially challenging cross-lingual retrieval problem by considering the molecules as a language with a very unique grammar. We construct a paired dataset of molecules and their corresponding text descriptions, which we use to learn an aligned common semantic embedding space for retrieval. We extend this to create a cross-modal attention-based model for explainability and reranking by interpreting the attentions as association rules. We also employ an ensemble approach to integrate our different architectures, which significantly improves results from 0.372 to 0.499 MRR. This new multimodal approach opens a new perspective on solving problems in chemistry literature understanding and molecular machine learning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_48", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_48.flac", "type": "audio", "duration": 713.493}, {"from": "Assistant", "value": "We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories. While graph neural networks can efficiently handle the variable structure of hierarchical documents using the permutation invariant message passing operations, we show that we can gain extra performance improvements using our proposed selective graph pooling operation that arises from the fact that some parts of the hierarchy are invariable across different documents. We applied our model to classify clinical trial (CT) protocols into completed and terminated categories. We use bag-of-words based, as well as pre-trained transformer-based embeddings to featurize the graph nodes, achieving f1-scoresaround 0.85 on a publicly available large scale CT registry of around 360K protocols. We further demonstrate how the selective pooling can add insights into the CT termination status prediction. We make the source code and dataset splits accessible.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_49", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_49.flac", "type": "audio", "duration": 645.863}, {"from": "Assistant", "value": "Recently, many datasets have been proposed to test the systematic generalization ability of neural networks. The companion baseline Transformers, typically trained with default hyper-parameters from standard tasks, are shown to fail dramatically. Here we demonstrate that by revisiting model configurations as basic as scaling of embeddings, early stopping, relative positional embedding, and Universal Transformer variants, we can drastically improve the performance of Transformers on systematic generalization. We report improvements on five popular datasets: SCAN, CFQ, PCFG, COGS, and Mathematics dataset. Our models improve accuracy from 50% to 85% on the PCFG productivity split, and from 35% to 81% on COGS. On SCAN, relative positional embedding largely mitigates the EOS decision problem (Newman et al., 2020), yielding 100% accuracy on the length split with a cutoff at 26. Importantly, performance differences between these models are typically invisible on the IID data split. This calls for proper generalization validation sets for developing neural networks that generalize systematically. We publicly release the code to reproduce our results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_50", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_50.flac", "type": "audio", "duration": 651.923}, {"from": "Assistant", "value": "The impressive capabilities of recent generative models to create texts that are challenging to distinguish from the human-written ones can be misused for generating fake news, product reviews, and even abusive content. Despite the prominent performance of existing methods for artificial text detection, they still lack interpretability and robustness towards unseen models. To this end, we propose three novel types of interpretable topological features for this task based on Topological Data Analysis (TDA) which is currently understudied in the field of NLP. We empirically show that the features derived from the BERT model outperform count- and neural-based baselines up to 10% on three common datasets, and tend to be the most robust towards unseen GPT-style generation models as opposed to existing methods. The probing analysis of the features reveals their sensitivity to the surface and syntactic properties. The results demonstrate that TDA is a promising line with respect to NLP tasks, specifically the ones that incorporate surface and structural information.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_51", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_51.flac", "type": "audio", "duration": 803.062}, {"from": "Assistant", "value": "Common acquisition functions for active learning use either uncertainty or diversity sampling, aiming to select difficult and diverse data points from the pool of unlabeled data, respectively. In this work, leveraging the best of both worlds, we propose an acquisition function that opts for selecting contrastive examples, i.e. data points that are similar in the model feature space and yet the model outputs maximally different predictive likelihoods. We compare our approach, CAL (Contrastive Active Learning), with a diverse set of acquisition functions in four natural language understanding tasks and seven datasets. Our experiments show that CAL performs consistently better or equal than the best performing baseline across all tasks, on both in-domain and out-of-domain data. We also conduct an extensive ablation study of our method and we further analyze all actively acquired datasets showing that CAL achieves a better trade-off between uncertainty and diversity compared to other strategies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_52", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_52.flac", "type": "audio", "duration": 728.062}, {"from": "Assistant", "value": "Beam search is the default decoding strategy for many sequence generation tasks in NLP. The set of approximate K-best items returned by the algorithm is a useful summary of the distribution for many applications; however, the candidates typically exhibit high overlap and may give a highly biased estimate for expectations under our model. These problems can be addressed by instead using stochastic decoding strategies. In this work, we propose a new method for turning beam search into a stochastic process: Conditional Poisson stochastic beam search. Rather than taking the maximizing set at each iteration, we sample K candidates without replacement according to the conditional Poisson sampling design. We view this as a more natural alternative to Kool et al. (2019)’s stochastic beam search (SBS). Furthermore, we show how samples generated under the CPSBS design can be used to build consistent estimators and sample diverse sets from sequence models. In our experiments, we observe CPSBS produces lower variance and more efficient estimators than SBS, even showing improvements in high entropy settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_53", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_53.flac", "type": "audio", "duration": 751.019}, {"from": "Assistant", "value": "We propose a novel framework to train models to classify acceptability of responses generated by natural language generation (NLG) models, improving upon existing sentence transformation and model-based approaches. An NLG response is considered acceptable if it is both semantically correct and grammatical. We don’t make use of any human references making the classifiers suitable for runtime deployment. Training data for the classifiers is obtained using a 2-stage approach of first generating synthetic data using a combination of existing and new model-based approaches followed by a novel validation framework to filter and sort the synthetic data into acceptable and unacceptable classes. Our 2-stage approach adapts to a wide range of data representations and does not require additional data beyond what the NLG models are trained on. It is also independent of the underlying NLG model architecture, and is able to generate more realistic samples close to the distribution of the NLG model-generated responses. We present results on 5 datasets (WebNLG, Cleaned E2E, ViGGO, Alarm, and Weather) with varying data representations. We compare our framework with existing techniques that involve synthetic data generation using simple sentence transformations and/or model-based techniques, and show that building acceptability classifiers using data that resembles the generation model outputs followed by a validation framework outperforms the existing techniques, achieving state-of-the-art results. We also show that our techniques can be used in few-shot settings using self-training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_54", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_54.flac", "type": "audio", "duration": 954.062}, {"from": "Assistant", "value": "In social settings, much of human behavior is governed by unspoken rules of conduct rooted in societal norms. For artificial systems to be fully integrated into social environments, adherence to such norms is a central prerequisite. To investigate whether language generation models can serve as behavioral priors for systems deployed in social settings, we evaluate their ability to generate action descriptions that achieve predefined goals under normative constraints. Moreover, we examine if models can anticipate likely consequences of actions that either observe or violate known norms, or explain why certain actions are preferable by generating relevant norm hypotheses. For this purpose, we introduce Moral Stories, a crowd-sourced dataset of structured, branching narratives for the study of grounded, goal-oriented social reasoning. Finally, we propose decoding strategies that combine multiple expert models to significantly improve the quality of generated actions, consequences, and norms compared to strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_55", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_55.flac", "type": "audio", "duration": 712.043}, {"from": "Assistant", "value": "In this paper, we explore the task of automatically generating natural language descriptions of salient patterns in a time series, such as stock prices of a company over a week. A model for this task should be able to extract high-level patterns such as presence of a peak or a dip. While typical contemporary neural models with attention mechanisms can generate fluent output descriptions for this task, they often generate factually incorrect descriptions. We propose a computational model with a truth-conditional architecture which first runs small learned programs on the input time series, then identifies the programs/patterns which hold true for the given input, and finally conditions on *only* the chosen valid program (rather than the input time series) to generate the output text description. A program in our model is constructed from modules, which are small neural networks that are designed to capture numerical patterns and temporal information. The modules are shared across multiple programs, enabling compositionality as well as efficient learning of module parameters. The modules, as well as the composition of the modules, are unobserved in data, and we learn them in an end-to-end fashion with the only training signal coming from the accompanying natural language text descriptions. We find that the proposed model is able to generate high-precision captions even though we consider a small and simple space of module types.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_56", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_56.flac", "type": "audio", "duration": 413.896}, {"from": "Assistant", "value": "Recent successes in deep generative modeling have led to significant advances in natural language generation (NLG). Incorporating entities into neural generation models has demonstrated great improvements by assisting to infer the summary topic and to generate coherent content. To enhance the role of entity in NLG, in this paper, we aim to model the entity type in the decoding phase to generate contextual words accurately. We develop a novel NLG model to produce a target sequence based on a given list of entities. Our model has a multi-step decoder that injects the entity types into the process of entity mention generation. Experiments on two public news datasets demonstrate type injection performs better than existing type embedding concatenation baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_57", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_57.flac", "type": "audio", "duration": 480.043}, {"from": "Assistant", "value": "Recent work on multilingual AMR-to-text generation has exclusively focused on data augmentation strategies that utilize silver AMR. However, this assumes a high quality of generated AMRs, potentially limiting the transferability to the target task. In this paper, we investigate different techniques for automatically generating AMR annotations, where we aim to study which source of information yields better multilingual results. Our models trained on gold AMR with silver (machine translated) sentences outperform approaches which leverage generated silver AMR. We find that combining both complementary sources of information further improves multilingual AMR-to-text generation. Our models surpass the previous state of the art for German, Italian, Spanish, and Chinese by a large margin.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_58", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_58.flac", "type": "audio", "duration": 498.045}, {"from": "Assistant", "value": "Recent developments in machine translation and multilingual text generation have led researchers to adopt trained metrics such as COMET or BLEURT, which treat evaluation as a regression problem and use representations from multilingual pre-trained models such as XLM-RoBERTa or mBERT. Yet studies on related tasks suggest that these models are most efficient when they are large, which is costly and impractical for evaluation. We investigate the trade-off between multilinguality and model capacity with RemBERT, a state-of-the-art multilingual language model, using data from the WMT Metrics Shared Task. We present a series of experiments which show that model size is indeed a bottleneck for cross-lingual transfer, then demonstrate how distillation can help addressing this bottleneck, by leveraging synthetic data generation and transferring knowledge from one teacher to multiple students trained on related languages. Our method yields up to 10.5% improvement over vanilla fine-tuning and reaches 92.6% of RemBERT’s performance using only a third of its parameters.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_59", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_59.flac", "type": "audio", "duration": 720.051}, {"from": "Assistant", "value": "In order to preserve word-order information in a non-autoregressive setting, transformer architectures tend to include positional knowledge, by (for instance) adding positional encodings to token embeddings. Several modifications have been proposed over the sinusoidal positional encodings used in the original transformer architecture; these include, for instance, separating position encodings and token embeddings, or directly modifying attention weights based on the distance between word pairs. We first show that surprisingly, while these modifications tend to improve monolingual language models, none of them result in better multilingual language models. We then answer why that is: sinusoidal encodings were explicitly designed to facilitate compositionality by allowing linear projections over arbitrary time steps. Higher variances in multilingual training distributions requires higher compression, in which case, compositionality becomes indispensable. Learned absolute positional encodings (e.g., in mBERT) tend to approximate sinusoidal embeddings in multilingual settings, but more complex positional encoding architectures lack the inductive bias to effectively learn cross-lingual alignment. In other words, while sinusoidal positional encodings were designed for monolingual applications, they are particularly useful in multilingual language models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_60", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_60.flac", "type": "audio", "duration": 683.921}, {"from": "Assistant", "value": "Representations from large pretrained models such as BERT encode a range of features into monolithic vectors, affording strong predictive accuracy across a range of downstream tasks. In this paper we explore whether it is possible to learn disentangled representations by identifying existing subnetworks within pretrained models that encode distinct, complementary aspects. Concretely, we learn binary masks over transformer weights or hidden units to uncover subsets of features that correlate with a specific factor of variation; this eliminates the need to train a disentangled model from scratch for a particular task. We evaluate this method with respect to its ability to disentangle representations of sentiment from genre in movie reviews, toxicity from dialect in Tweets, and syntax from semantics. By combining masking with magnitude pruning we find that we can identify sparse subnetworks within BERT that strongly encode particular aspects (e.g., semantics) while only weakly encoding others (e.g., syntax). Moreover, despite only learning masks, disentanglement-via-masking performs as well as — and often better than —previously proposed methods based on variational autoencoders and adversarial training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_61", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_61.flac", "type": "audio", "duration": 486.342}, {"from": "Assistant", "value": "Several studies have been carried out on revealing linguistic features captured by BERT. This is usually achieved by training a diagnostic classifier on the representations obtained from different layers of BERT. The subsequent classification accuracy is then interpreted as the ability of the model in encoding the corresponding linguistic property. Despite providing insights, these studies have left out the potential role of token representations. In this paper, we provide a more in-depth analysis on the representation space of BERT in search for distinct and meaningful subspaces that can explain the reasons behind these probing results. Based on a set of probing tasks and with the help of attribution methods we show that BERT tends to encode meaningful knowledge in specific token representations (which are often ignored in standard classification setups), allowing the model to detect syntactic and semantic abnormalities, and to distinctively separate grammatical number and tense subspaces.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_62", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_62.flac", "type": "audio", "duration": 739.045}, {"from": "Assistant", "value": "Language models are generally trained on short, truncated input sequences, which limits their ability to use discourse-level information present in long-range context to improve their predictions. Recent efforts to improve the efficiency of self-attention have led to a proliferation of long-range Transformer language models, which can process much longer sequences than models of the past. However, the ways in which such models take advantage of the long-range context remain unclear. In this paper, we perform a fine-grained analysis of two long-range Transformer language models (including the Routing Transformer, which achieves state-of-the-art perplexity on the PG-19 long-sequence LM benchmark dataset) that accept input sequences of up to 8K tokens. Our results reveal that providing long-range context (i.e., beyond the previous 2K tokens) to these models only improves their predictions on a small set of tokens (e.g., those that can be copied from the distant context) and does not help at all for sentence-level prediction tasks. Finally, we discover that PG-19 contains a variety of different document types and domains, and that long-range context helps most for literary novels (as opposed to textbooks or magazines).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_64", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_64.flac", "type": "audio", "duration": 599.04}, {"from": "Assistant", "value": "We introduce SelfExplain, a novel self-explaining model that explains a text classifier’s predictions using phrase-based concepts. SelfExplain augments existing neural classifiers by adding (1) a globally interpretable layer that identifies the most influential concepts in the training set for a given sample and (2) a locally interpretable layer that quantifies the contribution of each local input concept by computing a relevance score relative to the predicted label. Experiments across five text-classification datasets show that SelfExplain facilitates interpretability without sacrificing performance. Most importantly, explanations from SelfExplain show sufficiency for model predictions and are perceived as adequate, trustworthy and understandable by human judges compared to existing widely-used baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_65", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_65.flac", "type": "audio", "duration": 727.969}, {"from": "Assistant", "value": "Measuring event salience is essential in the understanding of stories. This paper takes a recent unsupervised method for salience detection derived from Barthes Cardinal Functions and theories of surprise and applies it to longer narrative forms. We improve the standard transformer language model by incorporating an external knowledgebase (derived from Retrieval Augmented Generation) and adding a memory mechanism to enhance performance on longer works. We use a novel approach to derive salience annotation using chapter-aligned summaries from the Shmoop corpus for classic literary works. Our evaluation against this data demonstrates that our salience detection model improves performance over and above a non-knowledgebase and memory augmented language model, both of which are crucial to this improvement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_66", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_66.flac", "type": "audio", "duration": 700.053}, {"from": "Assistant", "value": "This paper proposes to study a fine-grained semantic novelty detection task, which can be illustrated with the following example. It is normal that a person walks a dog in the park, but if someone says “A man is walking a chicken in the park”, it is novel. Given a set of natural language descriptions of normal scenes, we want to identify descriptions of novel scenes. We are not aware of any existing work that solves the problem. Although existing novelty or anomaly detection algorithms are applicable, since they are usually topic-based, they perform poorly on our fine-grained semantic novelty detection task. This paper proposes an effective model (called GAT-MA) to solve the problem and also contributes a new dataset. Experimental evaluation shows that GAT-MA outperforms 11 baselines by large margins.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_67", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_67.flac", "type": "audio", "duration": 727.979}, {"from": "Assistant", "value": "A challenge in designing high-stakes language assessments is calibrating the test item difficulties, either a priori or from limited pilot test data. While prior work has addressed ‘cold start’ estimation of item difficulties without piloting, we devise a multi-task generalized linear model with BERT features to jump-start these estimates, rapidly improving their quality with as few as 500 test-takers and a small sample of item exposures (≈6 each) from a large item bank (≈4,000 items). Our joint model provides a principled way to compare test-taker proficiency, item difficulty, and language proficiency frameworks like the Common European Framework of Reference (CEFR). This also enables new item difficulty estimates without piloting them first, which in turn limits item exposure and thus enhances test item security. Finally, using operational data from the Duolingo English Test, a high-stakes English proficiency test, we find that the difficulty estimates derived using this method correlate strongly with lexico-grammatical features that correlate with reading complexity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_68", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_68.flac", "type": "audio", "duration": 526.336}, {"from": "Assistant", "value": "Query auto completion (QAC) is the task of predicting a search engine user’s final query from their intermediate, incomplete query. In this paper, we extend QAC to the streaming voice search setting, where automatic speech recognition systems produce intermediate transcriptions as users speak. Naively applying existing methods fails because the intermediate transcriptions often don’t form prefixes or even substrings of the final transcription. To address this issue, we propose to condition QAC approaches on intermediate transcriptions to complete voice queries. We evaluate our models on a speech-enabled smart television with real-life voice search traffic, finding that this ASR-aware conditioning improves the completion quality. Our best method obtains an 18% relative improvement in mean reciprocal rank over previous methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_69", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_69.flac", "type": "audio", "duration": 477.056}, {"from": "Assistant", "value": "Large-Scale Multi-Label Text Classification (LMTC) includes tasks with hierarchical label spaces, such as automatic assignment of ICD-9 codes to discharge summaries. Performance of models in prior art is evaluated with standard precision, recall, and F1 measures without regard for the rich hierarchical structure. In this work we argue for hierarchical evaluation of the predictions of neural LMTC models. With the example of the ICD-9 ontology we describe a structural issue in the representation of the structured label space in prior art, and propose an alternative representation based on the depth of the ontology. We propose a set of metrics for hierarchical evaluation using the depth-based representation. We compare the evaluation scores from the proposed metrics with previously used metrics on prior art LMTC models for ICD-9 coding in MIMIC-III. We also propose further avenues of research involving the proposed ontological representation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_70", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_70.flac", "type": "audio", "duration": 479.701}, {"from": "Assistant", "value": "Determining whether two documents were composed by the same author, also known as authorship verification, has traditionally been tackled using statistical methods. Recently, authorship representations learned using neural networks have been found to outperform alternatives, particularly in large-scale settings involving hundreds of thousands of authors. But do such representations learned in a particular domain transfer to other domains? Or are these representations inherently entangled with domain-specific features? To study these questions, we conduct the first large-scale study of cross-domain transfer for authorship verification considering zero-shot transfers involving three disparate domains: Amazon reviews, fanfiction short stories, and Reddit comments. We find that although a surprising degree of transfer is possible between certain domains, it is not so successful between others. We examine properties of these domains that influence generalization and propose simple but effective methods to improve transfer.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_71", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_71.flac", "type": "audio", "duration": 706.688}, {"from": "Assistant", "value": "Natural language relies on a finite lexicon to express an unbounded set of emerging ideas. One result of this tension is the formation of new compositions, such that existing linguistic units can be combined with emerging items into novel expressions. We develop a framework that exploits the cognitive mechanisms of chaining and multimodal knowledge to predict emergent compositional expressions through time. We present the syntactic frame extension model (SFEM) that draws on the theory of chaining and knowledge from “percept”, “concept”, and “language” to infer how verbs extend their frames to form new compositions with existing and novel nouns. We evaluate SFEM rigorously on the 1) modalities of knowledge and 2) categorization models of chaining, in a syntactically parsed English corpus over the past 150 years. We show that multimodal SFEM predicts newly emerged verb syntax and arguments substantially better than competing models using purely linguistic or unimodal knowledge. We find support for an exemplar view of chaining as opposed to a prototype view and reveal how the joint approach of multimodal chaining may be fundamental to the creation of literal and figurative language uses including metaphor and metonymy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_72", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_72.flac", "type": "audio", "duration": 630.05}, {"from": "Assistant", "value": "Pre-trained language models perform well on a variety of linguistic tasks that require symbolic reasoning, raising the question of whether such models implicitly represent abstract symbols and rules. We investigate this question using the case study of BERT’s performance on English subject–verb agreement. Unlike prior work, we train multiple instances of BERT from scratch, allowing us to perform a series of controlled interventions at pre-training time. We show that BERT often generalizes well to subject–verb pairs that never occurred in training, suggesting a degree of rule-governed behavior. We also find, however, that performance is heavily influenced by word frequency, with experiments showing that both the absolute frequency of a verb form, as well as the frequency relative to the alternate inflection, are causally implicated in the predictions BERT makes at inference time. Closer analysis of these frequency effects reveals that BERT’s behavior is consistent with a system that correctly applies the SVA rule in general but struggles to overcome strong training priors and to estimate agreement features (singular vs. plural) on infrequent lexical items.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_74", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_74.flac", "type": "audio", "duration": 717.055}, {"from": "Assistant", "value": "The uniform information density (UID) hypothesis posits a preference among language users for utterances structured such that information is distributed uniformly across a signal. While its implications on language production have been well explored, the hypothesis potentially makes predictions about language comprehension and linguistic acceptability as well. Further, it is unclear how uniformity in a linguistic signal—or lack thereof—should be measured, and over which linguistic unit, e.g., the sentence or language level, this uniformity should hold. Here we investigate these facets of the UID hypothesis using reading time and acceptability data. While our reading time results are generally consistent with previous work, they are also consistent with a weakly super-linear effect of surprisal, which would be compatible with UID’s predictions. For acceptability judgments, we find clearer evidence that non-uniformity in information density is predictive of lower acceptability. We then explore multiple operationalizations of UID, motivated by different interpretations of the original hypothesis, and analyze the scope over which the pressure towards uniformity is exerted. The explanatory power of a subset of the proposed operationalizations suggests that the strongest trend may be a regression towards a mean surprisal across the language, rather than the phrase, sentence, or document—a finding that supports a typical interpretation of UID, namely that it is the byproduct of language users maximizing the use of a (hypothetical) communication channel.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_75", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_75.flac", "type": "audio", "duration": 695.019}, {"from": "Assistant", "value": "Pre-trained Transformer language models (LM) have become go-to text representation encoders. Prior research fine-tunes deep LMs to encode text sequences such as sentences and passages into single dense vector representations for efficient text comparison and retrieval. However, dense encoders require a lot of data and sophisticated techniques to effectively train and suffer in low data situations. This paper finds a key reason is that standard LMs’ internal attention structure is not ready-to-use for dense encoders, which needs to aggregate text information into the dense representation. We propose to pre-train towards dense encoder with a novel Transformer architecture, Condenser, where LM prediction CONditions on DENSE Representation. Our experiments show Condenser improves over standard LM by large margins on various text retrieval and similarity tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_76", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_76.flac", "type": "audio", "duration": 645.056}, {"from": "Assistant", "value": "Slow emerging topic detection is a task between event detection, where we aggregate behaviors of different words on short period of time, and language evolution, where we monitor their long term evolution. In this work, we tackle the problem of early detection of slowly emerging new topics. To this end, we gather evidence of weak signals at the word level. We propose to monitor the behavior of words representation in an embedding space and use one of its geometrical properties to characterize the emergence of topics. As evaluation is typically hard for this kind of task, we present a framework for quantitative evaluation and show positive results that outperform state-of-the-art methods. Our method is evaluated on two public datasets of press and scientific articles.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_77", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_77.flac", "type": "audio", "duration": 704.052}, {"from": "Assistant", "value": "This paper describes a compact and effective model for low-latency passage retrieval in conversational search based on learned dense representations. Prior to our work, the state-of-the-art approach uses a multi-stage pipeline comprising conversational query reformulation and information retrieval modules. Despite its effectiveness, such a pipeline often includes multiple neural models that require long inference times. In addition, independently optimizing each module ignores dependencies among them. To address these shortcomings, we propose to integrate conversational query reformulation directly into a dense retrieval model. To aid in this goal, we create a dataset with pseudo-relevance labels for conversational search to overcome the lack of training data and to explore different training strategies. We demonstrate that our model effectively rewrites conversational queries as dense representations in conversational search and open-domain question answering datasets. Finally, after observing that our model learns to adjust the L2 norm of query token embeddings, we leverage this property for hybrid retrieval and to support error analysis.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_78", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_78.flac", "type": "audio", "duration": 719.052}, {"from": "Assistant", "value": "The semantic matching capabilities of neural information retrieval can ameliorate synonymy and polysemy problems of symbolic approaches. However, neural models’ dense representations are more suitable for re-ranking, due to their inefficiency. Sparse representations, either in symbolic or latent form, are more efficient with an inverted index. Taking the merits of the sparse and dense representations, we propose an ultra-high dimensional (UHD) representation scheme equipped with directly controllable sparsity. UHD’s large capacity and minimal noise and interference among the dimensions allow for binarized representations, which are highly efficient for storage and search. Also proposed is a bucketing method, where the embeddings from multiple layers of BERT are selected/merged to represent diverse linguistic aspects. We test our models with MS MARCO and TREC CAR, showing that our models outperforms other sparse models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_79", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_79.flac", "type": "audio", "duration": 658.053}, {"from": "Assistant", "value": "With the advent of contextualized embeddings, attention towards neural ranking approaches for Information Retrieval increased considerably. However, two aspects have remained largely neglected: i) queries usually consist of few keywords only, which increases ambiguity and makes their contextualization harder, and ii) performing neural ranking on non-English documents is still cumbersome due to shortage of labeled datasets. In this paper we present SIR (Sense-enhanced Information Retrieval) to mitigate both problems by leveraging word sense information. At the core of our approach lies a novel multilingual query expansion mechanism based on Word Sense Disambiguation that provides sense definitions as additional semantic information for the query. Importantly, we use senses as a bridge across languages, thus allowing our model to perform considerably better than its supervised and unsupervised alternatives across French, German, Italian and Spanish languages on several CLEF benchmarks, while being trained on English Robust04 data only. We release SIR athttps://github.com/SapienzaNLP/sir.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_80", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_80.flac", "type": "audio", "duration": 700.058}, {"from": "Assistant", "value": "Neural topic models (NTMs) apply deep neural networks to topic modelling. Despite their success, NTMs generally ignore two important aspects: (1) only document-level word count information is utilized for the training, while more fine-grained sentence-level information is ignored, and (2) external semantic knowledge regarding documents, sentences and words are not exploited for the training. To address these issues, we propose a variational autoencoder (VAE) NTM model that jointly reconstructs the sentence and document word counts using combinations of bag-of-words (BoW) topical embeddings and pre-trained semantic embeddings. The pre-trained embeddings are first transformed into a common latent topical space to align their semantics with the BoW embeddings. Our model also features hierarchical KL divergence to leverage embeddings of each document to regularize those of their sentences, paying more attention to semantically relevant sentences. Both quantitative and qualitative experiments have shown the efficacy of our model in 1) lowering the reconstruction errors at both the sentence and document levels, and 2) discovering more coherent topics from real-world datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_81", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_81.flac", "type": "audio", "duration": 737.094}, {"from": "Assistant", "value": "Relational knowledge bases (KBs) are commonly used to represent world knowledge in machines. However, while advantageous for their high degree of precision and interpretability, KBs are usually organized according to manually-defined schemas, which limit their expressiveness and require significant human efforts to engineer and maintain. In this review, we take a natural language processing perspective to these limitations, examining how they may be addressed in part by training deep contextual language models (LMs) to internalize and express relational knowledge in more flexible forms. We propose to organize knowledge representation strategies in LMs by the level of KB supervision provided, from no KB supervision at all to entity- and relation-level supervision. Our contributions are threefold: (1) We provide a high-level, extensible taxonomy for knowledge representation in LMs; (2) Within our taxonomy, we highlight notable models, evaluation tasks, and findings, in order to provide an up-to-date review of current knowledge representation capabilities in LMs; and (3) We suggest future research directions that build upon the complementary aspects of LMs and KBs as knowledge representations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_83", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_83.flac", "type": "audio", "duration": 682.063}, {"from": "Assistant", "value": "Automatic construction of relevant Knowledge Bases (KBs) from text, and generation of semantically meaningful text from KBs are both long-standing goals in Machine Learning. In this paper, we present ReGen, a bidirectional generation of text and graph leveraging Reinforcement Learning to improve performance. Graph linearization enables us to re-frame both tasks as a sequence to sequence generation problem regardless of the generative direction, which in turn allows the use of Reinforcement Learning for sequence training where the model itself is employed as its own critic leading to Self-Critical Sequence Training (SCST). We present an extensive investigation demonstrating that the use of RL via SCST benefits graph and text generation on WebNLG+ 2020 and TekGen datasets. Our system provides state-of-the-art results on WebNLG+ 2020 by significantly improving upon published results from the WebNLG 2020+ Challenge for both text-to-graph and graph-to-text generation tasks. More details athttps://github.com/IBM/regen.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_84", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_84.flac", "type": "audio", "duration": 545.493}, {"from": "Assistant", "value": "Pretrained Transformers achieve remarkable performance when training and test data are from the same distribution. However, in real-world scenarios, the model often faces out-of-distribution (OOD) instances that can cause severe semantic shift problems at inference time. Therefore, in practice, a reliable model should identify such instances, and then either reject them during inference or pass them over to models that handle another distribution. In this paper, we develop an unsupervised OOD detection method, in which only the in-distribution (ID) data are used in training. We propose to fine-tune the Transformers with a contrastive loss, which improves the compactness of representations, such that OOD instances can be better differentiated from ID ones. These OOD instances can then be accurately detected using the Mahalanobis distance in the model’s penultimate layer. We experiment with comprehensive settings and achieve near-perfect OOD detection performance, outperforming baselines drastically. We further investigate the rationales behind the improvement, finding that more compact representations through margin-based contrastive learning bring the improvement. We release our code to the community for future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_85", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_85.flac", "type": "audio", "duration": 638.618}, {"from": "Assistant", "value": "An ideal integration of autonomous agents in a human world implies that they are able to collaborate on human terms. In particular, theory of mind plays an important role in maintaining common ground during human collaboration and communication. To enable theory of mind modeling in situated interactions, we introduce a fine-grained dataset of collaborative tasks performed by pairs of human subjects in the 3D virtual blocks world of Minecraft. It provides information that captures partners’ beliefs of the world and of each other as an interaction unfolds, bringing abundant opportunities to study human collaborative behaviors in situated language communication. As a first step towards our goal of developing embodied AI agents able to infer belief states of collaborative partners in situ, we build and present results on computational models for several theory of mind tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_86", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_86.flac", "type": "audio", "duration": 569.045}, {"from": "Assistant", "value": "Personas are useful for dialogue response prediction. However, the personas used in current studies are pre-defined and hard to obtain before a conversation. To tackle this issue, we study a new task, named Speaker Persona Detection (SPD), which aims to detect speaker personas based on the plain conversational text. In this task, a best-matched persona is searched out from candidates given the conversational text. This is a many-to-many semantic matching task because both contexts and personas in SPD are composed of multiple sentences. The long-term dependency and the dynamic redundancy among these sentences increase the difficulty of this task. We build a dataset for SPD, dubbed as Persona Match on Persona-Chat (PMPC). Furthermore, we evaluate several baseline models and propose utterance-to-profile (U2P) matching networks for this task. The U2P models operate at a fine granularity which treat both contexts and personas as sets of multiple sequences. Then, each sequence pair is scored and an interpretable overall score is obtained for a context-persona pair through aggregation. Evaluation results show that the U2P models outperform their baseline counterparts significantly.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_87", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_87.flac", "type": "audio", "duration": 714.362}, {"from": "Assistant", "value": "Recent progress in task-oriented neural dialogue systems is largely focused on a handful of languages, as annotation of training data is tedious and expensive. Machine translation has been used to make systems multilingual, but this can introduce a pipeline of errors. Another promising solution is using cross-lingual transfer learning through pretrained multilingual models. Existing methods train multilingual models with additional code-mixed task data or refine the cross-lingual representations through parallel ontologies. In this work, we enhance the transfer learning process by intermediate fine-tuning of pretrained multilingual models, where the multilingual models are fine-tuned with different but related data and/or tasks. Specifically, we use parallel and conversational movie subtitles datasets to design cross-lingual intermediate tasks suitable for downstream dialogue tasks. We use only 200K lines of parallel data for intermediate fine-tuning which is already available for 1782 language pairs. We test our approach on the cross-lingual dialogue state tracking task for the parallel MultiWoZ (English -> Chinese, Chinese -> English) and Multilingual WoZ (English -> German, English -> Italian) datasets. We achieve impressive improvements (> 20% on joint goal accuracy) on the parallel MultiWoZ dataset and the Multilingual WoZ dataset over the vanilla baseline with only 10% of the target language task data and zero-shot setup respectively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_88", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_88.flac", "type": "audio", "duration": 923.349}, {"from": "Assistant", "value": "Transformer-based language models (LMs) pretrained on large text collections are proven to store a wealth of semantic knowledge. However, 1) they are not effective as sentence encoders when used off-the-shelf, and 2) thus typically lag behind conversationally pretrained (e.g., via response selection) encoders on conversational tasks such as intent detection (ID). In this work, we propose ConvFiT, a simple and efficient two-stage procedure which turns any pretrained LM into a universal conversational encoder (after Stage 1 ConvFiT-ing) and task-specialised sentence encoder (after Stage 2). We demonstrate that 1) full-blown conversational pretraining is not required, and that LMs can be quickly transformed into effective conversational encoders with much smaller amounts of unannotated data; 2) pretrained LMs can be fine-tuned into task-specialised sentence encoders, optimised for the fine-grained semantics of a particular task. Consequently, such specialised sentence encoders allow for treating ID as a simple semantic similarity task based on interpretable nearest neighbours retrieval. We validate the robustness and versatility of the ConvFiT framework with such similarity-based inference on the standard ID evaluation sets: ConvFiT-ed LMs achieve state-of-the-art ID performance across the board, with particular gains in the most challenging, few-shot setups.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_90", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_90.flac", "type": "audio", "duration": 483.051}, {"from": "Assistant", "value": "Incremental processing allows interactive systems to respond based on partial inputs, which is a desirable property e.g. in dialogue agents. The currently popular Transformer architecture inherently processes sequences as a whole, abstracting away the notion of time. Recent work attempts to apply Transformers incrementally via restart-incrementality by repeatedly feeding, to an unchanged model, increasingly longer input prefixes to produce partial outputs. However, this approach is computationally costly and does not scale efficiently for long sequences. In parallel, we witness efforts to make Transformers more efficient, e.g. the Linear Transformer (LT) with a recurrence mechanism. In this work, we examine the feasibility of LT for incremental NLU in English. Our results show that the recurrent LT model has better incremental performance and faster inference speed compared to the standard Transformer and LT with restart-incrementality, at the cost of part of the non-incremental (full sequence) quality. We show that the performance drop can be mitigated by training the model to wait for right context before committing to an output and that training with input prefixes is beneficial for delivering correct partial outputs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_91", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_91.flac", "type": "audio", "duration": 482.046}, {"from": "Assistant", "value": "With counterfactual bandit learning, models can be trained based on positive and negative feedback received for historical predictions, with no labeled data needed. Such feedback is often available in real-world dialog systems, however, the modularized architecture commonly used in large-scale systems prevents the direct application of such algorithms. In this paper, we study the feedback attribution problem that arises when using counterfactual bandit learning for multi-domain spoken language understanding. We introduce an experimental setup to simulate the problem on small-scale public datasets, propose attribution methods inspired by multi-agent reinforcement learning and evaluate them against multiple baselines. We find that while directly using overall feedback leads to disastrous performance, our proposed attribution methods can allow training competitive models from user feedback.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_92", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_92.flac", "type": "audio", "duration": 601.049}, {"from": "Assistant", "value": "Relation extraction systems require large amounts of labeled examples which are costly to annotate. In this work we reformulate relation extraction as an entailment task, with simple, hand-made, verbalizations of relations produced in less than 15 min per relation. The system relies on a pretrained textual entailment engine which is run as-is (no training examples, zero-shot) or further fine-tuned on labeled examples (few-shot or fully trained). In our experiments on TACRED we attain 63% F1 zero-shot, 69% with 16 examples per relation (17% points better than the best supervised system on the same conditions), and only 4 points short to the state-of-the-art (which uses 20 times more training data). We also show that the performance can be improved significantly with larger entailment models, up to 12 points in zero-shot, allowing to report the best results to date on TACRED when fully trained. The analysis shows that our few-shot systems are specially effective when discriminating between relations, and that the performance difference in low data regimes comes mainly from identifying no-relation cases.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_94", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_94.flac", "type": "audio", "duration": 669.06}, {"from": "Assistant", "value": "We cast a suite of information extraction tasks into a text-to-triple translation framework. Instead of solving each task relying on task-specific datasets and models, we formalize the task as a translation between task-specific input text and output triples. By taking the task-specific input, we enable a task-agnostic translation by leveraging the latent knowledge that a pre-trained language model has about the task. We further demonstrate that a simple pre-training task of predicting which relational information corresponds to which input text is an effective way to produce task-specific outputs. This enables the zero-shot transfer of our framework to downstream tasks. We study the zero-shot performance of this framework on open information extraction (OIE2016, NYT, WEB, PENN), relation classification (FewRel and TACRED), and factual probe (Google-RE and T-REx). The model transfers non-trivially to most tasks and is often competitive with a fully supervised method without the need for any task-specific training. For instance, we significantly outperform the F1 score of the supervised open information extraction without needing to use its training set.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_95", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_95.flac", "type": "audio", "duration": 717.102}, {"from": "Assistant", "value": "Document-level relation extraction aims to identify relations between entities in a whole document. Prior efforts to capture long-range dependencies have relied heavily on implicitly powerful representations learned through (graph) neural networks, which makes the model less transparent. To tackle this challenge, in this paper, we propose LogiRE, a novel probabilistic model for document-level relation extraction by learning logic rules. LogiRE treats logic rules as latent variables and consists of two modules: a rule generator and a relation extractor. The rule generator is to generate logic rules potentially contributing to final predictions, and the relation extractor outputs final predictions based on the generated logic rules. Those two modules can be efficiently optimized with the expectation-maximization (EM) algorithm. By introducing logic rules into neural networks, LogiRE can explicitly capture long-range dependencies as well as enjoy better interpretation. Empirical results show that significantly outperforms several strong baselines in terms of relation performance and logical consistency. Our code is available athttps://github.com/rudongyu/LogiRE.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_96", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_96.flac", "type": "audio", "duration": 701.01}, {"from": "Assistant", "value": "Recent development in NLP shows a strong trend towards refining pre-trained models with a domain-specific dataset. This is especially the case for response generation where emotion plays an important role. However, existing empathetic datasets remain small, delaying research efforts in this area, for example, the development of emotion-aware chatbots. One main technical challenge has been the cost of manually annotating dialogues with the right emotion labels. In this paper, we describe a large-scale silver dataset consisting of 1M dialogues annotated with 32 fine-grained emotions, eight empathetic response intents, and the Neutral category. To achieve this goal, we have developed a novel data curation pipeline starting with a small seed of manually annotated data and eventually scaling it to a satisfactory size. We compare its quality against a state-of-the-art gold dataset using both offline experiments and visual validation methods. The resultant procedure can be used to create similar datasets in the same domain as well as in other domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_97", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_97.flac", "type": "audio", "duration": 729.06}, {"from": "Assistant", "value": "Recent text generation research has increasingly focused on open-ended domains such as story and poetry generation. Because models built for such tasks are difficult to evaluate automatically, most researchers in the space justify their modeling choices by collecting crowdsourced human judgments of text quality (e.g., Likert scores of coherence or grammaticality) from Amazon Mechanical Turk (AMT). In this paper, we first conduct a survey of 45 open-ended text generation papers and find that the vast majority of them fail to report crucial details about their AMT tasks, hindering reproducibility. We then run a series of story evaluation experiments with both AMT workers and English teachers and discover that even with strict qualification filters, AMT workers (unlike teachers) fail to distinguish between model-generated text and human-generated references. We show that AMT worker judgments improve when they are shown model-generated output alongside human-generated references, which enables the workers to better calibrate their ratings. Finally, interviews with the English teachers provide deeper insights into the challenges of the evaluation process, particularly when rating model-generated text.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_98", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_98.flac", "type": "audio", "duration": 706.048}, {"from": "Assistant", "value": "Large language models have led to remarkable progress on many NLP tasks, and researchers are turning to ever-larger text corpora to train them. Some of the largest corpora available are made by scraping significant portions of the internet, and are frequently introduced with only minimal documentation. In this work we provide some of the first documentation for the Colossal Clean Crawled Corpus (C4; Raffel et al., 2020), a dataset created by applying a set of filters to a single snapshot of Common Crawl. We begin by investigating where the data came from, and find a significant amount of text from unexpected sources like patents and US military websites. Then we explore the content of the text itself, and find machine-generated text (e.g., from machine translation systems) and evaluation examples from other benchmark NLP datasets. To understand the impact of the filters applied to create this dataset, we evaluate the text that was removed, and show that blocklist filtering disproportionately removes text from and about minority individuals. Finally, we conclude with some recommendations for how to created and document web-scale datasets from a scrape of the internet.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_99", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_99.flac", "type": "audio", "duration": 480.043}, {"from": "Assistant", "value": "Reproducible benchmarks are crucial in driving progress of machine translation research. However, existing machine translation benchmarks have been mostly limited to high-resource or well-represented languages. Despite an increasing interest in low-resource machine translation, there are no standardized reproducible benchmarks for many African languages, many of which are used by millions of speakers but have less digitized textual data. To tackle these challenges, we propose AfroMT, a standardized, clean, and reproducible machine translation benchmark for eight widely spoken African languages. We also develop a suite of analysis tools for system diagnosis taking into account the unique properties of these languages. Furthermore, we explore the newly considered case of low-resource focused pretraining and develop two novel data augmentation-based strategies, leveraging word-level alignment information and pseudo-monolingual data for pretraining multilingual sequence-to-sequence models. We demonstrate significant improvements when pretraining on 11 languages, with gains of up to 2 BLEU points over strong baselines. We also show gains of up to 12 BLEU points over cross-lingual transfer baselines in data-constrained scenarios. All code and pretrained models will be released as further steps towards larger reproducible benchmarks for African languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_100", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_100.flac", "type": "audio", "duration": 705.051}, {"from": "Assistant", "value": "While the field of style transfer (ST) has been growing rapidly, it has been hampered by a lack of standardized practices for automatic evaluation. In this paper, we evaluate leading automatic metrics on the oft-researched task of formality style transfer. Unlike previous evaluations, which focus solely on English, we expand our focus to Brazilian-Portuguese, French, and Italian, making this work the first multilingual evaluation of metrics in ST. We outline best practices for automatic evaluation in (formality) style transfer and identify several models that correlate well with human judgments and are robust across languages. We hope that this work will help accelerate development in ST, where human evaluation is often challenging to collect.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_101", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_101.flac", "type": "audio", "duration": 535.011}, {"from": "Assistant", "value": "Material science synthesis procedures are a promising domain for scientific NLP, as proper modeling of these recipes could provide insight into new ways of creating materials. However, a fundamental challenge in building information extraction models for material science synthesis procedures is getting accurate labels for the materials, operations, and other entities of those procedures. We present a new corpus of entity mention annotations over 595 Material Science synthesis procedural texts (157,488 tokens), which greatly expands the training data available for the Named Entity Recognition task. We outline a new label inventory designed to provide consistent annotations and a new annotation approach intended to maximize the consistency and annotation speed of domain experts. Inter-annotator agreement studies and baseline models trained upon the data suggest that the corpus provides high-quality annotations of these mention types. This corpus helps lay a foundation for future high-quality modeling of synthesis procedures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_102", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_102.flac", "type": "audio", "duration": 771.019}, {"from": "Assistant", "value": "Politicians often have underlying agendas when reacting to events. Arguments in contexts of various events reflect a fairly consistent set of agendas for a given entity. In spite of recent advances in Pretrained Language Models, those text representations are not designed to capture such nuanced patterns. In this paper, we propose a Compositional Reader model consisting of encoder and composer modules, that captures and leverages such information to generate more effective representations for entities, issues, and events. These representations are contextualized by tweets, press releases, issues, news articles, and participating entities. Our model processes several documents at once and generates composed representations for multiple entities over several issues or events. Via qualitative and quantitative empirical analysis, we show that these representations are meaningful and effective.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_103", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_103.flac", "type": "audio", "duration": 721.045}, {"from": "Assistant", "value": "Despite recent promising results on the application of span-based models for event reference interpretation, there is a lack of understanding of what has been improved. We present an empirical analysis of a state-of-the-art span-based event reference systems with the goal of providing the general NLP audience with a better understanding of the state of the art and reference researchers with directions for future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_104", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_104.flac", "type": "audio", "duration": 709.857}, {"from": "Assistant", "value": "Discourse segmentation, the first step of discourse analysis, has been shown to improve results for text summarization, translation and other NLP tasks. While segmentation models for written text tend to perform well, they are not directly applicable to spontaneous, oral conversation, which has linguistic features foreign to written text. Segmentation is less studied for this type of language, where annotated data is scarce, and existing corpora more heterogeneous. We develop a weak supervision approach to adapt, using minimal annotation, a state of the art discourse segmenter trained on written text to French conversation transcripts. Supervision is given by a latent model bootstrapped by manually defined heuristic rules that use linguistic and acoustic information. The resulting model improves the original segmenter, especially in contexts where information on speaker turns is lacking or noisy, gaining up to 13% in F-score. Evaluation is performed on data like those used to define our heuristic rules, but also on transcripts from two other corpora.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_105", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_105.flac", "type": "audio", "duration": 619.903}, {"from": "Assistant", "value": "Narrative analysis is becoming increasingly important for a number of linguistic tasks including summarization, knowledge extraction, and question answering. We present a novel approach for narrative event representation using attention to re-contextualize events across the whole story. Comparing to previous analysis we find an unexpected attachment of event semantics to predicate tokens within a popular transformer model. We test the utility of our approach on narrative completion prediction, achieving state of the art performance on Multiple Choice Narrative Cloze and scoring competitively on the Story Cloze Task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_524", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_524.flac", "type": "audio", "duration": 669.525}, {"from": "Assistant", "value": "The aim of knowledge base completion is to predict unseen facts from existing facts in knowledge bases. In this work, we introduce the first approach for transfer of knowledge from one collection of facts to another without the need for entity or relation matching. The method works for both canonicalized knowledge bases and uncanonicalized or open knowledge bases, i.e., knowledge bases where more than one copy of a real-world entity or relation may exist. Such knowledge bases are a natural output of automated information extraction tools that extract structured data from unstructured text. Our main contribution is a method that can make use of a large-scale pretraining on facts, collected from unstructured text, to improve predictions on structured data from a specific domain. The introduced method is the most impactful on small datasets such as ReVerb20K, where we obtained a 6% absolute increase of mean reciprocal rank and 65% relative decrease of mean rank over the previously best method, despite not relying on large pre-trained models like BERT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_525", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_525.flac", "type": "audio", "duration": 619.044}, {"from": "Assistant", "value": "Selective rationalization aims to produce decisions along with rationales (e.g., text highlights or word alignments between two sentences). Commonly, rationales are modeled as stochastic binary masks, requiring sampling-based gradient estimators, which complicates training and requires careful hyperparameter tuning. Sparse attention mechanisms are a deterministic alternative, but they lack a way to regularize the rationale extraction (e.g., to control the sparsity of a text highlight or the number of alignments). In this paper, we present a unified framework for deterministic extraction of structured explanations via constrained inference on a factor graph, forming a differentiable layer. Our approach greatly eases training and rationale regularization, generally outperforming previous work on what comes to performance and plausibility of the extracted rationales. We further provide a comparative study of stochastic and deterministic methods for rationale extraction for classification and natural language inference tasks, jointly assessing their predictive power, quality of the explanations, and model variability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_526", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_526.flac", "type": "audio", "duration": 874.046}, {"from": "Assistant", "value": "Knowledge distillation (KD) is a common knowledge transfer algorithm used for model compression across a variety of deep learning based natural language processing (NLP) solutions. In its regular manifestations, KD requires access to the teacher’s training data for knowledge transfer to the student network. However, privacy concerns, data regulations and proprietary reasons may prevent access to such data. We present, to the best of our knowledge, the first work on Zero-shot Knowledge Distillation for NLP, where the student learns from the much larger teacher without any task specific data. Our solution combines out-of-domain data and adversarial training to learn the teacher’s output distribution. We investigate six tasks from the GLUE benchmark and demonstrate that we can achieve between 75% and 92% of the teacher’s classification score (accuracy or F1) while compressing the model 30 times.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_527", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_527.flac", "type": "audio", "duration": 701.823}, {"from": "Assistant", "value": "Adversarial regularization has been shown to improve the generalization performance of deep learning models in various natural language processing tasks. Existing works usually formulate the method as a zero-sum game, which is solved by alternating gradient descent/ascent algorithms. Such a formulation treats the adversarial and the defending players equally, which is undesirable because only the defending player contributes to the generalization performance. To address this issue, we propose Stackelberg Adversarial Regularization (SALT), which formulates adversarial regularization as a Stackelberg game. This formulation induces a competition between a leader and a follower, where the follower generates perturbations, and the leader trains the model subject to the perturbations. Different from conventional approaches, in SALT, the leader is in an advantageous position. When the leader moves, it recognizes the strategy of the follower and takes the anticipated follower’s outcomes into consideration. Such a leader’s advantage enables us to improve the model fitting to the unperturbed data. The leader’s strategic information is captured by the Stackelberg gradient, which is obtained using an unrolling algorithm. Our experimental results on a set of machine translation and natural language understanding tasks show that SALT outperforms existing adversarial regularization baselines across all tasks. Our code is publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_528", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_528.flac", "type": "audio", "duration": 685.012}, {"from": "Assistant", "value": "Recent work on opinion summarization produces general summaries based on a set of input reviews and the popularity of opinions expressed in them. In this paper, we propose an approach that allows the generation of customized summaries based on aspect queries (e.g., describing the location and room of a hotel). Using a review corpus, we create a synthetic training dataset of (review, summary) pairs enriched with aspect controllers which are induced by a multi-instance learning model that predicts the aspects of a document at different levels of granularity. We fine-tune a pretrained model using our synthetic dataset and generate aspect-specific summaries by modifying the aspect controllers. Experiments on two benchmarks show that our model outperforms the previous state of the art and generates personalized summaries by controlling the number of aspects discussed in them.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_529", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_529.flac", "type": "audio", "duration": 726.059}, {"from": "Assistant", "value": "Summarization evaluation remains an open research problem: current metrics such as ROUGE are known to be limited and to correlate poorly with human judgments. To alleviate this issue, recent work has proposed evaluation metrics which rely on question answering models to assess whether a summary contains all the relevant information in its source document. Though promising, the proposed approaches have so far failed to correlate better than ROUGE with human judgments. In this paper, we extend previous approaches and propose a unified framework, named QuestEval. In contrast to established metrics such as ROUGE or BERTScore, QuestEval does not require any ground-truth reference. Nonetheless, QuestEval substantially improves the correlation with human judgments over four evaluation dimensions (consistency, coherence, fluency, and relevance), as shown in extensive experiments.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_530", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_530.flac", "type": "audio", "duration": 757.04}, {"from": "Assistant", "value": "Abstractive conversation summarization has received growing attention while most current state-of-the-art summarization models heavily rely on human-annotated summaries. To reduce the dependence on labeled summaries, in this work, we present a simple yet effective set of Conversational Data Augmentation (CODA) methods for semi-supervised abstractive conversation summarization, such as random swapping/deletion to perturb the discourse relations inside conversations, dialogue-acts-guided insertion to interrupt the development of conversations, and conditional-generation-based substitution to substitute utterances with their paraphrases generated based on the conversation context. To further utilize unlabeled conversations, we combine CODA with two-stage noisy self-training where we first pre-train the summarization model on unlabeled conversations with pseudo summaries and then fine-tune it on labeled conversations. Experiments conducted on the recent conversation summarization datasets demonstrate the effectiveness of our methods over several state-of-the-art data augmentation baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_531", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_531.flac", "type": "audio", "duration": 798.059}, {"from": "Assistant", "value": "Human evaluation for summarization tasks is reliable but brings in issues of reproducibility and high costs. Automatic metrics are cheap and reproducible but sometimes poorly correlated with human judgment. In this work, we propose flexible semiautomatic to automatic summary evaluation metrics, following the Pyramid human evaluation method. Semi-automatic Lite2Pyramid retains the reusable human-labeled Summary Content Units (SCUs) for reference(s) but replaces the manual work of judging SCUs’ presence in system summaries with a natural language inference (NLI) model. Fully automatic Lite3Pyramid further substitutes SCUs with automatically extracted Semantic Triplet Units (STUs) via a semantic role labeling (SRL) model. Finally, we propose in-between metrics, Lite2.xPyramid, where we use a simple regressor to predict how well the STUs can simulate SCUs and retain SCUs that are more difficult to simulate, which provides a smooth transition and balance between automation and manual evaluation. Comparing to 15 existing metrics, we evaluate human-metric correlations on 3 existing meta-evaluation datasets and our newly collected PyrXSum (with 100/10 XSum examples/systems). It shows that Lite2Pyramid consistently has the best summary-level correlations; Lite3Pyramid works better than or comparable to other automatic metrics; Lite2.xPyramid trades off small correlation drops for larger manual effort reduction, which can reduce costs for future data collection.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_532", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_532.flac", "type": "audio", "duration": 709.045}, {"from": "Assistant", "value": "We study generating abstractive summaries that are faithful and factually consistent with the given articles. A novel contrastive learning formulation is presented, which leverages both reference summaries, as positive training data, and automatically generated erroneous summaries, as negative training data, to train summarization systems that are better at distinguishing between them. We further design four types of strategies for creating negative samples, to resemble errors made commonly by two state-of-the-art models, BART and PEGASUS, found in our new human annotations of summary errors. Experiments on XSum and CNN/Daily Mail show that our contrastive learning framework is robust across datasets and models. It consistently produces more factual summaries than strong comparisons with post error correction, entailment-based reranking, and unlikelihood training, according to QA-based factuality evaluation. Human judges echo the observation and find that our model summaries correct more errors.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_533", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_533.flac", "type": "audio", "duration": 621.041}, {"from": "Assistant", "value": "We consider the problem of multilingual unsupervised machine translation, translating to and from languages that only have monolingual data by using auxiliary parallel language pairs. For this problem the standard procedure so far to leverage the monolingual data is _back-translation_, which is computationally costly and hard to tune. In this paper we propose instead to use _denoising adapters_, adapter layers with a denoising objective, on top of pre-trained mBART-50. In addition to the modularity and flexibility of such an approach we show that the resulting translations are on-par with back-translating as measured by BLEU, and furthermore it allows adding unseen languages incrementally.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_534", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_534.flac", "type": "audio", "duration": 748.727}, {"from": "Assistant", "value": "The success of bidirectional encoders using masked language models, such as BERT, on numerous natural language processing tasks has prompted researchers to attempt to incorporate these pre-trained models into neural machine translation (NMT) systems. However, proposed methods for incorporating pre-trained models are non-trivial and mainly focus on BERT, which lacks a comparison of the impact that other pre-trained models may have on translation performance. In this paper, we demonstrate that simply using the output (contextualized embeddings) of a tailored and suitable bilingual pre-trained language model (dubbed BiBERT) as the input of the NMT encoder achieves state-of-the-art translation performance. Moreover, we also propose a stochastic layer selection approach and a concept of a dual-directional translation model to ensure the sufficient utilization of contextualized embeddings. In the case of without using back translation, our best models achieve BLEU scores of 30.45 for En→De and 38.61 for De→En on the IWSLT’14 dataset, and 31.26 for En→De and 34.94 for De→En on the WMT’14 dataset, which exceeds all published numbers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_535", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_535.flac", "type": "audio", "duration": 713.6}, {"from": "Assistant", "value": "Fine-grained control of machine translation (MT) outputs along multiple attributes is critical for many modern MT applications and is a requirement for gaining users’ trust. A standard approach for exerting control in MT is to prepend the input with a special tag to signal the desired output attribute. Despite its simplicity, attribute tagging has several drawbacks: continuous values must be binned into discrete categories, which is unnatural for certain applications; interference between multiple tags is poorly understood. We address these problems by introducing vector-valued interventions which allow for fine-grained control over multiple attributes simultaneously via a weighted linear combination of the corresponding vectors. For some attributes, our approach even allows for fine-tuning a model trained without annotations to support such interventions. In experiments with three attributes (length, politeness and monotonicity) and two language pairs (English to German and Japanese) our models achieve better control over a wider range of tasks compared to tagging, and translation quality does not degrade when no control is requested. Finally, we demonstrate how to enable control in an already trained model after a relatively cheap fine-tuning stage.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_536", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_536.flac", "type": "audio", "duration": 719.168}, {"from": "Assistant", "value": "We propose a generative framework for simultaneous machine translation. Conventional approaches use a fixed number of source words to translate or learn dynamic policies for the number of source words by reinforcement learning. Here we formulate simultaneous translation as a structural sequence-to-sequence learning problem. A latent variable is introduced to model read or translate actions at every time step, which is then integrated out to consider all the possible translation policies. A re-parameterised Poisson prior is used to regularise the policies which allows the model to explicitly balance translation quality and latency. The experiments demonstrate the effectiveness and robustness of the generative framework, which achieves the best BLEU scores given different average translation latencies on benchmark datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_537", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_537.flac", "type": "audio", "duration": 414.058}, {"from": "Assistant", "value": "Most existing simultaneous machine translation (SiMT) systems are trained and evaluated on offline translation corpora. We argue that SiMT systems should be trained and tested on real interpretation data. To illustrate this argument, we propose an interpretation test set and conduct a realistic evaluation of SiMT trained on offline translations. Our results, on our test set along with 3 existing smaller scale language pairs, highlight the difference of up-to 13.83 BLEU score when SiMT models are evaluated on translation vs interpretation data. In the absence of interpretation training data, we propose a translation-to-interpretation (T2I) style transfer method which allows converting existing offline translations into interpretation-style data, leading to up-to 2.8 BLEU improvement. However, the evaluation gap remains notable, calling for constructing large-scale interpretation corpora better suited for evaluating and developing SiMT systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_538", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_538.flac", "type": "audio", "duration": 476.565}, {"from": "Assistant", "value": "Recent multilingual pre-trained language models have achieved remarkable zero-shot performance, where the model is only finetuned on one source language and directly evaluated on target languages. In this work, we propose a self-learning framework that further utilizes unlabeled data of target languages, combined with uncertainty estimation in the process to select high-quality silver labels. Three different uncertainties are adapted and analyzed specifically for the cross lingual transfer: Language Heteroscedastic/Homoscedastic Uncertainty (LEU/LOU), Evidential Uncertainty (EVI). We evaluate our framework with uncertainties on two cross-lingual tasks including Named Entity Recognition (NER) and Natural Language Inference (NLI) covering 40 languages in total, which outperforms the baselines significantly by 10 F1 for NER on average and 2.5 accuracy for NLI.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_539", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_539.flac", "type": "audio", "duration": 478.052}, {"from": "Assistant", "value": "We propose a novel scheme to use the Levenshtein Transformer to perform the task of word-level quality estimation. A Levenshtein Transformer is a natural fit for this task: trained to perform decoding in an iterative manner, a Levenshtein Transformer can learn to post-edit without explicit supervision. To further minimize the mismatch between the translation task and the word-level QE task, we propose a two-stage transfer learning procedure on both augmented data and human post-editing data. We also propose heuristics to construct reference labels that are compatible with subword-level finetuning and inference. Results on WMT 2020 QE shared task dataset show that our proposed method has superior data efficiency under the data-constrained setting and competitive performance under the unconstrained setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_540", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_540.flac", "type": "audio", "duration": 729.966}, {"from": "Assistant", "value": "Interactive machine reading comprehension (iMRC) is machine comprehension tasks where knowledge sources are partially observable. An agent must interact with an environment sequentially to gather necessary knowledge in order to answer a question. We hypothesize that graph representations are good inductive biases, which can serve as an agent’s memory mechanism in iMRC tasks. We explore four different categories of graphs that can capture text information at various levels. We describe methods that dynamically build and update these graphs during information gathering, as well as neural models to encode graph representations in RL agents. Extensive experiments on iSQuAD suggest that graph representations can result in significant performance improvements for RL agents.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_541", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_541.flac", "type": "audio", "duration": 683.851}, {"from": "Assistant", "value": "Automatic Speech Recognition (ASR) systems are often optimized to work best for speakers with canonical speech patterns. Unfortunately, these systems perform poorly when tested on atypical speech and heavily accented speech. It has previously been shown that personalization through model fine-tuning substantially improves performance. However, maintaining such large models per speaker is costly and difficult to scale. We show that by adding a relatively small number of extra parameters to the encoder layers via so-called residual adapter, we can achieve similar adaptation gains compared to model fine-tuning, while only updating a tiny fraction (less than 0.5%) of the model parameters. We demonstrate this on two speech adaptation tasks (atypical and accented speech) and for two state-of-the-art ASR architectures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_542", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_542.flac", "type": "audio", "duration": 635.019}, {"from": "Assistant", "value": "We propose Visual News Captioner, an entity-aware model for the task of news image captioning. We also introduce Visual News, a large-scale benchmark consisting of more than one million news images along with associated news articles, image captions, author information, and other metadata. Unlike the standard image captioning task, news images depict situations where people, locations, and events are of paramount importance. Our proposed method can effectively combine visual and textual features to generate captions with richer information such as events and entities. More specifically, built upon the Transformer architecture, our model is further equipped with novel multi-modal feature fusion techniques and attention mechanisms, which are designed to generate named entities more accurately. Our method utilizes much fewer parameters while achieving slightly better prediction results than competing methods. Our larger and more diverse Visual News dataset further highlights the remaining challenges in captioning news images.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_543", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_543.flac", "type": "audio", "duration": 694.059}, {"from": "Assistant", "value": "While much research has been done in text-to-image synthesis, little work has been done to explore the usage of linguistic structure of the input text. Such information is even more important for story visualization since its inputs have an explicit narrative structure that needs to be translated into an image sequence (or visual story). Prior work in this domain has shown that there is ample room for improvement in the generated image sequence in terms of visual quality, consistency and relevance. In this paper, we first explore the use of constituency parse trees using a Transformer-based recurrent architecture for encoding structured input. Second, we augment the structured input with commonsense information and study the impact of this external knowledge on the generation of visual story. Third, we also incorporate visual structure via bounding boxes and dense captioning to provide feedback about the characters/objects in generated images within a dual learning setup. We show that off-the-shelf dense-captioning models trained on Visual Genome can improve the spatial structure of images from a different target domain without needing fine-tuning. We train the model end-to-end using intra-story contrastive loss (between words and image sub-regions) and show significant improvements in visual quality. Finally, we provide an analysis of the linguistic and visuo-spatial information.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_544", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_544.flac", "type": "audio", "duration": 660.885}, {"from": "Assistant", "value": "We present VideoCLIP, a contrastive approach to pre-train a unified model for zero-shot video and text understanding, without using any labels on downstream tasks. VideoCLIP trains a transformer for video and text by contrasting temporally overlapping positive video-text pairs with hard negatives from nearest neighbor retrieval. Our experiments on a diverse series of downstream tasks, including sequence-level text-video retrieval, VideoQA, token-level action localization, and action segmentation reveal state-of-the-art performance, surpassing prior work, and in some cases even outperforming supervised approaches. Code is made available athttps://github.com/pytorch/fairseq/examples/MMPT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_545", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_545.flac", "type": "audio", "duration": 670.523}, {"from": "Assistant", "value": "Online misinformation is a prevalent societal issue, with adversaries relying on tools ranging from cheap fakes to sophisticated deep fakes. We are motivated by the threat scenario where an image is used out of context to support a certain narrative. While some prior datasets for detecting image-text inconsistency generate samples via text manipulation, we propose a dataset where both image and text are unmanipulated but mismatched. We introduce several strategies for automatically retrieving convincing images for a given caption, capturing cases with inconsistent entities or semantic context. Our large-scale automatically generated the NewsCLIPpings Dataset: (1) demonstrates that machine-driven image repurposing is now a realistic threat, and (2) provides samples that represent challenging instances of mismatch between text and image in news that are able to mislead humans. We benchmark several state-of-the-art multimodal models on our dataset and analyze their performance across different pretraining domains and visual backbones.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_546", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_546.flac", "type": "audio", "duration": 702.059}, {"from": "Assistant", "value": "We study Comparative Preference Classification (CPC) which aims at predicting whether a preference comparison exists between two entities in a given sentence and, if so, which entity is preferred over the other. High-quality CPC models can significantly benefit applications such as comparative question answering and review-based recommendation. Among the existing approaches, non-deep learning methods suffer from inferior performances. The state-of-the-art graph neural network-based ED-GAT (Ma et al., 2020) only considers syntactic information while ignoring the critical semantic relations and the sentiments to the compared entities. We propose Sentiment Analysis Enhanced COmparative Network (SAECON) which improves CPC accuracy with a sentiment analyzer that learns sentiments to individual entities via domain adaptive knowledge transfer. Experiments on the CompSent-19 (Panchenko et al., 2019) dataset present a significant improvement on the F1 scores over the best existing CPC approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_547", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_547.flac", "type": "audio", "duration": 683.085}, {"from": "Assistant", "value": "We study the problem of performing automatic stance classification on social media with neural architectures such as BERT. Although these architectures deliver impressive results, their level is not yet comparable to the one of humans and they might produce errors that have a significant impact on the downstream task (e.g., fact-checking). To improve the performance, we present a new neural architecture where the input also includes automatically generated negated perspectives over a given claim. The model is jointly learned to make simultaneously multiple predictions, which can be used either to improve the classification of the original perspective or to filter out doubtful predictions. In the first case, we propose a weakly supervised method for combining the predictions into a final one. In the second case, we show that using the confidence scores to remove doubtful predictions allows our method to achieve human-like performance over the retained information, which is still a sizable part of the original input.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_548", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_548.flac", "type": "audio", "duration": 666.645}, {"from": "Assistant", "value": "Darknet market forums are frequently used to exchange illegal goods and services between parties who use encryption to conceal their identities. The Tor network is used to host these markets, which guarantees additional anonymization from IP and location tracking, making it challenging to link across malicious users using multiple accounts (sybils). Additionally, users migrate to new forums when one is closed further increasing the difficulty of linking users across multiple forums. We develop a novel stylometry-based multitask learning approach for natural language and model interactions using graph embeddings to construct low-dimensional representations of short episodes of user activity for authorship attribution. We provide a comprehensive evaluation of our methods across four different darknet forums demonstrating its efficacy over the state-of-the-art, with a lift of up to 2.5X on Mean Retrieval Rank and 2X on Recall@10.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_549", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_549.flac", "type": "audio", "duration": 716.245}, {"from": "Assistant", "value": "Several recent studies on dyadic human-human interactions have been done on conversations without specific business objectives. However, many companies might benefit from studies dedicated to more precise environments such as after sales services or customer satisfaction surveys. In this work, we place ourselves in the scope of a live chat customer service in which we want to detect emotions and their evolution in the conversation flow. This context leads to multiple challenges that range from exploiting restricted, small and mostly unlabeled datasets to finding and adapting methods for such context. We tackle these challenges by using Few-Shot Learning while making the hypothesis it can serve conversational emotion classification for different languages and sparse labels. We contribute by proposing a variation of Prototypical Networks for sequence labeling in conversation that we name ProtoSeq. We test this method on two datasets with different languages: daily conversations in English and customer service chat conversations in French. When applied to emotion classification in conversations, our method proved to be competitive even when compared to other ones.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_550", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_550.flac", "type": "audio", "duration": 671.04}, {"from": "Assistant", "value": "This paper studies continual learning (CL) of a sequence of aspect sentiment classification (ASC) tasks in a particular CL setting called domain incremental learning (DIL). Each task is from a different domain or product. The DIL setting is particularly suited to ASC because in testing the system needs not know the task/domain to which the test data belongs. To our knowledge, this setting has not been studied before for ASC. This paper proposes a novel model called CLASSIC. The key novelty is a contrastive continual learning method that enables both knowledge transfer across tasks and knowledge distillation from old tasks to the new task, which eliminates the need for task ids in testing. Experimental results show the high effectiveness of CLASSIC.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_551", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_551.flac", "type": "audio", "duration": 683.061}, {"from": "Assistant", "value": "Implicit sentiment analysis, aiming at detecting the sentiment of a sentence without sentiment words, has become an attractive research topic in recent years. In this paper, we focus on event-centric implicit sentiment analysis that utilizes the sentiment-aware event contained in a sentence to infer its sentiment polarity. Most existing methods in implicit sentiment analysis simply view noun phrases or entities in text as events or indirectly model events with sophisticated models. Since events often trigger sentiments in sentences, we argue that this task would benefit from explicit modeling of events and event representation learning. To this end, we represent an event as the combination of its event type and the event triplet <subject, predicate, object>. Based on such event representation, we further propose a novel model with hierarchical tensor-based composition mechanism to detect sentiment in text. In addition, we present a dataset for event-centric implicit sentiment analysis where each sentence is labeled with the event representation described above. Experimental results on our constructed dataset and an existing benchmark dataset show the effectiveness of the proposed approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_552", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_552.flac", "type": "audio", "duration": 718.449}, {"from": "Assistant", "value": "This paper presents SimCSE, a simple contrastive learning framework that greatly advances the state-of-the-art sentence embeddings. We first describe an unsupervised approach, which takes an input sentence and predicts itself in a contrastive objective, with only standard dropout used as noise. This simple method works surprisingly well, performing on par with previous supervised counterparts. We find that dropout acts as minimal data augmentation and removing it leads to a representation collapse. Then, we propose a supervised approach, which incorporates annotated pairs from natural language inference datasets into our contrastive learning framework, by using “entailment” pairs as positives and “contradiction” pairs as hard negatives. We evaluate SimCSE on standard semantic textual similarity (STS) tasks, and our unsupervised and supervised models using BERT base achieve an average of 76.3% and 81.6% Spearman’s correlation respectively, a 4.2% and 2.2% improvement compared to previous best results. We also show—both theoretically and empirically—that contrastive learning objective regularizes pre-trained embeddings’ anisotropic space to be more uniform, and it better aligns positive pairs when supervised signals are available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_553", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_553.flac", "type": "audio", "duration": 664.857}, {"from": "Assistant", "value": "Learning fine-grained distinctions between vocabulary items is a key challenge in learning a new language. For example, the noun “wall” has different lexical manifestations in Spanish – “pared” refers to an indoor wall while “muro” refers to an outside wall. However, this variety of lexical distinction may not be obvious to non-native learners unless the distinction is explained in such a way. In this work, we present a method for automatically identifying fine-grained lexical distinctions, and extracting rules explaining these distinctions in a human- and machine-readable format. We confirm the quality of these extracted rules in a language learning setup for two languages, Spanish and Greek, where we use the rules to teach non-native speakers when to translate a given ambiguous word into its different possible translations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_554", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_554.flac", "type": "audio", "duration": 680.043}, {"from": "Assistant", "value": "Recipe texts are an idiosyncratic form of instructional language that pose unique challenges for automatic understanding. One challenge is that a cooking step in one recipe can be explained in another recipe in different words, at a different level of abstraction, or not at all. Previous work has annotated correspondences between recipe instructions at the sentence level, often glossing over important correspondences between cooking steps across recipes. We present a novel and fully-parsed English recipe corpus, ARA (Aligned Recipe Actions), which annotates correspondences between individual actions across similar recipes with the goal of capturing information implicit for accurate recipe understanding. We represent this information in the form of recipe graphs, and we train a neural model for predicting correspondences on ARA. We find that substantial gains in accuracy can be obtained by taking fine-grained structural information about the recipes into account.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_555", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_555.flac", "type": "audio", "duration": 496.001}, {"from": "Assistant", "value": "To obtain high-quality sentence embeddings from pretrained language models (PLMs), they must either be augmented with additional pretraining objectives or finetuned on a large set of labeled text pairs. While the latter approach typically outperforms the former, it requires great human effort to generate suitable datasets of sufficient size. In this paper, we show how PLMs can be leveraged to obtain high-quality sentence embeddings without the need for labeled data, finetuning or modifications to the pretraining objective: We utilize the generative abilities of large and high-performing PLMs to generate entire datasets of labeled text pairs from scratch, which we then use for finetuning much smaller and more efficient models. Our fully unsupervised approach outperforms strong baselines on several semantic textual similarity datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_556", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_556.flac", "type": "audio", "duration": 414.059}, {"from": "Assistant", "value": "Combining a pretrained language model (PLM) with textual patterns has been shown to help in both zero- and few-shot settings. For zero-shot performance, it makes sense to design patterns that closely resemble the text seen during self-supervised pretraining because the model has never seen anything else. Supervised training allows for more flexibility. If we allow for tokens outside the PLM’s vocabulary, patterns can be adapted more flexibly to a PLM’s idiosyncrasies. Contrasting patterns where a “token” can be any continuous vector from those where a discrete choice between vocabulary elements has to be made, we call our method CONtinous pAtterNs (CONAN). We evaluate CONAN on two established benchmarks for lexical inference in context (LIiC) a.k.a. predicate entailment, a challenging natural language understanding task with relatively small training data. In a direct comparison with discrete patterns, CONAN consistently leads to improved performance, setting a new state of the art. Our experiments give valuable insights on the kind of pattern that enhances a PLM’s performance on LIiC and raise important questions regarding our understanding of PLMs using text patterns.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_557", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_557.flac", "type": "audio", "duration": 479.887}, {"from": "Assistant", "value": "Specialized number representations in NLP have shown improvements on numerical reasoning tasks like arithmetic word problems and masked number prediction. But humans also use numeracy to make better sense of world concepts, e.g., you can seat 5 people in your ‘room’ but not 500. Does a better grasp of numbers improve a model’s understanding of other concepts and words? This paper studies the effect of using six different number encoders on the task of masked word prediction (MWP), as a proxy for evaluating literacy. To support this investigation, we develop Wiki-Convert, a 900,000 sentence dataset annotated with numbers and units, to avoid conflating nominal and ordinal number occurrences. We find a significant improvement in MWP for sentences containing numbers, that exponent embeddings are the best number encoders, yielding over 2 points jump in prediction accuracy over a BERT baseline, and that these enhanced literacy skills also generalize to contexts without annotated numbers. We release all code athttps://git.io/JuZXn.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_558", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_558.flac", "type": "audio", "duration": 509.005}, {"from": "Assistant", "value": "While neural networks produce state-of-the- art performance in several NLP tasks, they generally depend heavily on lexicalized information, which transfer poorly between domains. Previous works have proposed delexicalization as a form of knowledge distillation to reduce the dependency on such lexical artifacts. However, a critical unsolved issue that remains is how much delexicalization to apply: a little helps reduce overfitting, but too much discards useful information. We propose Group Learning, a knowledge and model distillation approach for fact verification in which multiple student models have access to different delexicalized views of the data, but are encouraged to learn from each other through pair-wise consistency losses. In several cross-domain experiments between the FEVER and FNC fact verification datasets, we show that our approach learns the best delexicalization strategy for the given training dataset, and outperforms state-of-the-art classifiers that rely on the original data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_559", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_559.flac", "type": "audio", "duration": 570.048}, {"from": "Assistant", "value": "We introduce MULTI-EURLEX, a new multilingual dataset for topic classification of legal documents. The dataset comprises 65k European Union (EU) laws, officially translated in 23 languages, annotated with multiple labels from the EUROVOC taxonomy. We highlight the effect of temporal concept drift and the importance of chronological, instead of random splits. We use the dataset as a testbed for zero-shot cross-lingual transfer, where we exploit annotated training documents in one language (source) to classify documents in another language (target). We find that fine-tuning a multilingually pretrained model (XLM-ROBERTA, MT5) in a single source language leads to catastrophic forgetting of multilingual knowledge and, consequently, poor zero-shot transfer to other languages. Adaptation strategies, namely partial fine-tuning, adapters, BITFIT, LNFIT, originally proposed to accelerate fine-tuning for new end-tasks, help retain multilingual knowledge from pretraining, substantially improving zero-shot cross-lingual transfer, but their impact also depends on the pretrained model used and the size of the label set.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_560", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_560.flac", "type": "audio", "duration": 709.091}, {"from": "Assistant", "value": "We study multi-answer retrieval, an under-explored problem that requires retrieving passages to cover multiple distinct answers for a given question. This task requires joint modeling of retrieved passages, as models should not repeatedly retrieve passages containing the same answer at the cost of missing a different valid answer. Prior work focusing on single-answer retrieval is limited as it cannot reason about the set of passages jointly. In this paper, we introduce JPR, a joint passage retrieval model focusing on reranking. To model the joint probability of the retrieved passages, JPR makes use of an autoregressive reranker that selects a sequence of passages, equipped with novel training and decoding algorithms. Compared to prior approaches, JPR achieves significantly better answer coverage on three multi-answer datasets. When combined with downstream question answering, the improved retrieval enables larger answer generation models since they need to consider fewer passages, establishing a new state-of-the-art.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_561", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_561.flac", "type": "audio", "duration": 441.063}, {"from": "Assistant", "value": "Compositional reasoning tasks such as multi-hop question answering require models to learn how to make latent decisions using only weak supervision from the final answer. Crowdsourced datasets gathered for these tasks, however, often contain only a slice of the underlying task distribution, which can induce unanticipated biases such as shallow word overlap between the question and context. Recent works have shown that discriminative training results in models that exploit these underlying biases to achieve a better held-out performance, without learning the right way to reason. We propose a generative context selection model for multi-hop QA that reasons about how the given question could have been generated given a context pair and not just independent contexts. We show that on HotpotQA, while being comparable to the state-of-the-art answering performance, our proposed generative passage selection model has a better performance (4.9% higher than baseline) on adversarial held-out set which tests robustness of model’s multi-hop reasoning capabilities.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_562", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_562.flac", "type": "audio", "duration": 501.739}, {"from": "Assistant", "value": "Coupled with the availability of large scale datasets, deep learning architectures have enabled rapid progress on the Question Answering task. However, most of those datasets are in English, and the performances of state-of-the-art multilingual models are significantly lower when evaluated on non-English data. Due to high data collection costs, it is not realistic to obtain annotated data for each language one desires to support. We propose a method to improve the Cross-lingual Question Answering performance without requiring additional annotated data, leveraging Question Generation models to produce synthetic samples in a cross-lingual fashion. We show that the proposed method allows to significantly outperform the baselines trained on English data only. We report a new state-of-the-art on four datasets: MLQA, XQuAD, SQuAD-it and PIAF (fr).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_563", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_563.flac", "type": "audio", "duration": 494.059}, {"from": "Assistant", "value": "Numerical reasoning in machine reading comprehension (MRC) has shown drastic improvements over the past few years. While the previous models for numerical MRC are able to interpolate the learned numerical reasoning capabilities, it is not clear whether they can perform just as well on numbers unseen in the training dataset. Our work rigorously tests state-of-the-art models on DROP, a numerical MRC dataset, to see if they can handle passages that contain out-of-range numbers. One of the key findings is that the models fail to extrapolate to unseen numbers. Presenting numbers as digit-by-digit input to the model, we also propose theE-digitnumber form that alleviates the lack of extrapolation in models and reveals the need to treat numbers differently from regular words in the text. Our work provides a valuable insight into the numerical MRC models and the way to represent number forms in MRC.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_564", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_564.flac", "type": "audio", "duration": 718.379}, {"from": "Assistant", "value": "Large language models have shown promising results in zero-shot settings. For example, they can perform multiple choice tasks simply by conditioning on a question and selecting the answer with the highest probability. However, ranking by string probability can be problematic due to surface form competition—wherein different surface forms compete for probability mass, even if they represent the same underlying concept in a given context, e.g. “computer” and “PC.” Since probability mass is finite, this lowers the probability of the correct answer, due to competition from other strings that are valid answers (but not one of the multiple choice options). We introduce Domain Conditional Pointwise Mutual Information, an alternative scoring function that directly compensates for surface form competition by simply reweighing each option according to its a priori likelihood within the context of a specific task. It achieves consistent gains in zero-shot performance over both calibrated and uncalibrated scoring functions on all GPT-2 and GPT-3 models on a variety of multiple choice datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_565", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_565.flac", "type": "audio", "duration": 717.055}, {"from": "Assistant", "value": "Knowledge-dependent tasks typically use two sources of knowledge: parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts, where the contextual information contradicts the learned information. Analyzing the behaviour of popular models, we measure their over-reliance on memorized information (the cause of hallucinations), and uncover important factors that exacerbate this behaviour. Lastly, we propose a simple method to mitigate over-reliance on parametric knowledge, which minimizes hallucination, and improves out-of-distribution generalization by 4% - 7%. Our findings demonstrate the importance for practitioners to evaluate model tendency to hallucinate rather than read, and show that our mitigation strategy encourages generalization to evolving information (i.e. time-dependent queries). To encourage these practices, we have released our framework for generating knowledge conflicts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_566", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_566.flac", "type": "audio", "duration": 720.051}, {"from": "Assistant", "value": "In this work, we introduce back-training, an alternative to self-training for unsupervised domain adaptation (UDA). While self-training generates synthetic training data where natural inputs are aligned with noisy outputs, back-training results in natural outputs aligned with noisy inputs. This significantly reduces the gap between target domain and synthetic data distribution, and reduces model overfitting to source domain. We run UDA experiments on question generation and passage retrieval from the Natural Questions domain to machine learning and biomedical domains. We find that back-training vastly outperforms self-training by a mean improvement of 7.8 BLEU-4 points on generation, and 17.6% top-20 retrieval accuracy across both domains. We further propose consistency filters to remove low-quality synthetic data before training. We also release a new domain-adaptation dataset - MLQuestions containing 35K unaligned questions, 50K unaligned passages, and 3K aligned question-passage pairs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_569", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_569.flac", "type": "audio", "duration": 704.493}, {"from": "Assistant", "value": "Style is an integral part of natural language. However, evaluation methods for style measures are rare, often task-specific and usually do not control for content. We propose the modular, fine-grained and content-controlled similarity-based STyle EvaLuation framework (STEL) to test the performance of any model that can compare two sentences on style. We illustrate STEL with two general dimensions of style (formal/informal and simple/complex) as well as two specific characteristics of style (contrac’tion and numb3r substitution). We find that BERT-based methods outperform simple versions of commonly used style measures like 3-grams, punctuation frequency and LIWC-based approaches. We invite the addition of further tasks and task instances to STEL and hope to facilitate the improvement of style-sensitive measures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_570", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_570.flac", "type": "audio", "duration": 684.013}, {"from": "Assistant", "value": "Text generation systems are ubiquitous in natural language processing applications. However, evaluation of these systems remains a challenge, especially in multilingual settings. In this paper, we propose L’AMBRE – a metric to evaluate the morphosyntactic well-formedness of text using its dependency parse and morphosyntactic rules of the language. We present a way to automatically extract various rules governing morphosyntax directly from dependency treebanks. To tackle the noisy outputs from text generation systems, we propose a simple methodology to train robust parsers. We show the effectiveness of our metric on the task of machine translation through a diachronic study of systems translating into morphologically-rich languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_571", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_571.flac", "type": "audio", "duration": 784.161}, {"from": "Assistant", "value": "Capturing word meaning in context and distinguishing between correspondences and variations across languages is key to building successful multilingual and cross-lingual text representation models. However, existing multilingual evaluation datasets that evaluate lexical semantics “in-context” have various limitations. In particular, 1) their language coverage is restricted to high-resource languages and skewed in favor of only a few language families and areas, 2) a design that makes the task solvable via superficial cues, which results in artificially inflated (and sometimes super-human) performances of pretrained encoders, and 3) no support for cross-lingual evaluation. In order to address these gaps, we present AM2iCo (Adversarial and Multilingual Meaning in Context), a wide-coverage cross-lingual and multilingual evaluation set; it aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts for 14 language pairs. We conduct a series of experiments in a wide range of setups and demonstrate the challenging nature of AM2iCo. The results reveal that current SotA pretrained encoders substantially lag behind human performance, and the largest gaps are observed for low-resource languages and languages dissimilar to English.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_572", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_572.flac", "type": "audio", "duration": 628.48}, {"from": "Assistant", "value": "Humans can learn a new language task efficiently with only few examples, by leveraging their knowledge obtained when learning prior tasks. In this paper, we explore whether and how such cross-task generalization ability can be acquired, and further applied to build better few-shot learners across diverse NLP tasks. We introduce CrossFit, a problem setup for studying cross-task generalization ability, which standardizes seen/unseen task partitions, data access during different learning stages, and the evaluation protocols. To instantiate different seen/unseen task partitions in CrossFit and facilitate in-depth analysis, we present the NLP Few-shot Gym, a repository of 160 diverse few-shot NLP tasks created from open-access NLP datasets and converted to a unified text-to-text format. Our analysis reveals that the few-shot learning ability on unseen tasks can be improved via an upstream learning stage using a set of seen tasks. We also observe that the selection of upstream learning tasks can significantly influence few-shot performance on unseen tasks, asking further analysis on task similarity and transferability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_573", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_573.flac", "type": "audio", "duration": 668.928}, {"from": "Assistant", "value": "Current NLP models are predominantly trained through a two-stage “pre-train then fine-tune” pipeline. Prior work has shown that inserting an intermediate pre-training stage, using heuristic masking policies for masked language modeling (MLM), can significantly improve final performance. However, it is still unclear (1) in what cases such intermediate pre-training is helpful, (2) whether hand-crafted heuristic objectives are optimal for a given task, and (3) whether a masking policy designed for one task is generalizable beyond that task. In this paper, we perform a large-scale empirical study to investigate the effect of various masking policies in intermediate pre-training with nine selected tasks across three categories. Crucially, we introduce methods to automate the discovery of optimal masking policies via direct supervision or meta-learning. We conclude that the success of intermediate pre-training is dependent on appropriate pre-train corpus, selection of output format (i.e., masked spans or full sentence), and clear understanding of the role that MLM plays for the downstream task. In addition, we find our learned masking policies outperform the heuristic of masking named entities on TriviaQA, and policies learned from one task can positively transfer to other tasks in certain cases, inviting future research in this direction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_574", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_574.flac", "type": "audio", "duration": 723.051}, {"from": "Assistant", "value": "Word embeddings learn implicit biases from linguistic regularities captured by word co-occurrence statistics. By extending methods that quantify human-like biases in word embeddings, we introduce ValNorm, a novel intrinsic evaluation task and method to quantify the valence dimension of affect in human-rated word sets from social psychology. We apply ValNorm on static word embeddings from seven languages (Chinese, English, German, Polish, Portuguese, Spanish, and Turkish) and from historical English text spanning 200 years. ValNorm achieves consistently high accuracy in quantifying the valence of non-discriminatory, non-social group word sets. Specifically, ValNorm achieves a Pearson correlation of r=0.88 for human judgment scores of valence for 399 words collected to establish pleasantness norms in English. In contrast, we measure gender stereotypes using the same set of word embeddings and find that social biases vary across languages. Our results indicate that valence associations of non-discriminatory, non-social group words represent widely-shared associations, in seven languages and over 200 years.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_575", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_575.flac", "type": "audio", "duration": 583.053}, {"from": "Assistant", "value": "Natural Language Generation (NLG) evaluation is a multifaceted task requiring assessment of multiple desirable criteria, e.g., fluency, coherency, coverage, relevance, adequacy, overall quality, etc. Across existing datasets for 6 NLG tasks, we observe that the human evaluation scores on these multiple criteria are often not correlated. For example, there is a very low correlation between human scores on fluency and data coverage for the task of structured data to text generation. This suggests that the current recipe of proposing new automatic evaluation metrics for NLG by showing that they correlate well with scores assigned by humans for a single criteria (overall quality) alone is inadequate. Indeed, our extensive study involving 25 automatic evaluation metrics across 6 different tasks and 18 different evaluation criteria shows that there is no single metric which correlates well with human scores on all desirable criteria, for most NLG tasks. Given this situation, we propose CheckLists for better design and evaluation of automatic metrics. We design templates which target a specific criteria (e.g., coverage) and perturb the output such that the quality gets affected only along this specific criteria (e.g., the coverage drops). We show that existing evaluation metrics are not robust against even such simple perturbations and disagree with scores assigned by humans to the perturbed output. The proposed templates thus allow for a fine-grained assessment of automatic evaluation metrics exposing their limitations and will facilitate better design, analysis and evaluation of such metrics. Our templates and code are available athttps://iitmnlp.github.io/EvalEval/", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_576", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_576.flac", "type": "audio", "duration": 709.973}, {"from": "Assistant", "value": "Machine translation models have discrete vocabularies and commonly use subword segmentation techniques to achieve an ‘open vocabulary.’ This approach relies on consistent and correct underlying unicode sequences, and makes models susceptible to degradation from common types of noise and variation. Motivated by the robustness of human language processing, we propose the use of visual text representations, which dispense with a finite set of text embeddings in favor of continuous vocabularies created by processing visually rendered text with sliding windows. We show that models using visual text representations approach or match performance of traditional text models on small and larger datasets. More importantly, models with visual embeddings demonstrate significant robustness to varied types of noise, achieving e.g., 25.9 BLEU on a character permuted German–English task where subword models degrade to 1.9.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_577", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_577.flac", "type": "audio", "duration": 713.456}, {"from": "Assistant", "value": "Despite constant improvements in machine translation quality, automatic poetry translation remains a challenging problem due to the lack of open-sourced parallel poetic corpora, and to the intrinsic complexities involved in preserving the semantics, style and figurative nature of poetry. We present an empirical investigation for poetry translation along several dimensions: 1) size and style of training data (poetic vs. non-poetic), including a zero-shot setup; 2) bilingual vs. multilingual learning; and 3) language-family-specific models vs. mixed-language-family models. To accomplish this, we contribute a parallel dataset of poetry translations for several language pairs. Our results show that multilingual fine-tuning on poetic text significantly outperforms multilingual fine-tuning on non-poetic text that is 35X larger in size, both in terms of automatic metrics (BLEU, BERTScore, COMET) and human evaluation metrics such as faithfulness (meaning and poetic style). Moreover, multilingual fine-tuning on poetic data outperforms bilingual fine-tuning on poetic data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_578", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_578.flac", "type": "audio", "duration": 712.063}, {"from": "Assistant", "value": "Multilingual Neural Machine Translation (NMT) enables one model to serve all translation directions, including ones that are unseen during training, i.e. zero-shot translation. Despite being theoretically attractive, current models often produce low quality translations – commonly failing to even produce outputs in the right target language. In this work, we observe that off-target translation is dominant even in strong multilingual systems, trained on massive multilingual corpora. To address this issue, we propose a joint approach to regularize NMT models at both representation-level and gradient-level. At the representation level, we leverage an auxiliary target language prediction task to regularize decoder outputs to retain information about the target language. At the gradient level, we leverage a small amount of direct data (in thousands of sentence pairs) to regularize model gradients. Our results demonstrate that our approach is highly effective in both reducing off-target translation occurrences and improving zero-shot translation performance by +5.59 and +10.38 BLEU on WMT and OPUS datasets respectively. Moreover, experiments show that our method also works well when the small amount of direct data is not available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_579", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_579.flac", "type": "audio", "duration": 657.218}, {"from": "Assistant", "value": "How to effectively adapt neural machine translation (NMT) models according to emerging cases without retraining? Despite the great success of neural machine translation, updating the deployed models online remains a challenge. Existing non-parametric approaches that retrieve similar examples from a database to guide the translation process are promising but are prone to overfit the retrieved examples. However, non-parametric methods are prone to overfit the retrieved examples. In this work, we propose to learn Kernel-Smoothed Translation with Example Retrieval (KSTER), an effective approach to adapt neural machine translation models online. Experiments on domain adaptation and multi-domain machine translation datasets show that even without expensive retraining, KSTER is able to achieve improvement of 1.1 to 1.5 BLEU scores over the best existing online adaptation methods. The code and trained models are released athttps://github.com/jiangqn/KSTER.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_580", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_580.flac", "type": "audio", "duration": 710.972}, {"from": "Assistant", "value": "Learning multilingual and multi-domain translation model is challenging as the heterogeneous and imbalanced data make the model converge inconsistently over different corpora in real world. One common practice is to adjust the share of each corpus in the training, so that the learning process is balanced and low-resource cases can benefit from the high resource ones. However, automatic balancing methods usually depend on the intra- and inter-dataset characteristics, which is usually agnostic or requires human priors. In this work, we propose an approach, MultiUAT, that dynamically adjusts the training data usage based on the model’s uncertainty on a small set of trusted clean data for multi-corpus machine translation. We experiments with two classes of uncertainty measures on multilingual (16 languages with 4 settings) and multi-domain settings (4 for in-domain and 2 for out-of-domain on English-German translation) and demonstrate our approach MultiUAT substantially outperforms its baselines, including both static and dynamic strategies. We analyze the cross-domain transfer and show the deficiency of static and similarity based methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_581", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_581.flac", "type": "audio", "duration": 714.223}, {"from": "Assistant", "value": "Simultaneous machine translation (SiMT) generates translation before reading the entire source sentence and hence it has to trade off between translation quality and latency. To fulfill the requirements of different translation quality and latency in practical applications, the previous methods usually need to train multiple SiMT models for different latency levels, resulting in large computational costs. In this paper, we propose a universal SiMT model with Mixture-of-Experts Wait-k Policy to achieve the best translation quality under arbitrary latency with only one trained model. Specifically, our method employs multi-head attention to accomplish the mixture of experts where each head is treated as a wait-k expert with its own waiting words number, and given a test latency and source inputs, the weights of the experts are accordingly adjusted to produce the best translation. Experiments on three datasets show that our method outperforms all the strong baselines under different latency, including the state-of-the-art adaptive policy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_582", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_582.flac", "type": "audio", "duration": 554.423}, {"from": "Assistant", "value": "Many real-world problems require the combined application of multiple reasoning abilities—employing suitable abstractions, commonsense knowledge, and creative synthesis of problem-solving strategies. To help advance AI systems towards such capabilities, we propose a new reasoning challenge, namely Fermi Problems (FPs), which are questions whose answers can only be approximately estimated because their precise computation is either impractical or impossible. For example, “How much would the sea level rise if all ice in the world melted?” FPs are commonly used in quizzes and interviews to bring out and evaluate the creative reasoning abilities of humans. To do the same for AI systems, we present two datasets: 1) A collection of 1k real-world FPs sourced from quizzes and olympiads; and 2) a bank of 10k synthetic FPs of intermediate complexity to serve as a sandbox for the harder real-world challenge. In addition to question-answer pairs, the datasets contain detailed solutions in the form of an executable program and supporting facts, helping in supervision and evaluation of intermediate steps. We demonstrate that even extensively fine-tuned large-scale language models perform poorly on these datasets, on average making estimates that are off by two orders of magnitude. Our contribution is thus the crystallization of several unsolved AI problems into a single, new challenge that we hope will spur further advances in building systems that can reason.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_583", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_583.flac", "type": "audio", "duration": 727.083}, {"from": "Assistant", "value": "In this paper we propose a novel approach towards improving the efficiency of Question Answering (QA) systems by filtering out questions that will not be answered by them. This is based on an interesting new finding: the answer confidence scores of state-of-the-art QA systems can be approximated well by models solely using the input question text. This enables preemptive filtering of questions that are not answered by the system due to their answer confidence scores being lower than the system threshold. Specifically, we learn Transformer-based question models by distilling Transformer-based answering models. Our experiments on three popular QA datasets and one industrial QA benchmark demonstrate the ability of our question models to approximate the Precision/Recall curves of the target QA system well. These question models, when used as filters, can effectively trade off lower computation cost of QA systems for lower Recall, e.g., reducing computation by ~60%, while only losing ~3-4% of Recall.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_584", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_584.flac", "type": "audio", "duration": 615.04}, {"from": "Assistant", "value": "When training most modern reading comprehension models, all the questions associated with a context are treated as being independent from each other. However, closely related questions and their corresponding answers are not independent, and leveraging these relationships could provide a strong supervision signal to a model. Drawing on ideas from contrastive estimation, we introduce several new supervision losses that compare question-answer scores across multiple related instances. Specifically, we normalize these scores across various neighborhoods of closely contrasting questions and/or answers, adding a cross entropy loss term in addition to traditional maximum likelihood estimation. Our techniques require bundles of related question-answer pairs, which we either mine from within existing data or create using automated heuristics. We empirically demonstrate the effectiveness of training with instance bundles on two datasets—HotpotQA and ROPES—showing up to 9% absolute gains in accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_585", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_585.flac", "type": "audio", "duration": 719.168}, {"from": "Assistant", "value": "Our goal, in the context of open-domain textual question-answering (QA), is to explain answers by showing the line of reasoning from what is known to the answer, rather than simply showing a fragment of textual evidence (a “rationale”). If this could be done, new opportunities for understanding and debugging the system’s reasoning become possible. Our approach is to generate explanations in the form of entailment trees, namely a tree of multipremise entailment steps from facts that are known, through intermediate conclusions, to the hypothesis of interest (namely the question + answer). To train a model with this skill, we created ENTAILMENTBANK, the first dataset to contain multistep entailment trees. Given a hypothesis (question + answer), we define three increasingly difficult explanation tasks: generate a valid entailment tree given (a) all relevant sentences (b) all relevant and some irrelevant sentences, or (c) a corpus. We show that a strong language model can partially solve these tasks, in particular when the relevant sentences are included in the input (e.g., 35% of trees for (a) are perfect), and with indications of generalization to other domains. This work is significant as it provides a new type of dataset (multistep entailments) and baselines, offering a new avenue for the community to generate richer, more systematic explanations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_586", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_586.flac", "type": "audio", "duration": 669.056}, {"from": "Assistant", "value": "Answers to the same question may change depending on the extra-linguistic contexts (when and where the question was asked). To study this challenge, we introduce SituatedQA, an open-retrieval QA dataset where systems must produce the correct answer to a question given the temporal or geographical context. To construct SituatedQA, we first identify such questions in existing QA datasets. We find that a significant proportion of information seeking questions have context-dependent answers (e.g. roughly 16.5% of NQ-Open). For such context-dependent questions, we then crowdsource alternative contexts and their corresponding answers. Our study shows that existing models struggle with producing answers that are frequently updated or from uncommon locations. We further quantify how existing models, which are trained on data collected in the past, fail to generalize to answering questions asked in the present, even when provided with an updated evidence corpus (a roughly 15 point drop in accuracy). Our analysis suggests that open-retrieval QA benchmarks should incorporate extra-linguistic context to stay relevant globally and in the future. Our data, code, and datasheet are available athttps://situatedqa.github.io/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_587", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_587.flac", "type": "audio", "duration": 637.063}, {"from": "Assistant", "value": "We present the first English corpus study on abusive language towards three conversational AI systems gathered ‘in the wild’: an open-domain social bot, a rule-based chatbot, and a task-based system. To account for the complexity of the task, we take a more ‘nuanced’ approach where our ConvAI dataset reflects fine-grained notions of abuse, as well as views from multiple expert annotators. We find that the distribution of abuse is vastly different compared to other commonly used datasets, with more sexually tinted aggression towards the virtual persona of these systems. Finally, we report results from bench-marking existing models against this data. Unsurprisingly, we find that there is substantial room for improvement with F1 scores below 90%.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_588", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_588.flac", "type": "audio", "duration": 726.018}, {"from": "Assistant", "value": "One of the challenges faced by conversational agents is their inability to identify unstated presumptions of their users’ commands, a task trivial for humans due to their common sense. In this paper, we propose a zero-shot commonsense reasoning system for conversational agents in an attempt to achieve this. Our reasoner uncovers unstated presumptions from user commands satisfying a general template of if-(state), then-(action), because-(goal). Our reasoner uses a state-of-the-art transformer-based generative commonsense knowledge base (KB) as its source of background knowledge for reasoning. We propose a novel and iterative knowledge query mechanism to extract multi-hop reasoning chains from the neural KB which uses symbolic logic rules to significantly reduce the search space. Similar to any KBs gathered to date, our commonsense KB is prone to missing knowledge. Therefore, we propose to conversationally elicit the missing knowledge from human users with our novel dynamic question generation strategy, which generates and presents contextualized queries to human users. We evaluate the model with a user study with human users that achieves a 35% higher success rate compared to SOTA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_589", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_589.flac", "type": "audio", "duration": 691.003}, {"from": "Assistant", "value": "Reliable automatic evaluation of dialogue systems under an interactive environment has long been overdue. An ideal environment for evaluating dialog systems, also known as the Turing test, needs to involve human interaction, which is usually not affordable for large-scale experiments. Though researchers have attempted to use metrics for language generation tasks (e.g., perplexity, BLEU) or some model-based reinforcement learning methods (e.g., self-play evaluation) for automatic evaluation, these methods only show very weak correlation with the actual human evaluation in practice. To bridge such a gap, we propose a new framework named ENIGMA for estimating human evaluation scores based on recent advances of off-policy evaluation in reinforcement learning. ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation, making automatic evaluations feasible. More importantly, ENIGMA is model-free and agnostic to the behavior policies for collecting the experience data, which significantly alleviates the technical difficulties of modeling complex dialogue environments and human behaviors. Our experiments show that ENIGMA significantly outperforms existing methods in terms of correlation with human evaluation scores.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_590", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_590.flac", "type": "audio", "duration": 716.963}, {"from": "Assistant", "value": "Continual learning in task-oriented dialogue systems allows the system to add new domains and functionalities overtime after deployment, without incurring the high cost of retraining the whole system each time. In this paper, we propose a first-ever continual learning benchmark for task-oriented dialogue systems with 37 domains to be learned continuously in both modularized and end-to-end learning settings. In addition, we implement and compare multiple existing continual learning baselines, and we propose a simple yet effective architectural method based on residual adapters. We also suggest that the upper bound performance of continual learning should be equivalent to multitask learning when data from all domain is available at once. Our experiments demonstrate that the proposed architectural method and a simple replay-based strategy perform better, by a large margin, compared to other continuous learning techniques, and only slightly worse than the multitask learning upper bound while being 20X faster in learning new domains. We also report several trade-offs in terms of parameter usage, memory size and training time, which are important in the design of a task-oriented dialogue system. The proposed benchmark is released to promote more research in this direction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_591", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_591.flac", "type": "audio", "duration": 526.848}, {"from": "Assistant", "value": "We present a systematic study on multilingual and cross-lingual intent detection (ID) from spoken data. The study leverages a new resource put forth in this work, termed MInDS-14, a first training and evaluation resource for the ID task with spoken data. It covers 14 intents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties. Our key results indicate that combining machine translation models with state-of-the-art multilingual sentence encoders (e.g., LaBSE) yield strong intent detectors in the majority of target languages covered in MInDS-14, and offer comparative analyses across different axes: e.g., translation direction, impact of speech recognition, data augmentation from a related domain. We see this work as an important step towards more inclusive development and evaluation of multilingual ID from spoken data, hopefully in a much wider spectrum of languages compared to prior work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_592", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_592.flac", "type": "audio", "duration": 510.059}, {"from": "Assistant", "value": "Humans often employ figurative language use in communication, including during interactions with dialog systems. Thus, it is important for real-world dialog systems to be able to handle popular figurative language constructs like metaphor and simile. In this work, we analyze the performance of existing dialog models in situations where the input dialog context exhibits use of figurative language. We observe large gaps in handling of figurative language when evaluating the models on two open domain dialog datasets. When faced with dialog contexts consisting of figurative language, some models show very large drops in performance compared to contexts without figurative language. We encourage future research in dialog modeling to separately analyze and report results on figurative language in order to better test model capabilities relevant to real-world use. Finally, we propose lightweight solutions to help existing models become more robust to figurative language by simply using an external resource to translate figurative language to literal (non-figurative) forms while preserving the meaning to the best extent possible.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_593", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_593.flac", "type": "audio", "duration": 472.131}, {"from": "Assistant", "value": "Sequence-to-sequence models have been applied to a wide variety of NLP tasks, but how to properly use them for dialogue state tracking has not been systematically investigated. In this paper, we study this problem from the perspectives of pre-training objectives as well as the formats of context representations. We demonstrate that the choice of pre-training objective makes a significant difference to the state tracking quality. In particular, we find that masked span prediction is more effective than auto-regressive language modeling. We also explore using Pegasus, a span prediction-based pre-training objective for text summarization, for the state tracking model. We found that pre-training for the seemingly distant summarization task works surprisingly well for dialogue state tracking. In addition, we found that while recurrent state context representation works also reasonably well, the model may have a hard time recovering from earlier mistakes. We conducted experiments on the MultiWOZ 2.1-2.4, WOZ 2.0, and DSTC2 datasets with consistent observations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_595", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_595.flac", "type": "audio", "duration": 724.927}, {"from": "Assistant", "value": "Image captioning has conventionally relied on reference-based automatic evaluations, where machine captions are compared against captions written by humans. This is in contrast to the reference-free manner in which humans assess caption quality. In this paper, we report the surprising empirical finding that CLIP (Radford et al., 2021), a cross-modal model pretrained on 400M image+caption pairs from the web, can be used for robust automatic evaluation of image captioning without the need for references. Experiments spanning several corpora demonstrate that our new reference-free metric, CLIPScore, achieves the highest correlation with human judgements, outperforming existing reference-based metrics like CIDEr and SPICE. Information gain experiments demonstrate that CLIPScore, with its tight focus on image-text compatibility, is complementary to existing reference-based metrics that emphasize text-text similarities. Thus, we also present a reference-augmented version, RefCLIPScore, which achieves even higher correlation. Beyond literal description tasks, several case studies reveal domains where CLIPScore performs well (clip-art images, alt-text rating), but also where it is relatively weaker in comparison to reference-based metrics, e.g., news captions that require richer contextual knowledge.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_596", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_596.flac", "type": "audio", "duration": 722.453}, {"from": "Assistant", "value": "Building compositional explanations requires models to combine two or more facts that, together, describe why the answer to a question is correct. Typically, these “multi-hop” explanations are evaluated relative to one (or a small number of) gold explanations. In this work, we show these evaluations substantially underestimate model performance, both in terms of the relevance of included facts, as well as the completeness of model-generated explanations, because models regularly discover and produce valid explanations that are different than gold explanations. To address this, we construct a large corpus of 126k domain-expert (science teacher) relevance ratings that augment a corpus of explanations to standardized science exam questions, discovering 80k additional relevant facts not rated as gold. We build three strong models based on different methodologies (generation, ranking, and schemas), and empirically show that while expert-augmented ratings provide better estimates of explanation quality, both original (gold) and expert-augmented automatic evaluations still substantially underestimate performance by up to 36% when compared with full manual expert judgements, with different models being disproportionately affected. This poses a significant methodological challenge to accurately evaluating explanations produced by compositional reasoning models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_597", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_597.flac", "type": "audio", "duration": 684.053}, {"from": "Assistant", "value": "Understanding how events are semantically related to each other is the essence of reading comprehension. Recent event-centric reading comprehension datasets focus mostly on event arguments or temporal relations. While these tasks partially evaluate machines’ ability of narrative understanding, human-like reading comprehension requires the capability to process event-based information beyond arguments and temporal reasoning. For example, to understand causality between events, we need to infer motivation or purpose; to establish event hierarchy, we need to understand the composition of events. To facilitate these tasks, we introduce **ESTER**, a comprehensive machine reading comprehension (MRC) dataset for Event Semantic Relation Reasoning. The dataset leverages natural language queries to reason about the five most common event semantic relations, provides more than 6K questions, and captures 10.1K event relation pairs. Experimental results show that the current SOTA systems achieve 22.1%, 63.3% and 83.5% for token-based exact-match (**EM**), **F1** and event-based **HIT@1** scores, which are all significantly below human performances (36.0%, 79.6%, 100% respectively), highlighting our dataset as a challenging benchmark.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_598", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_598.flac", "type": "audio", "duration": 750.613}, {"from": "Assistant", "value": "Pre-trained language models (PTLMs) have achieved impressive performance on commonsense inference benchmarks, but their ability to employ commonsense to make robust inferences, which is crucial for effective communications with humans, is debated. In the pursuit of advancing fluid human-AI communication, we propose a new challenge, RICA: Robust Inference using Commonsense Axioms, that evaluates robust commonsense inference despite textual perturbations. To generate data for this challenge, we develop a systematic and scalable procedure using commonsense knowledge bases and probe PTLMs across two different evaluation settings. Extensive experiments on our generated probe sets with more than 10k statements show that PTLMs perform no better than random guessing on the zero-shot setting, are heavily impacted by statistical biases, and are not robust to perturbation attacks. We also find that fine-tuning on similar statements offer limited gains, as PTLMs still fail to generalize to unseen inferences. Our new large-scale benchmark exposes a significant gap between PTLMs and human-level language understanding and offers a new challenge for PTLMs to demonstrate commonsense.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_599", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_599.flac", "type": "audio", "duration": 705.051}, {"from": "Assistant", "value": "Natural language generation (NLG) spans a broad range of tasks, each of which serves for specific objectives and desires different properties of generated text. The complexity makes automatic evaluation of NLG particularly challenging. Previous work has typically focused on a single task and developed individual evaluation metrics based on specific intuitions. In this paper, we propose a unifying perspective based on the nature of information change in NLG tasks, including compression (e.g., summarization), transduction (e.g., text rewriting), and creation (e.g., dialog). _Information alignment_ between input, context, and output text plays a common central role in characterizing the generation. With automatic alignment prediction models, we develop a family of interpretable metrics that are suitable for evaluating key aspects of different NLG tasks, often without need of gold reference data. Experiments show the uniformly designed metrics achieve stronger or comparable correlations with human judgement compared to state-of-the-art metrics in each of diverse tasks, including text summarization, style transfer, and knowledge-grounded dialog.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_600", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_600.flac", "type": "audio", "duration": 661.699}, {"from": "Assistant", "value": "This work presents a sparse-attention Transformer architecture for modeling documents that contain large tables. Tables are ubiquitous on the web, and are rich in information. However, more than 20% of relational tables on the web have 20 or more rows (Cafarella et al., 2008), and these large tables present a challenge for current Transformer models, which are typically limited to 512 tokens. Here we propose MATE, a novel Transformer architecture designed to model the structure of web tables. MATE uses sparse attention in a way that allows heads to efficiently attend to either rows or columns in a table. This architecture scales linearly with respect to speed and memory, and can handle documents containing more than 8000 tokens with current accelerators. MATE also has a more appropriate inductive bias for tabular data, and sets a new state-of-the-art for three table reasoning datasets. For HybridQA (Chen et al., 2020), a dataset that involves large documents containing tables, we improve the best prior result by 19 points.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_601", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_601.flac", "type": "audio", "duration": 556.053}, {"from": "Assistant", "value": "Training NLP systems typically assumes access to annotated data that has a single human label per example. Given imperfect labeling from annotators and inherent ambiguity of language, we hypothesize that single label is not sufficient to learn the spectrum of language interpretation. We explore new annotation distribution schemes, assigning multiple labels per example for a small subset of training examples. Introducing such multi label examples at the cost of annotating fewer examples brings clear gains on natural language inference task and entity typing task, even when we simply first train with a single label data and then fine tune with multi label examples. Extending a MixUp data augmentation framework, we propose a learning algorithm that can learn from training examples with different amount of annotation (with zero, one, or multiple labels). This algorithm efficiently combines signals from uneven training data and brings additional gains in low annotation budget and cross domain settings. Together, our method achieves consistent gains in two tasks, suggesting distributing labels unevenly among training examples can be beneficial for many NLP tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_602", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_602.flac", "type": "audio", "duration": 690.048}, {"from": "Assistant", "value": "Large language models have become increasingly difficult to train because of the growing computation time and cost. In this work, we present SRU++, a highly-efficient architecture that combines fast recurrence and attention for sequence modeling. SRU++ exhibits strong modeling capacity and training efficiency. On standard language modeling tasks such as Enwik8, Wiki-103 and Billion Word datasets, our model obtains better bits-per-character and perplexity while using 3x-10x less training cost compared to top-performing Transformer models. For instance, our model achieves a state-of-the-art result on the Enwik8 dataset using 1.6 days of training on an 8-GPU machine. We further demonstrate that SRU++ requires minimal attention for near state-of-the-art performance. Our results suggest jointly leveraging fast recurrence with little attention as a promising direction for accelerating model training and inference.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_603", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_603.flac", "type": "audio", "duration": 718.1}, {"from": "Assistant", "value": "Intermediate layer matching is shown as an effective approach for improving knowledge distillation (KD). However, this technique applies matching in the hidden spaces of two different networks (i.e. student and teacher), which lacks clear interpretability. Moreover, intermediate layer KD cannot easily deal with other problems such as layer mapping search and architecture mismatch (i.e. it requires the teacher and student to be of the same model type). To tackle the aforementioned problems all together, we propose Universal-KD to match intermediate layers of the teacher and the student in the output space (by adding pseudo classifiers on intermediate layers) via the attention-based layer projection. By doing this, our unified approach has three merits: (i) it can be flexibly combined with current intermediate layer distillation techniques to improve their results (ii) the pseudo classifiers of the teacher can be deployed instead of extra expensive teacher assistant networks to address the capacity gap problem in KD which is a common issue when the gap between the size of the teacher and student networks becomes too large; (iii) it can be used in cross-architecture intermediate layer KD. We did comprehensive experiments in distilling BERT-base into BERT-4, RoBERTa-large into DistilRoBERTa and BERT-base into CNN and LSTM-based models. Results on the GLUE tasks show that our approach is able to outperform other KD techniques.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_604", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_604.flac", "type": "audio", "duration": 421.001}, {"from": "Assistant", "value": "Generative approaches have been recently shown to be effective for both Entity Disambiguation and Entity Linking (i.e., joint mention detection and disambiguation). However, the previously proposed autoregressive formulation for EL suffers from i) high computational cost due to a complex (deep) decoder, ii) non-parallelizable decoding that scales with the source sequence length, and iii) the need for training on a large amount of data. In this work, we propose a very efficient approach that parallelizes autoregressive linking across all potential mentions and relies on a shallow and efficient decoder. Moreover, we augment the generative objective with an extra discriminative component, i.e., a correction term which lets us directly optimize the generator’s ranking. When taken together, these techniques tackle all the above issues: our model is >70 times faster and more accurate than the previous generative method, outperforming state-of-the-art approaches on the standard English dataset AIDA-CoNLL. Source code available athttps://github.com/nicola-decao/efficient-autoregressive-EL", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_605", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_605.flac", "type": "audio", "duration": 411.968}, {"from": "Assistant", "value": "Recent coreference resolution models rely heavily on span representations to find coreference links between word spans. As the number of spans isO(n2)in the length of text and the number of potential links isO(n4), various pruning techniques are necessary to make this approach computationally feasible. We propose instead to consider coreference links between individual words rather than word spans and then reconstruct the word spans. This reduces the complexity of the coreference model toO(n2)and allows it to consider all potential mentions without pruning any of them out. We also demonstrate that, with these changes, SpanBERT for coreference resolution will be significantly outperformed by RoBERTa. While being highly efficient, our model performs competitively with recent coreference resolution systems on the OntoNotes benchmark.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_606", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_606.flac", "type": "audio", "duration": 467.604}, {"from": "Assistant", "value": "In this work, we consider the problem of designing secure and efficient federated learning (FL) frameworks for NLP. Existing solutions under this literature either consider a trusted aggregator or require heavy-weight cryptographic primitives, which makes the performance significantly degraded. Moreover, many existing secure FL designs work only under the restrictive assumption that none of the clients can be dropped out from the training protocol. To tackle these problems, we propose SEFL, a secure and efficient federated learning framework that (1) eliminates the need for the trusted entities; (2) achieves similar and even better model accuracy compared with existing FL designs; (3) is resilient to client dropouts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_607", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_607.flac", "type": "audio", "duration": 691.606}, {"from": "Assistant", "value": "In practical applications of semantic parsing, we often want to rapidly change the behavior of the parser, such as enabling it to handle queries in a new domain, or changing its predictions on certain targeted queries. While we can introduce new training examples exhibiting the target behavior, a mechanism for enacting such behavior changes without expensive model re-training would be preferable. To this end, we propose ControllAble Semantic Parser via Exemplar Retrieval (CASPER). Given an input query, the parser retrieves related exemplars from a retrieval index, augments them to the query, and then applies a generative seq2seq model to produce an output parse. The exemplars act as a control mechanism over the generic generative model: by manipulating the retrieval index or how the augmented query is constructed, we can manipulate the behavior of the parser. On the MTOP dataset, in addition to achieving state-of-the-art on the standard setup, we show that CASPER can parse queries in a new domain, adapt the prediction toward the specified patterns, or adapt to new semantic schemas without having to further re-train the model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_608", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_608.flac", "type": "audio", "duration": 711.04}, {"from": "Assistant", "value": "We explore the use of large pretrained language models as few-shot semantic parsers. The goal in semantic parsing is to generate a structured meaning representation given a natural language input. However, language models are trained to generate natural language. To bridge the gap, we use language models to paraphrase inputs into a controlled sublanguage resembling English that can be automatically mapped to a target meaning representation. Our results demonstrate that with only a small amount of data and very little code to convert into English-like representations, our blueprint for rapidly bootstrapping semantic parsers leads to surprisingly effective performance on multiple community tasks, greatly exceeding baseline methods also trained on the same limited data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_609", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_609.flac", "type": "audio", "duration": 708.053}, {"from": "Assistant", "value": "Recent commonsense-reasoning tasks are typically discriminative in nature, where a model answers a multiple-choice question for a certain context. Discriminative tasks are limiting because they fail to adequately evaluate the model’s ability to reason and explain predictions with underlying commonsense knowledge. They also allow such models to use reasoning shortcuts and not be “right for the right reasons”. In this work, we present ExplaGraphs, a new generative and structured commonsense-reasoning task (and an associated dataset) of explanation graph generation for stance prediction. Specifically, given a belief and an argument, a model has to predict if the argument supports or counters the belief and also generate a commonsense-augmented graph that serves as non-trivial, complete, and unambiguous explanation for the predicted stance. We collect explanation graphs through a novel Create-Verify-And-Refine graph collection framework that improves the graph quality (up to 90%) via multiple rounds of verification and refinement. A significant 79% of our graphs contain external commonsense nodes with diverse structures and reasoning depths. Next, we propose a multi-level evaluation framework, consisting of automatic metrics and human evaluation, that check for the structural and semantic correctness of the generated graphs and their degree of match with ground-truth graphs. Finally, we present several structured, commonsense-augmented, and text generation models as strong starting points for this explanation graph generation task, and observe that there is a large gap with human performance, thereby encouraging future work for this new challenging task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_610", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_610.flac", "type": "audio", "duration": 696.043}, {"from": "Assistant", "value": "Word Sense Disambiguation (WSD) aims to automatically identify the exact meaning of one word according to its context. Existing supervised models struggle to make correct predictions on rare word senses due to limited training data and can only select the best definition sentence from one predefined word sense inventory (e.g., WordNet). To address the data sparsity problem and generalize the model to be independent of one predefined inventory, we propose a gloss alignment algorithm that can align definition sentences (glosses) with the same meaning from different sense inventories to collect rich lexical knowledge. We then train a model to identify semantic equivalence between a target word in context and one of its glosses using these aligned inventories, which exhibits strong transfer capability to many WSD tasks. Experiments on benchmark datasets show that the proposed method improves predictions on both frequent and rare word senses, outperforming prior work by 1.2% on the All-Words WSD Task and 4.3% on the Low-Shot WSD Task. Evaluation on WiC Task also indicates that our method can better capture word meanings in context.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_611", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_611.flac", "type": "audio", "duration": 720.376}, {"from": "Assistant", "value": "Grammatical error correction (GEC) requires a set of labeled ungrammatical / grammatical sentence pairs for training, but obtaining such annotation can be prohibitively expensive. Recently, the Break-It-Fix-It (BIFI) framework has demonstrated strong results on learning to repair a broken program without any labeled examples, but this relies on a perfect critic (e.g., a compiler) that returns whether an example is valid or not, which does not exist for the GEC task. In this work, we show how to leverage a pretrained language model (LM) in defining an LM-Critic, which judges a sentence to be grammatical if the LM assigns it a higher probability than its local perturbations. We apply this LM-Critic and BIFI along with a large set of unlabeled sentences to bootstrap realistic ungrammatical / grammatical pairs for training a corrector. We evaluate our approach on GEC datasets on multiple domains (CoNLL-2014, BEA-2019, GMEG-wiki and GMEG-yahoo) and show that it outperforms existing methods in both the unsupervised setting (+7.7 F0.5) and the supervised setting (+0.5 F0.5).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_612", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_612.flac", "type": "audio", "duration": 704.043}, {"from": "Assistant", "value": "We propose a method to distill a language-agnostic meaning embedding from a multilingual sentence encoder. By removing language-specific information from the original embedding, we retrieve an embedding that fully represents the sentence’s meaning. The proposed method relies only on parallel corpora without any human annotations. Our meaning embedding allows efficient cross-lingual sentence similarity estimation by simple cosine similarity calculation. Experimental results on both quality estimation of machine translation and cross-lingual semantic textual similarity tasks reveal that our method consistently outperforms the strong baselines using the original multilingual embedding. Our method consistently improves the performance of any pre-trained multilingual sentence encoder, even in low-resource language pairs where only tens of thousands of parallel sentence pairs are available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_613", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_613.flac", "type": "audio", "duration": 609.57}, {"from": "Assistant", "value": "Understanding the origins of militarized conflict is a complex, yet important undertaking. Existing research seeks to build this understanding by considering bi-lateral relationships between entity pairs (dyadic causes) and multi-lateral relationships among multiple entities (systemic causes). The aim of this work is to compare these two causes in terms of how they correlate with conflict between two entities. We do this by devising a set of textual and graph-based features which represent each of the causes. The features are extracted from Wikipedia and modeled as a large graph. Nodes in this graph represent entities connected by labeled edges representing ally or enemy-relationships. This allows casting the problem as an edge classification task, which we term dyad classification. We propose and evaluate classifiers to determine if a particular pair of entities are allies or enemies. Our results suggest that our systemic features might be slightly better correlates of conflict. Further, we find that Wikipedia articles of allies are semantically more similar than enemies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_614", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_614.flac", "type": "audio", "duration": 625.801}, {"from": "Assistant", "value": "Point-of-interest (POI) type prediction is the task of inferring the type of a place from where a social media post was shared. Inferring a POI’s type is useful for studies in computational social science including sociolinguistics, geosemiotics, and cultural geography, and has applications in geosocial networking technologies such as recommendation and visualization systems. Prior efforts in POI type prediction focus solely on text, without taking visual information into account. However in reality, the variety of modalities, as well as their semiotic relationships with one another, shape communication and interactions in social media. This paper presents a study on POI type prediction using multimodal information from text and images available at posting time. For that purpose, we enrich a currently available data set for POI type prediction with the images that accompany the text messages. Our proposed method extracts relevant information from each modality to effectively capture interactions between text and image achieving a macro F1 of 47.21 across 8 categories significantly outperforming the state-of-the-art method for POI type prediction based on text-only methods. Finally, we provide a detailed analysis to shed light on cross-modal interactions and the limitations of our best performing model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_615", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_615.flac", "type": "audio", "duration": 581.497}, {"from": "Assistant", "value": "In this paper, we introduce the task of political coalition signal prediction from text, that is, the task of recognizing from the news coverage leading up to an election the (un)willingness of political parties to form a government coalition. We decompose our problem into two related, but distinct tasks: (i) predicting whether a reported statement from a politician or a journalist refers to a potential coalition and (ii) predicting the polarity of the signal – namely, whether the speaker is in favour of or against the coalition. For this, we explore the benefits of multi-task learning and investigate which setup and task formulation is best suited for each sub-task. We evaluate our approach, based on hand-coded newspaper articles, covering elections in three countries (Ireland, Germany, Austria) and two languages (English, German). Our results show that the multi-task learning approach can further improve results over a strong monolingual transfer learning baseline.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_616", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_616.flac", "type": "audio", "duration": 680.043}, {"from": "Assistant", "value": "Millions of hashtags are created on social media every day to cross-refer messages concerning similar topics. To help people find the topics they want to discuss, this paper characterizes a user’s hashtagging preferences via predicting how likely they will post with a hashtag. It is hypothesized that one’s interests in a hashtag are related with what they said before (user history) and the existing posts present the hashtag (hashtag contexts). These factors are married in the deep semantic space built with a pre-trained BERT and a neural topic model via multitask learning. In this way, user interests learned from the past can be customized to match future hashtags, which is beyond the capability of existing methods assuming unchanged hashtag semantics. Furthermore, we propose a novel personalized topic attention to capture salient contents to personalize hashtag contexts. Experiments on a large-scale Twitter dataset show that our model significantly outperforms the state-of-the-art recommendation approach without exploiting latent topics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_617", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_617.flac", "type": "audio", "duration": 383.083}, {"from": "Assistant", "value": "The task of Conversational Recommendation System (CRS), i.e., recommender dialog system, aims to recommend precise items to users through natural language interactions. Though recent end-to-end neural models have shown promising progress on this task, two key challenges still remain. First, the recommended items cannot be always incorporated into the generated response precisely and appropriately. Second, only the items mentioned in the training corpus have a chance to be recommended in the conversation. To tackle these challenges, we introduce a novel framework called NTRD for recommender dialogue system that can decouple the dialogue generation from the item recommendation. NTRD has two key components, i.e., response template generator and item selector. The former adopts an encoder-decoder model to generate a response template with slot locations tied to target items, while the latter fills in slot locations with the proper items using a sufficient attention mechanism. Our approach combines the strengths of both classical slot filling approaches (that are generally controllable) and modern neural NLG approaches (that are generally more natural and accurate). Extensive experiments on the benchmark ReDial show our approach significantly outperforms the previous state-of-the-art methods. Besides, our approach has the unique advantage to produce novel items that do not appear in the training set of dialogue corpus. The code is available athttps://github.com/jokieleung/NTRD.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_618", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_618.flac", "type": "audio", "duration": 706.537}, {"from": "Assistant", "value": "The automatic evaluation of open-domain dialogues remains a largely unsolved challenge. Despite the abundance of work done in the field, human judges have to evaluate dialogues’ quality. As a consequence, performing such evaluations at scale is usually expensive. This work investigates using a deep-learning model trained on the General Language Understanding Evaluation (GLUE) benchmark to serve as a quality indication of open-domain dialogues. The aim is to use the various GLUE tasks as different perspectives on judging the quality of conversation, thus reducing the need for additional training data or responses that serve as quality references. Due to this nature, the method can infer various quality metrics and can derive a component-based overall score. We achieve statistically significant correlation coefficients of up to 0.7.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_619", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_619.flac", "type": "audio", "duration": 692.001}, {"from": "Assistant", "value": "Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability. Inspired by recent work on evaluating factual consistency in abstractive summarization, we propose an automatic evaluation metric for factual consistency in knowledge-grounded dialogue using automatic question generation and question answering. Our metric, denotedQ2, compares answer spans using natural language inference (NLI), instead of token-based matching as done in previous work. To foster proper evaluation, we curate a novel dataset of dialogue system outputs for the Wizard-of-Wikipedia dataset, manually annotated for factual consistency. We perform a thorough meta-evaluation ofQ2against other metrics using this dataset and two others, where it consistently shows higher correlation with human judgements.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_620", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_620.flac", "type": "audio", "duration": 691.051}, {"from": "Assistant", "value": "Dialogue State Tracking is central to multi-domain task-oriented dialogue systems, responsible for extracting information from user utterances. We present a novel hybrid architecture that augments GPT-2 with representations derived from Graph Attention Networks in such a way to allow causal, sequential prediction of slot values. The model architecture captures inter-slot relationships and dependencies across domains that otherwise can be lost in sequential prediction. We report improvements in state tracking performance in MultiWOZ 2.0 against a strong GPT-2 baseline and investigate a simplified sparse training scenario in which DST models are trained only on session-level annotations but evaluated at the turn level. We further report detailed analyses to demonstrate the effectiveness of graph models in DST by showing that the proposed graph modules capture inter-slot dependencies and improve the predictions of values that are common to multiple domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_621", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_621.flac", "type": "audio", "duration": 469.043}, {"from": "Assistant", "value": "Most reinforcement learning methods for dialog policy learning train a centralized agent that selects a predefined joint action concatenating domain name, intent type, and slot name. The centralized dialog agent suffers from a great many user-agent interaction requirements due to the large action space. Besides, designing the concatenated actions is laborious to engineers and maybe struggled with edge cases. To solve these problems, we model the dialog policy learning problem with a novel multi-agent framework, in which each part of the action is led by a different agent. The framework reduces labor costs for action templates and decreases the size of the action space for each agent. Furthermore, we relieve the non-stationary problem caused by the changing dynamics of the environment as evolving of agents’ policies by introducing a joint optimization process that makes agents can exchange their policy information. Concurrently, an independent experience replay buffer mechanism is integrated to reduce the dependence between gradients of samples to improve training efficiency. The effectiveness of the proposed framework is demonstrated in a multi-domain environment with both user simulator evaluation and human evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_622", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_622.flac", "type": "audio", "duration": 761.011}, {"from": "Assistant", "value": "Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data. In this work, we propose to transfer the cross-task knowledge from general question answering (QA) corpora for the zero-shot DST task. Specifically, we propose TransferQA, a transferable generative QA model that seamlessly combines extractive QA and multi-choice QA via a text-to-text transformer framework, and tracks both categorical slots and non-categorical slots in DST. In addition, we introduce two effective ways to construct unanswerable questions, namely, negative question sampling and context truncation, which enable our model to handle none value slots in the zero-shot DST setting. The extensive experiments show that our approaches substantially improve the existing zero-shot and few-shot results on MultiWoz. Moreover, compared to the fully trained baseline on the Schema-Guided Dialogue dataset, our approach shows better generalization ability in unseen domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_623", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_623.flac", "type": "audio", "duration": 689.045}, {"from": "Assistant", "value": "The ability to identify and resolve uncertainty is crucial for the robustness of a dialogue system. Indeed, this has been confirmed empirically on systems that utilise Bayesian approaches to dialogue belief tracking. However, such systems consider only confidence estimates and have difficulty scaling to more complex settings. Neural dialogue systems, on the other hand, rarely take uncertainties into account. They are therefore overconfident in their decisions and less robust. Moreover, the performance of the tracking task is often evaluated in isolation, without consideration of its effect on the downstream policy optimisation. We propose the use of different uncertainty measures in neural belief tracking. The effects of these measures on the downstream task of policy optimisation are evaluated by adding selected measures of uncertainty to the feature space of the policy and training policies through interaction with a user simulator. Both human and simulated user results show that incorporating these measures leads to improvements both of the performance and of the robustness of the downstream dialogue policy. This highlights the importance of developing neural dialogue belief trackers that take uncertainty into account.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_624", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_624.flac", "type": "audio", "duration": 487.062}, {"from": "Assistant", "value": "Online conversations can sometimes take a turn for the worse, either due to systematic cultural differences, accidental misunderstandings, or mere malice. Automatically forecasting derailment in public online conversations provides an opportunity to take early action to moderate it. Previous work in this space is limited, and we extend it in several ways. We apply a pretrained language encoder to the task, which outperforms earlier approaches. We further experiment with shifting the training paradigm for the task from a static to a dynamic one to increase the forecast horizon. This approach shows mixed results: in a high-quality data setting, a longer average forecast horizon can be achieved at the cost of a small drop in F1; in a low-quality data setting, however, dynamic training propagates the noise and is highly detrimental to performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_625", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_625.flac", "type": "audio", "duration": 551.102}, {"from": "Assistant", "value": "Knowledge graph embedding, representing entities and relations in the knowledge graphs with high-dimensional vectors, has made significant progress in link prediction. More researchers have explored the representational capabilities of models in recent years. That is, they investigate better representational models to fit symmetry/antisymmetry and combination relationships. The current embedding models are more inclined to utilize the identical vector for the same entity in various triples to measure the matching performance. The observation that measuring the rationality of specific triples means comparing the matching degree of the specific attributes associated with the relations is well-known. Inspired by this fact, this paper designs Semantic Filter Based on Relations(SFBR) to extract the required attributes of the entities. Then the rationality of triples is compared under these extracted attributes through the traditional embedding models. The semantic filter module can be added to most geometric and tensor decomposition models with minimal additional memory. experiments on the benchmark datasets show that the semantic filter based on relations can suppress the impact of other attribute dimensions and improve link prediction performance. The tensor decomposition models with SFBR have achieved state-of-the-art.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_626", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_626.flac", "type": "audio", "duration": 475.73}, {"from": "Assistant", "value": "Transformer models are expensive to fine-tune, slow for inference, and have large storage requirements. Recent approaches tackle these shortcomings by training smaller models, dynamically reducing the model size, and by training light-weight adapters. In this paper, we propose AdapterDrop, removing adapters from lower transformer layers during training and inference, which incorporates concepts from all three directions. We show that AdapterDrop can dynamically reduce the computational overhead when performing inference over multiple tasks simultaneously, with minimal decrease in task performances. We further prune adapters from AdapterFusion, which improves the inference efficiency while maintaining the task performances entirely.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_627", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_627.flac", "type": "audio", "duration": 684.803}, {"from": "Assistant", "value": "Transformer-based architectures have become the de-facto standard models for a wide range of Natural Language Processing tasks. However, their memory footprint and high latency are prohibitive for efficient deployment and inference on resource-limited devices. In this work, we explore quantization for transformers. We show that transformers have unique quantization challenges – namely, high dynamic activation ranges that are difficult to represent with a low bit fixed-point format. We establish that these activations contain structured outliers in the residual connections that encourage specific attention patterns, such as attending to the special separator token. To combat these challenges, we present three solutions based on post-training quantization and quantization-aware training, each with a different set of compromises for accuracy, model size, and ease of use. In particular, we introduce a novel quantization scheme – per-embedding-group quantization. We demonstrate the effectiveness of our methods on the GLUE benchmark using BERT, establishing state-of-the-art results for post-training quantization. Finally, we show that transformer weights and embeddings can be quantized to ultra-low bit-widths, leading to significant memory savings with a minimum accuracy loss. Our source code is available athttps://github.com/qualcomm-ai-research/transformer-quantization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_628", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_628.flac", "type": "audio", "duration": 394.368}, {"from": "Assistant", "value": "Neural language models have contributed to state-of-the-art results in a number of downstream applications including sentiment analysis, intent classification and others. However, obtaining text representations or embeddings using these models risks encoding personally identifiable information learned from language and context cues that may lead to privacy leaks. To ameliorate this issue, we propose Context-Aware Private Embeddings (CAPE), a novel approach which combines differential privacy and adversarial learning to preserve privacy during training of embeddings. Specifically, CAPE firstly applies calibrated noise through differential privacy to maintain the privacy of text representations by preserving the encoded semantic links while obscuring sensitive information. Next, CAPE employs an adversarial training regime that obscures identified private variables. Experimental results demonstrate that our proposed approach is more effective in reducing private information leakage than either single intervention, with approximately a 3% reduction in attacker performance compared to the best-performing current method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_629", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_629.flac", "type": "audio", "duration": 711.808}, {"from": "Assistant", "value": "We present two novel unsupervised methods for eliminating toxicity in text. Our first method combines two recent ideas: (1) guidance of the generation process with small style-conditional language models and (2) use of paraphrasing models to perform style transfer. We use a well-performing paraphraser guided by style-trained language models to keep the text content and remove toxicity. Our second method uses BERT to replace toxic words with their non-offensive synonyms. We make the method more flexible by enabling BERT to replace mask tokens with a variable number of words. Finally, we present the first large-scale comparative study of style transfer models on the task of toxicity removal. We compare our models with a number of methods for style transfer. The models are evaluated in a reference-free way using a combination of unsupervised style transfer metrics. Both methods we suggest yield new SOTA results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_630", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_630.flac", "type": "audio", "duration": 591.644}, {"from": "Assistant", "value": "Text simplification is a valuable technique. However, current research is limited to sentence simplification. In this paper, we define and investigate a new task of document-level text simplification, which aims to simplify a document consisting of multiple sentences. Based on Wikipedia dumps, we first construct a large-scale dataset named D-Wikipedia and perform analysis and human evaluation on it to show that the dataset is reliable. Then, we propose a new automatic evaluation metric called D-SARI that is more suitable for the document-level simplification task. Finally, we select several representative models as baseline models for this task and perform automatic evaluation and human evaluation. We analyze the results and point out the shortcomings of the baseline models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_631", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_631.flac", "type": "audio", "duration": 435.003}, {"from": "Assistant", "value": "Dialogue summarization comes with its own peculiar challenges as opposed to news or scientific articles summarization. In this work, we explore four different challenges of the task: handling and differentiating parts of the dialogue belonging to multiple speakers, negation understanding, reasoning about the situation, and informal language understanding. Using a pretrained sequence-to-sequence language model, we explore speaker name substitution, negation scope highlighting, multi-task learning with relevant tasks, and pretraining on in-domain data. Our experiments show that our proposed techniques indeed improve summarization performance, outperforming strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_633", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_633.flac", "type": "audio", "duration": 498.048}, {"from": "Assistant", "value": "QuestEval is a reference-less metric used in text-to-text tasks, that compares the generated summaries directly to the source text, by automatically asking and answering questions. Its adaptation to Data-to-Text tasks is not straightforward, as it requires multimodal Question Generation and Answering systems on the considered tasks, which are seldom available. To this purpose, we propose a method to build synthetic multimodal corpora enabling to train multimodal components for a data-QuestEval metric. The resulting metric is reference-less and multimodal; it obtains state-of-the-art correlations with human judgment on the WebNLG and WikiBio benchmarks. We make data-QuestEval’s code and models available for reproducibility purpose, as part of the QuestEval project.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_635", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_635.flac", "type": "audio", "duration": 580.894}, {"from": "Assistant", "value": "Joint extraction of entities and relations from unstructured texts to form factual triples is a fundamental task of constructing a Knowledge Base (KB). A common method is to decode triples by predicting entity pairs to obtain the corresponding relation. However, it is still challenging to handle this task efficiently, especially for the overlapping triple problem. To address such a problem, this paper proposes a novel efficient entities and relations extraction model calledTDEER, which stands forTranslatingDecoding Schema for JointExtraction ofEntities andRelations. Unlike the common approaches, the proposed translating decoding schema regards the relation as a translating operation from subject to objects, i.e., TDEER decodes triples assubject + relation → objects. TDEER can naturally handle the overlapping triple problem, because the translating decoding schema can recognize all possible triples, including overlapping and non-overlapping triples. To enhance model robustness, we introduce negative samples to alleviate error accumulation at different stages. Extensive experiments on public datasets demonstrate that TDEER produces competitive results compared with the state-of-the-art (SOTA) baselines. Furthermore, the computation complexity analysis indicates that TDEER is more efficient than powerful baselines. Especially, the proposed TDEER is 2 times faster than the recent SOTA models. The code is available athttps://github.com/4AI/TDEER.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_636", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_636.flac", "type": "audio", "duration": 599.539}, {"from": "Assistant", "value": "Detecting events and their evolution through time is a crucial task in natural language understanding. Recent neural approaches to event temporal relation extraction typically map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs. However, embeddings in the Euclidean space cannot capture richer asymmetric relations such as event temporal relations. We thus propose to embed events into hyperbolic spaces, which are intrinsically oriented at modeling hierarchical structures. We introduce two approaches to encode events and their temporal relations in hyperbolic spaces. One approach leverages hyperbolic embeddings to directly infer event relations through simple geometrical operations. In the second one, we devise an end-to-end architecture composed of hyperbolic neural units tailored for the temporal relation extraction task. Thorough experimental assessments on widely used datasets have shown the benefits of revisiting the tasks on a different geometrical space, resulting in state-of-the-art performance on several standard metrics. Finally, the ablation study and several qualitative analyses highlighted the rich event semantics implicitly encoded into hyperbolic spaces.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_637", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_637.flac", "type": "audio", "duration": 360.043}, {"from": "Assistant", "value": "Event detection has long been troubled by the trigger curse: overfitting the trigger will harm the generalization ability while underfitting it will hurt the detection performance. This problem is even more severe in few-shot scenario. In this paper, we identify and solve the trigger curse problem in few-shot event detection (FSED) from a causal view. By formulating FSED with a structural causal model (SCM), we found that the trigger is a confounder of the context and the result, which makes previous FSED methods much easier to overfit triggers. To resolve this problem, we propose to intervene on the context via backdoor adjustment during training. Experiments show that our method significantly improves the FSED on both ACE05 and MAVEN datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_638", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_638.flac", "type": "audio", "duration": 535.011}, {"from": "Assistant", "value": "Term weighting schemes are widely used in Natural Language Processing and Information Retrieval. In particular, term weighting is the basis for keyword extraction. However, there are relatively few evaluation studies that shed light about the strengths and shortcomings of each weighting scheme. In fact, in most cases researchers and practitioners resort to the well-known tf-idf as default, despite the existence of other suitable alternatives, including graph-based models. In this paper, we perform an exhaustive and large-scale empirical comparison of both statistical and graph-based term weighting methods in the context of keyword extraction. Our analysis reveals some interesting findings such as the advantages of the less-known lexical specificity with respect to tf-idf, or the qualitative differences between statistical and graph-based methods. Finally, based on our findings we discuss and devise some suggestions for practitioners. Source code to reproduce our experimental results, including a keyword extraction library, are available in the following repository:https://github.com/asahi417/kex", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_639", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_639.flac", "type": "audio", "duration": 655.04}, {"from": "Assistant", "value": "Various temporal knowledge graph (KG) completion models have been proposed in the recent literature. The models usually contain two parts, a temporal embedding layer and a score function derived from existing static KG modeling approaches. Since the approaches differ along several dimensions, including different score functions and training strategies, the individual contributions of different temporal embedding techniques to model performance are not always clear. In this work, we systematically study six temporal embedding approaches and empirically quantify their performance across a wide range of configurations with about 3000 experiments and 13159 GPU hours. We classify the temporal embeddings into two classes: (1) timestamp embeddings and (2) time-dependent entity embeddings. Despite the common belief that the latter is more expressive, an extensive experimental study shows that timestamp embeddings can achieve on-par or even better performance with significantly fewer parameters. Moreover, we find that when trained appropriately, the relative performance differences between various temporal embeddings often shrink and sometimes even reverse when compared to prior results. For example, TTransE (CITATION), one of the first temporal KG models, can outperform more recent architectures on ICEWS datasets. To foster further research, we provide the first unified open-source framework for temporal KG completion models with full composability, where temporal embeddings, score functions, loss functions, regularizers, and the explicit modeling of reciprocal relations can be combined arbitrarily.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_640", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_640.flac", "type": "audio", "duration": 466.419}, {"from": "Assistant", "value": "Product quantization (PQ) is a widely used technique for ad-hoc retrieval. Recent studies propose supervised PQ, where the embedding and quantization models can be jointly trained with supervised learning. However, there is a lack of appropriate formulation of the joint training objective; thus, the improvements over previous non-supervised baselines are limited in reality. In this work, we propose the Matching-oriented Product Quantization (MoPQ), where a novel objective Multinoulli Contrastive Loss (MCL) is formulated. With the minimization of MCL, we are able to maximize the matching probability of query and ground-truth key, which contributes to the optimal retrieval accuracy. Given that the exact computation of MCL is intractable due to the demand of vast contrastive samples, we further propose the Differentiable Cross-device Sampling (DCS), which significantly augments the contrastive samples for precise approximation of MCL. We conduct extensive experimental studies on four real-world datasets, whose results verify the effectiveness of MoPQ. The code is available athttps://github.com/microsoft/MoPQ.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_641", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_641.flac", "type": "audio", "duration": 669.083}, {"from": "Assistant", "value": "A mind-map is a diagram that represents the central concept and key ideas in a hierarchical way. Converting plain text into a mind-map will reveal its key semantic structure and be easier to understand. Given a document, the existing automatic mind-map generation method extracts the relationships of every sentence pair to generate the directed semantic graph for this document. The computation complexity increases exponentially with the length of the document. Moreover, it is difficult to capture the overall semantics. To deal with the above challenges, we propose an efficient mind-map generation network that converts a document into a graph via sequence-to-graph. To guarantee a meaningful mind-map, we design a graph refinement module to adjust the relation graph in a reinforcement learning manner. Extensive experimental results demonstrate that the proposed approach is more effective and efficient than the existing methods. The inference time is reduced by thousands of times compared with the existing methods. The case studies verify that the generated mind-maps better reveal the underlying semantic structures of the document.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_642", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_642.flac", "type": "audio", "duration": 556.048}, {"from": "Assistant", "value": "Text classification is a fundamental task with broad applications in natural language processing. Recently, graph neural networks (GNNs) have attracted much attention due to their powerful representation ability. However, most existing methods for text classification based on GNNs consider only one-hop neighborhoods and low-frequency information within texts, which cannot fully utilize the rich context information of documents. Moreover, these models suffer from over-smoothing issues if many graph layers are stacked. In this paper, a Deep Attention Diffusion Graph Neural Network (DADGNN) model is proposed to learn text representations, bridging the chasm of interaction difficulties between a word and its distant neighbors. Experimental results on various standard benchmark datasets demonstrate the superior performance of the present approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_643", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_643.flac", "type": "audio", "duration": 472.062}, {"from": "Assistant", "value": "Multi-label text classification is a challenging task because it requires capturing label dependencies. It becomes even more challenging when class distribution is long-tailed. Resampling and re-weighting are common approaches used for addressing the class imbalance problem, however, they are not effective when there is label dependency besides class imbalance because they result in oversampling of common labels. Here, we introduce the application of balancing loss functions for multi-label text classification. We perform experiments on a general domain dataset with 90 labels (Reuters-21578) and a domain-specific dataset from PubMed with 18211 labels. We find that a distribution-balanced loss function, which inherently addresses both the class imbalance and label linkage problems, outperforms commonly used loss functions. Distribution balancing methods have been successfully used in the image recognition field. Here, we show their effectiveness in natural language processing. Source code is available athttps://github.com/blessu/BalancedLossNLP.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_644", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_644.flac", "type": "audio", "duration": 974.983}, {"from": "Assistant", "value": "Causal inference using observational text data is becoming increasingly popular in many research areas. This paper presents the Bayesian Topic Regression (BTR) model that uses both text and numerical information to model an outcome variable. It allows estimation of both discrete and continuous treatment effects. Furthermore, it allows for the inclusion of additional numerical confounding factors next to text data. To this end, we combine a supervised Bayesian topic model with a Bayesian regression framework and perform supervised representation learning for the text features jointly with the regression parameter training, respecting the Frisch-Waugh-Lovell theorem. Our paper makes two main contributions. First, we provide a regression framework that allows causal inference in settings when both text and numerical confounders are of relevance. We show with synthetic and semi-synthetic datasets that our joint approach recovers ground truth with lower bias than any benchmark model, when text and numerical features are correlated. Second, experiments on two real-world datasets demonstrate that a joint and supervised learning strategy also yields superior prediction results compared to strategies that estimate regression weights for text and non-text features separately, being even competitive with more complex deep neural networks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_645", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_645.flac", "type": "audio", "duration": 321.132}, {"from": "Assistant", "value": "Pretrained transformer-based models such as BERT have demonstrated state-of-the-art predictive performance when adapted into a range of natural language processing tasks. An open problem is how to improve the faithfulness of explanations (rationales) for the predictions of these models. In this paper, we hypothesize that salient information extracted a priori from the training data can complement the task-specific information learned by the model during fine-tuning on a downstream task. In this way, we aim to help BERT not to forget assigning importance to informative input tokens when making predictions by proposing SaLoss; an auxiliary loss function for guiding the multi-head attention mechanism during training to be close to salient information extracted a priori using TextRank. Experiments for explanation faithfulness across five datasets, show that models trained with SaLoss consistently provide more faithful explanations across four different feature attribution methods compared to vanilla BERT. Using the rationales extracted from vanilla BERT and SaLoss models to train inherently faithful classifiers, we further show that the latter result in higher predictive performance in downstream tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_646", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_646.flac", "type": "audio", "duration": 771.051}, {"from": "Assistant", "value": "The primary paradigm for multi-task training in natural language processing is to represent the input with a shared pre-trained language model, and add a small, thin network (head) per task. Given an input, a target head is the head that is selected for outputting the final prediction. In this work, we examine the behaviour of non-target heads, that is, the output of heads when given input that belongs to a different task than the one they were trained for. We find that non-target heads exhibit emergent behaviour, which may either explain the target task, or generalize beyond their original task. For example, in a numerical reasoning task, a span extraction head extracts from the input the arguments to a computation that results in a number generated by a target generative head. In addition, a summarization head that is trained with a target question answering head, outputs query-based summaries when given a question and a context from which the answer is to be extracted. This emergent behaviour suggests that multi-task training leads to non-trivial extrapolation of skills, which can be harnessed for interpretability and generalization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_647", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_647.flac", "type": "audio", "duration": 277.06}, {"from": "Assistant", "value": "Recently more attention has been given to adversarial attacks on neural networks for natural language processing (NLP). A central research topic has been the investigation of search algorithms and search constraints, accompanied by benchmark algorithms and tasks. We implement an algorithm inspired by zeroth order optimization-based attacks and compare with the benchmark results in the TextAttack framework. Surprisingly, we find that optimization-based methods do not yield any improvement in a constrained setup and slightly benefit from approximate gradient information only in unconstrained setups where search spaces are larger. In contrast, simple heuristics exploiting nearest neighbors without querying the target function yield substantial success rates in constrained setups, and nearly full success rate in unconstrained setups, at an order of magnitude fewer queries. We conclude from these results that current TextAttack benchmark tasks are too easy and constraints are too strict, preventing meaningful research on black-box adversarial text attacks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_648", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_648.flac", "type": "audio", "duration": 676.049}, {"from": "Assistant", "value": "Despite the widespread use of Knowledge Graph Embeddings (KGE), little is known about the security vulnerabilities that might disrupt their intended behaviour. We study data poisoning attacks against KGE models for link prediction. These attacks craft adversarial additions or deletions at training time to cause model failure at test time. To select adversarial deletions, we propose to use the model-agnostic instance attribution methods from Interpretable Machine Learning, which identify the training instances that are most influential to a neural model’s predictions on test instances. We use these influential triples as adversarial deletions. We further propose a heuristic method to replace one of the two entities in each influential triple to generate adversarial additions. Our experiments show that the proposed strategies outperform the state-of-art data poisoning attacks on KGE models and improve the MRR degradation due to the attacks by up to 62% over the baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_649", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_649.flac", "type": "audio", "duration": 475.8}, {"from": "Assistant", "value": "I highlight a simple failure mode of state-of-the-art machine reading systems: when contexts do not align with commonly shared beliefs. For example, machine reading systems fail to answerWhat did Elizabeth want?correctly in the context of ‘My kingdom for a cough drop, cried Queen Elizabeth.’ Biased by co-occurrence statistics in the training data of pretrained language models, systems predictmy kingdom, rather thana cough drop. I argue such biases are analogous to human belief biases and present a carefully designed challenge dataset for English machine reading, called Auto-Locke, to quantify such effects. Evaluations of machine reading systems on Auto-Locke show the pervasiveness of belief bias in machine reading.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_650", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_650.flac", "type": "audio", "duration": 456.063}, {"from": "Assistant", "value": "Transformer-based sequence-to-sequence architectures, while achieving state-of-the-art results on a large number of NLP tasks, can still suffer from overfitting during training. In practice, this is usually countered either by applying regularization methods (e.g. dropout, L2-regularization) or by providing huge amounts of training data. Additionally, Transformer and other architectures are known to struggle when generating very long sequences. For example, in machine translation, the neural-based systems perform worse on very long sequences when compared to the preceding phrase-based translation approaches (Koehn and Knowles, 2017). We present results which suggest that the issue might also be in the mismatch between the length distributions of the training and validation data combined with the aforementioned tendency of the neural networks to overfit to the training data. We demonstrate on a simple string editing tasks and a machine translation task that the Transformer model performance drops significantly when facing sequences of length diverging from the length distribution in the training data. Additionally, we show that the observed drop in performance is due to the hypothesis length corresponding to the lengths seen by the model during training rather than the length of the input sequence.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_651", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_651.flac", "type": "audio", "duration": 715.051}, {"from": "Assistant", "value": "Research shows that natural language processing models are generally considered to be vulnerable to adversarial attacks; but recent work has drawn attention to the issue of validating these adversarial inputs against certain criteria (e.g., the preservation of semantics and grammaticality). Enforcing constraints to uphold such criteria may render attacks unsuccessful, raising the question of whether valid attacks are actually feasible. In this work, we investigate this through the lens of human language ability. We report on crowdsourcing studies in which we task humans with iteratively modifying words in an input text, while receiving immediate model feedback, with the aim of causing a sentiment classification model to misclassify the example. Our findings suggest that humans are capable of generating a substantial amount of adversarial examples using semantics-preserving word substitutions. We analyze how human-generated adversarial examples compare to the recently proposed TextFooler, Genetic, BAE and SememePSO attack algorithms on the dimensions naturalness, preservation of sentiment, grammaticality and substitution rate. Our findings suggest that human-generated adversarial examples are not more able than the best algorithms to generate natural-reading, sentiment-preserving examples, though they do so by being much more computationally efficient.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_652", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_652.flac", "type": "audio", "duration": 448.006}, {"from": "Assistant", "value": "The Uniform Information Density principle states that speakers plan their utterances to reduce fluctuations in the density of the information transmitted. In this paper, we test whether, and within which contextual units this principle holds in task-oriented dialogues. We show that there is evidence supporting the principle in written dialogues where participants play a cooperative reference game as well as in spoken dialogues involving instruction giving and following. Our study underlines the importance of identifying the relevant contextual components, showing that information content increases particularly within topically and referentially related contextual units.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_653", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_653.flac", "type": "audio", "duration": 506.683}, {"from": "Assistant", "value": "Homophony’s widespread presence in natural languages is a controversial topic. Recent theories of language optimality have tried to justify its prevalence, despite its negative effects on cognitive processing time, e.g., Piantadosi et al. (2012) argued homophony enables the reuse of efficient wordforms and is thus beneficial for languages. This hypothesis has recently been challenged by Trott and Bergen (2020), who posit that good wordforms are more often homophonous simply because they are more phonotactically probable. In this paper, we join in on the debate. We first propose a new information-theoretic quantification of a language’s homophony: the sample Rényi entropy. Then, we use this quantification to revisit Trott and Bergen’s claims. While their point is theoretically sound, a specific methodological issue in their experiments raises doubts about their results. After addressing this issue, we find no clear pressure either towards or against homophony—a much more nuanced result than either Piantadosi et al.’s or Trott and Bergen’s findings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_654", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_654.flac", "type": "audio", "duration": 643.371}, {"from": "Assistant", "value": "Basic-level categories (BLC) are an important psycholinguistic concept introduced by Rosch et al. (1976); they are defined as the most inclusive categories for which a concrete mental image of the category as a whole can be formed, and also as those categories which are acquired early in life. Rosch’s original algorithm for detecting BLC (called cue-validity) is based on the availability of semantic features such as “has tail” for “cat”, and has remained untested at large. An at-scale algorithm for the automatic determination of BLC exists, but it operates without Rosch-style semantic features, and is thus unable to verify Rosch’s hypothesis. We present the first method for the detection of BLC at scale that makes use of Rosch-style semantic features. For both English and Mandarin, we test three methods of generating such features for any synset within Wordnet (WN): extraction of textual features from Wikipedia pages, Distributional Memory (DM) and BART. The best of our methods outperforms the current SoA in BLC detection, with an accuracy of English BLC detection of 75.0%, and of Mandarin BLC detection 80.7% on a test set. When applied to all of WordNet, our model predicts that 1,118 synsets in English Wordnet (1.4%) are BLC, far fewer than existing methods, and with a precision improvement of over 200% over these. As well as confirming the usefulness of Rosch’s cue validity algorithm, we also developed and evaluated our own new indicator for BLC, which models the fact that BLC features tend to be BLC themselves.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_655", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_655.flac", "type": "audio", "duration": 678.325}, {"from": "Assistant", "value": "Temporal knowledge graph (TKG) reasoning is a crucial task that has gained increasing research interest in recent years. Most existing methods focus on reasoning at past timestamps to complete the missing facts, and there are only a few works of reasoning on known TKGs to forecast future facts. Compared with the completion task, the forecasting task is more difficult that faces two main challenges: (1) how to effectively model the time information to handle future timestamps? (2) how to make inductive inference to handle previously unseen entities that emerge over time? To address these challenges, we propose the first reinforcement learning method for forecasting. Specifically, the agent travels on historical knowledge graph snapshots to search for the answer. Our method defines a relative time encoding function to capture the timespan information, and we design a novel time-shaped reward based on Dirichlet distribution to guide the model learning. Furthermore, we propose a novel representation method for unseen entities to improve the inductive inference ability of the model. We evaluate our method for this link prediction task at future timestamps. Extensive experiments on four benchmark datasets demonstrate substantial performance improvement meanwhile with higher explainability, less calculation, and fewer parameters when compared with existing state-of-the-art methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_657", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_657.flac", "type": "audio", "duration": 646.08}, {"from": "Assistant", "value": "Knowledge graph embeddings (KGEs) compactly encode multi-relational knowledge graphs (KGs). Existing KGE models rely on geometric operations to model relational patterns. Euclidean (circular) rotation is useful for modeling patterns such as symmetry, but cannot represent hierarchical semantics. In contrast, hyperbolic models are effective at modeling hierarchical relations, but do not perform as well on patterns on which circular rotation excels. It is crucial for KGE models to unify multiple geometric transformations so as to fully cover the multifarious relations in KGs. To do so, we propose BiQUE, a novel model that employsbiquaternionsto integrate multiple geometric transformations, viz., scaling, translation, Euclidean rotation, and hyperbolic rotation. BiQUE makes the best trade-offs among geometric operators during training, picking the best one (or their best combination) for each relation. Experiments on five datasets show BiQUE’s effectiveness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_658", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_658.flac", "type": "audio", "duration": 593.045}, {"from": "Assistant", "value": "There has been an increasing interest in inferring future links on temporal knowledge graphs (KG). While links on temporal KGs vary continuously over time, the existing approaches model the temporal KGs in discrete state spaces. To this end, we propose a novel continuum model by extending the idea of neural ordinary differential equations (ODEs) to multi-relational graph convolutional networks. The proposed model preserves the continuous nature of dynamic multi-relational graph data and encodes both temporal and structural information into continuous-time dynamic embeddings. In addition, a novel graph transition layer is applied to capture the transitions on the dynamic graph, i.e., edge formation and dissolution. We perform extensive experiments on five benchmark datasets for temporal KG reasoning, showing our model’s superior performance on the future link forecasting task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_659", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_659.flac", "type": "audio", "duration": 720.0}, {"from": "Assistant", "value": "Backdoor attacks, which maliciously control a well-trained model’s outputs of the instances with specific triggers, are recently shown to be serious threats to the safety of reusing deep neural networks (DNNs). In this work, we propose an efficient online defense mechanism based on robustness-aware perturbations. Specifically, by analyzing the backdoor training process, we point out that there exists a big gap of robustness between poisoned and clean samples. Motivated by this observation, we construct a word-based robustness-aware perturbation to distinguish poisoned samples from clean samples to defend against the backdoor attacks on natural language processing (NLP) models. Moreover, we give a theoretical analysis about the feasibility of our robustness-aware perturbation-based defense method. Experimental results on sentiment analysis and toxic detection tasks show that our method achieves better defending performance and much lower computational costs than existing online defense methods. Our code is available athttps://github.com/lancopku/RAP.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_660", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_660.flac", "type": "audio", "duration": 596.892}, {"from": "Assistant", "value": "Combining several embeddings typically improves performance in downstream tasks as different embeddings encode different information. It has been shown that even models using embeddings from transformers still benefit from the inclusion of standard word embeddings. However, the combination of embeddings of different types and dimensions is challenging. As an alternative to attention-based meta-embeddings, we propose feature-based adversarial meta-embeddings (FAME) with an attention function that is guided by features reflecting word-specific properties, such as shape and frequency, and show that this is beneficial to handle subword-based embeddings. In addition, FAME uses adversarial training to optimize the mappings of differently-sized embeddings to the same space. We demonstrate that FAME works effectively across languages and domains for sequence labeling and sentence classification, in particular in low-resource settings. FAME sets the new state of the art for POS tagging in 27 languages, various NER settings and question classification in different domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_661", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_661.flac", "type": "audio", "duration": 718.449}, {"from": "Assistant", "value": "Existing black box search methods have achieved high success rate in generating adversarial attacks against NLP models. However, such search methods are inefficient as they do not consider the amount of queries required to generate adversarial attacks. Also, prior attacks do not maintain a consistent search space while comparing different search methods. In this paper, we propose a query efficient attack strategy to generate plausible adversarial examples on text classification and entailment tasks. Our attack jointly leverages attention mechanism and locality sensitive hashing (LSH) to reduce the query count. We demonstrate the efficacy of our approach by comparing our attack with four baselines across three different search spaces. Further, we benchmark our results across the same search space used in prior attacks. In comparison to attacks proposed, on an average, we are able to reduce the query count by 75% across all datasets and target models. We also demonstrate that our attack achieves a higher success rate when compared to prior attacks in a limited query setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_662", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_662.flac", "type": "audio", "duration": 694.044}, {"from": "Assistant", "value": "Beam search is the go-to method for decoding auto-regressive machine translation models. While it yields consistent improvements in terms of BLEU, it is only concerned with finding outputs with high model likelihood, and is thus agnostic to whatever end metric or score practitioners care about. Our aim is to establish whether beam search can be replaced by a more powerful metric-driven search technique. To this end, we explore numerous decoding algorithms, including some which rely on a value function parameterised by a neural network, and report results on a variety of metrics. Notably, we introduce a Monte-Carlo Tree Search (MCTS) based method and showcase its competitiveness. We provide a blueprint for how to use MCTS fruitfully in language applications, which opens promising future directions. We find that which algorithm is best heavily depends on the characteristics of the goal metric; we believe that our extensive experiments and analysis will inform further research in this area.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_663", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_663.flac", "type": "audio", "duration": 491.051}, {"from": "Assistant", "value": "Previous works have shown that contextual information can improve the performance of neural machine translation (NMT). However, most existing document-level NMT methods failed to leverage contexts beyond a few set of previous sentences. How to make use of the whole document as global contexts is still a challenge. To address this issue, we hypothesize that a document can be represented as a graph that connects relevant contexts regardless of their distances. We employ several types of relations, including adjacency, syntactic dependency, lexical consistency, and coreference, to construct the document graph. Then, we incorporate both source and target graphs into the conventional Transformer architecture with graph convolutional networks. Experiments on various NMT benchmarks, including IWSLT English–French, Chinese-English, WMT English–German and Opensubtitle English–Russian, demonstrate that using document graphs can significantly improve the translation quality. Extensive analysis verifies that the document graph is beneficial for capturing discourse phenomena.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_664", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_664.flac", "type": "audio", "duration": 477.056}, {"from": "Assistant", "value": "Zero-shot translations is a fascinating feature of Multilingual Neural Machine Translation (MNMT) systems. These MNMT models are usually trained on English-centric data, i.e. English either as the source or target language, and with a language label prepended to the input indicating the target language. However, recent work has highlighted several flaws of these models in zero-shot scenarios where language labels are ignored and the wrong language is generated or different runs show highly unstable results. In this paper, we investigate the benefits of an explicit alignment to language labels in Transformer-based MNMT models in the zero-shot context, by jointly training one cross attention head with word alignment supervision to stress the focus on the target language label. We compare and evaluate several MNMT systems on three multilingual MT benchmarks of different sizes, showing that simply supervising one cross attention head to focus both on word alignments and language labels reduces the bias towards translating into the wrong language, improving the zero-shot performance overall. Moreover, as an additional advantage, we find that our alignment supervision leads to more stable results across different training runs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_665", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_665.flac", "type": "audio", "duration": 705.728}, {"from": "Assistant", "value": "With the advent of end-to-end deep learning approaches in machine translation, interest in word alignments initially decreased; however, they have again become a focus of research more recently. Alignments are useful for typological research, transferring formatting like markup to translated texts, and can be used in the decoding of machine translation systems. At the same time, massively multilingual processing is becoming an important NLP scenario, and pretrained language and machine translation models that are truly multilingual are proposed. However, most alignment algorithms rely on bitexts only and do not leverage the fact that many parallel corpora are multiparallel. In this work, we exploit the multiparallelity of corpora by representing an initial set of bilingual alignments as a graph and then predicting additional edges in the graph. We present two graph algorithms for edge prediction: one inspired by recommender systems and one based on network link prediction. Our experimental results show absolute improvements in F1 of up to 28% over the baseline bilingual word aligner in different datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_666", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_666.flac", "type": "audio", "duration": 458.048}, {"from": "Assistant", "value": "Building neural machine translation systems to perform well on a specific target domain is a well-studied problem. Optimizing system performance for multiple, diverse target domains however remains a challenge. We study this problem in an adaptation setting where the goal is to preserve the existing system quality while incorporating data for domains that were not the focus of the original translation system. We find that we can improve over the performance trade-off offered by Elastic Weight Consolidation with a relatively simple data mixing strategy. At comparable performance on the new domains, catastrophic forgetting is mitigated significantly on strong WMT baselines. Combining both approaches improves the Pareto frontier on this task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_667", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_667.flac", "type": "audio", "duration": 783.04}, {"from": "Assistant", "value": "Differently from the traditional statistical MT that decomposes the translation task into distinct separately learned components, neural machine translation uses a single neural network to model the entire translation process. Despite neural machine translation being de-facto standard, it is still not clear how NMT models acquire different competences over the course of training, and how this mirrors the different models in traditional SMT. In this work, we look at the competences related to three core SMT components and find that during training, NMT first focuses on learning target-side language modeling, then improves translation quality approaching word-by-word translation, and finally learns more complicated reordering patterns. We show that this behavior holds for several models and language pairs. Additionally, we explain how such an understanding of the training process can be useful in practice and, as an example, show how it can be used to improve vanilla non-autoregressive neural machine translation by guiding teacher model selection.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_668", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_668.flac", "type": "audio", "duration": 719.052}, {"from": "Assistant", "value": "Large scale multilingual pre-trained language models have shown promising results in zero- and few-shot cross-lingual tasks. However, recent studies have shown their lack of generalizability when the languages are structurally dissimilar. In this work, we propose a novel fine-tuning method based on co-training that aims to learn more generalized semantic equivalences as a complementary to multilingual language modeling using the unlabeled data in the target language. We also propose an adaption method based on contrastive learning to better capture the semantic relationship in the parallel data, when a few translation pairs are available. To show our method’s effectiveness, we conduct extensive experiments on cross-lingual inference and review classification tasks across various languages. We report significant gains compared to directly fine-tuning multilingual pre-trained models and other semi-supervised alternatives.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_669", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_669.flac", "type": "audio", "duration": 711.134}, {"from": "Assistant", "value": "In the context of neural machine translation, data augmentation (DA) techniques may be used for generating additional training samples when the available parallel data are scarce. Many DA approaches aim at expanding the support of the empirical data distribution by generating new sentence pairs that contain infrequent words, thus making it closer to the true data distribution of parallel sentences. In this paper, we propose to follow a completely different approach and present a multi-task DA approach in which we generate new sentence pairs with transformations, such as reversing the order of the target sentence, which produce unfluent target sentences. During training, these augmented sentences are used as auxiliary tasks in a multi-task framework with the aim of providing new contexts where the target prefix is not informative enough to predict the next word. This strengthens the encoder and forces the decoder to pay more attention to the source representations of the encoder. Experiments carried out on six low-resource translation tasks show consistent improvements over the baseline and over DA methods aiming at extending the support of the empirical data distribution. The systems trained with our approach rely more on the source tokens, are more robust against domain shift and suffer less hallucinations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_670", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_670.flac", "type": "audio", "duration": 944.054}, {"from": "Assistant", "value": "Winograd schemas are a well-established tool for evaluating coreference resolution (CoR) and commonsense reasoning (CSR) capabilities of computational models. So far, schemas remained largely confined to English, limiting their utility in multilingual settings. This work presents Wino-X, a parallel dataset of German, French, and Russian schemas, aligned with their English counterparts. We use this resource to investigate whether neural machine translation (NMT) models can perform CoR that requires commonsense knowledge and whether multilingual language models (MLLMs) are capable of CSR across multiple languages. Our findings show Wino-X to be exceptionally challenging for NMT systems that are prone to undesirable biases and unable to detect disambiguating information. We quantify biases using established statistical methods and define ways to address both of these issues. We furthermore present evidence of active cross-lingual knowledge transfer in MLLMs, whereby fine-tuning models on English schemas yields CSR improvements in other languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_671", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_671.flac", "type": "audio", "duration": 749.053}, {"from": "Assistant", "value": "Machine translation is generally understood as generating one target text from an input source document. In this paper, we consider a stronger requirement: to jointly generate two texts so that each output side effectively depends on the other. As we discuss, such a device serves several practical purposes, from multi-target machine translation to the generation of controlled variations of the target text. We present an analysis of possible implementations of dual decoding, and experiment with four applications. Viewing the problem from multiple angles allows us to better highlight the challenges of dual decoding and to also thoroughly analyze the benefits of generating matched, rather than independent, translations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_672", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_672.flac", "type": "audio", "duration": 323.051}, {"from": "Assistant", "value": "It has been shown for English that discrete and soft prompting perform strongly in few-shot learning with pretrained language models (PLMs). In this paper, we show that discrete and soft prompting perform better than finetuning in multilingual cases: Crosslingual transfer and in-language training of multilingual natural language inference. For example, with 48 English training examples, finetuning obtains 33.74% accuracy in crosslingual transfer, barely surpassing the majority baseline (33.33%). In contrast, discrete and soft prompting outperform finetuning, achieving 36.43% and 38.79%. We also demonstrate good performance of prompting with training data in multiple languages other than English.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_673", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_673.flac", "type": "audio", "duration": 434.048}, {"from": "Assistant", "value": "Multimodal machine translation (MMT) systems have been shown to outperform their text-only neural machine translation (NMT) counterparts when visual context is available. However, recent studies have also shown that the performance of MMT models is only marginally impacted when the associated image is replaced with an unrelated image or noise, which suggests that the visual context might not be exploited by the model at all. We hypothesize that this might be caused by the nature of the commonly used evaluation benchmark, also known as Multi30K, where the translations of image captions were prepared without actually showing the images to human translators. In this paper, we present a qualitative study that examines the role of datasets in stimulating the leverage of visual modality and we propose methods to highlight the importance of visual signals in the datasets which demonstrate improvements in reliance of models on the source images. Our findings suggest the research on effective MMT architectures is currently impaired by the lack of suitable datasets and careful consideration must be taken in creation of future MMT datasets, for which we also provide useful insights.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_674", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_674.flac", "type": "audio", "duration": 701.056}, {"from": "Assistant", "value": "Multilingual NMT has become an attractive solution for MT deployment in production. But to match bilingual quality, it comes at the cost of larger and slower models. In this work, we consider several ways to make multilingual NMT faster at inference without degrading its quality. We experiment with several “light decoder” architectures in two 20-language multi-parallel settings: small-scale on TED Talks and large-scale on ParaCrawl. Our experiments demonstrate that combining a shallow decoder with vocabulary filtering leads to almost 2 times faster inference with no loss in translation quality. We validate our findings with BLEU and chrF (on 380 language pairs), robustness evaluation and human evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_675", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_675.flac", "type": "audio", "duration": 735.04}, {"from": "Assistant", "value": "We explore the impact of leveraging the relatedness of languages that belong to the same family in NLP models using multilingual fine-tuning. We hypothesize and validate that multilingual fine-tuning of pre-trained language models can yield better performance on downstream NLP applications, compared to models fine-tuned on individual languages. A first of its kind detailed study is presented to track performance change as languages are added to a base language in a graded and greedy (in the sense of best boost of performance) manner; which reveals that careful selection of subset of related languages can significantly improve performance than utilizing all related languages. The Indo-Aryan (IA) language family is chosen for the study, the exact languages being Bengali, Gujarati, Hindi, Marathi, Oriya, Punjabi and Urdu. The script barrier is crossed by simple rule-based transliteration of the text of all languages to Devanagari. Experiments are performed on mBERT, IndicBERT, MuRIL and two RoBERTa-based LMs, the last two being pre-trained by us. Low resource languages, such as Oriya and Punjabi, are found to be the largest beneficiaries of multilingual fine-tuning. Textual Entailment, Entity Classification, Section Title Prediction, tasks of IndicGLUE and POS tagging form our test bed. Compared to monolingual fine tuning we get relative performance improvement of up to 150% in the downstream tasks. The surprise take-away is that for any language there is a particular combination of other languages which yields the best performance, and any additional language is in fact detrimental.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_676", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_676.flac", "type": "audio", "duration": 683.2}, {"from": "Assistant", "value": "Traditional hand-crafted linguistically-informed features have often been used for distinguishing between translated and original non-translated texts. By contrast, to date, neural architectures without manual feature engineering have been less explored for this task. In this work, we (i) compare the traditional feature-engineering-based approach to the feature-learning-based one and (ii) analyse the neural architectures in order to investigate how well the hand-crafted features explain the variance in the neural models’ predictions. We use pre-trained neural word embeddings, as well as several end-to-end neural architectures in both monolingual and multilingual settings and compare them to feature-engineering-based SVM classifiers. We show that (i) neural architectures outperform other approaches by more than 20 accuracy points, with the BERT-based model performing the best in both the monolingual and multilingual settings; (ii) while many individual hand-crafted translationese features correlate with neural model predictions, feature importance analysis shows that the most important features for neural and classical architectures differ; and (iii) our multilingual experiments provide empirical evidence for translationese universals across languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_677", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_677.flac", "type": "audio", "duration": 686.379}, {"from": "Assistant", "value": "Neural Machine Translation (NMT) is known to suffer from a beam-search problem: after a certain point, increasing beam size causes an overall drop in translation quality. This effect is especially pronounced for long sentences. While much work was done analyzing this phenomenon, primarily for autoregressive NMT models, there is still no consensus on its underlying cause. In this work, we analyze errors that cause major quality degradation with large beams in NMT and Automatic Speech Recognition (ASR). We show that a factor that strongly contributes to the quality degradation with large beams is dataset length-bias - NMT datasets are strongly biased towards short sentences. To mitigate this issue, we propose a new data augmentation technique – Multi-Sentence Resampling (MSR). This technique extends the training examples by concatenating several sentences from the original dataset to make a long training example. We demonstrate that MSR significantly reduces degradation with growing beam size and improves final translation quality on the IWSTL15 En-Vi, IWSTL17 En-Fr, and WMT14 En-De datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_678", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_678.flac", "type": "audio", "duration": 658.1}, {"from": "Assistant", "value": "Policy compliance detection is the task of ensuring that a scenario conforms to a policy (e.g. a claim is valid according to government rules or a post in an online platform conforms to community guidelines). This task has been previously instantiated as a form of textual entailment, which results in poor accuracy due to the complexity of the policies. In this paper we propose to address policy compliance detection via decomposing it into question answering, where questions check whether the conditions stated in the policy apply to the scenario, and an expression tree combines the answers to obtain the label. Despite the initial upfront annotation cost, we demonstrate that this approach results in better accuracy, especially in the cross-policy setup where the policies during testing are unseen in training. In addition, it allows us to use existing question answering models pre-trained on existing large datasets. Finally, it explicitly identifies the information missing from a scenario in case policy compliance cannot be determined. We conduct our experiments using a recent dataset consisting of government policies, which we augment with expert annotations and find that the cost of annotating question answering decomposition is largely offset by improved inter-annotator agreement and speed.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_679", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_679.flac", "type": "audio", "duration": 635.762}, {"from": "Assistant", "value": "Large-scale multi-label text classification (LMTC) tasks often face long-tailed label distributions, where many labels have few or even no training instances. Although current methods can exploit prior knowledge to handle these few/zero-shot labels, they neglect the meta-knowledge contained in the dataset that can guide models to learn with few samples. In this paper, for the first time, this problem is addressed from a meta-learning perspective. However, the simple extension of meta-learning approaches to multi-label classification is sub-optimal for LMTC tasks due to long-tailed label distribution and coexisting of few- and zero-shot scenarios. We propose a meta-learning approach named META-LMTC. Specifically, it constructs more faithful and more diverse tasks according to well-designed sampling strategies and directly incorporates the objective of adapting to new low-resource tasks into the meta-learning phase. Extensive experiments show that META-LMTC achieves state-of-the-art performance against strong baselines and can still enhance powerful BERTlike models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_680", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_680.flac", "type": "audio", "duration": 454.972}, {"from": "Assistant", "value": "We investigate post-OCR correction in a setting where we have access to different OCR views of the same document. The goal of this study is to understand if a pretrained language model (LM) can be used in an unsupervised way to reconcile the different OCR views such that their combination contains fewer errors than each individual view. This approach is motivated by scenarios in which unconstrained text generation for error correction is too risky. We evaluated different pretrained LMs on two datasets and found significant gains in realistic scenarios with up to 15% WER improvement over the best OCR view. We also show the importance of domain adaptation for post-OCR correction on out-of-domain documents.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_681", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_681.flac", "type": "audio", "duration": 721.045}, {"from": "Assistant", "value": "Lexically constrained text generation aims to control the generated text by incorporating certain pre-specified keywords into the output. Previous work injects lexical constraints into the output by controlling the decoding process or refining the candidate output iteratively, which tends to generate generic or ungrammatical sentences, and has high computational complexity. To address these challenges, we proposed Constrained BART (CBART) for lexically constrained text generation. CBART leverages the pre-trained model, BART and transfers part of the generation burden from the decoder to the encoder by decomposing this task into two sub-tasks, thereby improving the sentence quality. Concretely, we extended BART by adding a token-level classifier over the encoder, aiming at instructing the decoder where to replace and insert. Guided by the encoder, the decoder refines multiple tokens of the input in one step by inserting tokens before specific positions and re-predicting tokens at a low confidence level. To further reduce the inference latency, the decoder predicts all tokens in parallel. Experiment results on One-Billion-Word and Yelp show that CBART can generate plausible text with high quality and diversity while largely accelerating inference.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_682", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_682.flac", "type": "audio", "duration": 472.043}, {"from": "Assistant", "value": "Text moderation for user generated content, which helps to promote healthy interaction among users, has been widely studied and many machine learning models have been proposed. In this work, we explore an alternative perspective by augmenting reactive reviews with proactive forecasting. Specifically, we propose a new concept text toxicity propensity to characterize the extent to which a text tends to attract toxic comments. Beta regression is then introduced to do the probabilistic modeling, which is demonstrated to function well in comprehensive experiments. We also propose an explanation method to communicate the model decision clearly. Both propensity scoring and interpretation benefit text moderation in a novel manner. Finally, the proposed scaling mechanism for the linear model offers useful insights beyond this work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_683", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_683.flac", "type": "audio", "duration": 795.776}, {"from": "Assistant", "value": "Sentence order prediction is the task of finding the correct order of sentences in a randomly ordered document. Correctly ordering the sentences requires an understanding of coherence with respect to the chronological sequence of events described in the text. Document-level contextual understanding and commonsense knowledge centered around these events are often essential in uncovering this coherence and predicting the exact chronological order. In this paper, we introduce STaCK — a framework based on graph neural networks and temporal commonsense knowledge to model global information and predict the relative order of sentences. Our graph network accumulates temporal evidence using knowledge of ‘past’ and ‘future’ and formulates sentence ordering as a constrained edge classification problem. We report results on five different datasets, and empirically show that the proposed method is naturally suitable for order prediction. The implementation of this work is available at:https://github.com/declare-lab/sentence-ordering.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_684", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_684.flac", "type": "audio", "duration": 474.731}, {"from": "Assistant", "value": "Documents as short as a single sentence may inadvertently reveal sensitive information about their authors, including e.g. their gender or ethnicity. Style transfer is an effective way of transforming texts in order to remove any information that enables author profiling. However, for a number of current state-of-the-art approaches the improved privacy is accompanied by an undesirable drop in the down-stream utility of the transformed data. In this paper, we propose a simple, zero-shot way to effectively lower the risk of author profiling through multilingual back-translation using off-the-shelf translation models. We compare our models with five representative text style transfer models on three datasets across different domains. Results from both an automatic and a human evaluation show that our approach achieves the best overall performance while requiring no training data. We are able to lower the adversarial prediction of gender and race by up to 22% while retaining 95% of the original utility on downstream tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_685", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_685.flac", "type": "audio", "duration": 715.136}, {"from": "Assistant", "value": "Pre-trained models for Natural Languages (NL) like BERT and GPT have been recently shown to transfer well to Programming Languages (PL) and largely benefit a broad set of code-related tasks. Despite their success, most current methods either rely on an encoder-only (or decoder-only) pre-training that is suboptimal for generation (resp. understanding) tasks or process the code snippet in the same way as NL, neglecting the special characteristics of PL such as token types. We present CodeT5, a unified pre-trained encoder-decoder Transformer model that better leverages the code semantics conveyed from the developer-assigned identifiers. Our model employs a unified framework to seamlessly support both code understanding and generation tasks and allows for multi-task learning. Besides, we propose a novel identifier-aware pre-training task that enables the model to distinguish which code tokens are identifiers and to recover them when they are masked. Furthermore, we propose to exploit the user-written code comments with a bimodal dual generation task for better NL-PL alignment. Comprehensive experiments show that CodeT5 significantly outperforms prior methods on understanding tasks such as code defect detection and clone detection, and generation tasks across various directions including PL-NL, NL-PL, and PL-PL. Further analysis reveals that our model can better capture semantic information from code. Our code and pre-trained models are released athttps://github.com/salesforce/CodeT5.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_686", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_686.flac", "type": "audio", "duration": 709.056}, {"from": "Assistant", "value": "A health outcome is a measurement or an observation used to capture and assess the effect of a treatment. Automatic detection of health outcomes from text would undoubtedly speed up access to evidence necessary in healthcare decision making. Prior work on outcome detection has modelled this task as either (a) a sequence labelling task, where the goal is to detect which text spans describe health outcomes, or (b) a classification task, where the goal is to classify a text into a predefined set of categories depending on an outcome that is mentioned somewhere in that text. However, this decoupling of span detection and classification is problematic from a modelling perspective and ignores global structural correspondences between sentence-level and word-level information present in a given text. To address this, we propose a method that uses both word-level and sentence-level information to simultaneously perform outcome span detection and outcome type classification. In addition to injecting contextual information to hidden vectors, we use label attention to appropriately weight both word and sentence level information. Experimental results on several benchmark datasets for health outcome detection show that our proposed method consistently outperforms decoupled methods, reporting competitive results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_687", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_687.flac", "type": "audio", "duration": 695.808}, {"from": "Assistant", "value": "In this paper, we show how a multi-class grammatical error detection (GED) system can be used to improve grammatical error correction (GEC) for English. Specifically, we first develop a new state-of-the-art binary detection system based on pre-trained ELECTRA, and then extend it to multi-class detection using different error type tagsets derived from the ERRANT framework. Output from this detection system is used as auxiliary input to fine-tune a novel encoder-decoder GEC model, and we subsequently re-rank the N-best GEC output to find the hypothesis that most agrees with the GED output. Results show that fine-tuning the GEC system using 4-class GED produces the best model, but re-ranking using 55-class GED leads to the best performance overall. This suggests that different multi-class GED systems benefit GEC in different ways. Ultimately, our system outperforms all other previous work that combines GED and GEC, and achieves a new single-model NMT-based state of the art on the BEA-test benchmark.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_688", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_688.flac", "type": "audio", "duration": 467.051}, {"from": "Assistant", "value": "Can we get existing language models and refine them for zero-shot commonsense reasoning? This paper presents an initial study exploring the feasibility of zero-shot commonsense reasoning for the Winograd Schema Challenge by formulating the task as self-supervised refinement of a pre-trained language model. In contrast to previous studies that rely on fine-tuning annotated datasets, we seek to boost conceptualization via loss landscape refinement. To this end, we propose a novel self-supervised learning approach that refines the language model utilizing a set of linguistic perturbations of similar concept relationships. Empirical analysis of our conceptually simple framework demonstrates the viability of zero-shot commonsense reasoning on multiple benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_689", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_689.flac", "type": "audio", "duration": 470.808}, {"from": "Assistant", "value": "In low-resource settings, model transfer can help to overcome a lack of labeled data for many tasks and domains. However, predicting useful transfer sources is a challenging problem, as even the most similar sources might lead to unexpected negative transfer results. Thus, ranking methods based on task and text similarity — as suggested in prior work — may not be sufficient to identify promising sources. To tackle this problem, we propose a new approach to automatically determine which and how many sources should be exploited. For this, we study the effects of model transfer on sequence labeling across various domains and tasks and show that our methods based on model similarity and support vector machines are able to predict promising sources, resulting in performance increases of up to 24 F1 points.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_690", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_690.flac", "type": "audio", "duration": 735.051}, {"from": "Assistant", "value": "Contextualised word embeddings is a powerful tool to detect contextual synonyms. However, most of the current state-of-the-art (SOTA) deep learning concept extraction methods remain supervised and underexploit the potential of the context. In this paper, we propose a self-supervised pre-training approach which is able to detect contextual synonyms of concepts being training on the data created by shallow matching. We apply our methodology in the sparse multi-class setting (over 15,000 concepts) to extract phenotype information from electronic health records. We further investigate data augmentation techniques to address the problem of the class sparsity. Our approach achieves a new SOTA for the unsupervised phenotype concept annotation on clinical text on F1 and Recall outperforming the previous SOTA with a gain of up to 4.5 and 4.0 absolute points, respectively. After fine-tuning with as little as 20% of the labelled data, we also outperform BioBERT and ClinicalBERT. The extrinsic evaluation on three ICU benchmarks also shows the benefit of using the phenotypes annotated by our model as features.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_691", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_691.flac", "type": "audio", "duration": 355.285}, {"from": "Assistant", "value": "Contracts are a common type of legal document that frequent in several day-to-day business workflows. However, there has been very limited NLP research in processing such documents, and even lesser in generating them. These contracts are made up of clauses, and the unique nature of these clauses calls for specific methods to understand and generate such documents. In this paper, we introduce the task of clause recommendation, as a first step to aid and accelerate the authoring of contract documents. We propose a two-staged pipeline to first predict if a specific clause type is relevant to be added in a contract, and then recommend the top clauses for the given type based on the contract context. We pre-train BERT on an existing library of clauses with two additional tasks and use it for our prediction and recommendation. We experiment with classification methods and similarity-based heuristics for clause relevance prediction, and generation-based methods for clause recommendation, and evaluate the results from various methods on several clause types. We provide analyses on the results, and further outline the limitations and future directions of this line of research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_692", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_692.flac", "type": "audio", "duration": 491.328}, {"from": "Assistant", "value": "Finnish is a language with multiple dialects that not only differ from each other in terms of accent (pronunciation) but also in terms of morphological forms and lexical choice. We present the first approach to automatically detect the dialect of a speaker based on a dialect transcript and transcript with audio recording in a dataset consisting of 23 different dialects. Our results show that the best accuracy is received by combining both of the modalities, as text only reaches to an overall accuracy of 57%, where as text and audio reach to 85%. Our code, models and data have been released openly on Github and Zenodo.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_693", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_693.flac", "type": "audio", "duration": 648.649}, {"from": "Assistant", "value": "This paper surveys 60 English Machine Reading Comprehension datasets, with a view to providing a convenient resource for other researchers interested in this problem. We categorize the datasets according to their question and answer form and compare them across various dimensions including size, vocabulary, data source, method of creation, human performance level, and first question word. Our analysis reveals that Wikipedia is by far the most common data source and that there is a relative lack of why, when, and where questions across datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_694", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_694.flac", "type": "audio", "duration": 467.051}, {"from": "Assistant", "value": "End-to-end question answering using a differentiable knowledge graph is a promising technique that requires only weak supervision, produces interpretable results, and is fully differentiable. Previous implementations of this technique (Cohen et al, 2020) have focused on single-entity questions using a relation following operation. In this paper, we propose a model that explicitly handles multiple-entity questions by implementing a new intersection operation, which identifies the shared elements between two sets of entities. We find that introducing intersection improves performance over a baseline model on two datasets, WebQuestionsSP (69.6% to 73.3% Hits@1) and ComplexWebQuestions (39.8% to 48.7% Hits@1), and in particular, improves performance on questions with multiple entities by over 14% on WebQuestionsSP and by 19% on ComplexWebQuestions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_695", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_695.flac", "type": "audio", "duration": 744.06}, {"from": "Assistant", "value": "We tackle the problem of weakly-supervised conversational Question Answering over large Knowledge Graphs using a neural semantic parsing approach. We introduce a new Logical Form (LF) grammar that can model a wide range of queries on the graph while remaining sufficiently simple to generate supervision data efficiently. Our Transformer-based model takes a JSON-like structure as input, allowing us to easily incorporate both Knowledge Graph and conversational contexts. This structured input is transformed to lists of embeddings and then fed to standard attention layers. We validate our approach, both in terms of grammar coverage and LF execution accuracy, on two publicly available datasets, CSQA and ConvQuestions, both grounded in Wikidata. On CSQA, our approach increases the coverage from 80% to 96.2%, and the LF execution accuracy from 70.6% to 75.6%, with respect to previous state-of-the-art results. On ConvQuestions, we achieve competitive results with respect to the state-of-the-art.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_696", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_696.flac", "type": "audio", "duration": 820.338}, {"from": "Assistant", "value": "Despite recent progress, state-of-the-art question answering models remain vulnerable to a variety of adversarial attacks. While dynamic adversarial data collection, in which a human annotator tries to write examples that fool a model-in-the-loop, can improve model robustness, this process is expensive which limits the scale of the collected data. In this work, we are the first to use synthetic adversarial data generation to make question answering models more robust to human adversaries. We develop a data generation pipeline that selects source passages, identifies candidate answers, generates questions, then finally filters or re-labels them to improve quality. Using this approach, we amplify a smaller human-written adversarial dataset to a much larger set of synthetic question-answer pairs. By incorporating our synthetic data, we improve the state-of-the-art on the AdversarialQA dataset by 3.7F1 and improve model generalisation on nine of the twelve MRQA datasets. We further conduct a novel human-in-the-loop evaluation and show that our models are considerably more robust to new human-written adversarial examples: crowdworkers can fool our model only 8.8% of the time on average, compared to 17.6% for a model trained without synthetic data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_697", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_697.flac", "type": "audio", "duration": 758.619}, {"from": "Assistant", "value": "Although pretrained language models (PTLMs) contain significant amounts of world knowledge, they can still produce inconsistent answers to questions when probed, even after specialized training. As a result, it can be hard to identify what the model actually “believes” about the world, making it susceptible to inconsistent behavior and simple errors. Our goal is to reduce these problems. Our approach is to embed a PTLM in a broader system that also includes an evolving, symbolic memory of beliefs – a BeliefBank – that records but then may modify the raw PTLM answers. We describe two mechanisms to improve belief consistency in the overall system. First, a reasoning component – a weighted MaxSAT solver – revises beliefs that significantly clash with others. Second, a feedback component issues future queries to the PTLM using known beliefs as context. We show that, in a controlled experimental setting, these two mechanisms result in more consistent beliefs in the overall system, improving both the accuracy and consistency of its answers over time. This is significant as it is a first step towards PTLM-based architectures with a systematic notion of belief, enabling them to construct a more coherent picture of the world, and improve over time without model retraining.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_698", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_698.flac", "type": "audio", "duration": 601.211}, {"from": "Assistant", "value": "Question Answering (QA) has been successfully applied in scenarios of human-computer interaction such as chatbots and search engines. However, for the specific biomedical domain, QA systems are still immature due to expert-annotated datasets being limited by category and scale. In this paper, we present MLEC-QA, the largest-scale Chinese multi-choice biomedical QA dataset, collected from the National Medical Licensing Examination in China. The dataset is composed of five subsets with 136,236 biomedical multi-choice questions with extra materials (images or tables) annotated by human experts, and first covers the following biomedical sub-fields: Clinic, Stomatology, Public Health, Traditional Chinese Medicine, and Traditional Chinese Medicine Combined with Western Medicine. We implement eight representative control methods and open-domain QA methods as baselines. Experimental results demonstrate that even the current best model can only achieve accuracies between 40% to 55% on five subsets, especially performing poorly on questions that require sophisticated reasoning ability. We hope the release of the MLEC-QA dataset can serve as a valuable resource for research and evaluation in open-domain QA, and also make advances for biomedical QA systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_699", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_699.flac", "type": "audio", "duration": 718.379}, {"from": "Assistant", "value": "Natural language generation (NLG) benchmarks provide an important avenue to measure progress and develop better NLG systems. Unfortunately, the lack of publicly available NLG benchmarks for low-resource languages poses a challenging barrier for building NLG systems that work well for languages with limited amounts of data. Here we introduce IndoNLG, the first benchmark to measure natural language generation (NLG) progress in three low-resource—yet widely spoken—languages of Indonesia: Indonesian, Javanese, and Sundanese. Altogether, these languages are spoken by more than 100 million native speakers, and hence constitute an important use case of NLG systems today. Concretely, IndoNLG covers six tasks: summarization, question answering, chit-chat, and three different pairs of machine translation (MT) tasks. We collate a clean pretraining corpus of Indonesian, Sundanese, and Javanese datasets, Indo4B-Plus, which is used to pretrain our models: IndoBART and IndoGPT. We show that IndoBART and IndoGPT achieve competitive performance on all tasks—despite using only one-fifth the parameters of a larger multilingual model, mBART-large (Liu et al., 2020). This finding emphasizes the importance of pretraining on closely related, localized languages to achieve more efficient learning and faster inference at very low-resource languages like Javanese and Sundanese.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_700", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_700.flac", "type": "audio", "duration": 604.021}, {"from": "Assistant", "value": "Multi-hop reasoning has been widely studied in recent years to obtain more interpretable link prediction. However, we find in experiments that many paths given by these models are actually unreasonable, while little work has been done on interpretability evaluation for them. In this paper, we propose a unified framework to quantitatively evaluate the interpretability of multi-hop reasoning models so as to advance their development. In specific, we define three metrics, including path recall, local interpretability, and global interpretability for evaluation, and design an approximate strategy to calculate these metrics using the interpretability scores of rules. We manually annotate all possible rules and establish a benchmark. In experiments, we verify the effectiveness of our benchmark. Besides, we run nine representative baselines on our benchmark, and the experimental results show that the interpretability of current multi-hop reasoning models is less satisfactory and is 51.7% lower than the upper bound given by our benchmark. Moreover, the rule-based models outperform the multi-hop reasoning models in terms of performance and interpretability, which points to a direction for future research, i.e., how to better incorporate rule information into the multi-hop reasoning model. We will publish our codes and datasets upon acceptance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_701", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_701.flac", "type": "audio", "duration": 719.052}, {"from": "Assistant", "value": "Evaluation metrics are a key ingredient for progress of text generation systems. In recent years, several BERT-based evaluation metrics have been proposed (including BERTScore, MoverScore, BLEURT, etc.) which correlate much better with human assessment of text generation quality than BLEU or ROUGE, invented two decades ago. However, little is known what these metrics, which are based on black-box language model representations, actually capture (it is typically assumed they model semantic similarity). In this work, we use a simple regression based global explainability technique to disentangle metric scores along linguistic factors, including semantics, syntax, morphology, and lexical overlap. We show that the different metrics capture all aspects to some degree, but that they are all substantially sensitive to lexical overlap, just like BLEU and ROUGE. This exposes limitations of these novelly proposed metrics, which we also highlight in an adversarial test scenario.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_702", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_702.flac", "type": "audio", "duration": 329.6}, {"from": "Assistant", "value": "Recently, there has been significant progress in studying neural networks for translating text descriptions into SQL queries under the zero-shot cross-domain setting. Despite achieving good performance on some public benchmarks, we observe that existing text-to-SQL models do not generalize when facing domain knowledge that does not frequently appear in the training data, which may render the worse prediction performance for unseen domains. In this work, we investigate the robustness of text-to-SQL models when the questions require rarely observed domain knowledge. In particular, we define five types of domain knowledge and introduce Spider-DK (DK is the abbreviation of domain knowledge), a human-curated dataset based on the Spider benchmark for text-to-SQL translation. NL questions in Spider-DK are selected from Spider, and we modify some samples by adding domain knowledge that reflects real-world question paraphrases. We demonstrate that the prediction accuracy dramatically drops on samples that require such domain knowledge, even if the domain knowledge appears in the training set, and the model provides the correct predictions for related training samples.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_703", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_703.flac", "type": "audio", "duration": 509.376}, {"from": "Assistant", "value": "Previous work has shown that human evaluations in NLP are notoriously under-powered. Here, we argue that there are two common factors which make this problem even worse: NLP studies usually (a) treat ordinal data as interval data and (b) operate under high variance settings while the differences they are hoping to detect are often subtle. We demonstrate through simulation that ordinal mixed effects models are better able to detect small differences between models, especially in high variance settings common in evaluations of generated texts. We release tools for researchers to conduct their own power analysis and test their assumptions. We also make recommendations for improving statistical power.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_704", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_704.flac", "type": "audio", "duration": 474.407}, {"from": "Assistant", "value": "Recent years have seen an increasing need for gender-neutral and inclusive language. Within the field of NLP, there are various mono- and bilingual use cases where gender inclusive language is appropriate, if not preferred due to ambiguity or uncertainty in terms of the gender of referents. In this work, we present a rule-based and a neural approach to gender-neutral rewriting for English along with manually curated synthetic data (WinoBias+) and natural data (OpenSubtitles and Reddit) benchmarks. A detailed manual and automatic evaluation highlights how our NeuTral Rewriter, trained on data generated by the rule-based approach, obtains word error rates (WER) below 0.18% on synthetic, in-domain and out-domain test sets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_705", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_705.flac", "type": "audio", "duration": 682.62}, {"from": "Assistant", "value": "Reasoning over commonsense knowledge bases (CSKB) whose elements are in the form of free-text is an important yet hard task in NLP. While CSKB completion only fills the missing links within the domain of the CSKB, CSKB population is alternatively proposed with the goal of reasoning unseen assertions from external resources. In this task, CSKBs are grounded to a large-scale eventuality (activity, state, and event) graph to discriminate whether novel triples from the eventuality graph are plausible or not. However, existing evaluations on the population task are either not accurate (automatic evaluation with randomly sampled negative examples) or of small scale (human annotation). In this paper, we benchmark the CSKB population task with a new large-scale dataset by first aligning four popular CSKBs, and then presenting a high-quality human-annotated evaluation set to probe neural models’ commonsense reasoning ability. We also propose a novel inductive commonsense reasoning model that reasons over graphs. Experimental results show that generalizing commonsense reasoning on unseen assertions is inherently a hard task. Models achieving high accuracy during training perform poorly on the evaluation set, with a large gap between human performance. We will make the data publicly available for future contributions. Codes and data are available athttps://github.com/HKUST-KnowComp/CSKB-Population.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_706", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_706.flac", "type": "audio", "duration": 493.331}, {"from": "Assistant", "value": "In previous similarity-based WSD systems, studies have allocated much effort on learning comprehensive sense embeddings using contextual representations and knowledge sources. However, the context embedding of an ambiguous word is learned using only the sentence where the word appears, neglecting its global context. In this paper, we investigate the contribution of both word-level and sense-level global context of an ambiguous word for disambiguation. Experiments have shown that the Context-Oriented Embedding (COE) can enhance a similarity-based system’s performance on WSD by relatively large margins, achieving state-of-the-art on all-words WSD benchmarks in knowledge-based category.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_707", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_707.flac", "type": "audio", "duration": 652.016}, {"from": "Assistant", "value": "Data augmentation has attracted a lot of research attention in the deep learning era for its ability in alleviating data sparseness. The lack of labeled data for unseen evaluation databases is exactly the major challenge for cross-domain text-to-SQL parsing. Previous works either require human intervention to guarantee the quality of generated data, or fail to handle complex SQL queries. This paper presents a simple yet effective data augmentation framework. First, given a database, we automatically produce a large number of SQL queries based on an abstract syntax tree grammar. For better distribution matching, we require that at least 80% of SQL patterns in the training data are covered by generated queries. Second, we propose a hierarchical SQL-to-question generation model to obtain high-quality natural language questions, which is the major contribution of this work. Finally, we design a simple sampling strategy that can greatly improve training efficiency given large amounts of generated data. Experiments on three cross-domain datasets, i.e., WikiSQL and Spider in English, and DuSQL in Chinese, show that our proposed data augmentation framework can consistently improve performance over strong baselines, and the hierarchical generation component is the key for the improvement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_708", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_708.flac", "type": "audio", "duration": 629.888}, {"from": "Assistant", "value": "To translate natural language questions into executable database queries, most approaches rely on a fully annotated training set. Annotating a large dataset with queries is difficult as it requires query-language expertise. We reduce this burden using grounded in databases intermediate question representations. These representations are simpler to collect and were originally crowdsourced within the Break dataset (Wolfson et al., 2020). Our pipeline consists of two parts: a neural semantic parser that converts natural language questions into the intermediate representations and a non-trainable transpiler to the SPARQL query language (a standard language for accessing knowledge graphs and semantic web). We chose SPARQL because its queries are structurally closer to our intermediate representations (compared to SQL). We observe that the execution accuracy of queries constructed by our model on the challenging Spider dataset is comparable with the state-of-the-art text-to-SQL methods trained with annotated SQL queries. Our code and data are publicly available (https://github.com/yandex-research/sparqling-queries).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_709", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_709.flac", "type": "audio", "duration": 639.779}, {"from": "Assistant", "value": "Entity alignment aims to identify equivalent entity pairs between different knowledge graphs (KGs). Recently, the availability of temporal KGs (TKGs) that contain time information created the need for reasoning over time in such TKGs. Existing embedding-based entity alignment approaches disregard time information that commonly exists in many large-scale KGs, leaving much room for improvement. In this paper, we focus on the task of aligning entity pairs between TKGs and propose a novel Time-aware Entity Alignment approach based on Graph Neural Networks (TEA-GNN). We embed entities, relations and timestamps of different KGs into a vector space and use GNNs to learn entity representations. To incorporate both relation and time information into the GNN structure of our model, we use a self-attention mechanism which assigns different weights to different nodes with orthogonal transformation matrices computed from embeddings of the relevant relations and timestamps in a neighborhood. Experimental results on multiple real-world TKG datasets show that our method significantly outperforms the state-of-the-art methods due to the inclusion of time information.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_710", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_710.flac", "type": "audio", "duration": 572.929}, {"from": "Assistant", "value": "Stance detection concerns the classification of a writer’s viewpoint towards a target. There are different task variants, e.g., stance of a tweet vs. a full article, or stance with respect to a claim vs. an (implicit) topic. Moreover, task definitions vary, which includes the label inventory, the data collection, and the annotation protocol. All these aspects hinder cross-domain studies, as they require changes to standard domain adaptation approaches. In this paper, we perform an in-depth analysis of 16 stance detection datasets, and we explore the possibility for cross-domain learning from them. Moreover, we propose an end-to-end unsupervised framework for out-of-domain prediction of unseen, user-defined labels. In particular, we combine domain adaptation techniques such as mixture of experts and domain-adversarial training with label embeddings, and we demonstrate sizable performance gains over strong baselines, both (i) in-domain, i.e., for seen targets, and (ii) out-of-domain, i.e., for unseen targets. Finally, we perform an exhaustive analysis of the cross-domain results, and we highlight the important factors influencing the model performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_711", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_711.flac", "type": "audio", "duration": 673.936}, {"from": "Assistant", "value": "Data augmentation aims to enrich training samples for alleviating the overfitting issue in low-resource or class-imbalanced situations. Traditional methods first devise task-specific operations such as Synonym Substitute, then preset the corresponding parameters such as the substitution rate artificially, which require a lot of prior knowledge and are prone to fall into the sub-optimum. Besides, the number of editing operations is limited in the previous methods, which decreases the diversity of the augmented data and thus restricts the performance gain. To overcome the above limitations, we propose a framework named Text AutoAugment (TAA) to establish a compositional and learnable paradigm for data augmentation. We regard a combination of various operations as an augmentation policy and utilize an efficient Bayesian Optimization algorithm to automatically search for the best policy, which substantially improves the generalization capability of models. Experiments on six benchmark datasets show that TAA boosts classification accuracy in low-resource and class-imbalanced regimes by an average of 8.8% and 9.7%, respectively, outperforming strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_712", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_712.flac", "type": "audio", "duration": 642.055}, {"from": "Assistant", "value": "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository:https://github.com/asahi417/relbert", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_713", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_713.flac", "type": "audio", "duration": 449.045}, {"from": "Assistant", "value": "Recent prompt-based approaches allow pretrained language models to achieve strong performances on few-shot finetuning by reformulating downstream tasks as a language modeling problem. In this work, we demonstrate that, despite its advantages on low data regimes, finetuned prompt-based models for sentence pair classification tasks still suffer from a common pitfall of adopting inference heuristics based on lexical overlap, e.g., models incorrectly assuming a sentence pair is of the same meaning because they consist of the same set of words. Interestingly, we find that this particular inference heuristic is significantly less present in the zero-shot evaluation of the prompt-based model, indicating how finetuning can be destructive to useful knowledge learned during the pretraining. We then show that adding a regularization that preserves pretraining weights is effective in mitigating this destructive tendency of few-shot finetuning. Our evaluation on three datasets demonstrates promising improvements on the three corresponding challenge datasets used to diagnose the inference heuristics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_714", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_714.flac", "type": "audio", "duration": 716.053}, {"from": "Assistant", "value": "Abstract Meaning Representations (AMR) are a broad-coverage semantic formalism which represents sentence meaning as a directed acyclic graph. To train most AMR parsers, one needs to segment the graph into subgraphs and align each such subgraph to a word in a sentence; this is normally done at preprocessing, relying on hand-crafted rules. In contrast, we treat both alignment and segmentation as latent variables in our model and induce them as part of end-to-end training. As marginalizing over the structured latent variables is infeasible, we use the variational autoencoding framework. To ensure end-to-end differentiable optimization, we introduce a differentiable relaxation of the segmentation and alignment problems. We observe that inducing segmentation yields substantial gains over using a ‘greedy’ segmentation heuristic. The performance of our method also approaches that of a model that relies on the segmentation rules of Lyu and Titov (2018), which were hand-crafted to handle individual AMR constructions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_715", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_715.flac", "type": "audio", "duration": 449.051}, {"from": "Assistant", "value": "Neural Word Sense Disambiguation (WSD) has recently been shown to benefit from the incorporation of pre-existing knowledge, such as that coming from the WordNet graph. However, state-of-the-art approaches have been successful in exploiting only the local structure of the graph, with only close neighbors of a given synset influencing the prediction. In this work, we improve a classification model by recomputing logits as a function of both the vanilla independently produced logits and the global WordNet graph. We achieve this by incorporating an online neural approximated PageRank, which enables us to refine edge weights as well. This method exploits the global graph structure while keeping space requirements linear in the number of edges. We obtain strong improvements, matching the current state of the art. Code is available athttps://github.com/SapienzaNLP/neural-pagerank-wsd", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_716", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_716.flac", "type": "audio", "duration": 689.54}, {"from": "Assistant", "value": "Multilingual sentence embeddings capture rich semantic information not only for measuring similarity between texts but also for catering to a broad range of downstream cross-lingual NLP tasks. State-of-the-art multilingual sentence embedding models require large parallel corpora to learn efficiently, which confines the scope of these models. In this paper, we propose a novel sentence embedding framework based on an unsupervised loss function for generating effective multilingual sentence embeddings, eliminating the need for parallel corpora. We capture semantic similarity and relatedness between sentences using a multi-task loss function for training a dual encoder model mapping different languages onto the same vector space. We demonstrate the efficacy of an unsupervised as well as a weakly supervised variant of our framework on STS, BUCC and Tatoeba benchmark tasks. The proposed unsupervised sentence embedding framework outperforms even supervised state-of-the-art methods for certain under-resourced languages on the Tatoeba dataset and on a monolingual benchmark. Further, we show enhanced zero-shot learning capabilities for more than 30 languages, with the model being trained on only 13 languages. Our model can be extended to a wide range of languages from any language family, as it overcomes the requirement of parallel corpora for training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_717", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_717.flac", "type": "audio", "duration": 480.043}, {"from": "Assistant", "value": "While Masked Language Models (MLM) are pre-trained on massive datasets, the additional training with the MLM objective on domain or task-specific data before fine-tuning for the final task is known to improve the final performance. This is usually referred to as the domain or task adaptation step. However, unlike the initial pre-training, this step is performed for each domain or task individually and is still rather slow, requiring several GPU days compared to several GPU hours required for the final task fine-tuning. We argue that the standard MLM objective leads to inefficiency when it is used for the adaptation step because it mostly learns to predict the most frequent words, which are not necessarily related to a final task. We propose a technique for more efficient adaptation that focuses on predicting words with large weights of the Naive Bayes classifier trained for the task at hand, which are likely more relevant than the most frequent words. The proposed method provides faster adaptation and better final performance for sentiment analysis compared to the standard approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_718", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_718.flac", "type": "audio", "duration": 701.376}, {"from": "Assistant", "value": "As unlabeled data carry rich task-relevant information, they are proven useful for few-shot learning of language model. The question is how to effectively make use of such data. In this work, we revisit the self-training technique for language model fine-tuning and present a state-of-the-art prompt-based few-shot learner, SFLM. Given two views of a text sample via weak and strong augmentation techniques, SFLM generates a pseudo label on the weakly augmented version. Then, the model predicts the same pseudo label when fine-tuned with the strongly augmented version. This simple approach is shown to outperform other state-of-the-art supervised and semi-supervised counterparts on six sentence classification and six sentence-pair classification benchmarking tasks. In addition, SFLM only relies on a few in-domain unlabeled data. We conduct a comprehensive analysis to demonstrate the robustness of our proposed approach under various settings, including augmentation techniques, model scale, and few-shot knowledge transfer across tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_719", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_719.flac", "type": "audio", "duration": 404.693}, {"from": "Assistant", "value": "There is a growing consensus that surface form alone does not enable models to learn meaning and gain language understanding. This warrants an interest in hybrid systems that combine the strengths of neural and symbolic methods. We favour triadic systems consisting of neural networks, knowledge bases, and inference engines. The network provides perception, that is, the interface between the system and its environment. The knowledge base provides explicit memory and thus immediate access to established facts. Finally, inference capabilities are provided by the inference engine which reflects on the perception, supported by memory, to reason and discover new facts. In this work, we probe six popular language models for semantic relations and outline a future line of research to study how the constituent subsystems can be jointly realised and integrated.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_720", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_720.flac", "type": "audio", "duration": 537.937}, {"from": "Assistant", "value": "Effective unimodal representation and complementary crossmodal representation fusion are both important in multimodal representation learning. Prior works often modulate one modal feature to another straightforwardly and thus, underutilizing both unimodal and crossmodal representation refinements, which incurs a bottleneck of performance improvement. In this paper, Unimodal and Crossmodal Refinement Network (UCRN) is proposed to enhance both unimodal and crossmodal representations. Specifically, to improve unimodal representations, a unimodal refinement module is designed to refine modality-specific learning via iteratively updating the distribution with transformer-based attention layers. Self-quality improvement layers are followed to generate the desired weighted representations progressively. Subsequently, those unimodal representations are projected into a common latent space, regularized by a multimodal Jensen-Shannon divergence loss for better crossmodal refinement. Lastly, a crossmodal refinement module is employed to integrate all information. By hierarchical explorations on unimodal, bimodal, and trimodal interactions, UCRN is highly robust against missing modality and noisy data. Experimental results on MOSI and MOSEI datasets illustrated that the proposed UCRN outperforms recent state-of-the-art techniques and its robustness is highly preferred in real multimodal sequence fusion scenarios. Codes will be shared publicly.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_721", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_721.flac", "type": "audio", "duration": 713.131}, {"from": "Assistant", "value": "Current TSA evaluation in a cross-domain setup is restricted to the small set of review domains available in existing datasets. Such an evaluation is limited, and may not reflect true performance on sites like Amazon or Yelp that host diverse reviews from many domains. To address this gap, we present YASO – a new TSA evaluation dataset of open-domain user reviews. YASO contains 2,215 English sentences from dozens of review domains, annotated with target terms and their sentiment. Our analysis verifies the reliability of these annotations, and explores the characteristics of the collected data. Benchmark results using five contemporary TSA systems show there is ample room for improvement on this challenging new dataset. YASO is available athttps://github.com/IBM/yaso-tsa.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_722", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_722.flac", "type": "audio", "duration": 473.942}, {"from": "Assistant", "value": "Target-oriented opinion words extraction (TOWE) (Fan et al., 2019b) is a new subtask of target-oriented sentiment analysis that aims to extract opinion words for a given aspect in text. Current state-of-the-art methods leverage position embeddings to capture the relative position of a word to the target. However, the performance of these methods depends on the ability to incorporate this information into word representations. In this paper, we explore a variety of text encoders based on pretrained word embeddings or language models that leverage part-of-speech and position embeddings, aiming to examine the actual contribution of each component in TOWE. We also adapt a graph convolutional network (GCN) to enhance word representations by incorporating syntactic information. Our experimental results demonstrate that BiLSTM-based models can effectively encode position information into word representations while using a GCN only achieves marginal gains. Interestingly, our simple methods outperform several state-of-the-art complex neural structures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_723", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_723.flac", "type": "audio", "duration": 711.616}, {"from": "Assistant", "value": "In multimodal sentiment analysis (MSA), the performance of a model highly depends on the quality of synthesized embeddings. These embeddings are generated from the upstream process called multimodal fusion, which aims to extract and combine the input unimodal raw data to produce a richer multimodal representation. Previous work either back-propagates the task loss or manipulates the geometric property of feature spaces to produce favorable fusion results, which neglects the preservation of critical task-related information that flows from input to the fusion results. In this work, we propose a framework named MultiModal InfoMax (MMIM), which hierarchically maximizes the Mutual Information (MI) in unimodal input pairs (inter-modality) and between multimodal fusion result and unimodal input in order to maintain task-related information through multimodal fusion. The framework is jointly trained with the main task (MSA) to improve the performance of the downstream MSA task. To address the intractable issue of MI bounds, we further formulate a set of computationally simple parametric and non-parametric methods to approximate their truth value. Experimental results on the two widely used datasets demonstrate the efficacy of our approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_724", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_724.flac", "type": "audio", "duration": 452.053}, {"from": "Assistant", "value": "Graph-based Aspect-based Sentiment Classification (ABSC) approaches have yielded state-of-the-art results, expecially when equipped with contextual word embedding from pre-training language models (PLMs). However, they ignore sequential features of the context and have not yet made the best of PLMs. In this paper, we propose a novel model, BERT4GCN, which integrates the grammatical sequential features from the PLM of BERT, and the syntactic knowledge from dependency graphs. BERT4GCN utilizes outputs from intermediate layers of BERT and positional information between words to augment GCN (Graph Convolutional Network) to better encode the dependency graphs for the downstream classification. Experimental results demonstrate that the proposed BERT4GCN outperforms all state-of-the-art baselines, justifying that augmenting GCN with the grammatical features from intermediate layers of BERT can significantly empower ABSC models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_725", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_725.flac", "type": "audio", "duration": 477.056}, {"from": "Assistant", "value": "Online forums such as ChangeMyView have been explored to research aspects of persuasion and argumentative quality in language. While previous research has focused on arguments between a view-holder and a persuader, we explore the premise that apart from the merits of arguments, persuasion is influenced by the ambient social community. We hypothesize that comments from the rest of the community can either affirm the original view or implicitly exert pressure to change it. We develop a structured model to capture the ambient community’s sentiment towards the discussion and its effect on persuasion. Our experiments show that social features themselves are significantly predictive of persuasion (even without looking at the actual content of discussion), with performance comparable to some earlier approaches that use content features. Combining community and content features leads to overall performance of 78.5% on the persuasion prediction task. Our analyses suggest that the effect of social pressure is comparable to the difference between persuasive and non-persuasive language strategies in driving persuasion and that social pressure might be a causal factor for persuasion.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_726", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_726.flac", "type": "audio", "duration": 706.773}, {"from": "Assistant", "value": "Aspect-based sentiment analysis (ABSA) has been extensively studied in recent years, which typically involves four fundamental sentiment elements, including the aspect category, aspect term, opinion term, and sentiment polarity. Existing studies usually consider the detection of partial sentiment elements, instead of predicting the four elements in one shot. In this work, we introduce the Aspect Sentiment Quad Prediction (ASQP) task, aiming to jointly detect all sentiment elements in quads for a given opinionated sentence, which can reveal a more comprehensive and complete aspect-level sentiment structure. We further propose a novel Paraphrase modeling paradigm to cast the ASQP task to a paraphrase generation process. On one hand, the generation formulation allows solving ASQP in an end-to-end manner, alleviating the potential error propagation in the pipeline solution. On the other hand, the semantics of the sentiment elements can be fully exploited by learning to generate them in the natural language form. Extensive experiments on benchmark datasets show the superiority of our proposed method and the capacity of cross-task transfer with the proposed unified Paraphrase modeling framework.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_727", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_727.flac", "type": "audio", "duration": 712.128}, {"from": "Assistant", "value": "Many efforts have been made in solving the Aspect-based sentiment analysis (ABSA) task. While most existing studies focus on English texts, handling ABSA in resource-poor languages remains a challenging problem. In this paper, we consider the unsupervised cross-lingual transfer for the ABSA task, where only labeled data in the source language is available and we aim at transferring its knowledge to the target language having no labeled data. To this end, we propose an alignment-free label projection method to obtain high-quality pseudo-labeled data of the target language with the help of the translation system, which could preserve more accurate task-specific knowledge in the target language. For better utilizing the source and translated data, as well as enhancing the cross-lingual alignment, we design an aspect code-switching mechanism to augment the training data with code-switched bilingual sentences. To further investigate the importance of language-specific knowledge in solving the ABSA problem, we distill the above model on the unlabeled target language data which improves the performance to the same level of the supervised method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_728", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_728.flac", "type": "audio", "duration": 708.046}, {"from": "Assistant", "value": "Research in emotion analysis is scattered across different label formats (e.g., polarity types, basic emotion categories, and affective dimensions), linguistic levels (word vs. sentence vs. discourse), and, of course, (few well-resourced but much more under-resourced) natural languages and text genres (e.g., product reviews, tweets, news). The resulting heterogeneity makes data and software developed under these conflicting constraints hard to compare and challenging to integrate. To resolve this unsatisfactory state of affairs we here propose a training scheme that learns a shared latent representation of emotion independent from different label formats, natural languages, and even disparate model architectures. Experiments on a wide range of datasets indicate that this approach yields the desired interoperability without penalizing prediction quality. Code and data are archived under DOI 10.5281/zenodo.5466068.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_729", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_729.flac", "type": "audio", "duration": 685.058}, {"from": "Assistant", "value": "Unsupervised text style transfer aims to alter the underlying style of the text to a desired value while keeping its style-independent semantics, without the support of parallel training corpora. Existing methods struggle to achieve both high style conversion rate and low content loss, exhibiting the over-transfer and under-transfer problems. We attribute these problems to the conflicting driving forces of the style conversion goal and content preservation goal. In this paper, we propose a collaborative learning framework for unsupervised text style transfer using a pair of bidirectional decoders, one decoding from left to right while the other decoding from right to left. In our collaborative learning mechanism, each decoder is regularized by knowledge from its peer which has a different knowledge acquisition process. The difference is guaranteed by their opposite decoding directions and a distinguishability constraint. As a result, mutual knowledge distillation drives both decoders to a better optimum and alleviates the over-transfer and under-transfer problems. Experimental results on two benchmark datasets show that our framework achieves strong empirical results on both style compatibility and content preservation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_730", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_730.flac", "type": "audio", "duration": 479.051}, {"from": "Assistant", "value": "In this paper, we explore Non-AutoRegressive (NAR) decoding for unsupervised text style transfer. We first propose a base NAR model by directly adapting the common training scheme from its AutoRegressive (AR) counterpart. Despite the faster inference speed over the AR model, this NAR model sacrifices its transfer performance due to the lack of conditional dependence between output tokens. To this end, we investigate three techniques, i.e., knowledge distillation, contrastive learning, and iterative decoding, for performance enhancement. Experimental results on two benchmark datasets suggest that, although the base NAR model is generally inferior to AR decoding, their performance gap can be clearly narrowed when empowering NAR decoding with knowledge distillation, contrastive learning, and iterative decoding.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_731", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_731.flac", "type": "audio", "duration": 616.722}, {"from": "Assistant", "value": "Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinion term/span explaining the rationale behind the sentiment. Existing research efforts are majorly tagging-based. Among the methods taking a sequence tagging approach, some fail to capture the strong interdependence between the three opinion factors, whereas others fall short of identifying triplets with overlapping aspect/opinion spans. A recent grid tagging approach on the other hand fails to capture the span-level semantics while predicting the sentiment between an aspect-opinion pair. Different from these, we present a tagging-free solution for the task, while addressing the limitations of the existing works. We adapt an encoder-decoder architecture with a Pointer Network-based decoding framework that generates an entire opinion triplet at each time step thereby making our solution end-to-end. Interactions between the aspects and opinions are effectively captured by the decoder by considering their entire detected spans while predicting their connecting sentiment. Extensive experiments on several benchmark datasets establish the better efficacy of our proposed approach, especially in recall, and in predicting multiple and aspect/opinion-overlapped triplets from the same review sentence. We report our results both with and without BERT and also demonstrate the utility of domain-specific BERT post-training for the task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_732", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_732.flac", "type": "audio", "duration": 619.462}, {"from": "Assistant", "value": "We address the problem of temporal sentence localization in videos (TSLV). Traditional methods follow a top-down framework which localizes the target segment with pre-defined segment proposals. Although they have achieved decent performance, the proposals are handcrafted and redundant. Recently, bottom-up framework attracts increasing attention due to its superior efficiency. It directly predicts the probabilities for each frame as a boundary. However, the performance of bottom-up model is inferior to the top-down counterpart as it fails to exploit the segment-level interaction. In this paper, we propose an Adaptive Proposal Generation Network (APGN) to maintain the segment-level interaction while speeding up the efficiency. Specifically, we first perform a foreground-background classification upon the video and regress on the foreground frames to adaptively generate proposals. In this way, the handcrafted proposal design is discarded and the redundant proposals are decreased. Then, a proposal consolidation module is further developed to enhance the semantics of the generated proposals. Finally, we locate the target moments with these generated proposals following the top-down framework. Extensive experiments show that our proposed APGN significantly outperforms previous state-of-the-art methods on three challenging benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_733", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_733.flac", "type": "audio", "duration": 570.259}, {"from": "Assistant", "value": "A key solution to temporal sentence grounding (TSG) exists in how to learn effective alignment between vision and language features extracted from an untrimmed video and a sentence description. Existing methods mainly leverage vanilla soft attention to perform the alignment in a single-step process. However, such single-step attention is insufficient in practice, since complicated relations between inter- and intra-modality are usually obtained through multi-step reasoning. In this paper, we propose an Iterative Alignment Network (IA-Net) for TSG task, which iteratively interacts inter- and intra-modal features within multiple steps for more accurate grounding. Specifically, during the iterative reasoning process, we pad multi-modal features with learnable parameters to alleviate the nowhere-to-attend problem of non-matched frame-word pairs, and enhance the basic co-attention mechanism in a parallel manner. To further calibrate the misaligned attention caused by each reasoning step, we also devise a calibration module following each attention module to refine the alignment knowledge. With such iterative alignment scheme, our IA-Net can robustly capture the fine-grained relations between vision and language domains step-by-step for progressively reasoning the temporal boundaries. Extensive experiments conducted on three challenging benchmarks demonstrate that our proposed model performs better than the state-of-the-arts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_734", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_734.flac", "type": "audio", "duration": 347.092}, {"from": "Assistant", "value": "Pretrained language models demonstrate strong performance in most NLP tasks when fine-tuned on small task-specific datasets. Hence, these autoregressive models constitute ideal agents to operate in text-based environments where language understanding and generative capabilities are essential. Nonetheless, collecting expert demonstrations in such environments is a time-consuming endeavour. We introduce a two-stage procedure to learn from a small set of demonstrations and further improve by interacting with an environment. We show that language models fine-tuned with only 1.2% of the expert demonstrations and a simple reinforcement learning algorithm achieve a 51% absolute improvement in success rate over existing methods in the ALFWorld environment.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_735", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_735.flac", "type": "audio", "duration": 643.564}, {"from": "Assistant", "value": "Change captioning is to use a natural language sentence to describe the fine-grained disagreement between two similar images. Viewpoint change is the most typical distractor in this task, because it changes the scale and location of the objects and overwhelms the representation of real change. In this paper, we propose a Relation-embedded Representation Reconstruction Network (Rˆ3Net) to explicitly distinguish the real change from the large amount of clutter and irrelevant changes. Specifically, a relation-embedded module is first devised to explore potential changed objects in the large amount of clutter. Then, based on the semantic similarities of corresponding locations in the two images, a representation reconstruction module (RRM) is designed to learn the reconstruction representation and further model the difference representation. Besides, we introduce a syntactic skeleton predictor (SSP) to enhance the semantic interaction between change localization and caption generation. Extensive experiments show that the proposed method achieves the state-of-the-art results on two public datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_736", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_736.flac", "type": "audio", "duration": 472.921}, {"from": "Assistant", "value": "Generating goal-oriented questions in Visual Dialogue tasks is a challenging and longstanding problem. State-Of-The-Art systems are shown to generate questions that, although grammatically correct, often lack an effective strategy and sound unnatural to humans. Inspired by the cognitive literature on information search and cross-situational word learning, we design Confirm-it, a model based on a beam search re-ranking algorithm that guides an effective goal-oriented strategy by asking questions that confirm the model’s conjecture about the referent. We take the GuessWhat?! game as a case-study. We show that dialogues generated by Confirm-it are more natural and effective than beam search decoding without re-ranking.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_737", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_737.flac", "type": "audio", "duration": 725.056}, {"from": "Assistant", "value": "Transformer models have been used in automatic speech recognition (ASR) successfully and yields state-of-the-art results. However, its performance is still affected by speaker mismatch between training and test data. Further finetuning a trained model with target speaker data is the most natural approach for adaptation, but it takes a lot of compute and may cause catastrophic forgetting to the existing speakers. In this work, we propose a unified speaker adaptation approach consisting of feature adaptation and model adaptation. For feature adaptation, we employ a speaker-aware persistent memory model which generalizes better to unseen test speakers by making use of speaker i-vectors to form a persistent memory. For model adaptation, we use a novel gradual pruning method to adapt to target speakers without changing the model architecture, which to the best of our knowledge, has never been explored in ASR. Specifically, we gradually prune less contributing parameters on model encoder to a certain sparsity level, and use the pruned parameters for adaptation, while freezing the unpruned parameters to keep the original model performance. We conduct experiments on the Librispeech dataset. Our proposed approach brings relative 2.74-6.52% word error rate (WER) reduction on general speaker adaptation. On target speaker adaptation, our method outperforms the baseline with up to 20.58% relative WER reduction, and surpasses the finetuning method by up to relative 2.54%. Besides, with extremely low-resource adaptation data (e.g., 1 utterance), our method could improve the WER by relative 6.53% with only a few epochs of training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_738", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_738.flac", "type": "audio", "duration": 393.045}, {"from": "Assistant", "value": "The recently introduced hateful meme challenge demonstrates the difficulty of determining whether a meme is hateful or not. Specifically, both unimodal language models and multimodal vision-language models cannot reach the human level of performance. Motivated by the need to model the contrast between the image content and the overlayed text, we suggest applying an off-the-shelf image captioning tool in order to capture the first. We demonstrate that the incorporation of such automatic captions during fine-tuning improves the results for various unimodal and multimodal models. Moreover, in the unimodal case, continuing the pre-training of language models on augmented and original caption pairs, is highly beneficial to the classification accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_739", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_739.flac", "type": "audio", "duration": 364.902}, {"from": "Assistant", "value": "Transformer models have achieved state-of-the-art results in a wide range of NLP tasks including summarization. Training and inference using large transformer models can be computationally expensive. Previous work has focused on one important bottleneck, the quadratic self-attention mechanism in the encoder. Modified encoder architectures such as LED or LoBART use local attention patterns to address this problem for summarization. In contrast, this work focuses on the transformer’s encoder-decoder attention mechanism. The cost of this attention becomes more significant in inference or training approaches that require model-generated histories. First, we examine the complexity of the encoder-decoder attention. We demonstrate empirically that there is a sparse sentence structure in document summarization that can be exploited by constraining the attention mechanism to a subset of input sentences, whilst maintaining system performance. Second, we propose a modified architecture that selects the subset of sentences to constrain the encoder-decoder attention. Experiments are carried out on abstractive summarization tasks, including CNN/DailyMail, XSum, Spotify Podcast, and arXiv.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_740", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_740.flac", "type": "audio", "duration": 714.88}, {"from": "Assistant", "value": "Inductive transfer learning has taken the entire NLP field by storm, with models such as BERT and BART setting new state of the art on countless NLU tasks. However, most of the available models and research have been conducted for English. In this work, we introduce BARThez, the first large-scale pretrained seq2seq model for French. Being based on BART, BARThez is particularly well-suited for generative tasks. We evaluate BARThez on five discriminative tasks from the FLUE benchmark and two generative tasks from a novel summarization dataset, OrangeSum, that we created for this research. We show BARThez to be very competitive with state-of-the-art BERT-based French language models such as CamemBERT and FlauBERT. We also continue the pretraining of a multilingual BART on BARThez’ corpus, and show our resulting model, mBARThez, to significantly boost BARThez’ generative performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_741", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_741.flac", "type": "audio", "duration": 643.053}, {"from": "Assistant", "value": "Abstractive text summarization is one of the areas influenced by the emergence of pre-trained language models. Current pre-training works in abstractive summarization give more points to the summaries with more words in common with the main text and pay less attention to the semantic similarity between generated sentences and the original document. We propose ARMAN, a Transformer-based encoder-decoder model pre-trained with three novel objectives to address this issue. In ARMAN, salient sentences from a document are selected according to a modified semantic score to be masked and form a pseudo summary. To summarize more accurately and similar to human writing patterns, we applied modified sentence reordering. We evaluated our proposed models on six downstream Persian summarization tasks. Experimental results show that our proposed model achieves state-of-the-art performance on all six summarization tasks measured by ROUGE and BERTScore. Our models also outperform prior works in textual entailment, question paraphrasing, and multiple choice question answering. Finally, we established a human evaluation and show that using the semantic score significantly improves summarization results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_742", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_742.flac", "type": "audio", "duration": 627.055}, {"from": "Assistant", "value": "We present a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in a target language. The corpus covers twelve language pairs and directions for four European languages, namely Czech, English, French and German, and the methodology for its creation can be applied to several other languages. We derive cross-lingual document-summary instances from Wikipedia by combining lead paragraphs and articles’ bodies from language aligned Wikipedia titles. We analyse the proposed cross-lingual summarisation task with automatic metrics and validate it with a human study. To illustrate the utility of our dataset we report experiments with multi-lingual pre-trained models in supervised, zero- and few-shot, and out-of-domain scenarios.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_743", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_743.flac", "type": "audio", "duration": 618.88}, {"from": "Assistant", "value": "Opinion summarization has been traditionally approached with unsupervised, weakly-supervised and few-shot learning techniques. In this work, we collect a large dataset of summaries paired with user reviews for over 31,000 products, enabling supervised training. However, the number of reviews per product is large (320 on average), making summarization – and especially training a summarizer – impractical. Moreover, the content of many reviews is not reflected in the human-written summaries, and, thus, the summarizer trained on random review subsets hallucinates. In order to deal with both of these challenges, we formulate the task as jointly learning to select informative subsets of reviews and summarizing the opinions expressed in these subsets. The choice of the review subset is treated as a latent variable, predicted by a small and simple selector. The subset is then fed into a more powerful summarizer. For joint training, we use amortized variational inference and policy gradient methods. Our experiments demonstrate the importance of selecting informative reviews resulting in improved quality of summaries and reduced hallucinations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_744", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_744.flac", "type": "audio", "duration": 424.043}, {"from": "Assistant", "value": "Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries. Nevertheless, these models still suffer from the short-range dependency problem, causing them to produce summaries that miss the key points of document. In this paper, we attempt to address this issue by introducing a neural topic model empowered with normalizing flow to capture the global semantics of the document, which are then integrated into the summarization model. In addition, to avoid the overwhelming effect of global semantics on contextualized representation, we introduce a mechanism to control the amount of global semantics supplied to the text generation module. Our method outperforms state-of-the-art summarization models on five common text summarization datasets, namely CNN/DailyMail, XSum, Reddit TIFU, arXiv, and PubMed.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_745", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_745.flac", "type": "audio", "duration": 608.043}, {"from": "Assistant", "value": "We compare two orthogonal semi-supervised learning techniques, namely tri-training and pretrained word embeddings, in the task of dependency parsing. We explore language-specific FastText and ELMo embeddings and multilingual BERT embeddings. We focus on a low resource scenario as semi-supervised learning can be expected to have the most impact here. Based on treebank size and available ELMo models, we select Hungarian, Uyghur (a zero-shot language for mBERT) and Vietnamese. Furthermore, we include English in a simulated low-resource setting. We find that pretrained word embeddings make more effective use of unlabelled data than tri-training but that the two approaches can be successfully combined.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_746", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_746.flac", "type": "audio", "duration": 479.616}, {"from": "Assistant", "value": "Zero-shot cross-domain slot filling alleviates the data dependence in the case of data scarcity in the target domain, which has aroused extensive research. However, as most of the existing methods do not achieve effective knowledge transfer to the target domain, they just fit the distribution of the seen slot and show poor performance on unseen slot in the target domain. To solve this, we propose a novel approach based on prototypical contrastive learning with a dynamic label confusion strategy for zero-shot slot filling. The prototypical contrastive learning aims to reconstruct the semantic constraints of labels, and we introduce the label confusion strategy to establish the label dependence between the source domains and the target domain on-the-fly. Experimental results show that our model achieves significant improvement on the unseen slots, while also set new state-of-the-arts on slot filling task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_747", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_747.flac", "type": "audio", "duration": 712.063}, {"from": "Assistant", "value": "Neural models and symbolic rules such as regular expressions have their respective merits and weaknesses. In this paper, we study the integration of the two approaches for the slot filling task by converting regular expressions into neural networks. Specifically, we first convert regular expressions into a special form of finite-state transducers, then unfold its approximate inference algorithm as a bidirectional recurrent neural model that performs slot filling via sequence labeling. Experimental results show that our model has superior zero-shot and few-shot performance and stays competitive when there are sufficient training data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_748", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_748.flac", "type": "audio", "duration": 753.139}, {"from": "Assistant", "value": "The principle of independent causal mechanisms (ICM) states that generative processes of real world data consist of independent modules which do not influence or inform each other. While this idea has led to fruitful developments in the field of causal inference, it is not widely-known in the NLP community. In this work, we argue that the causal direction of the data collection process bears nontrivial implications that can explain a number of published NLP findings, such as differences in semi-supervised learning (SSL) and domain adaptation (DA) performance across different settings. We categorize common NLP tasks according to their causal direction and empirically assay the validity of the ICM principle for text data using minimum description length. We conduct an extensive meta-analysis of over 100 published SSL and 30 DA studies, and find that the results are consistent with our expectations based on causal insights. This work presents the first attempt to analyze the ICM principle in NLP, and provides constructive suggestions for future modeling choices.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_749", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_749.flac", "type": "audio", "duration": 698.967}, {"from": "Assistant", "value": "Recent pretrained language models extend from millions to billions of parameters. Thus the need to fine-tune an extremely large pretrained model with a limited training corpus arises in various downstream tasks. In this paper, we propose a straightforward yet effective fine-tuning technique, Child-Tuning, which updates a subset of parameters (called child network) of large pretrained models via strategically masking out the gradients of the non-child network during the backward process. Experiments on various downstream tasks in GLUE benchmark show that Child-Tuning consistently outperforms the vanilla fine-tuning by 1.5 8.6 average score among four different pretrained models, and surpasses the prior fine-tuning techniques by 0.6 1.3 points. Furthermore, empirical results on domain transfer and task transfer show that Child-Tuning can obtain better generalization performance by large margins.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_750", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_750.flac", "type": "audio", "duration": 700.693}, {"from": "Assistant", "value": "Knowledge Graph Embeddings (KGEs) have shown promising performance on link prediction tasks by mapping the entities and relations from a knowledge graph into a geometric space. The capability of KGEs in preserving graph characteristics including structural aspects and semantics, highly depends on the design of their score function, as well as the inherited abilities from the underlying geometry. Many KGEs use the Euclidean geometry which renders them incapable of preserving complex structures and consequently causes wrong inferences by the models. To address this problem, we propose a neuro differential KGE that embeds nodes of a KG on the trajectories of Ordinary Differential Equations (ODEs). To this end, we represent each relation (edge) in a KG as a vector field on several manifolds. We specifically parameterize ODEs by a neural network to represent complex manifolds and complex vector fields on the manifolds. Therefore, the underlying embedding space is capable to assume the shape of various geometric forms to encode heterogeneous subgraphs. Experiments on synthetic and benchmark datasets using state-of-the-art KGE models justify the ODE trajectories as a means to enable structure preservation and consequently avoiding wrong inferences.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_751", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_751.flac", "type": "audio", "duration": 470.059}, {"from": "Assistant", "value": "The absence of labeled data for training neural models is often addressed by leveraging knowledge about the specific task, resulting in heuristic but noisy labels. The knowledge is captured in labeling functions, which detect certain regularities or patterns in the training samples and annotate corresponding labels for training. This process of weakly supervised training may result in an over-reliance on the signals captured by the labeling functions and hinder models to exploit other signals or to generalize well. We propose KnowMAN, an adversarial scheme that enables to control influence of signals associated with specific labeling functions. KnowMAN forces the network to learn representations that are invariant to those signals and to pick up other signals that are more generally associated with an output label. KnowMAN strongly improves results compared to direct weakly supervised learning with a pre-trained transformer language model and a feature-based baseline.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_752", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_752.flac", "type": "audio", "duration": 267.006}, {"from": "Assistant", "value": "Backdoor attacks are a kind of emergent training-time threat to deep neural networks (DNNs). They can manipulate the output of DNNs and possess high insidiousness. In the field of natural language processing, some attack methods have been proposed and achieve very high attack success rates on multiple popular models. Nevertheless, there are few studies on defending against textual backdoor attacks. In this paper, we propose a simple and effective textual backdoor defense named ONION, which is based on outlier word detection and, to the best of our knowledge, is the first method that can handle all the textual backdoor attack situations. Experiments demonstrate the effectiveness of our model in defending BiLSTM and BERT against five different backdoor attacks. All the code and data of this paper can be obtained athttps://github.com/thunlp/ONION.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_753", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_753.flac", "type": "audio", "duration": 465.049}, {"from": "Assistant", "value": "Following the success of dot-product attention in Transformers, numerous approximations have been recently proposed to address its quadratic complexity with respect to the input length. However, all approximations thus far have ignored the contribution of the *value vectors* to the quality of approximation. In this work, we argue that research efforts should be directed towards approximating the true output of the attention sub-layer, which includes the value vectors. We propose a value-aware objective, and show theoretically and empirically that an optimal approximation of a value-aware objective substantially outperforms an optimal approximation that ignores values, in the context of language modeling. Moreover, we show that the choice of kernel function for computing attention similarity can substantially affect the quality of sparse approximations, where kernel functions that are less skewed are more affected by the value vectors.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_754", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_754.flac", "type": "audio", "duration": 595.778}, {"from": "Assistant", "value": "Question generation has recently shown impressive results in customizing question answering (QA) systems to new domains. These approaches circumvent the need for manually annotated training data from the new domain and, instead, generate synthetic question-answer pairs that are used for training. However, existing methods for question generation rely on large amounts of synthetically generated datasets and costly computational resources, which render these techniques widely inaccessible when the text corpora is of limited size. This is problematic as many niche domains rely on small text corpora, which naturally restricts the amount of synthetic data that can be generated. In this paper, we propose a novel framework for domain adaptation called contrastive domain adaptation for QA (CAQA). Specifically, CAQA combines techniques from question generation and domain-invariant learning to answer out-of-domain questions in settings with limited text corpora. Here, we train a QA system on both source data and generated data from the target domain with a contrastive adaptation loss that is incorporated in the training objective. By combining techniques from question generation and domain-invariant learning, our model achieved considerable improvements compared to state-of-the-art baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_755", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_755.flac", "type": "audio", "duration": 765.051}, {"from": "Assistant", "value": "It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions — a paradigm known as case-based reasoning (CBR). We propose a neuro-symbolic CBR approach (CBR-KBQA) for question answering over large knowledge bases. CBR-KBQA consists of a nonparametric memory that stores cases (question and logical forms) and a parametric model that can generate a logical form for a new question by retrieving cases that are relevant to it. On several KBQA datasets that contain complex questions, CBR-KBQA achieves competitive performance. For example, on the CWQ dataset, CBR-KBQA outperforms the current state of the art by 11% on accuracy. Furthermore, we show that CBR-KBQA is capable of using new caseswithoutany further training: by incorporating a few human-labeled examples in the case memory, CBR-KBQA is able to successfully generate logical forms containing unseen KB entities as well as relations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_756", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_756.flac", "type": "audio", "duration": 720.619}, {"from": "Assistant", "value": "Open-domain question answering answers a question based on evidence retrieved from a large corpus. State-of-the-art neural approaches require intermediate evidence annotations for training. However, such intermediate annotations are expensive, and methods that rely on them cannot transfer to the more common setting, where only question–answer pairs are available. This paper investigates whether models can learn to find evidence from a large corpus, with only distant supervision from answer labels for model training, thereby generating no additional annotation cost. We introduce a novel approach (DistDR) that iteratively improves over a weak retriever by alternately finding evidence from the up-to-date model and encouraging the model to learn the most likely evidence. Without using any evidence labels, DistDR is on par with fully-supervised state-of-the-art methods on both multi-hop and single-hop QA benchmarks. Our analysis confirms that DistDR finds more accurate evidence over iterations, which leads to model improvements. The code is available athttps://github.com/henryzhao5852/DistDR.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_758", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_758.flac", "type": "audio", "duration": 486.08}, {"from": "Assistant", "value": "Question answering (QA) primarily descends from two branches of research: (1) Alan Turing’s investigation of machine intelligence at Manchester University and (2) Cyril Cleverdon’s comparison of library card catalog indices at Cranfield University. This position paper names and distinguishes these paradigms. Despite substantial overlap, subtle but significant distinctions exert an outsize influence on research. While one evaluation paradigm values creating more intelligent QA systems, the other paradigm values building QA systems that appeal to users. By better understanding the epistemic heritage of QA, researchers, academia, and industry can more effectively accelerate QA research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_759", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_759.flac", "type": "audio", "duration": 321.411}, {"from": "Assistant", "value": "Numerical reasoning based machine reading comprehension is a task that involves reading comprehension along with using arithmetic operations such as addition, subtraction, sorting and counting. The DROP benchmark (Dua et al., 2019) is a recent dataset that has inspired the design of NLP models aimed at solving this task. The current standings of these models in the DROP leaderboard, over standard metrics, suggests that the models have achieved near-human performance. However, does this mean that these models have learned to reason? In this paper, we present a controlled study on some of the top-performing model architectures for the task of numerical reasoning. Our observations suggest that the standard metrics are incapable of measuring progress towards such tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_760", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_760.flac", "type": "audio", "duration": 376.001}, {"from": "Assistant", "value": "The task of knowledge base population (KBP) aims to discover facts about entities from texts and expand a knowledge base with these facts. Previous studies shape end-to-end KBP as a machine translation task, which is required to convert unordered fact into a sequence according to a pre-specified order. However, the facts stated in a sentence are unordered in essence. In this paper, we formulate end-to-end KBP as a direct set generation problem, avoiding considering the order of multiple facts. To solve the set generation problem, we propose networks featured by transformers with non-autoregressive parallel decoding. Unlike previous approaches that use an autoregressive decoder to generate facts one by one, the proposed networks can directly output the final set of facts in one shot. Furthermore, to train the networks, we also design a set-based loss that forces unique predictions via bipartite matching. Compared with cross-entropy loss that highly penalizes small shifts in fact order, the proposed bipartite matching loss is invariant to any permutation of predictions. Benefiting from getting rid of the burden of predicting the order of multiple facts, our proposed networks achieve state-of-the-art (SoTA) performance on two benchmark datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_761", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_761.flac", "type": "audio", "duration": 637.016}, {"from": "Assistant", "value": "Distantly supervised relation extraction (RE) automatically aligns unstructured text with relation instances in a knowledge base (KB). Due to the incompleteness of current KBs, sentences implying certain relations may be annotated as N/A instances, which causes the so-called false negative (FN) problem. Current RE methods usually overlook this problem, inducing improper biases in both training and testing procedures. To address this issue, we propose a two-stage approach. First, it finds out possible FN samples by heuristically leveraging the memory mechanism of deep neural networks. Then, it aligns those unlabeled data with the training data into a unified feature space by adversarial training to assign pseudo labels and further utilize the information contained in them. Experiments on two wildly-used benchmark datasets demonstrate the effectiveness of our approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_762", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_762.flac", "type": "audio", "duration": 702.336}, {"from": "Assistant", "value": "Bootstrapping has become the mainstream method for entity set expansion. Conventional bootstrapping methods mostly define the expansion boundary using seed-based distance metrics, which heavily depend on the quality of selected seeds and are hard to be adjusted due to the extremely sparse supervision. In this paper, we propose BootstrapGAN, a new learning method for bootstrapping which jointly models the bootstrapping process and the boundary learning process in a GAN framework. Specifically, the expansion boundaries of different bootstrapping iterations are learned via different discriminator networks; the bootstrapping network is the generator to generate new positive entities, and the discriminator networks identify the expansion boundaries by trying to distinguish the generated entities from known positive entities. By iteratively performing the above adversarial learning, the generator and the discriminators can reinforce each other and be progressively refined along the whole bootstrapping process. Experiments show that BootstrapGAN achieves the new state-of-the-art entity set expansion performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_763", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_763.flac", "type": "audio", "duration": 453.056}, {"from": "Assistant", "value": "Information Extraction (IE) aims to extract structural information from unstructured texts. In practice, long-tailed distributions caused by the selection bias of a dataset may lead to incorrect correlations, also known as spurious correlations, between entities and labels in the conventional likelihood models. This motivates us to propose counterfactual IE (CFIE), a novel framework that aims to uncover the main causalities behind data in the view of causal inference. Specifically, 1) we first introduce a unified structural causal model (SCM) for various IE tasks, describing the relationships among variables; 2) with our SCM, we then generate counterfactuals based on an explicit language structure to better calculate the direct causal effect during the inference stage; 3) we further propose a novel debiasing approach to yield more robust predictions. Experiments on three IE tasks across five public datasets show the effectiveness of our CFIE model in mitigating the spurious correlation issues.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_764", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_764.flac", "type": "audio", "duration": 734.037}, {"from": "Assistant", "value": "Open Information Extraction (OpenIE) aims to discover textual facts from a given sentence. In essence, the facts contained in plain text are unordered. However, the popular OpenIE systems usually output facts sequentially in the way of predicting the next fact conditioned on the previous decoded ones, which enforce an unnecessary order on the facts and involve the error accumulation between autoregressive steps. To break this bottleneck, we propose MacroIE, a novel non-autoregressive framework for OpenIE. MacroIE firstly constructs a fact graph based on the table filling scheme, in which each node denotes a fact element, and an edge links two nodes that belong to the same fact. Then OpenIE can be reformulated as a non-parametric process of finding maximal cliques from the graph. It directly outputs the final set of facts in one go, thus getting rid of the burden of predicting fact order, as well as the error propagation between facts. Experiments conducted on two benchmark datasets show that our proposed model significantly outperforms current state-of-the-art methods, beats the previous systems by as much as 5.7 absolute gain in F1 score.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_765", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_765.flac", "type": "audio", "duration": 675.051}, {"from": "Assistant", "value": "The clustering-based unsupervised relation discovery method has gradually become one of the important methods of open relation extraction (OpenRE). However, high-dimensional vectors can encode complex linguistic information which leads to the problem that the derived clusters cannot explicitly align with the relational semantic classes. In this work, we propose a relation-oriented clustering model and use it to identify the novel relations in the unlabeled data. Specifically, to enable the model to learn to cluster relational data, our method leverages the readily available labeled data of pre-defined relations to learn a relation-oriented representation. We minimize distance between the instance with same relation by gathering the instances towards their corresponding relation centroids to form a cluster structure, so that the learned representation is cluster-friendly. To reduce the clustering bias on predefined classes, we optimize the model by minimizing a joint objective on both labeled and unlabeled data. Experimental results show that our method reduces the error rate by 29.2% and 15.7%, on two datasets respectively, compared with current SOTA methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_766", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_766.flac", "type": "audio", "duration": 688.007}, {"from": "Assistant", "value": "The task of generating explanatory notes for language learners is known as feedback comment generation. Although various generation techniques are available, little is known about which methods are appropriate for this task. Nagata (2019) demonstrates the effectiveness of neural-retrieval-based methods in generating feedback comments for preposition use. Retrieval-based methods have limitations in that they can only output feedback comments existing in a given training data. Furthermore, feedback comments can be made on other grammatical and writing items than preposition use, which is still unaddressed. To shed light on these points, we investigate a wider range of methods for generating many feedback comments in this study. Our close analysis of the type of task leads us to investigate three different architectures for comment generation: (i) a neural-retrieval-based method as a baseline, (ii) a pointer-generator-based generation method as a neural seq2seq method, (iii) a retrieve-and-edit method, a hybrid of (i) and (ii). Intuitively, the pointer-generator should outperform neural-retrieval, and retrieve-and-edit should perform best. However, in our experiments, this expectation is completely overturned. We closely analyze the results to reveal the major causes of these counter-intuitive results and report on our findings from the experiments.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_767", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_767.flac", "type": "audio", "duration": 719.099}, {"from": "Assistant", "value": "Chatbot is increasingly thriving in different domains, however, because of unexpected discourse complexity and training data sparseness, its potential distrust hatches vital apprehension. Recently, Machine-Human Chatting Handoff (MHCH), predicting chatbot failure and enabling human-algorithm collaboration to enhance chatbot quality, has attracted increasing attention from industry and academia. In this study, we propose a novel model, Role-Selected Sharing Network (RSSN), which integrates both dialogue satisfaction estimation and handoff prediction in one multi-task learning framework. Unlike prior efforts in dialog mining, by utilizing local user satisfaction as a bridge, global satisfaction detector and handoff predictor can effectively exchange critical information. Specifically, we decouple the relation and interaction between the two tasks by the role information after the shared encoder. Extensive experiments on two public datasets demonstrate the effectiveness of our model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_768", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_768.flac", "type": "audio", "duration": 624.129}, {"from": "Assistant", "value": "With the wide availability of Pre-trained Language Models (PLMs), multi-task fine-tuning across domains has been extensively applied. For tasks related to distant domains with different class label sets, PLMs may memorize non-transferable knowledge for the target domain and suffer from negative transfer. Inspired by meta-learning, we propose the Meta Distant Transfer Learning (Meta-DTL) framework to learn the cross-task knowledge for PLM-based methods. Meta-DTL first employs task representation learning to mine implicit relations among multiple tasks and classes. Based on the results, it trains a PLM-based meta-learner to capture the transferable knowledge across tasks. The weighted maximum entropy regularizers are proposed to make meta-learner more task-agnostic and unbiased. Finally, the meta-learner can be fine-tuned to fit each task with better parameter initialization. We evaluate Meta-DTL using both BERT and ALBERT on seven public datasets. Experiment results confirm the superiority of Meta-DTL as it consistently outperforms strong baselines. We find that Meta-DTL is highly effective when very few data is available for the target task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_769", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_769.flac", "type": "audio", "duration": 822.219}, {"from": "Assistant", "value": "Knowledge graph inference has been studied extensively due to its wide applications. It has been addressed by two lines of research, i.e., the more traditional logical rule reasoning and the more recent knowledge graph embedding (KGE). Several attempts have been made to combine KGE and logical rules for better knowledge graph inference. Unfortunately, they either simply treat logical rules as additional constraints into KGE loss or use probabilistic model to approximate the exact logical inference (i.e., MAX-SAT). Even worse, both approaches need to sample ground rules to tackle the scalability issue, as the total number of ground rules is intractable in practice, making them less effective in handling logical rules. In this paper, we propose a novel framework UniKER to address these challenges by restricting logical rules to be definite Horn rules, which can fully exploit the knowledge in logical rules and enable the mutual enhancement of logical rule-based reasoning and KGE in an extremely efficient way. Extensive experiments have demonstrated that our approach is superior to existing state-of-the-art algorithms in terms of both efficiency and effectiveness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_770", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_770.flac", "type": "audio", "duration": 607.062}, {"from": "Assistant", "value": "Transfer learning (TL) seeks to improve the learning of a data-scarce target domain by using information from source domains. However, the source and target domains usually have different data distributions, which may lead to negative transfer. To alleviate this issue, we propose a Wasserstein Selective Transfer Learning (WSTL) method. Specifically, the proposed method considers a reinforced selector to select helpful data for transfer learning. We further use a Wasserstein-based discriminator to maximize the empirical distance between the selected source data and target data. The TL module is then trained to minimize the estimated Wasserstein distance in an adversarial manner and provides domain invariant features for the reinforced selector. We adopt an evaluation metric based on the performance of the TL module as delayed reward and a Wasserstein-based metric as immediate rewards to guide the reinforced selector learning. Compared with the competing TL approaches, the proposed method selects data samples that are closer to the target domain. It also provides better state features and reward signals that lead to better performance with faster convergence. Extensive experiments on three real-world text mining tasks demonstrate the effectiveness of the proposed method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_771", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_771.flac", "type": "audio", "duration": 673.054}, {"from": "Assistant", "value": "We propose a novel task of jointly repairing program codes and generating commit messages. Code repair and commit message generation are two essential and related tasks for software development. However, existing work usually performs the two tasks independently. We construct a multilingual triple dataset including buggy code, fixed code, and commit messages for this novel task. We first introduce a cascaded method with two models, one is to generate the fixed code first, and the other generates the commit message based on the fixed and original codes. We enhance the cascaded method with different training approaches, including the teacher-student method, the multi-task method, and the back-translation method. To deal with the error propagation problem of the cascaded method, we also propose a joint model that can both repair the program code and generate the commit message in a unified framework. Massive experiments on our constructed buggy-fixed-commit dataset reflect the challenge of this task and that the enhanced cascaded model and the proposed joint model significantly outperform baselines in both quality of code and commit messages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_772", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_772.flac", "type": "audio", "duration": 714.083}, {"from": "Assistant", "value": "By exploiting the cross-modal attention, cross-BERT methods have achieved state-of-the-art accuracy in cross-modal retrieval. Nevertheless, the heavy text-image interactions in the cross-BERT model are prohibitively slow for large-scale retrieval. Late-interaction methods trade off retrieval accuracy and efficiency by exploiting cross-modal interaction only in the late stage, attaining a satisfactory retrieval speed. In this work, we propose an inflating and shrinking approach to further boost the efficiency and accuracy of late-interaction methods. The inflating operation plugs several codes in the input of the encoder to exploit the text-image interactions more thoroughly for higher retrieval accuracy. Then the shrinking operation gradually reduces the text-image interactions through knowledge distilling for higher efficiency. Through an inflating operation followed by a shrinking operation, both efficiency and accuracy of a late-interaction model are boosted. Systematic experiments on public benchmarks demonstrate the effectiveness of our inflating and shrinking approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_773", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_773.flac", "type": "audio", "duration": 523.703}, {"from": "Assistant", "value": "Video grounding aims to localize the temporal segment corresponding to a sentence query from an untrimmed video. Almost all existing video grounding methods fall into two frameworks: 1) Top-down model: It predefines a set of segment candidates and then conducts segment classification and regression. 2) Bottom-up model: It directly predicts frame-wise probabilities of the referential segment boundaries. However, all these methods are not end-to-end, i.e., they always rely on some time-consuming post-processing steps to refine predictions. To this end, we reformulate video grounding as a set prediction task and propose a novel end-to-end multi-modal Transformer model, dubbed as GTR. Specifically, GTR has two encoders for video and language encoding, and a cross-modal decoder for grounding prediction. To facilitate the end-to-end training, we use a Cubic Embedding layer to transform the raw videos into a set of visual tokens. To better fuse these two modalities in the decoder, we design a new Multi-head Cross-Modal Attention. The whole GTR is optimized via a Many-to-One matching loss. Furthermore, we conduct comprehensive studies to investigate different model design choices. Extensive results on three benchmarks have validated the superiority of GTR. All three typical GTR variants achieve record-breaking performance on all datasets and metrics, with several times faster inference speed.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_774", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_774.flac", "type": "audio", "duration": 693.056}, {"from": "Assistant", "value": "While interest in models that generalize at test time to new compositions has risen in recent years, benchmarks in the visually-grounded domain have thus far been restricted to synthetic images. In this work, we propose COVR, a new test-bed for visually-grounded compositional generalization with real images. To create COVR, we use real images annotated with scene graphs, and propose an almost fully automatic procedure for generating question-answer pairs along with a set of context images. COVR focuses on questions that require complex reasoning, including higher-order operations such as quantification and aggregation. Due to the automatic generation process, COVR facilitates the creation of compositional splits, where models at test time need to generalize to new concepts and compositions in a zero- or few-shot setting. We construct compositional splits using COVR and demonstrate a myriad of cases where state-of-the-art pre-trained language-and-vision models struggle to compositionally generalize.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_775", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_775.flac", "type": "audio", "duration": 539.051}, {"from": "Assistant", "value": "Pretrained vision-and-language BERTs aim to learn representations that combine information from both modalities. We propose a diagnostic method based on cross-modal input ablation to assess the extent to which these models actually integrate cross-modal information. This method involves ablating inputs from one modality, either entirely or selectively based on cross-modal grounding alignments, and evaluating the model prediction performance on the other modality. Model performance is measured by modality-specific tasks that mirror the model pretraining objectives (e.g. masked language modelling for text). Models that have learned to construct cross-modal representations using both modalities are expected to perform worse when inputs are missing from a modality. We find that recently proposed models have much greater relative difficulty predicting text when visual information is ablated, compared to predicting visual object categories when text is ablated, indicating that these models are not symmetrically cross-modal.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_776", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_776.flac", "type": "audio", "duration": 719.893}, {"from": "Assistant", "value": "Interpolation-based regularisation methods for data augmentation have proven to be effective for various tasks and modalities. These methods involve performing mathematical operations over the raw input samples or their latent states representations - vectors that often possess complex hierarchical geometries. However, these operations are performed in the Euclidean space, simplifying these representations, which may lead to distorted and noisy interpolations. We propose HypMix, a novel model-, data-, and modality-agnostic interpolative data augmentation technique operating in the hyperbolic space, which captures the complex geometry of input and hidden state hierarchies better than its contemporaries. We evaluate HypMix on benchmark and low resource datasets across speech, text, and vision modalities, showing that HypMix consistently outperforms state-of-the-art data augmentation techniques. In addition, we demonstrate the use of HypMix in semi-supervised settings. We further probe into the adversarial robustness and qualitative inferences we draw from HypMix that elucidate the efficacy of the Riemannian hyperbolic manifolds for interpolation-based data augmentation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_777", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_777.flac", "type": "audio", "duration": 625.058}, {"from": "Assistant", "value": "Scripts are structured sequences of events together with the participants, which are extracted from the texts. Script event prediction aims to predict the subsequent event given the historical events in the script. Two kinds of information facilitate this task, namely, the event-level information and the script-level information. At the event level, existing studies view an event as a verb with its participants, while neglecting other useful properties, such as the state of the participants. At the script level, most existing studies only consider a single event sequence corresponding to one common protagonist. In this paper, we propose a Transformer-based model, called MCPredictor, which integrates deep event-level and script-level information for script event prediction. At the event level, MCPredictor utilizes the rich information in the text to obtain more comprehensive event semantic representations. At the script-level, it considers multiple event sequences corresponding to different participants of the subsequent event. The experimental results on the widely-used New York Times corpus demonstrate the effectiveness and superiority of the proposed model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_778", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_778.flac", "type": "audio", "duration": 754.048}, {"from": "Assistant", "value": "Multi-text applications, such as multi-document summarization, are typically required to model redundancies across related texts. Current methods confronting consolidation struggle to fuse overlapping information. In order to explicitly represent content overlap, we propose to align predicate-argument relations across texts, providing a potential scaffold for information consolidation. We go beyond clustering coreferring mentions, and instead model overlap with respect to redundancy at a propositional level, rather than merely detecting shared referents. Our setting exploits QA-SRL, utilizing question-answer pairs to capture predicate-argument relations, facilitating laymen annotation of cross-text alignments. We employ crowd-workers for constructing a dataset of QA-based alignments, and present a baseline QA alignment model trained over our dataset. Analyses show that our new task is semantically challenging, capturing content overlap beyond lexical similarity and complements cross-document coreference with proposition-level links, offering potential use for downstream tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_779", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_779.flac", "type": "audio", "duration": 461.611}, {"from": "Assistant", "value": "Large pre-trained language models for textual data have an unconstrained output space; at each decoding step, they can produce any of 10,000s of sub-word tokens. When fine-tuned to target constrained formal languages like SQL, these models often generate invalid code, rendering it unusable. We propose PICARD (code available athttps://github.com/ElementAI/picard), a method for constraining auto-regressive decoders of language models through incremental parsing. PICARD helps to find valid output sequences by rejecting inadmissible tokens at each decoding step. On the challenging Spider and CoSQL text-to-SQL translation tasks, we show that PICARD transforms fine-tuned T5 models with passable performance into state-of-the-art solutions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_780", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_780.flac", "type": "audio", "duration": 482.047}, {"from": "Assistant", "value": "Semantic sentence embeddings are usually supervisedly built minimizing distances between pairs of embeddings of sentences labelled as semantically similar by annotators. Since big labelled datasets are rare, in particular for non-English languages, and expensive, recent studies focus on unsupervised approaches that require not-paired input sentences. We instead propose a language-independent approach to build large datasets of pairs of informal texts weakly similar, without manual human effort, exploiting Twitter’s intrinsic powerful signals of relatedness: replies and quotes of tweets. We use the collected pairs to train a Transformer model with triplet-like structures, and we test the generated embeddings on Twitter NLP similarity tasks (PIT and TURL) and STSb. We also introduce four new sentence ranking evaluation benchmarks of informal texts, carefully extracted from the initial collections of tweets, proving not only that our best model learns classical Semantic Textual Similarity, but also excels on tasks where pairs of sentences are not exact paraphrases. Ablation studies reveal how increasing the corpus size influences positively the results, even at 2M samples, suggesting that bigger collections of Tweets still do not contain redundant information about semantic similarities. Code available athttps://github.com/marco-digio/Twitter4SSE", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_781", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_781.flac", "type": "audio", "duration": 483.486}, {"from": "Assistant", "value": "What do linguistic models reveal about the emotions associated with words? In this study, we consider the task of estimating word-level emotion intensity scores for specific emotions, exploring unsupervised, supervised, and finally a self-supervised method of extracting emotional associations from pretrained vectors and models. Overall, we find that linguistic models carry substantial potential for inducing fine-grained emotion intensity scores, showing a far higher correlation with human ground truth ratings than state-of-the-art emotion lexicons based on labeled data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_783", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_783.flac", "type": "audio", "duration": 710.925}, {"from": "Assistant", "value": "Extracting moral sentiment from text is a vital component in understanding public opinion, social movements, and policy decisions. The Moral Foundation Theory identifies five moral foundations, each associated with a positive and negative polarity. However, moral sentiment is often motivated by its targets, which can correspond to individuals or collective entities. In this paper, we introduce morality frames, a representation framework for organizing moral attitudes directed at different entities, and come up with a novel and high-quality annotated dataset of tweets written by US politicians. Then, we propose a relational learning model to predict moral attitudes towards entities and moral foundations jointly. We do qualitative and quantitative evaluations, showing that moral sentiment towards entities differs highly across political ideologies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_784", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_784.flac", "type": "audio", "duration": 721.769}, {"from": "Assistant", "value": "Certainty and uncertainty are fundamental to science communication. Hedges have widely been used as proxies for uncertainty. However, certainty is a complex construct, with authors expressing not only the degree but the type and aspects of uncertainty in order to give the reader a certain impression of what is known. Here, we introduce a new study of certainty that models both the level and the aspects of certainty in scientific findings. Using a new dataset of 2167 annotated scientific findings, we demonstrate that hedges alone account for only a partial explanation of certainty. We show that both the overall certainty and individual aspects can be predicted with pre-trained language models, providing a more complete picture of the author’s intended communication. Downstream analyses on 431K scientific findings from news and scientific abstracts demonstrate that modeling sentence-level and aspect-level certainty is meaningful for areas like science communication. Both the model and datasets used in this paper are released athttps://blablablab.si.umich.edu/projects/certainty/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_785", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_785.flac", "type": "audio", "duration": 551.962}, {"from": "Assistant", "value": "Various measures have been proposed to quantify human-like social biases in word embeddings. However, bias scores based on these measures can suffer from measurement error. One indication of measurement quality is reliability, concerning the extent to which a measure produces consistent results. In this paper, we assess three types of reliability of word embedding gender bias measures, namely test-retest reliability, inter-rater consistency and internal consistency. Specifically, we investigate the consistency of bias scores across different choices of random seeds, scoring rules and words. Furthermore, we analyse the effects of various factors on these measures’ reliability scores. Our findings inform better design of word embedding gender bias measures. Moreover, we urge researchers to be more critical about the application of such measures", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_786", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_786.flac", "type": "audio", "duration": 621.12}, {"from": "Assistant", "value": "Rumors are rampant in the era of social media. Conversation structures provide valuable clues to differentiate between real and fake claims. However, existing rumor detection methods are either limited to the strict relation of user responses or oversimplify the conversation structure. In this study, to substantially reinforces the interaction of user opinions while alleviating the negative impact imposed by irrelevant posts, we first represent the conversation thread as an undirected interaction graph. We then present a Claim-guided Hierarchical Graph Attention Network for rumor classification, which enhances the representation learning for responsive posts considering the entire social contexts and attends over the posts that can semantically infer the target claim. Extensive experiments on three Twitter datasets demonstrate that our rumor detection method achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_787", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_787.flac", "type": "audio", "duration": 657.045}, {"from": "Assistant", "value": "Bill writing is a critical element of representative democracy. However, it is often overlooked that most legislative bills are derived, or even directly copied, from other bills. Despite the significance of bill-to-bill linkages for understanding the legislative process, existing approaches fail to address semantic similarities across bills, let alone reordering or paraphrasing which are prevalent in legal document writing. In this paper, we overcome these limitations by proposing a 5-class classification task that closely reflects the nature of the bill generation process. In doing so, we construct a human-labeled dataset of 4,721 bill-to-bill relationships at the subsection-level and release this annotated dataset to the research community. To augment the dataset, we generate synthetic data with varying degrees of similarity, mimicking the complex bill writing process. We use BERT variants and apply multi-stage training, sequentially fine-tuning our models with synthetic and human-labeled datasets. We find that the predictive performance significantly improves when training with both human-labeled and synthetic data. Finally, we apply our trained model to infer section- and bill-level similarities. Our analysis shows that the proposed methodology successfully captures the similarities across legal documents at various levels of aggregation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_788", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_788.flac", "type": "audio", "duration": 461.45}, {"from": "Assistant", "value": "Understanding differences of viewpoints across corpora is a fundamental task for computational social sciences. In this paper, we propose the Sliced Word Embedding Association Test (SWEAT), a novel statistical measure to compute the relative polarization of a topical wordset across two distributional representations. To this end, SWEAT uses two additional wordsets, deemed to have opposite valence, to represent two different poles. We validate our approach and illustrate a case study to show the usefulness of the introduced measure.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_789", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_789.flac", "type": "audio", "duration": 475.057}, {"from": "Assistant", "value": "Computational Humour (CH) has attracted the interest of Natural Language Processing and Computational Linguistics communities. Creating datasets for automatic measurement of humour quotient is difficult due to multiple possible interpretations of the content. In this work, we create a multi-modal humour-annotated dataset (~40 hours) using stand-up comedy clips. We devise a novel scoring mechanism to annotate the training data with a humour quotient score using the audience’s laughter. The normalized duration (laughter duration divided by the clip duration) of laughter in each clip is used to compute this humour coefficient score on a five-point scale (0-4). This method of scoring is validated by comparing with manually annotated scores, wherein a quadratic weighted kappa of 0.6 is obtained. We use this dataset to train a model that provides a ‘funniness’ score, on a five-point scale, given the audio and its corresponding text. We compare various neural language models for the task of humour-rating and achieve an accuracy of 0.813 in terms of Quadratic Weighted Kappa (QWK). Our ‘Open Mic’ dataset is released for further research along with the code.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_791", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_791.flac", "type": "audio", "duration": 702.059}, {"from": "Assistant", "value": "Sentence embedding refers to a set of effective and versatile techniques for converting raw text into numerical vector representations that can be used in a wide range of natural language processing (NLP) applications. The majority of these techniques are either supervised or unsupervised. Compared to the unsupervised methods, the supervised ones make less assumptions about optimization objectives and usually achieve better results. However, the training requires a large amount of labeled sentence pairs, which is not available in many industrial scenarios. To that end, we propose a generic and end-to-end approach – PAUSE (Positive and Annealed Unlabeled Sentence Embedding), capable of learning high-quality sentence embeddings from a partially labeled dataset. We experimentally show that PAUSE achieves, and sometimes surpasses, state-of-the-art results using only a small fraction of labeled sentence pairs on various benchmark tasks. When applied to a real industrial use case where labeled samples are scarce, PAUSE encourages us to extend our dataset without the burden of extensive manual annotation work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_792", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_792.flac", "type": "audio", "duration": 470.042}, {"from": "Assistant", "value": "Powerful sentence encoders trained for multiple languages are on the rise. These systems are capable of embedding a wide range of linguistic properties into vector representations. While explicit probing tasks can be used to verify the presence of specific linguistic properties, it is unclear whether the vector representations can be manipulated to indirectly steer such properties. For efficient learning, we investigate the use of a geometric mapping in embedding space to transform linguistic properties, without any tuning of the pre-trained sentence encoder or decoder. We validate our approach on three linguistic properties using a pre-trained multilingual autoencoder and analyze the results in both monolingual and cross-lingual settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_793", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_793.flac", "type": "audio", "duration": 506.381}, {"from": "Assistant", "value": "Linguistic typology generally divides synthetic languages into groups based on their morphological fusion. However, this measure has long been thought to be best considered a matter of degree. We present an information-theoretic measure, called informational fusion, to quantify the degree of fusion of a given set of morphological features in a surface form, which naturally provides such a graded scale. Informational fusion is able to encapsulate not only concatenative, but also nonconcatenative morphological systems (e.g. Arabic), abstracting away from any notions of morpheme segmentation. We then show, on a sample of twenty-one languages, that our measure recapitulates the usual linguistic classifications for concatenative systems, and provides new measures for nonconcatenative ones. We also evaluate the long-standing hypotheses that more frequent forms are more fusional, and that paradigm size anticorrelates with degree of fusion. We do not find evidence for the idea that languages have characteristic levels of fusion; rather, the degree of fusion varies across part-of-speech within languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_794", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_794.flac", "type": "audio", "duration": 463.772}, {"from": "Assistant", "value": "Natural languages display a trade-off among different strategies to convey syntactic structure, such as word order or inflection. This trade-off, however, has not appeared in recent simulations of iterated language learning with neural network agents (Chaabouni et al., 2019b). We re-evaluate this result in light of three factors that play an important role in comparable experiments from the Language Evolution field: (i) speaker bias towards efficient messaging, (ii) non systematic input languages, and (iii) learning bottleneck. Our simulations show that neural agents mainly strive to maintain the utterance type distribution observed during learning, instead of developing a more efficient or systematic language.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_795", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_795.flac", "type": "audio", "duration": 461.055}, {"from": "Assistant", "value": "To ease the difficulty of argument stance classification, the task of same side stance classification (S3C) has been proposed. In contrast to actual stance classification, which requires a substantial amount of domain knowledge to identify whether an argument is in favor or against a certain issue, it is argued that, for S3C, only argument similarity within stances needs to be learned to successfully solve the task. We evaluate several transformer-based approaches on the dataset of the recent S3C shared task, followed by an in-depth evaluation and error analysis of our model and the task’s hypothesis. We show that, although we achieve state-of-the-art results, our model fails to generalize both within as well as across topics and domains when adjusting the sampling strategy of the training and test set to a more adversarial scenario. Our evaluation shows that current state-of-the-art approaches cannot determine same side stance by considering only domain-independent linguistic similarity features, but appear to require domain knowledge and semantic inference, too.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_796", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_796.flac", "type": "audio", "duration": 666.965}, {"from": "Assistant", "value": "Opinion Role Labeling (ORL), aiming to identify the key roles of opinion, has received increasing interest. Unlike most of the previous works focusing on the English language, in this paper, we present the first work of Chinese ORL. We construct a Chinese dataset by manually translating and projecting annotations from a standard English MPQA dataset. Then, we investigate the effectiveness of cross-lingual transfer methods, including model transfer and corpus translation. We exploit multilingual BERT with Contextual Parameter Generator and Adapter methods to examine the potentials of unsupervised cross-lingual learning and our experiments and analyses for both bilingual and multilingual transfers establish a foundation for the future research of this task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_797", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_797.flac", "type": "audio", "duration": 732.053}, {"from": "Assistant", "value": "Current research in automatic summarisation is unapologetically anglo-centered–a persistent state-of-affairs, which also predates neural net approaches. High-quality automatic summarisation datasets are notoriously expensive to create, posing a challenge for any language. However, with digitalisation, archiving, and social media advertising of newswire articles, recent work has shown how, with careful methodology application, large-scale datasets can now be simply gathered instead of written. In this paper, we present a large-scale multilingual summarisation dataset containing articles in 92 languages, spread across 28.8 million articles, in more than 35 writing scripts. This is both the largest, most inclusive, existing automatic summarisation dataset, as well as one of the largest, most inclusive, ever published datasets for any NLP task. We present the first investigation on the efficacy of resource building from news platforms in the low-resource language setting. Finally, we provide some first insight on how low-resource language settings impact state-of-the-art automatic summarisation system performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_798", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_798.flac", "type": "audio", "duration": 611.056}, {"from": "Assistant", "value": "Recent efforts to develop deep learning models for text generation tasks such as extractive and abstractive summarization have resulted in state-of-the-art performances on various datasets. However, obtaining the best model configuration for a given dataset requires an extensive knowledge of deep learning specifics like model architecture, tuning parameters etc., and is often extremely challenging for a non-expert. In this paper, we propose methods to automatically create deep learning models for the tasks of extractive and abstractive text summarization. Based on the recent advances in Automated Machine Learning and the success of large language models such as BERT and GPT-2 in encoding knowledge, we use a combination of Neural Architecture Search (NAS) and Knowledge Distillation (KD) techniques to perform model search and compression using the vast knowledge provided by these language models to develop smaller, customized models for any given dataset. We present extensive empirical results to illustrate the effectiveness of our model creation methods in terms of inference time and model size, while achieving near state-of-the-art performances in terms of accuracy across a range of datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_799", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_799.flac", "type": "audio", "duration": 717.404}, {"from": "Assistant", "value": "Compared to fully manual translation, post-editing (PE) machine translation (MT) output can save time and reduce errors. Automatic word-level quality estimation (QE) aims to predict the correctness of words in MT output and holds great promise to aid PE by flagging problematic output. Quality of QE is crucial, as incorrect QE might lead to translators missing errors or wasting time on already correct MT output. Achieving accurate automatic word-level QE is very hard, and it is currently not known (i) at what quality threshold QE is actually beginning to be useful for human PE, and (ii), how to best present word-level QE information to translators. In particular, should word-level QE visualization indicate uncertainty of the QE model or not? In this paper, we address both research questions with real and simulated word-level QE, visualizations, and user studies, where time, subjective ratings, and quality of the final translations are assessed. Results show that current word-level QE models are not yet good enough to support PE. Instead, quality levels of > 80% F1 are required. For helpful quality levels, a visualization reflecting the uncertainty of the QE model is preferred. Our analysis further shows that speed gains achieved through QE are not merely a result of blindly trusting the QE system, but that the quality of the final translations also improves. The threshold results from the paper establish a quality goal for future word-level QE research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_800", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_800.flac", "type": "audio", "duration": 707.86}, {"from": "Assistant", "value": "Massively multilingual language models such as multilingual BERT offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. However, due to limited capacity and large differences in pretraining data sizes, there is a profound performance gap between resource-rich and resource-poor target languages. The ultimate challenge is dealing with under-resourced languages not covered at all by the models and written in scripts unseen during pretraining. In this work, we propose a series of novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts. Relying on matrix factorization, our methods capitalize on the existing latent knowledge about multiple languages already available in the pretrained model’s embedding matrix. Furthermore, we show that learning of the new dedicated embedding matrix in the target language can be improved by leveraging a small number of vocabulary items (i.e., the so-called lexically overlapping tokens) shared between mBERT’s and target language vocabulary. Our adaptation techniques offer substantial performance gains for languages with unseen scripts. We also demonstrate that they can yield improvements for low-resource languages written in scripts covered by the pretrained model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_801", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_801.flac", "type": "audio", "duration": 595.179}, {"from": "Assistant", "value": "We test the natural expectation that using MT in professional translation saves human processing time. The last such study was carried out by Sanchez-Torron and Koehn (2016) with phrase-based MT, artificially reducing the translation quality. In contrast, we focus on neural MT (NMT) of high quality, which has become the state-of-the-art approach since then and also got adopted by most translation companies. Through an experimental study involving over 30 professional translators for English -> Czech translation, we examine the relationship between NMT performance and post-editing time and quality. Across all models, we found that better MT systems indeed lead to fewer changes in the sentences in this industry setting. The relation between system quality and post-editing time is however not straightforward and, contrary to the results on phrase-based MT, BLEU is definitely not a stable predictor of the time or final output quality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_802", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_802.flac", "type": "audio", "duration": 746.325}, {"from": "Assistant", "value": "Machine learning has brought striking advances in multilingual natural language processing capabilities over the past year. For example, the latest techniques have improved the state-of-the-art performance on the XTREME multilingual benchmark by more than 13 points. While a sizeable gap to human-level performance remains, improvements have been easier to achieve in some tasks than in others. This paper analyzes the current state of cross-lingual transfer learning and summarizes some lessons learned. In order to catalyze meaningful progress, we extend XTREME to XTREME-R, which consists of an improved set of ten natural language understanding tasks, including challenging language-agnostic retrieval tasks, and covers 50 typologically diverse languages. In addition, we provide a massively multilingual diagnostic suite and fine-grained multi-dataset evaluation capabilities through an interactive public leaderboard to gain a better understanding of such models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_803", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_803.flac", "type": "audio", "duration": 676.288}, {"from": "Assistant", "value": "Lexical disambiguation is a major challenge for machine translation systems, especially if some senses of a word are trained less often than others. Identifying patterns of overgeneralization requires evaluation methods that are both reliable and scalable. We propose contrastive conditioning as a reference-free black-box method for detecting disambiguation errors. Specifically, we score the quality of a translation by conditioning on variants of the source that provide contrastive disambiguation cues. After validating our method, we apply it in a case study to perform a targeted evaluation of sequence-level knowledge distillation. By probing word sense disambiguation and translation of gendered occupation names, we show that distillation-trained models tend to overgeneralize more than other models with a comparable BLEU score. Contrastive conditioning thus highlights a side effect of distillation that is not fully captured by standard evaluation metrics. Code and data to reproduce our findings are publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_804", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_804.flac", "type": "audio", "duration": 719.981}, {"from": "Assistant", "value": "In interpretable NLP, we require faithful rationales that reflect the model’s decision-making process for an explained instance. While prior work focuses on extractive rationales (a subset of the input words), we investigate their less-studied counterpart: free-text natural language rationales. We demonstrate that *pipelines*, models for faithful rationalization on information-extraction style tasks, do not work as well on “reasoning” tasks requiring free-text rationales. We turn to models that *jointly* predict and rationalize, a class of widely used high-performance models for free-text rationalization. We investigate the extent to which the labels and rationales predicted by these models are associated, a necessary property of faithful explanation. Via two tests, *robustness equivalence* and *feature importance agreement*, we find that state-of-the-art T5-based joint models exhibit desirable properties for explaining commonsense question-answering and natural language inference, indicating their potential for producing faithful free-text rationales.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_805", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_805.flac", "type": "audio", "duration": 630.05}, {"from": "Assistant", "value": "As a prominent attribution-based explanation algorithm, Integrated Gradients (IG) is widely adopted due to its desirable explanation axioms and the ease of gradient computation. It measures feature importance by averaging the model’s output gradient interpolated along a straight-line path in the input data space. However, such straight-line interpolated points are not representative of text data due to the inherent discreteness of the word embedding space. This questions the faithfulness of the gradients computed at the interpolated points and consequently, the quality of the generated explanations. Here we propose Discretized Integrated Gradients (DIG), which allows effective attribution along non-linear interpolation paths. We develop two interpolation strategies for the discrete word embedding space that generates interpolation points that lie close to actual words in the embedding space, yielding more faithful gradient computation. We demonstrate the effectiveness of DIG over IG through experimental and human evaluations on multiple sentiment classification datasets. We provide the source code of DIG to encourage reproducible research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_807", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_807.flac", "type": "audio", "duration": 710.043}, {"from": "Assistant", "value": "Sequence models are a critical component of modern NLP systems, but their predictions are difficult to explain. We consider model explanations though rationales, subsets of context that can explain individual model predictions. We find sequential rationales by solving a combinatorial optimization: the best rationale is the smallest subset of input tokens that would predict the same output as the full sequence. Enumerating all subsets is intractable, so we propose an efficient greedy algorithm to approximate this objective. The algorithm, which is called greedy rationalization, applies to any model. For this approach to be effective, the model should form compatible conditional distributions when making predictions on incomplete subsets of the context. This condition can be enforced with a short fine-tuning step. We study greedy rationalization on language modeling and machine translation. Compared to existing baselines, greedy rationalization is best at optimizing the sequential objective and provides the most faithful rationales. On a new dataset of annotated sequential rationales, greedy rationales are most similar to human rationales.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_808", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_808.flac", "type": "audio", "duration": 792.043}, {"from": "Assistant", "value": "Influence functions approximate the “influences” of training data-points for test predictions and have a wide variety of applications. Despite the popularity, their computational cost does not scale well with model and training data size. We present FastIF, a set of simple modifications to influence functions that significantly improves their run-time. We use k-Nearest Neighbors (kNN) to narrow the search space down to a subset of good candidate data points, identify the configurations that best balance the speed-quality trade-off in estimating the inverse Hessian-vector product, and introduce a fast parallel variant. Our proposed method achieves about 80X speedup while being highly correlated with the original influence values. With the availability of the fast influence functions, we demonstrate their usefulness in four applications. First, we examine whether influential data-points can “explain” test time behavior using the framework of simulatability. Second, we visualize the influence interactions between training and test data-points. Third, we show that we can correct model errors by additional fine-tuning on certain influential data-points, improving the accuracy of a trained MultiNLI model by 2.5% on the HANS dataset. Finally, we experiment with a similar setup but fine-tuning on datapoints not seen during training, improving the model accuracy by 2.8% and 1.7% on HANS and ANLI datasets respectively. Overall, our fast influence functions can be efficiently applied to large models and datasets, and our experiments demonstrate the potential of influence functions in model interpretation and correcting model errors.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_809", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_809.flac", "type": "audio", "duration": 605.419}, {"from": "Assistant", "value": "As neural language models approach human performance on NLP benchmark tasks, their advances are widely seen as evidence of an increasingly complex understanding of syntax. This view rests upon a hypothesis that has not yet been empirically tested: that word order encodes meaning essential to performing these tasks. We refute this hypothesis in many cases: in the GLUE suite and in various genres of English text, the words in a sentence or phrase can rarely be permuted to form a phrase carrying substantially different information. Our surprising result relies on inference by iterative shuffling (IBIS), a novel, efficient procedure that finds the ordering of a bag of words having the highest likelihood under a fixed language model. IBIS can use any black-box model without additional training and is superior to existing word ordering algorithms. Coalescing our findings, we discuss how shuffling inference procedures such as IBIS can benefit language modeling and constrained generation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_810", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_810.flac", "type": "audio", "duration": 719.006}, {"from": "Assistant", "value": "We study the problem of training named entity recognition (NER) models using only distantly-labeled data, which can be automatically obtained by matching entity mentions in the raw text with entity types in a knowledge base. The biggest challenge of distantly-supervised NER is that the distant supervision may induce incomplete and noisy labels, rendering the straightforward application of supervised learning ineffective. In this paper, we propose (1) a noise-robust learning scheme comprised of a new loss function and a noisy label removal step, for training NER models on distantly-labeled data, and (2) a self-training method that uses contextualized augmentations created by pre-trained language models to improve the generalization ability of the NER model. On three benchmark datasets, our method achieves superior performance, outperforming existing distantly-supervised NER models by significant margins.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_811", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_811.flac", "type": "audio", "duration": 712.063}, {"from": "Assistant", "value": "Noun phrases and Relation phrases in open knowledge graphs are not canonicalized, leading to an explosion of redundant and ambiguous subject-relation-object triples. Existing approaches to solve this problem take a two-step approach. First, they generate embedding representations for both noun and relation phrases, then a clustering algorithm is used to group them using the embeddings as features. In this work, we propose Canonicalizing Using Variational AutoEncoders and Side Information (CUVA), a joint model to learn both embeddings and cluster assignments in an end-to-end approach, which leads to a better vector representation for the noun and relation phrases. Our evaluation over multiple benchmarks shows that CUVA outperforms the existing state-of-the-art approaches. Moreover, we introduce CanonicNell, a novel dataset to evaluate entity canonicalization systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_812", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_812.flac", "type": "audio", "duration": 715.051}, {"from": "Assistant", "value": "This paper examines the challenging problem of learning representations of entities and relations in a complex multi-relational knowledge graph. We propose HittER, a Hierarchical Transformer model to jointly learn Entity-relation composition and Relational contextualization based on a source entity’s neighborhood. Our proposed model consists of two different Transformer blocks: the bottom block extracts features of each entity-relation pair in the local neighborhood of the source entity and the top block aggregates the relational information from outputs of the bottom block. We further design a masked entity prediction task to balance information from the relational context and the source entity itself. Experimental results show that HittER achieves new state-of-the-art results on multiple link prediction datasets. We additionally propose a simple approach to integrate HittER into BERT and demonstrate its effectiveness on two Freebase factoid question answering datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_813", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_813.flac", "type": "audio", "duration": 739.045}, {"from": "Assistant", "value": "This paper presents an empirical study to efficiently build named entity recognition (NER) systems when a small amount of in-domain labeled data is available. Based upon recent Transformer-based self-supervised pre-trained language models (PLMs), we investigate three orthogonal schemes to improve model generalization ability in few-shot settings: (1) meta-learning to construct prototypes for different entity types, (2) task-specific supervised pre-training on noisy web data to extract entity-related representations and (3) self-training to leverage unlabeled in-domain data. On 10 public NER datasets, we perform extensive empirical comparisons over the proposed schemes and their combinations with various proportions of labeled data, our experiments show that (i)in the few-shot learning setting, the proposed NER schemes significantly improve or outperform the commonly used baseline, a PLM-based linear classifier fine-tuned using domain labels. (ii) We create new state-of-the-art results on both few-shot and training-free settings compared with existing methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_814", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_814.flac", "type": "audio", "duration": 478.563}, {"from": "Assistant", "value": "Cross-lingual named-entity lexica are an important resource to multilingual NLP tasks such as machine translation and cross-lingual wikification. While knowledge bases contain a large number of entities in high-resource languages such as English and French, corresponding entities for lower-resource languages are often missing. To address this, we propose Lexical-Semantic-Phonetic Align (LSP-Align), a technique to automatically mine cross-lingual entity lexica from mined web data. We demonstrate LSP-Align outperforms baselines at extracting cross-lingual entity pairs and mine 164 million entity pairs from 120 different languages aligned with English. We release these cross-lingual entity pairs along with the massively multilingual tagged named entity corpus as a resource to the NLP community.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_815", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_815.flac", "type": "audio", "duration": 475.684}, {"from": "Assistant", "value": "Event time is one of the most important features for event-event temporal relation extraction. However, explicit event time information in text is sparse. For example, only about 20% of event mentions in TimeBank-Dense have event-time links. In this paper, we propose a joint model for event-event temporal relation classification and an auxiliary task, relative event time prediction, which predicts the event time as real numbers. We adopt the Stack-Propagation framework to incorporate predicted relative event time for temporal relation classification and keep the differentiability. Our experiments on MATRES dataset show that our model can significantly improve the RoBERTa-based baseline and achieve state-of-the-art performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_816", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_816.flac", "type": "audio", "duration": 480.003}, {"from": "Assistant", "value": "State-of-the-art NLP models can adopt shallow heuristics that limit their generalization capability (McCoy et al., 2019). Such heuristics include lexical overlap with the training set in Named-Entity Recognition (Taille et al., 2020) and Event or Type heuristics in Relation Extraction (Rosenman et al., 2020). In the more realistic end-to-end RE setting, we can expect yet another heuristic: the mere retention of training relation triples. In this paper we propose two experiments confirming that retention of known facts is a key factor of performance on standard benchmarks. Furthermore, one experiment suggests that a pipeline model able to use intermediate type representations is less prone to over-rely on retention.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_817", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_817.flac", "type": "audio", "duration": 710.059}, {"from": "Assistant", "value": "A new metric BaryScore to evaluate text generation based on deep contextualized embeddings (e.g., BERT, Roberta, ELMo) is introduced. This metric is motivated by a new framework relying on optimal transport tools,i.e., Wasserstein distance and barycenter. By modelling the layer output of deep contextualized embeddings as a probability distribution rather than by a vector embedding; this framework provides a natural way to aggregate the different outputs through the Wasserstein space topology. In addition, it provides theoretical grounds to our metric and offers an alternative to available solutions (e.g., MoverScore and BertScore). Numerical evaluation is performed on four different tasks: machine translation, summarization, data2text generation and image captioning. Our results show that BaryScore outperforms other BERT based metrics and exhibits more consistent behaviour in particular for text summarization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_818", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_818.flac", "type": "audio", "duration": 696.018}, {"from": "Assistant", "value": "The design of widespread vision-and-language datasets and pre-trained encoders directly adopts, or draws inspiration from, the concepts and images of ImageNet. While one can hardly overestimate how much this benchmark contributed to progress in computer vision, it is mostly derived from lexical databases and image queries in English, resulting in source material with a North American or Western European bias. Therefore, we devise a new protocol to construct an ImageNet-style hierarchy representative of more languages and cultures. In particular, we let the selection of both concepts and images be entirely driven by native speakers, rather than scraping them automatically. Specifically, we focus on a typologically diverse set of languages, namely, Indonesian, Mandarin Chinese, Swahili, Tamil, and Turkish. On top of the concepts and images obtained through this new protocol, we create a multilingual dataset for Multicultural Reasoning over Vision and Language (MaRVL) by eliciting statements from native speaker annotators about pairs of images. The task consists of discriminating whether each grounded statement is true or false. We establish a series of baselines using state-of-the-art models and find that their cross-lingual transfer performance lags dramatically behind supervised performance in English. These results invite us to reassess the robustness and accuracy of current state-of-the-art models beyond a narrow domain, but also open up new exciting challenges for the development of truly multilingual and multicultural systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_819", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_819.flac", "type": "audio", "duration": 625.003}, {"from": "Assistant", "value": "The Winograd Schema (WS) has been proposed as a test for measuring commonsense capabilities of models. Recently, pre-trained language model-based approaches have boosted performance on some WS benchmarks but the source of improvement is still not clear. This paper suggests that the apparent progress on WS may not necessarily reflect progress in commonsense reasoning. To support this claim, we first show that the current evaluation method of WS is sub-optimal and propose a modification that uses twin sentences for evaluation. We also propose two new baselines that indicate the existence of artifacts in WS benchmarks. We then develop a method for evaluating WS-like sentences in a zero-shot setting to account for the commonsense reasoning abilities acquired during the pretraining and observe that popular language models perform randomly in this setting when using our more strict evaluation. We conclude that the observed progress is mostly due to the use of supervision in training WS models, which is not likely to successfully support all the required commonsense reasoning skills and knowledge.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_820", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_820.flac", "type": "audio", "duration": 729.493}, {"from": "Assistant", "value": "Entity disambiguation (ED) is the last step of entity linking (EL), when candidate entities are reranked according to the context they appear in. All datasets for training and evaluating models for EL consist of convenience samples, such as news articles and tweets, that propagate the prior probability bias of the entity distribution towards more frequently occurring entities. It was shown that the performance of the EL systems on such datasets is overestimated since it is possible to obtain higher accuracy scores by merely learning the prior. To provide a more adequate evaluation benchmark, we introduce the ShadowLink dataset, which includes 16K short text snippets annotated with entity mentions. We evaluate and report the performance of popular EL systems on the ShadowLink benchmark. The results show a considerable difference in accuracy between more and less common entities for all of the EL systems under evaluation, demonstrating the effect of prior probability bias and entity overshadowing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_821", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_821.flac", "type": "audio", "duration": 599.702}, {"from": "Assistant", "value": "We present IndoNLI, the first human-elicited NLI dataset for Indonesian. We adapt the data collection protocol for MNLI and collect ~18K sentence pairs annotated by crowd workers and experts. The expert-annotated data is used exclusively as a test set. It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning. Experiment results show that XLM-R outperforms other pre-trained models in our data. The best performance on the expert-annotated data is still far below human performance (13.4% accuracy gap), suggesting that this test set is especially challenging. Furthermore, our analysis shows that our expert-annotated data is more diverse and contains fewer annotation artifacts than the crowd-annotated data. We hope this dataset can help accelerate progress in Indonesian NLP research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_823", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_823.flac", "type": "audio", "duration": 706.091}, {"from": "Assistant", "value": "We describe two approaches to single-root dependency parsing that yield significant speed ups in such parsing. One approach has been previously used in dependency parsers in practice, but remains undocumented in the parsing literature, and is considered a heuristic. We show that this approach actually finds the optimal dependency tree. The second approach relies on simple reweighting of the inference graph being input to the dependency parser and has an optimal running time. Here, we again show that this approach is fully correct and identifies the highest-scoring parse tree. Our experiments demonstrate a manyfold speed up compared to a previous graph-based state-of-the-art parser without any loss in accuracy or optimality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_824", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_824.flac", "type": "audio", "duration": 518.4}, {"from": "Assistant", "value": "Probabilistic distributions over spanning trees in directed graphs are a fundamental model of dependency structure in natural language processing, syntactic dependency trees. In NLP, dependency trees often have an additional root constraint: only one edge may emanate from the root. However, no sampling algorithm has been presented in the literature to account for this additional constraint. In this paper, we adapt two spanning tree sampling algorithms to faithfully sample dependency trees from a graph subject to the root constraint. Wilson (1996(’s sampling algorithm has a running time of O(H) where H is the mean hitting time of the graph. Colbourn (1996)’s sampling algorithm has a running time of O(Nˆ3), which is often greater than the mean hitting time of a directed graph. Additionally, we build upon Colbourn’s algorithm and present a novel extension that can sample K trees without replacement in O(K Nˆ3 + Kˆ2 N) time. To the best of our knowledge, no algorithm has been given for sampling spanning trees without replacement from a directed graph.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_825", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_825.flac", "type": "audio", "duration": 457.015}, {"from": "Assistant", "value": "Discontinuous constituent parsers have always lagged behind continuous approaches in terms of accuracy and speed, as the presence of constituents with discontinuous yield introduces extra complexity to the task. However, a discontinuous tree can be converted into a continuous variant by reordering tokens. Based on that, we propose to reduce discontinuous parsing to a continuous problem, which can then be directly solved by any off-the-shelf continuous parser. To that end, we develop a Pointer Network capable of accurately generating the continuous token arrangement for a given input sentence and define a bijective function to recover the original order. Experiments on the main benchmarks with two continuous parsers prove that our approach is on par in accuracy with purely discontinuous state-of-the-art algorithms, but considerably faster.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_826", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_826.flac", "type": "audio", "duration": 470.613}, {"from": "Assistant", "value": "This paper proposes a new representation for CCG derivations. CCG derivations are represented as trees whose nodes are labeled with categories strictly restricted by CCG rule schemata. This characteristic is not suitable for span-based parsing models because they predict node labels independently. In other words, span-based models may generate invalid CCG derivations that violate the rule schemata. Our proposed representation decomposes CCG derivations into several independent pieces and prevents the span-based parsing models from violating the schemata. Our experimental result shows that an off-the-shelf span-based parser with our representation is comparable with previous CCG parsers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_827", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_827.flac", "type": "audio", "duration": 635.043}, {"from": "Assistant", "value": "Intermediate task fine-tuning has been shown to culminate in large transfer gains across many NLP tasks. With an abundance of candidate datasets as well as pre-trained language models, it has become infeasible to experiment with all combinations to find the best transfer setting. In this work, we provide a comprehensive comparison of different methods for efficiently identifying beneficial tasks for intermediate transfer learning. We focus on parameter and computationally efficient adapter settings, highlight different data-availability scenarios, and provide expense estimates for each method. We experiment with a diverse set of 42 intermediate and 11 target English classification, multiple choice, question answering, and sequence tagging tasks. Our results demonstrate that efficient embedding based methods, which rely solely on the respective datasets, outperform computational expensive few-shot fine-tuning approaches. Our best methods achieve an average Regret@3 of 1% across all target tasks, demonstrating that we are able to efficiently identify the best datasets for intermediate training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_828", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_828.flac", "type": "audio", "duration": 624.733}, {"from": "Assistant", "value": "A recent variation of Transformer, Performer, scales Transformer to longer sequences with a linear attention mechanism. However, it is not compatible with relative position encoding, which has advantages over absolute position encoding. In this paper, we discuss possible ways to add relative position encoding to Performer. Based on the analysis, we propose PermuteFormer, a Performer-based model with relative position encoding that scales linearly on long sequences. PermuteFormer applies position-dependent transformation on queries and keys to encode positional information into the attention module. This transformation is carefully crafted so that the final output of self-attention is not affected by absolute positions of tokens. PermuteFormer introduces negligible computational overhead by design that it runs as fast as Performer. We evaluate PermuteFormer on Long-Range Arena, a dataset for long sequences, as well as WikiText-103, a language modeling dataset. The experiments show that PermuteFormer uniformly improves the performance of Performer with almost no computational overhead and outperforms vanilla Transformer on most of the tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_829", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_829.flac", "type": "audio", "duration": 573.013}, {"from": "Assistant", "value": "Pre-training has improved model accuracy for both classification and generation tasks at the cost of introducing much larger and slower models. Pruning methods have proven to be an effective way of reducing model size, whereas distillation methods are proven for speeding up inference. We introduce a block pruning approach targeting both small and fast models. Our approach extends structured methods by considering blocks of any size and integrates this structure into the movement pruning paradigm for fine-tuning. We find that this approach learns to prune out full components of the underlying model, such as attention heads. Experiments consider classification and generation tasks, yielding among other results a pruned model that is a 2.4x faster, 74% smaller BERT on SQuAD v1, with a 1% drop on F1, competitive both with distilled models in speed and pruned models in size.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_830", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_830.flac", "type": "audio", "duration": 693.046}, {"from": "Assistant", "value": "Transformers have outperformed recurrent neural networks (RNNs) in natural language generation. But this comes with a signifi- cant computational cost, as the attention mechanism’s complexity scales quadratically with sequence length. Efficient transformer variants have received increasing interest in recent works. Among them, a linear-complexity recurrent variant has proven well suited for autoregressive generation. It approximates the softmax attention with randomized or heuristic feature maps, but can be difficult to train and may yield suboptimal accuracy. This work aims to convert a pretrained transformer into its efficient recurrent counterpart, improving efficiency while maintaining accuracy. Specifically, we propose a swap-then-finetune procedure: in an off-the-shelf pretrained transformer, we replace the softmax attention with its linear-complexity recurrent alternative and then finetune. With a learned feature map, our approach provides an improved tradeoff between efficiency and accuracy over the standard transformer and other recurrent variants. We also show that the finetuning process has lower training cost relative to training these recurrent variants from scratch. As many models for natural language tasks are increasingly dependent on large-scale pretrained transformers, this work presents a viable approach to improving inference efficiency without repeating the expensive pretraining process.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_831", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_831.flac", "type": "audio", "duration": 452.046}, {"from": "Assistant", "value": "While large language models a la BERT are used ubiquitously in NLP, pretraining them is considered a luxury that only a few well-funded industry labs can afford. How can one train such models with a more modest budget? We present a recipe for pretraining a masked language model in 24 hours using a single low-end deep learning server. We demonstrate that through a combination of software optimizations, design choices, and hyperparameter tuning, it is possible to produce models that are competitive with BERT-base on GLUE tasks at a fraction of the original pretraining cost.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_832", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_832.flac", "type": "audio", "duration": 257.416}, {"from": "Assistant", "value": "Recent studies on compression of pretrained language models (e.g., BERT) usually use preserved accuracy as the metric for evaluation. In this paper, we propose two new metrics, label loyalty and probability loyalty that measure how closely a compressed model (i.e., student) mimics the original model (i.e., teacher). We also explore the effect of compression with regard to robustness under adversarial attacks. We benchmark quantization, pruning, knowledge distillation and progressive module replacing with loyalty and robustness. By combining multiple compression techniques, we provide a practical strategy to achieve better accuracy, loyalty and robustness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_833", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_833.flac", "type": "audio", "duration": 383.936}, {"from": "Assistant", "value": "We present IndoBERTweet, the first large-scale pretrained model for Indonesian Twitter that is trained by extending a monolingually-trained Indonesian BERT model with additive domain-specific vocabulary. We focus in particular on efficient model adaptation under vocabulary mismatch, and benchmark different ways of initializing the BERT embedding layer for new word types. We find that initializing with the average BERT subword embedding makes pretraining five times faster, and is more effective than proposed methods for vocabulary adaptation in terms of extrinsic evaluation over seven Twitter-based datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_834", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_834.flac", "type": "audio", "duration": 693.092}, {"from": "Assistant", "value": "We report two essential improvements in readability assessment: 1. three novel features in advanced semantics and 2. the timely evidence that traditional ML models (e.g. Random Forest, using handcrafted features) can combine with transformers (e.g. RoBERTa) to augment model performance. First, we explore suitable transformers and traditional ML models. Then, we extract 255 handcrafted linguistic features using self-developed extraction software. Finally, we assemble those to create several hybrid models, achieving state-of-the-art (SOTA) accuracy on popular datasets in readability assessment. The use of handcrafted features help model performance on smaller datasets. Notably, our RoBERTA-RF-T1 hybrid achieves the near-perfect classification accuracy of 99%, a 20.3% increase from the previous SOTA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_835", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_835.flac", "type": "audio", "duration": 657.821}, {"from": "Assistant", "value": "Despite agreement on the importance of detecting out-of-distribution (OOD) examples, there is little consensus on the formal definition of the distribution shifts of OOD examples and how to best detect them. We categorize these examples as exhibiting a background shift or semantic shift, and find that the two major approaches to OOD detection, calibration and density estimation (language modeling for text), have distinct behavior on these types of OOD data. Across 14 pairs of in-distribution and OOD English natural language understanding datasets, we find that density estimation methods consistently beat calibration methods in background shift settings and perform worse in semantic shift settings. In addition, we find that both methods generally fail to detect examples from challenge data, indicating that these examples constitute a different type of OOD data. Overall, while the categorization we apply explains many of the differences between the two methods, our results call for a more explicit definition of OOD to create better benchmarks and build detectors that can target the type of OOD data expected at test time.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_836", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_836.flac", "type": "audio", "duration": 609.045}, {"from": "Assistant", "value": "While pre-trained language models have obtained state-of-the-art performance for several natural language understanding tasks, they are quite opaque in terms of their decision-making process. While some recent works focus on rationalizing neural predictions by highlighting salient concepts in the text as justifications or rationales, they rely on thousands of labeled training examples for both task labels as well as annotated rationales for every instance. Such extensive large-scale annotations are infeasible to obtain for many tasks. To this end, we develop a multi-task teacher-student framework based on self-training pre-trained language models with limited task-specific labels and rationales and judicious sample selection to learn from informative pseudo-labeled examples. We study several characteristics of what constitutes a good rationale and demonstrate that the neural model performance can be significantly improved by making it aware of its rationalized predictions, particularly in low-resource settings. Extensive experiments in several benchmark datasets demonstrate the effectiveness of our approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_837", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_837.flac", "type": "audio", "duration": 743.04}, {"from": "Assistant", "value": "When training neural models, it is common to combine multiple loss terms. The balancing of these terms requires considerable human effort and is computationally demanding. Moreover, the optimal trade-off between the loss terms can change as training progresses, e.g., for adversarial terms. In this work, we generalize the Adam optimization algorithm to handle multiple loss terms. The guiding principle is that for every layer, the gradient magnitude of the terms should be balanced. To this end, the Multi-Term Adam (MTAdam) computes the derivative of each loss term separately, infers the first and second moments per parameter and loss term, and calculates a first moment for the magnitude per layer of the gradients arising from each loss. This magnitude is used to continuously balance the gradients across all layers, in a manner that both varies from one layer to the next and dynamically changes over time. Our results show that training with the new method leads to fast recovery from suboptimal initial loss weighting and to training outcomes that match or improve conventional training with the prescribed hyperparameters of each method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_838", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_838.flac", "type": "audio", "duration": 718.933}, {"from": "Assistant", "value": "Classification problems having thousands or more classes naturally occur in NLP, for example language models or document classification. A softmax or one-vs-all classifier naturally handles many classes, but it is very slow at inference time, because every class score must be calculated to find the top class. We propose the “softmax tree”, consisting of a binary tree having sparse hyperplanes at the decision nodes (which make hard, not soft, decisions) and small softmax classifiers at the leaves. This is much faster at inference because the input instance follows a single path to a leaf (whose length is logarithmic on the number of leaves) and the softmax classifier at each leaf operates on a small subset of the classes. Although learning accurate tree-based models has proven difficult in the past, we are able to overcome this by using a variation of a recent algorithm, tree alternating optimization (TAO). Compared to a softmax and other classifiers, the resulting softmax trees are both more accurate in prediction and faster in inference, as shown in NLP problems having from one thousand to one hundred thousand classes.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_840", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_840.flac", "type": "audio", "duration": 700.053}, {"from": "Assistant", "value": "Drawing inferences between open-domain natural language predicates is a necessity for true language understanding. There has been much progress in unsupervised learning of entailment graphs for this purpose. We make three contributions: (1) we reinterpret the Distributional Inclusion Hypothesis to model entailment between predicates of different valencies, like DEFEAT(Biden, Trump) entails WIN(Biden); (2) we actualize this theory by learning unsupervised Multivalent Entailment Graphs of open-domain predicates; and (3) we demonstrate the capabilities of these graphs on a novel question answering task. We show that directional entailment is more helpful for inference than non-directional similarity on questions of fine-grained semantics. We also show that drawing on evidence across valencies answers more questions than by using only the same valency evidence.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_841", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_841.flac", "type": "audio", "duration": 739.413}, {"from": "Assistant", "value": "The task of organizing a shuffled set of sentences into a coherent text has been used to evaluate a machine’s understanding of causal and temporal relations. We formulate the sentence ordering task as a conditional text-to-marker generation problem. We present Reorder-BART (Re-BART) that leverages a pre-trained Transformer-based model to identify a coherent order for a given set of shuffled sentences. The model takes a set of shuffled sentences with sentence-specific markers as input and generates a sequence of position markers of the sentences in the ordered text. Re-BART achieves the state-of-the-art performance across 7 datasets in Perfect Match Ratio (PMR) and Kendall’s tau. We perform evaluations in a zero-shot setting, showcasing that our model is able to generalize well across other datasets. We additionally perform several experiments to understand the functioning and limitations of our framework.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_842", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_842.flac", "type": "audio", "duration": 801.216}, {"from": "Assistant", "value": "Ontology Alignment is an important research problem applied to various fields such as data integration, data transfer, data preparation, etc. State-of-the-art (SOTA) Ontology Alignment systems typically use naive domain-dependent approaches with handcrafted rules or domain-specific architectures, making them unscalable and inefficient. In this work, we propose VeeAlign, a Deep Learning based model that uses a novel dual-attention mechanism to compute the contextualized representation of a concept which, in turn, is used to discover alignments. By doing this, not only is our approach able to exploit both syntactic and semantic information encoded in ontologies, it is also, by design, flexible and scalable to different domains with minimal effort. We evaluate our model on four different datasets from different domains and languages, and establish its superiority through these results as well as detailed ablation studies. The code and datasets used are available athttps://github.com/Remorax/VeeAlign.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_843", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_843.flac", "type": "audio", "duration": 653.061}, {"from": "Assistant", "value": "Modern semantic parsers suffer from two principal limitations. First, training requires expensive collection of utterance-program pairs. Second, semantic parsers fail to generalize at test time to new compositions/structures that have not been observed during training. Recent research has shown that automatic generation of synthetic utterance-program pairs can alleviate the first problem, but its potential for the second has thus far been under-explored. In this work, we investigate automatic generation of synthetic utterance-program pairs for improving compositional generalization in semantic parsing. Given a small training set of annotated examples and an “infinite” pool of synthetic examples, we select a subset of synthetic examples that are structurally-diverse and use them to improve compositional generalization. We evaluate our approach on a new split of the schema2QA dataset, and show that it leads to dramatic improvements in compositional generalization as well as moderate improvements in the traditional i.i.d setup. Moreover, structurally-diverse sampling achieves these improvements with as few as 5K examples, compared to 1M examples when sampling uniformly at random – a 200x improvement in data efficiency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_844", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_844.flac", "type": "audio", "duration": 724.045}, {"from": "Assistant", "value": "The lexical substitution task aims at generating a list of suitable replacements for a target word in context, ideally keeping the meaning of the modified text unchanged. While its usage has increased in recent years, the paucity of annotated data prevents the finetuning of neural models on the task, hindering the full fruition of recently introduced powerful architectures such as language models. Furthermore, lexical substitution is usually evaluated in a framework that is strictly bound to a limited vocabulary, making it impossible to credit appropriate, but out-of-vocabulary, substitutes. To assess these issues, we proposed GeneSis (Generating Substitutes in contexts), the first generative approach to lexical substitution. Thanks to a seq2seq model, we generate substitutes for a word according to the context it appears in, attaining state-of-the-art results on different benchmarks. Moreover, our approach allows silver data to be produced for further improving the performances of lexical substitution systems. Along with an extensive analysis of GeneSis results, we also present a human evaluation of the generated substitutes in order to assess their quality. We release the fine-tuned models, the generated datasets, and the code to reproduce the experiments athttps://github.com/SapienzaNLP/genesis.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_845", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_845.flac", "type": "audio", "duration": 660.053}, {"from": "Assistant", "value": "Public trust in science depends on honest and factual communication of scientific papers. However, recent studies have demonstrated a tendency of news media to misrepresent scientific papers by exaggerating their findings. Given this, we present a formalization of and study into the problem of exaggeration detection in science communication. While there are an abundance of scientific papers and popular media articles written about them, very rarely do the articles include a direct link to the original paper, making data collection challenging, and necessitating the need for few-shot learning. We address this by curating a set of labeled press release/abstract pairs from existing expert annotated studies on exaggeration in press releases of scientific papers suitable for benchmarking the performance of machine learning models on the task. Using limited data from this and previous studies on exaggeration detection in science, we introduce MT-PET, a multi-task version of Pattern Exploiting Training (PET), which leverages knowledge from complementary cloze-style QA tasks to improve few-shot learning. We demonstrate that MT-PET outperforms PET and supervised learning both when data is limited, as well as when there is an abundance of data for the main task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_846", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_846.flac", "type": "audio", "duration": 711.04}, {"from": "Assistant", "value": "Phrase representations derived from BERT often do not exhibit complex phrasal compositionality, as the model relies instead on lexical similarity to determine semantic relatedness. In this paper, we propose a contrastive fine-tuning objective that enables BERT to produce more powerful phrase embeddings. Our approach (Phrase-BERT) relies on a dataset of diverse phrasal paraphrases, which is automatically generated using a paraphrase generation model, as well as a large-scale dataset of phrases in context mined from the Books3 corpus. Phrase-BERT outperforms baselines across a variety of phrase-level similarity tasks, while also demonstrating increased lexical diversity between nearest neighbors in the vector space. Finally, as a case study, we show that Phrase-BERT embeddings can be easily integrated with a simple autoencoder to build a phrase-based neural topic model that interprets topics as mixtures of words and phrases by performing a nearest neighbor search in the embedding space. Crowdsourced evaluations demonstrate that this phrase-based topic model produces more coherent and meaningful topics than baseline word and phrase-level topic models, further validating the utility of Phrase-BERT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_847", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_847.flac", "type": "audio", "duration": 759.808}, {"from": "Assistant", "value": "This study investigates the applicability of semantic change detection methods in descriptively oriented linguistic research. It specifically focuses on contact-induced semantic shifts in Quebec English. We contrast synchronic data from different regions in order to identify the meanings that are specific to Quebec and potentially related to language contact. Type-level embeddings are used to detect new semantic shifts, and token-level embeddings to isolate regionally specific occurrences. We introduce a new 80-item test set and conduct both quantitative and qualitative evaluations. We demonstrate that diachronic word embedding methods can be applied to contact-induced semantic shifts observed in synchrony, obtaining results comparable to the state of the art on similar tasks in diachrony. However, we show that encouraging evaluation results do not translate to practical value in detecting new semantic shifts. Finally, our application of token-level embeddings accelerates manual data exploration and provides an efficient way of scaling up sociolinguistic analyses.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_1", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_1.flac", "type": "audio", "duration": 596.614}, {"from": "Assistant", "value": "In this paper, we presentMiSS, an assistant for multi-style simultaneous translation. Our proposed translation system has five key features: highly accurate translation, simultaneous translation, translation for multiple text styles, back-translation for translation quality evaluation, and grammatical error correction. With this system, we aim to provide a complete translation experience for machine translation users. Our design goals are high translation accuracy, real-time translation, flexibility, and measurable translation quality. Compared with the free commercial translation systems commonly used, our translation assistance system regards the machine translation application as a more complete and fully-featured tool for users. By incorporating additional features and giving the user better control over their experience, we improve translation efficiency and performance. Additionally, our assistant system combines machine translation, grammatical error correction, and interactive edits, and uses a crowdsourcing mode to collect more data for further training to improve both the machine translation and grammatical error correction models. A short video demonstrating our system is available athttps://www.youtube.com/watch?v=ZGCo7KtRKd8.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_3", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_3.flac", "type": "audio", "duration": 720.538}, {"from": "Assistant", "value": "Text annotation tools assume that their user’s goal is to create a labeled corpus. However,users view annotation as a necessary evil on the way to deliver business value through NLP.Thus an annotation tool should optimize for the throughput of the global NLP process, not only the productivity of individual annotators. LightTag is a text annotation tool designed and built on that principle. This paper shares our design rationale, data modeling choices, and user interface decisions then illustrates how those choices serve the full NLP lifecycle.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_4", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_4.flac", "type": "audio", "duration": 594.013}, {"from": "Assistant", "value": "For many use cases, it is required that MT does not just translate raw text, but complex formatted documents (e.g. websites, slides, spreadsheets) and the result of the translation should reflect the formatting. This is challenging, as markup can be nested, apply to spans contiguous in source but non-contiguous in target etc. Here we present TransIns, a system for non-plain text document translation that builds on the Okapi framework and MT models trained with Marian NMT. We develop, implement and evaluate different strategies for reinserting markup into translated sentences using token alignments between source and target sentences. We propose a simple and effective strategy that compiles down all markup to single source tokens and transfers them to aligned target tokens. A first evaluation shows that this strategy yields highly accurate markup in the translated documents that outperforms the markup quality found in documents translated with popular translation services. We release TransIns under the MIT License as open-source software onhttps://github.com/DFKI-MLT/TransIns. An online demonstrator is available athttps://transins.dfki.de.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_5", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_5.flac", "type": "audio", "duration": 641.242}, {"from": "Assistant", "value": "In this paper we explore the functionalities of ET, a suite designed to support linguistic research and natural language processing tasks using corpora annotated in the CoNLL-U format. These goals are achieved by two integrated environments – Interrogatório, an environment for querying and editing annotated corpora, and Julgamento, an environment for assessing their quality. ET is open-source, built on different Python Web technologies and has Web demonstrations available on-line. ET has been intensively used in our research group for over two years, being the chosen framework for several linguistic and NLP-related studies conducted by its researchers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_6", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_6.flac", "type": "audio", "duration": 297.935}, {"from": "Assistant", "value": "We introduce N-LTP, an open-source neural language technology platform supporting six fundamental Chinese NLP tasks: lexical analysis (Chinese word segmentation, part-of-speech tagging, and named entity recognition), syntactic parsing (dependency parsing), and semantic parsing (semantic dependency parsing and semantic role labeling). Unlike the existing state-of-the-art toolkits, such as Stanza, that adopt an independent model for each task, N-LTP adopts the multi-task framework by using a shared pre-trained model, which has the advantage of capturing the shared knowledge across relevant Chinese tasks. In addition, a knowledge distillation method (Clark et al., 2019) where the single-task model teaches the multi-task model is further introduced to encourage the multi-task model to surpass its single-task teacher. Finally, we provide a collection of easy-to-use APIs and a visualization tool to make users to use and view the processing results more easily and directly. To the best of our knowledge, this is the first toolkit to support six Chinese NLP fundamental tasks. Source code, documentation, and pre-trained models are available athttps://github.com/HIT-SCIR/ltp.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_7", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_7.flac", "type": "audio", "duration": 595.383}, {"from": "Assistant", "value": "We introduce COMBO – a fully neural NLP system for accurate part-of-speech tagging, morphological analysis, lemmatisation, and (enhanced) dependency parsing. It predicts categorical morphosyntactic features whilst also exposes their vector representations, extracted from hidden layers. COMBO is an easy to install Python package with automatically downloadable pre-trained models for over 40 languages. It maintains a balance between efficiency and quality. As it is an end-to-end system and its modules are jointly trained, its training is competitively fast. As its models are optimised for accuracy, they achieve often better prediction quality than SOTA. The COMBO library is available at:https://gitlab.clarin-pl.eu/syntactic-tools/combo.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_8", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_8.flac", "type": "audio", "duration": 600.05}, {"from": "Assistant", "value": "Timely responses from policy makers to mitigate the impact of the COVID-19 pandemic rely on a comprehensive grasp of events, their causes, and their impacts. These events are reported at such a speed and scale as to be overwhelming. In this paper, we present ExcavatorCovid, a machine reading system that ingests open-source text documents (e.g., news and scientific publications), extracts COVID-19 related events and relations between them, and builds a Temporal and Causal Analysis Graph (TCAG). Excavator will help government agencies alleviate the information overload, understand likely downstream effects of political and economic decisions and events related to the pandemic, and respond in a timely manner to mitigate the impact of COVID-19. We expect the utility of Excavator to outlive the COVID-19 pandemic: analysts and decision makers will be empowered by Excavator to better understand and solve complex problems in the future. A demonstration video is available athttps://vimeo.com/528619007.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_9", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_9.flac", "type": "audio", "duration": 222.935}, {"from": "Assistant", "value": "Warning: This manuscript contains a certain level of offensive expression. As communication through social media platforms has grown immensely, the increasing prevalence of offensive language online has become a critical problem. Notably in Korea, one of the countries with the highest Internet usage, automatic detection of offensive expressions has recently been brought to attention. However, morphological richness and complex syntax of Korean causes difficulties in neural model training. Furthermore, most of previous studies mainly focus on the detection of abusive language, disregarding implicit offensiveness and underestimating a different degree of intensity. To tackle these problems, we present KOAS, a system that fully exploits both contextual and linguistic features and estimates an offensiveness score for a text. We carefully designed KOAS with a multi-task learning framework and constructed a Korean dataset for offensive analysis from various domains. Refer for a detailed demonstration.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_10", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_10.flac", "type": "audio", "duration": 594.245}, {"from": "Assistant", "value": "We present RepGraph, an open source visualisation and analysis tool for meaning representation graphs. Graph-based meaning representations provide rich semantic annotations, but visualising them clearly is more challenging than for fully lexicalized representations. Our application provides a seamless, unifying interface with which to visualise, manipulate and analyse semantically parsed graph data represented in a JSON-based serialisation format. RepGraph visualises graphs in multiple formats, with an emphasis on showing the relation between nodes and their corresponding token spans, whilst keeping the representation compact. Additionally, the web-based tool provides NLP researchers with a clear, visually intuitive way of interacting with these graphs, and includes a number of graph analysis features. The tool currently supports the DMRS, EDS, PTG, UCCA, and AMR semantic frameworks. A live demo is available athttps://repgraph.vercel.app/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_11", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_11.flac", "type": "audio", "duration": 573.324}, {"from": "Assistant", "value": "In the language domain, as in other domains, neural explainability takes an ever more important role, with feature attribution methods on the forefront. Many such methods require considerable computational resources and expert knowledge about implementation details and parameter choices. To facilitate research, we present Thermostat which consists of a large collection of model explanations and accompanying analysis tools. Thermostat allows easy access to over 200k explanations for the decisions of prominent state-of-the-art models spanning across different NLP tasks, generated with multiple explainers. The dataset took over 10k GPU hours (> one year) to compile; compute time that the community now saves. The accompanying software tools allow to analyse explanations instance-wise but also accumulatively on corpus level. Users can investigate and compare models, datasets and explainers without the need to orchestrate implementation details. Thermostat is fully open source, democratizes explainability research in the language domain, circumvents redundant computations and increases comparability and replicability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_12", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_12.flac", "type": "audio", "duration": 560.646}, {"from": "Assistant", "value": "While different language models are ubiquitous in NLP, it is hard to contrast their outputs and identify which contexts one can handle better than the other. To address this question, we introduce LMdiff, a tool that visually compares probability distributions of two models that differ, e.g., through finetuning, distillation, or simply training with different parameter sizes. LMdiff allows the generation of hypotheses about model behavior by investigating text instances token by token and further assists in choosing these interesting text instances by identifying the most interesting phrases from large corpora. We showcase the applicability of LMdiff for hypothesis generation across multiple case studies. A demo is available athttp://lmdiff.net.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_13", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_13.flac", "type": "audio", "duration": 591.807}, {"from": "Assistant", "value": "In this paper we present a prototype demonstrator showcasing a novel method to perform semantic exploration of user reviews. The system enables effective navigation in a rich contextual semantic schema with a large number of structured classes indicating relevant information. In order to identify instances of the structured classes in the reviews, we defined a new Information Extraction task called Semantic Context Path (SCP) labeling, which simultaneously assigns types and semantic roles to entity mentions. Reviews can rapidly be explored based on the fine-grained and structured semantic classes. As a proof-of-concept, we have implemented this system for reviews on Points-of-Interest, in English and Korean.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_14", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_14.flac", "type": "audio", "duration": 558.602}, {"from": "Assistant", "value": "On the way towards general Visual Question Answering (VQA) systems that are able to answer arbitrary questions, the need arises for evaluation beyond single-metric leaderboards for specific datasets. To this end, we propose a browser-based benchmarking tool for researchers and challenge organizers, with an API for easy integration of new models and datasets to keep up with the fast-changing landscape of VQA. Our tool helps test generalization capabilities of models across multiple datasets, evaluating not just accuracy, but also performance in more realistic real-world scenarios such as robustness to input noise. Additionally, we include metrics that measure biases and uncertainty, to further explain model behavior. Interactive filtering facilitates discovery of problematic behavior, down to the data sample level. As proof of concept, we perform a case study on four models. We find that state-of-the-art VQA models are optimized for specific tasks or datasets, but fail to generalize even to other in-domain test sets, for example they can not recognize text in images. Our metrics allow us to quantify which image and question embeddings provide most robustness to a model. All code s publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_15", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_15.flac", "type": "audio", "duration": 594.059}, {"from": "Assistant", "value": "Athena 2.0 is an Alexa Prize SocialBot that has been a finalist in the last two Alexa Prize Grand Challenges. One reason for Athena’s success is its novel dialogue management strategy, which allows it to dynamically construct dialogues and responses from component modules, leading to novel conversations with every interaction. Here we describe Athena’s system design and performance in the Alexa Prize during the 20/21 competition. A live demo of Athena as well as video recordings will provoke discussion on the state of the art in conversational AI.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_16", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_16.flac", "type": "audio", "duration": 552.054}, {"from": "Assistant", "value": "In this paper we present SPRING Online Services, a Web interface and RESTful APIs for our state-of-the-art AMR parsing and generation system, SPRING (Symmetric PaRsIng aNd Generation). The Web interface has been developed to be easily used by the Natural Language Processing community, as well as by the general public. It provides, among other things, a highly interactive visualization platform and a feedback mechanism to obtain user suggestions for further improvements of the system’s output. Moreover, our RESTful APIs enable easy integration of SPRING in downstream applications where AMR structures are needed. Finally, we make SPRING Online Services freely available athttp://nlp.uniroma1.it/springand, in addition, we release extra model checkpoints to be used with the original SPRING Python code.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_18", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_18.flac", "type": "audio", "duration": 605.042}, {"from": "Assistant", "value": "Freedom of the press and media is of vital importance for democratically organised states and open societies. We introduce the Press Freedom Monitor, a tool that aims to detect reported press and media freedom violations in news articles and tweets. It is used by press and media freedom organisations to support their daily monitoring and to trigger rapid response actions. The Press Freedom Monitor enables the monitoring experts to get a fast overview over recently reported incidents and it has shown an impressive performance in this regard. This paper presents our work on the tool, starting with the training phase, which comprises defining the topic-related keywords to be used for querying APIs for news and Twitter content and evaluating different machine learning models based on a training dataset specifically created for our use case. Then, we describe the components of the production pipeline, including data gathering, duplicates removal, country mapping, case mapping and the user interface. We also conducted a usability study to evaluate the effectiveness of the user interface, and describe improvement plans for future work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_19", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_19.flac", "type": "audio", "duration": 552.054}, {"from": "Assistant", "value": "We present UMR-Writer, a web-based application for annotating Uniform Meaning Representations (UMR), a graph-based, cross-linguistically applicable semantic representation developed recently to support the development of interpretable natural language applications that require deep semantic analysis of texts. We present the functionalities of UMR-Writer and discuss the challenges in developing such a tool and how they are addressed.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_20", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_20.flac", "type": "audio", "duration": 376.976}, {"from": "Assistant", "value": "Every day, millions of people sacrifice their privacy and browsing habits in exchange for online machine translation. Companies and governments with confidentiality requirements often ban online translation or pay a premium to disable logging. To bring control back to the end user and demonstrate speed, we developed translateLocally. Running locally on a desktop or laptop CPU, translateLocally delivers cloud-like translation speed and quality even on 10 year old hardware. The open-source software is based on Marian and runs on Linux, Windows, and macOS.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_21", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_21.flac", "type": "audio", "duration": 399.801}, {"from": "Assistant", "value": "The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for contemporary NLP designed to support this ecosystem. Datasets aims to standardize end-user interfaces, versioning, and documentation, while providing a lightweight front-end that behaves similarly for small datasets as for internet-scale corpora. The design of the library incorporates a distributed, community-driven approach to adding datasets and documenting usage. After a year of development, the library now includes more than 650 unique datasets, has more than 250 contributors, and has helped support a variety of novel cross-dataset research projects and shared tasks. The library is available athttps://github.com/huggingface/datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_22", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_22.flac", "type": "audio", "duration": 345.583}, {"from": "Assistant", "value": "This paper introduces Summary Explorer, a new tool to support the manual inspection of text summarization systems by compiling the outputs of 55 state-of-the-art single document summarization approaches on three benchmark datasets, and visually exploring them during a qualitative assessment. The underlying design of the tool considers three well-known summary quality criteria (coverage, faithfulness, and position bias), encapsulated in a guided assessment based on tailored visualizations. The tool complements existing approaches for locally debugging summarization models and improves upon them. The tool is available athttps://tldr.webis.de/", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_23", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_23.flac", "type": "audio", "duration": 120.813}, {"from": "Assistant", "value": "We present MeetDot, a videoconferencing system with live translation captions overlaid on screen. The system aims to facilitate conversation between people who speak different languages, thereby reducing communication barriers between multilingual participants. Currently, our system supports speech and captions in 4 languages and combines automatic speech recognition (ASR) and machine translation (MT) in a cascade. We use the re-translation strategy to translate the streamed speech, resulting in caption flicker. Additionally, our system has very strict latency requirements to have acceptable call quality. We implement several features to enhance user experience and reduce their cognitive load, such as smooth scrolling captions and reducing caption flicker. The modular architecture allows us to integrate different ASR and MT services in our backend. Our system provides an integrated evaluation suite to optimize key intrinsic evaluation metrics such as accuracy, latency and erasure. Finally, we present an innovative cross-lingual word-guessing game as an extrinsic evaluation metric to measure end-to-end system performance. We plan to make our system open-source for research purposes.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_24", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_24.flac", "type": "audio", "duration": 514.415}, {"from": "Assistant", "value": "A fundamental component to the success of modern representation learning is the ease of performing various vector operations. Recently, objects with more geometric structure (eg. distributions, complex or hyperbolic vectors, or regions such as cones, disks, or boxes) have been explored for their alternative inductive biases and additional representational capacity. In this work, we introduce Box Embeddings, a Python library that enables researchers to easily apply and extend probabilistic box embeddings. Fundamental geometric operations on boxes are implemented in a numerically stable way, as are modern approaches to training boxes which mitigate gradient sparsity. The library is fully open source, and compatible with both PyTorch and TensorFlow, which allows existing neural network layers to be replaced with or transformed into boxes easily. In this work, we present the implementation details of the fundamental components of the library, and the concepts required to use box representations alongside existing neural network architectures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_25", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_25.flac", "type": "audio", "duration": 452.325}, {"from": "Assistant", "value": "NLP systems are often challenged by difficulties arising from noisy, non-standard, and domain specific corpora. The task of lexical normalisation aims to standardise such corpora, but currently lacks suitable tools to acquire high-quality annotated data to support deep learning based approaches. In this paper, we present LexiClean, the first open-source web-based annotation tool for multi-task lexical normalisation. LexiClean’s main contribution is support for simultaneous in situ token-level modification and annotation that can be rapidly applied corpus wide. We demonstrate the usefulness of our tool through a case study on two sets of noisy corpora derived from the specialised-domain of industrial mining. We show that LexiClean allows for the rapid and efficient development of high-quality parallel corpora. A demo of our system is available at:https://youtu.be/P7_ooKrQPDU.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_26", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_26.flac", "type": "audio", "duration": 565.383}, {"from": "Assistant", "value": "Transformers are the dominant architecture in NLP, but their training and fine-tuning is still very challenging. In this paper, we present the design and implementation of a visual analytic framework for assisting researchers in such process, by providing them with valuable insights about the model’s intrinsic properties and behaviours. Our framework offers an intuitive overview that allows the user to explore different facets of the model (e.g., hidden states, attention) through interactive visualization, and allows a suite of built-in algorithms that compute the importance of model components and different parts of the input sequence. Case studies and feedback from a user focus group indicate that the framework is useful, and suggest several improvements. Our framework is available at:https://github.com/raymondzmc/T3-Vis.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_27", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_27.flac", "type": "audio", "duration": 623.804}, {"from": "Assistant", "value": "We demonstrate a library for the integration of domain knowledge in deep learning architectures. Using this library, the structure of the data is expressed symbolically via graph declarations and the logical constraints over outputs or latent variables can be seamlessly added to the deep models. The domain knowledge can be defined explicitly, which improves the explainability of the models in addition to their performance and generalizability in the low-data regime. Several approaches for such integration of symbolic and sub-symbolic models have been introduced; however, there is no library to facilitate the programming for such integration in a generic way while various underlying algorithms can be used. Our library aims to simplify programming for such integration in both training and inference phases while separating the knowledge representation from learning algorithms. We showcase various NLP benchmark tasks and beyond. The framework is publicly available at Github(https://github.com/HLR/DomiKnowS).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_28", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_28.flac", "type": "audio", "duration": 636.738}, {"from": "Assistant", "value": "When journalists cover a news story, they can cover the story from multiple angles or perspectives. These perspectives are called “frames,” and usage of one frame or another may influence public perception and opinion of the issue at hand. We develop a web-based system for analyzing frames in multilingual text documents. We propose and guide users through a five-step end-to-end computational framing analysis framework grounded in media framing theory in communication research. Users can use the framework to analyze multilingual text data, starting from the exploration of frames in user’s corpora and through review of previous framing literature (step 1-3) to frame classification (step 4) and prediction (step 5). The framework combines unsupervised and supervised machine learning and leverages a state-of-the-art (SoTA) multilingual language model, which can significantly enhance frame prediction performance while requiring a considerably small sample of manual annotations. Through the interactive website, anyone can perform the proposed computational framing analysis, making advanced computational analysis available to researchers without a programming background and bridging the digital divide within the communication research discipline in particular and the academic community in general. The system is available online athttp://www.openframing.org, via an APIhttp://www.openframing.org:5000/docs/, or through our GitHub pagehttps://github.com/vibss2397/openFraming.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_29", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_29.flac", "type": "audio", "duration": 370.056}, {"from": "Assistant", "value": "IrEne is an energy prediction system that accurately predicts the interpretable inference energy consumption of a wide range of Transformer-based NLP models. We present the IrEne-viz tool, an online platform for visualizing and exploring energy consumption of various Transformer-based models easily. Additionally, we release a public API that can be used to access granular information about energy consumption of transformer models and their components. The live demo is available athttp://stonybrooknlp.github.io/irene/demo/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_30", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_30.flac", "type": "audio", "duration": 423.021}, {"from": "Assistant", "value": "Since late 2019, COVID-19 has quickly emerged as the newest biomedical domain, resulting in a surge of new information. As with other emergent domains, the discussion surrounding the topic has been rapidly changing, leading to the spread of misinformation. This has created the need for a public space for users to ask questions and receive credible, scientific answers. To fulfill this need, we turn to the task of open-domain question-answering, which we can use to efficiently find answers to free-text questions from a large set of documents. In this work, we present such a system for the emergent domain of COVID-19. Despite the small data size available, we are able to successfully train the system to retrieve answers from a large-scale corpus of published COVID-19 scientific papers. Furthermore, we incorporate effective re-ranking and question-answering techniques, such as document diversity and multiple answer spans. Our open-domain question-answering system can further act as a model for the quick development of similar systems that can be adapted and modified for other developing emergent domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_31", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_31.flac", "type": "audio", "duration": 590.042}, {"from": "Assistant", "value": "Project Debater was revealed in 2019 as the first AI system that can debate human experts on complex topics. Engaging in a live debate requires a diverse set of skills, and Project Debater has been developed accordingly as a collection of components, each designed to perform a specific subtask. Project Debater APIs provide access to many of these capabilities, as well as to more recently developed ones. This diverse set of web services, publicly available for academic use, includes core NLP services, argument mining and analysis capabilities, and higher-level services for content summarization. We describe these APIs and their performance, and demonstrate how they can be used for building practical solutions. In particular, we will focus on Key Point Analysis, a novel technology that identifies the main points and their prevalence in a collection of texts such as survey responses and user reviews.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_32", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_32.flac", "type": "audio", "duration": 391.163}, {"from": "Assistant", "value": "In this paper, we introduce CroAno, a web-based crowd annotation platform for the Chinese named entity recognition (NER). Besides some basic features for crowd annotation like fast tagging and data management, CroAno provides a systematic solution for improving label consistency of Chinese NER dataset. 1) Disagreement Adjudicator: CroAno uses a multi-dimensional highlight mode to visualize instance-level inconsistent entities and makes the revision process user-friendly. 2) Inconsistency Detector: CroAno employs a detector to locate corpus-level label inconsistency and provides users an interface to correct inconsistent entities in batches. 3) Prediction Error Analyzer: We deconstruct the entity prediction error of the model to six fine-grained entity error types. Users can employ this error system to detect corpus-level inconsistency from a model perspective. To validate the effectiveness of our platform, we use CroAno to revise two public datasets. In the two revised datasets, we get an improvement of +1.96% and +2.57% F1 respectively in model performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_33", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_33.flac", "type": "audio", "duration": 483.045}, {"from": "Assistant", "value": "We introduce iFᴀᴄᴇᴛSᴜᴍ, a web application for exploring topical document collections. iFᴀᴄᴇᴛSᴜᴍ integrates interactive summarization together with faceted search, by providing a novel faceted navigation scheme that yields abstractive summaries for the user’s selections. This approach offers both a comprehensive overview as well as particular details regard-ing subtopics of choice. The facets are automatically produced based on cross-document coreference pipelines, rendering generic concepts, entities and statements surfacing in the source texts. We analyze the effectiveness of our application through small-scale user studies that suggest the usefulness of our tool.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_34", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_34.flac", "type": "audio", "duration": 597.612}, {"from": "Assistant", "value": "Over the past few years, Word Sense Disambiguation (WSD) has received renewed interest: recently proposed systems have shown the remarkable effectiveness of deep learning techniques in this task, especially when aided by modern pretrained language models. Unfortunately, such systems are still not available as ready-to-use end-to-end packages, making it difficult for researchers to take advantage of their performance. The only alternative for a user interested in applying WSD to downstream tasks is to rely on currently available end-to-end WSD systems, which, however, still rely on graph-based heuristics or non-neural machine learning algorithms. In this paper, we fill this gap and propose AMuSE-WSD, the first end-to-end system to offer high-quality sense information in 40 languages through a state-of-the-art neural model for WSD. We hope that AMuSE-WSD will provide a stepping stone for the integration of meaning into real-world applications and encourage further studies in lexical semantics. AMuSE-WSD is available online athttp://nlp.uniroma1.it/amuse-wsd.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_35", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_35.flac", "type": "audio", "duration": 469.367}, {"from": "Assistant", "value": "Named Entity Recognition is a fundamental task in information extraction and is an essential element for various Natural Language Processing pipelines. Adversarial attacks have been shown to greatly affect the performance of text classification systems but knowledge about their effectiveness against named entity recognition models is limited. This paper investigates the effectiveness and portability of adversarial attacks from text classification to named entity recognition and the ability of adversarial training to counteract these attacks. We find that character-level and word-level attacks are the most effective, but adversarial training can grant significant protection at little to no expense of standard performance. Alongside our results, we also release SeqAttack, a framework to conduct adversarial attacks against token classification models (used in this work for named entity recognition) and a companion web application to inspect and cherry pick adversarial examples.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_36", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_36.flac", "type": "audio", "duration": 595.058}, {"from": "Assistant", "value": "Notwithstanding the growing interest in cross-lingual techniques for Natural Language Processing, there has been a surprisingly small number of efforts aimed at the development of easy-to-use tools for cross-lingual Semantic Role Labeling. In this paper, we fill this gap and present InVeRo-XL, an off-the-shelf state-of-the-art system capable of annotating text with predicate sense and semantic role labels from 7 predicate-argument structure inventories in more than 40 languages. We hope that our system – with its easy-to-use RESTful API and Web interface – will become a valuable tool for the research community, encouraging the integration of sentence-level semantics into cross-lingual downstream tasks. InVeRo-XL is available online athttp://nlp.uniroma1.it/invero.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_37", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_37.flac", "type": "audio", "duration": 515.042}, {"from": "Assistant", "value": "Recent advances in summarization provide models that can generate summaries of higher quality. Such models now exist for a number of summarization tasks, including query-based summarization, dialogue summarization, and multi-document summarization. While such models and tasks are rapidly growing in the research field, it has also become challenging for non-experts to keep track of them. To make summarization methods more accessible to a wider audience, we develop SummerTime by rethinking the summarization task from the perspective of an NLP non-expert. SummerTime is a complete toolkit for text summarization, including various models, datasets, and evaluation metrics, for a full spectrum of summarization-related tasks. SummerTime integrates with libraries designed for NLP researchers, and enables users with easy-to-use APIs. With SummerTime, users can locate pipeline solutions and search for the best model with their own data, and visualize the differences, all with a few lines of code. We also provide explanations for models and evaluation metrics to help users understand the model behaviors and select models that best suit their needs. Our library, along with a notebook demo, is available athttps://github.com/Yale-LILY/SummerTime.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_38", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_38.flac", "type": "audio", "duration": 572.047}, {"from": "Assistant", "value": "We introduce Chandler, a system that generates sarcastic responses to a given utterance. Previous sarcasm generators assume the intended meaning that sarcasm conceals is the opposite of the literal meaning. We argue that this traditional theory of sarcasm provides a grounding that is neither necessary, nor sufficient, for sarcasm to occur. Instead, we ground our generation process on a formal theory that specifies conditions that unambiguously differentiate sarcasm from non-sarcasm. Furthermore, Chandler not only generates sarcastic responses, but also explanations for why each response is sarcastic. This provides accountability, crucial for avoiding miscommunication between humans and conversational agents, particularly considering that sarcastic communication can be offensive. In human evaluation, Chandler achieves comparable or higher sarcasm scores, compared to state-of-the-art generators, while generating more diverse responses, that are more specific and more coherent to the input.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_39", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_39.flac", "type": "audio", "duration": 678.046}, {"from": "Assistant", "value": "To grasp the true reasoning ability, the Natural Language Inference model should be evaluated on counterfactual data. TabPert facilitates this by generation of such counterfactual data for assessing model tabular reasoning issues. TabPert allows the user to update a table, change the hypothesis, change the labels, and highlight rows that are important for hypothesis classification. TabPert also details the technique used to automatically produce the table, as well as the strategies employed to generate the challenging hypothesis. These counterfactual tables and hypotheses, as well as the metadata, is then used to explore the existing model’s shortcomings methodically and quantitatively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_40", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_40.flac", "type": "audio", "duration": 580.406}, {"from": "Assistant", "value": "In this work, we present to the NLP community, and to the wider research community as a whole, an application for the diachronic analysis of research corpora. We open source an easy-to-use tool coined DRIFT, which allows researchers to track research trends and development over the years. The analysis methods are collated from well-cited research works, with a few of our own methods added for good measure. Succinctly put, some of the analysis methods are: keyword extraction, word clouds, predicting declining/stagnant/growing trends using Productivity, tracking bi-grams using Acceleration plots, finding the Semantic Drift of words, tracking trends using similarity, etc. To demonstrate the utility and efficacy of our tool, we perform a case study on the cs.CL corpus of the arXiv repository and draw inferences from the analysis methods. The toolkit and the associated code are available here:https://github.com/rajaswa/DRIFT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_41", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_41.flac", "type": "audio", "duration": 600.096}, {"from": "Assistant", "value": "Working with a wide range of annotators with the same attributes is crucial, as in real-world applications. Although such application cases often use crowd-sourcing mechanisms to gather a variety of annotators, most real-world users use mobile devices. In this paper, we propose “FAST,” an annotation tool for application tasks that focuses on the user experience of mobile devices, which has not yet been focused on thus far. We designed FAST as a web application for use on any device with a flexible interface that can be customized to fit various tasks. In our experiments, we conducted crowd-sourced annotation for a sentiment analysis task with several annotators and evaluated annotation metrics such as speed, quality, and ease of use from the tool’s logs and user surveys. Based on the results of our experiments, we conclude that our system can annotate faster than existing methods while maintaining the annotation quality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-demo_42", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-demo_42.flac", "type": "audio", "duration": 622.063}, {"from": "Assistant", "value": "We introduce deepQuest-py, a framework for training and evaluation of large and light-weight models for Quality Estimation (QE). deepQuest-py provides access to (1) state-of-the-art models based on pre-trained Transformers for sentence-level and word-level QE; (2) light-weight and efficient sentence-level models implemented via knowledge distillation; and (3) a web interface for testing models and visualising their predictions. deepQuest-py is available athttps://github.com/sheffieldnlp/deepQuest-pyunder a CC BY-NC-SA licence.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_1", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_1.flac", "type": "audio", "duration": 322.688}, {"from": "Assistant", "value": "Existing pre-trained language models (PLMs) have demonstrated the effectiveness of self-supervised learning for a broad range of natural language processing (NLP) tasks. However, most of them are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios. In this paper, we propose K-PLUG, a knowledge-injected pre-trained language model based on the encoder-decoder transformer that can be transferred to both natural language understanding and generation tasks. Specifically, we propose five knowledge-aware self-supervised pre-training objectives to formulate the learning of domain-specific knowledge, including e-commerce domain-specific knowledge-bases, aspects of product entities, categories of product entities, and unique selling propositions of product entities. We verify our method in a diverse range of e-commerce scenarios that require domain-specific knowledge, including product knowledge base completion, abstractive product summarization, and multi-turn dialogue. K-PLUG significantly outperforms baselines across the board, which demonstrates that the proposed method effectively learns a diverse set of domain-specific knowledge for both language understanding and generation tasks. Our code is available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_2", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_2.flac", "type": "audio", "duration": 337.177}, {"from": "Assistant", "value": "Short text nowadays has become a more fashionable form of text data, e.g., Twitter posts, news titles, and product reviews. Extracting semantic topics from short texts plays a significant role in a wide spectrum of NLP applications, and neural topic modeling is now a major tool to achieve it. Motivated by learning more coherent and semantic topics, in this paper we develop a novel neural topic model named Dual Word Graph Topic Model (DWGTM), which extracts topics from simultaneous word co-occurrence and semantic correlation graphs. To be specific, we learn word features from the global word co-occurrence graph, so as to ingest rich word co-occurrence information; we then generate text features with word features, and feed them into an encoder network to get topic proportions per-text; finally, we reconstruct texts and word co-occurrence graph with topical distributions and word features, respectively. Besides, to capture semantics of words, we also apply word features to reconstruct a word semantic correlation graph computed by pre-trained word embeddings. Upon those ideas, we formulate DWGTM in an auto-encoding paradigm and efficiently train it with the spirit of neural variational inference. Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_3", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_3.flac", "type": "audio", "duration": 318.973}, {"from": "Assistant", "value": "Spoken question answering (SQA) requires fine-grained understanding of both spoken documents and questions for the optimal answer prediction. In this paper, we propose novel training schemes for spoken question answering with a self-supervised training stage and a contrastive representation learning stage. In the self-supervised stage, we propose three auxiliary self-supervised tasks, including utterance restoration, utterance insertion, and question discrimination, and jointly train the model to capture consistency and coherence among speech documents without any additional data or annotations. We then propose to learn noise-invariant utterance representations in a contrastive objective by adopting multiple augmentation strategies, including span deletion and span substitution. Besides, we design a Temporal-Alignment attention to semantically align the speech-text clues in the learned common space and benefit the SQA tasks. By this means, the training schemes can more effectively guide the generation model to predict more proper answers. Experimental results show that our model achieves state-of-the-art results on three SQA benchmarks. Our code will be publicly available after publication.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_4", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_4.flac", "type": "audio", "duration": 173.012}, {"from": "Assistant", "value": "Recent work in multilingual natural language processing has shown progress in various tasks such as natural language inference and joint multilingual translation. Despite success in learning across many languages, challenges arise where multilingual training regimes often boost performance on some languages at the expense of others. For multilingual named entity recognition (NER) we propose a simple technique that groups similar languages together by using embeddings from a pre-trained masked language model, and automatically discovering language clusters in this embedding space. Specifically, we fine-tune an XLM-Roberta model on a language identification task, and use embeddings from this model for clustering. We conduct experiments on 15 diverse languages in the WikiAnn dataset and show our technique largely outperforms three baselines: (1) training a multilingual model jointly on all available languages, (2) training one monolingual model per language, and (3) grouping languages by linguistic family. We also conduct analyses showing meaningful multilingual transfer for low-resource languages (Swahili and Yoruba), despite being automatically grouped with other seemingly disparate languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_5", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_5.flac", "type": "audio", "duration": 473.792}, {"from": "Assistant", "value": "Automatic news recommendation has gained much attention from the academic community and industry. Recent studies reveal that the key to this task lies within the effective representation learning of both news and users. Existing works typically encode news title and content separately while neglecting their semantic interaction, which is inadequate for news text comprehension. Besides, previous models encode user browsing history without leveraging the structural correlation of user browsed news to reflect user interests explicitly. In this work, we propose a news recommendation framework consisting of collaborative news encoding (CNE) and structural user encoding (SUE) to enhance news and user representation learning. CNE equipped with bidirectional LSTMs encodes news title and content collaboratively with cross-selection and cross-attention modules to learn semantic-interactive news representations. SUE utilizes graph convolutional networks to extract cluster-structural features of user history, followed by intra-cluster and inter-cluster attention modules to learn hierarchical user interest representations. Experiment results on the MIND dataset validate the effectiveness of our model to improve the performance of news recommendation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_6", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_6.flac", "type": "audio", "duration": 179.049}, {"from": "Assistant", "value": "Despite considerable progress, most machine reading comprehension (MRC) tasks still lack sufficient training data to fully exploit powerful deep neural network models with millions of parameters, and it is laborious, expensive, and time-consuming to create large-scale, high-quality MRC data through crowdsourcing. This paper focuses on generating more training data for MRC tasks by leveraging existing question-answering (QA) data. We first collect a large-scale multi-subject multiple-choice QA dataset for Chinese, ExamQA. We next use incomplete, yet relevant snippets returned by a web search engine as the context for each QA instance to convert it into a weakly-labeled MRC instance. To better use the weakly-labeled data to improve a target MRC task, we evaluate and compare several methods and further propose a self-teaching paradigm. Experimental results show that, upon state-of-the-art MRC baselines, we can obtain +5.1% in accuracy on a multiple-choice Chinese MRC dataset, Cˆ3, and +3.8% in exact match on an extractive Chinese MRC dataset, CMRC 2018, demonstrating the usefulness of the generated QA-based weakly-labeled data for different types of MRC tasks as well as the effectiveness of self-teaching. ExamQA will be available athttps://dataset.org/examqa/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_7", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_7.flac", "type": "audio", "duration": 596.219}, {"from": "Assistant", "value": "Understanding the semantic meaning of content on the web through the lens of entities and concepts has many practical advantages. However, when building large-scale entity extraction systems, practitioners are facing unique challenges involving finding the best ways to leverage the scale and variety of data available on internet platforms. We present learnings from our efforts in building an entity extraction system for multiple document types at large scale using multi-modal Transformers. We empirically demonstrate the effectiveness of multi-lingual, multi-task and cross-document type learning. We also discuss the label collection schemes that help to minimize the amount of noise in the collected data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_8", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_8.flac", "type": "audio", "duration": 327.04}, {"from": "Assistant", "value": "Visual and textual modalities contribute complementary information about events described in multimedia documents. Videos contain rich dynamics and detailed unfoldings of events, while text describes more high-level and abstract concepts. However, existing event extraction methods either do not handle video or solely target video while ignoring other modalities. In contrast, we propose the first approach to jointly extract events from both video and text articles. We introduce the new task of Video MultiMedia Event Extraction and propose two novel components to build the first system towards this task. First, we propose the first self-supervised cross-modal event coreference model that can determine coreference between video events and text events without any manually annotated pairs. Second, we introduce the first cross-modal transformer architecture, which extracts structured event information from both videos and text documents. We also construct and will publicly release a new benchmark of video-article pairs, consisting of 860 video-article pairs with extensive annotations for evaluating methods on this task. Our experimental results demonstrate the effectiveness of our proposed method on our new benchmark dataset. We achieve 6.0% and 5.8% absolute F-score gain on multimodal event coreference resolution and multimedia event extraction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_9", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_9.flac", "type": "audio", "duration": 177.006}, {"from": "Assistant", "value": "Temporal language grounding (TLG) aims to localize a video segment in an untrimmed video based on a natural language description. To alleviate the expensive cost of manual annotations for temporal boundary labels,we are dedicated to the weakly supervised setting, where only video-level descriptions are provided for training. Most of the existing weakly supervised methods generate a candidate segment set and learn cross-modal alignment through a MIL-based framework. However, the temporal structure of the video as well as the complicated semantics in the sentence are lost during the learning. In this work, we propose a novel candidate-free framework: Fine-grained Semantic Alignment Network (FSAN), for weakly supervised TLG. Instead of view the sentence and candidate moments as a whole, FSAN learns token-by-clip cross-modal semantic alignment by an iterative cross-modal interaction module, generates a fine-grained cross-modal semantic alignment map, and performs grounding directly on top of the map. Extensive experiments are conducted on two widely-used benchmarks: ActivityNet-Captions, and DiDeMo, where our FSAN achieves state-of-the-art performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_10", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_10.flac", "type": "audio", "duration": 358.005}, {"from": "Assistant", "value": "Despite significant progress has been achieved in text summarization, factual inconsistency in generated summaries still severely limits its practical applications. Among the key factors to ensure factual consistency, a reliable automatic evaluation metric is the first and the most crucial one. However, existing metrics either neglect the intrinsic cause of the factual inconsistency or rely on auxiliary tasks, leading to an unsatisfied correlation with human judgments or increasing the inconvenience of usage in practice. In light of these challenges, we propose a novel metric to evaluate the factual consistency in text summarization via counterfactual estimation, which formulates the causal relationship among the source document, the generated summary, and the language prior. We remove the effect of language prior, which can cause factual inconsistency, from the total causal effect on the generated summary, and provides a simple yet effective way to evaluate consistency without relying on other auxiliary tasks. We conduct a series of experiments on three public abstractive text summarization datasets, and demonstrate the advantages of the proposed metric in both improving the correlation with human judgments and the convenience of usage. The source code is available athttps://github.com/xieyxclack/factual_coco.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_11", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_11.flac", "type": "audio", "duration": 313.539}, {"from": "Assistant", "value": "Recent advances in using retrieval components over external knowledge sources have shown impressive results for a variety of downstream tasks in natural language processing. Here, we explore the use of unstructured external knowledge sources of images and their corresponding captions for improving visual question answering (VQA). First, we train a novel alignment model for embedding images and captions in the same space, which achieves substantial improvement in performance on image-caption retrieval w.r.t. similar methods. Second, we show that retrieval-augmented multi-modal transformers using the trained alignment model improve results on VQA over strong baselines. We further conduct extensive experiments to establish the promise of this approach, and examine novel applications for inference time such as hot-swapping indices.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_14", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_14.flac", "type": "audio", "duration": 329.004}, {"from": "Assistant", "value": "Latent Dirichlet allocation (LDA), a widely used topic model, is often employed as a fundamental tool for text analysis in various applications. However, the training process of the LDA model typically requires massive text corpus data. On one hand, such massive data may expose private information in the training data, thereby incurring significant privacy concerns. On the other hand, the efficiency of the LDA model training may be impacted, since LDA training often needs to handle these massive text corpus data. To address the privacy issues in LDA model training, some recent works have combined LDA training algorithms that are based on collapsed Gibbs sampling (CGS) with differential privacy. Nevertheless, these works usually have a high accumulative privacy budget due to vast iterations in CGS. Moreover, these works always have low efficiency due to handling massive text corpus data. To improve the privacy guarantee and efficiency, we combine a subsampling method with CGS and propose a novel LDA training algorithm with differential privacy, SUB-LDA. We find that subsampling in CGS naturally improves efficiency while amplifying privacy. We propose a novel metric, the efficiency–privacy function, to evaluate improvements of the privacy guarantee and efficiency. Based on a conventional subsampling method, we propose an adaptive subsampling method to improve the model’s utility produced by SUB-LDA when the subsampling ratio is small. We provide a comprehensive analysis of SUB-LDA, and the experiment results validate its efficiency and privacy guarantee improvements.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_15", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_15.flac", "type": "audio", "duration": 330.002}, {"from": "Assistant", "value": "Writing mammography reports can be error-prone and time-consuming for radiologists. In this paper we propose a method to generate mammography reports given four images, corresponding to the four views used in screening mammography. To the best of our knowledge our work represents the first attempt to generate the mammography report using deep-learning. We propose an encoder-decoder model that includes an EfficientNet-based encoder and a Transformer-based decoder. We demonstrate that the Transformer-based attention mechanism can combine visual and semantic information to localize salient regions on the input mammograms and generate a visually interpretable report. The conducted experiments, including an evaluation by a certified radiologist, show the effectiveness of the proposed method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_16", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_16.flac", "type": "audio", "duration": 180.048}, {"from": "Assistant", "value": "It is a well-known approach for fringe groups and organizations to use euphemisms—ordinary-sounding and innocent-looking words with a secret meaning—to conceal what they are discussing. For instance, drug dealers often use “pot” for marijuana and “avocado” for heroin. From a social media content moderation perspective, though recent advances in NLP have enabled the automatic detection of such single-word euphemisms, no existing work is capable of automatically detecting multi-word euphemisms, such as “blue dream” (marijuana) and “black tar” (heroin). Our paper tackles the problem of euphemistic phrase detection without human effort for the first time, as far as we are aware. We first perform phrase mining on a raw text corpus (e.g., social media posts) to extract quality phrases. Then, we utilize word embedding similarities to select a set of euphemistic phrase candidates. Finally, we rank those candidates by a masked language model—SpanBERT. Compared to strong baselines, we report 20-50% higher detection accuracies using our algorithm for detecting euphemistic phrases.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_17", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_17.flac", "type": "audio", "duration": 306.016}, {"from": "Assistant", "value": "Multi-hop QA requires the machine to answer complex questions through finding multiple clues and reasoning, and provide explanatory evidence to demonstrate the machine’s reasoning process. We propose Relation Extractor-Reader and Comparator (RERC), a three-stage framework based on complex question decomposition. The Relation Extractor decomposes the complex question, and then the Reader answers the sub-questions in turn, and finally the Comparator performs numerical comparison and summarizes all to get the final answer, where the entire process itself constitutes a complete reasoning evidence path. In the 2WikiMultiHopQA dataset, our RERC model has achieved the state-of-the-art performance, with a winning joint F1 score of 53.58 on the leaderboard. All indicators of our RERC are close to human performance, with only 1.95 behind the human level in F1 score of support fact. At the same time, the evidence path provided by our RERC framework has excellent readability and faithfulness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_18", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_18.flac", "type": "audio", "duration": 155.016}, {"from": "Assistant", "value": "The span-based model enjoys great popularity in recent works of sequence segmentation. However, each of these methods suffers from its own defects, such as invalid predictions. In this work, we introduce a unified span-based model, lexical unit analysis (LUA), that addresses all these matters. Segmenting a lexical unit sequence involves two steps. Firstly, we embed every span by using the representations from a pretraining language model. Secondly, we define a score for every segmentation candidate and apply dynamic programming (DP) to extract the candidate with the maximum score. We have conducted extensive experiments on 3 tasks, (e.g., syntactic chunking), across 7 datasets. LUA has established new state-of-the-art performances on 6 of them. We have achieved even better results through incorporating label correlations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_19", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_19.flac", "type": "audio", "duration": 360.002}, {"from": "Assistant", "value": "Dense neural text retrieval has achieved promising results on open-domain Question Answering (QA), where latent representations of questions and passages are exploited for maximum inner product search in the retrieval process. However, current dense retrievers require splitting documents into short passages that usually contain local, partial and sometimes biased context, and highly depend on the splitting process. As a consequence, it may yield inaccurate and misleading hidden representations, thus deteriorating the final retrieval result. In this work, we propose Dense Hierarchical Retrieval (DHR), a hierarchical framework which can generate accurate dense representations of passages by utilizing both macroscopic semantics in the document and microscopic semantics specific to each passage. Specifically, a document-level retriever first identifies relevant documents, among which relevant passages are then retrieved by a passage-level retriever. The ranking of the retrieved passages will be further calibrated by examining the document-level relevance. In addition, hierarchical title structure and two negative sampling strategies (i.e., In-Doc and In-Sec negatives) are investigated. We apply DHR to large-scale open-domain QA datasets. DHR significantly outperforms the original dense passage retriever, and helps an end-to-end QA system outperform the strong baselines on multiple open-domain QA benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_20", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_20.flac", "type": "audio", "duration": 344.491}, {"from": "Assistant", "value": "We investigate ways to compose complex concepts in texts from primitive ones while grounding them in images. We propose Concept and Relation Graph (CRG), which builds on top of constituency analysis and consists of recursively combined concepts with predicate functions. Meanwhile, we propose a concept composition neural network called Composer to leverage the CRG for visually grounded concept learning. Specifically, we learn the grounding of both primitive and all composed concepts by aligning them to images and show that learning to compose leads to more robust grounding results, measured in text-to-image matching accuracy. Notably, our model can model grounded concepts forming at both the finer-grained sentence level and the coarser-grained intermediate level (or word-level). Composer leads to pronounced improvement in matching accuracy when the evaluation data has significant compound divergence from the training data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_21", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_21.flac", "type": "audio", "duration": 355.939}, {"from": "Assistant", "value": "Humans are remarkably flexible when understanding new sentences that include combinations of concepts they have never encountered before. Recent work has shown that while deep networks can mimic some human language abilities when presented with novel sentences, systematic variation uncovers the limitations in the language-understanding abilities of networks. We demonstrate that these limitations can be overcome by addressing the generalization challenges in the gSCAN dataset, which explicitly measures how well an agent is able to interpret novel linguistic commands grounded in vision, e.g., novel pairings of adjectives and nouns. The key principle we employ is compositionality: that the compositional structure of networks should reflect the compositional structure of the problem domain they address, while allowing other parameters to be learned end-to-end. We build a general-purpose mechanism that enables agents to generalize their language understanding to compositional domains. Crucially, our network has the same state-of-the-art performance as prior work while generalizing its knowledge when prior work does not. Our network also provides a level of interpretability that enables users to inspect what each part of networks learns. Robust grounded language understanding without dramatic failures and without corner cases is critical to building safe and fair robots; we demonstrate the significant role that compositionality can play in achieving that goal.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_22", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_22.flac", "type": "audio", "duration": 331.418}, {"from": "Assistant", "value": "The availability of parallel sentence simplification (SS) is scarce for neural SS modelings. We propose an unsupervised method to build SS corpora from large-scale bilingual translation corpora, alleviating the need for SS supervised corpora. Our method is motivated by the following two findings: neural machine translation model usually tends to generate more high-frequency tokens and the difference of text complexity levels exists between the source and target language of a translation corpus. By taking the pair of the source sentences of translation corpus and the translations of their references in a bridge language, we can construct large-scale pseudo parallel SS data. Then, we keep these sentence pairs with a higher complexity difference as SS sentence pairs. The building SS corpora with an unsupervised approach can satisfy the expectations that the aligned sentences preserve the same meanings and have difference in text complexity levels. Experimental results show that SS methods trained by our corpora achieve the state-of-the-art results and significantly outperform the results on English benchmark WikiLarge.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_24", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_24.flac", "type": "audio", "duration": 373.005}, {"from": "Assistant", "value": "In a typical customer service chat scenario, customers contact a support center to ask for help or raise complaints, and human agents try to solve the issues. In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue. The goal of the present article is advancing the automation of this task. We introduce the first large scale, high quality, customer care dialog summarization dataset with close to 6500 human annotated summaries. The data is based on real-world customer support dialogs and includes both extractive and abstractive summaries. We also introduce a new unsupervised, extractive summarization method specific to dialogs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_25", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_25.flac", "type": "audio", "duration": 326.96}, {"from": "Assistant", "value": "Sentence splitting involves the segmentation of a sentence into two or more shorter sentences. It is a key component of sentence simplification, has been shown to help human comprehension and is a useful preprocessing step for NLP tasks such as summarisation and relation extraction. While several methods and datasets have been proposed for developing sentence splitting models, little attention has been paid to how sentence splitting interacts with discourse structure. In this work, we focus on cases where the input text contains a discourse connective, which we refer to as discourse-based sentence splitting. We create synthetic and organic datasets for discourse-based splitting and explore different ways of combining these datasets using different model architectures. We show that pipeline models which use discourse structure to mediate sentence splitting outperform end-to-end models in learning the various ways of expressing a discourse relation but generate text that is less grammatical; that large scale synthetic data provides a better basis for learning than smaller scale organic data; and that training on discourse-focused, rather than on general sentence splitting data provides a better basis for discourse splitting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_26", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_26.flac", "type": "audio", "duration": 348.926}, {"from": "Assistant", "value": "Multi-task dense retrieval models can be used to retrieve documents from a common corpus (e.g., Wikipedia) for different open-domain question-answering (QA) tasks. However, Karpukhin et al. (2020) shows that jointly learning different QA tasks with one dense model is not always beneficial due to corpus inconsistency. For example, SQuAD only focuses on a small set of Wikipedia articles while datasets like NQ and Trivia cover more entries, and joint training on their union can cause performance degradation. To solve this problem, we propose to train individual dense passage retrievers (DPR) for different tasks and aggregate their predictions during test time, where we use uncertainty estimation as weights to indicate how probable a specific query belongs to each expert’s expertise. Our method reaches state-of-the-art performance on 5 benchmark QA datasets, with up to 10% improvement in top-100 accuracy compared to a joint-training multi-task DPR on SQuAD. We also show that our method handles corpus inconsistency better than the joint-training DPR on a mixed subset of different QA datasets. Code and data are available athttps://github.com/alexlimh/DPR_MUF.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_27", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_27.flac", "type": "audio", "duration": 464.98}, {"from": "Assistant", "value": "Mining the causes of political decision-making is an active research area in the field of political science. In the past, most studies have focused on long-term policies that are collected over several decades of time, and have primarily relied on surveys as the main source of predictors. However, the recent COVID-19 pandemic has given rise to a new political phenomenon, where political decision-making consists of frequent short-term decisions, all on the same controlled topic—the pandemic. In this paper, we focus on the question of how public opinion influences policy decisions, while controlling for confounders such as COVID-19 case increases or unemployment rates. Using a dataset consisting of Twitter data from the 50 US states, we classify the sentiments toward governors of each state, and conduct controlled studies and comparisons. Based on the compiled samples of sentiments, policies, and confounders, we conduct causal inference to discover trends in political decision-making across different states.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_28", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_28.flac", "type": "audio", "duration": 283.934}, {"from": "Assistant", "value": "Event detection (ED) task aims to classify events by identifying key event trigger words embedded in a piece of text. Previous research have proved the validity of fusing syntactic dependency relations into Graph Convolutional Networks(GCN). While existing GCN-based methods explore latent node-to-node dependency relations according to a stationary adjacency tensor, an attention-based dynamic tensor, which can pay much attention to the key node like event trigger or its neighboring nodes, has not been developed. Simultaneously, suffering from the phenomenon of graph information vanishing caused by the symmetric adjacency tensor, existing GCN models can not achieve higher overall performance. In this paper, we propose a novel model Self-Attention Graph Residual Convolution Networks (SA-GRCN) to mine node-to-node latent dependency relations via self-attention mechanism and introduce Graph Residual Network (GResNet) to solve graph information vanishing problem. Specifically, a self-attention module is constructed to generate an attention tensor, representing the dependency attention scores of all words in the sentence. Furthermore, a graph residual term is added to the baseline SA-GCN to construct a GResNet. Considering the syntactically connection of the network input, we initialize the raw adjacency tensor without processed by the self-attention module as the residual term. We conduct experiments on the ACE2005 dataset and the results show significant improvement over competitive baseline methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_29", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_29.flac", "type": "audio", "duration": 165.489}, {"from": "Assistant", "value": "Diverse machine translation aims at generating various target language translations for a given source language sentence. To leverage the linear relationship in the sentence latent space introduced by the mixup training, we propose a novel method, MixDiversity, to generate different translations for the input sentence by linearly interpolating it with different sentence pairs sampled from the training corpus during decoding. To further improve the faithfulness and diversity of the translations, we propose two simple but effective approaches to select diverse sentence pairs in the training corpus and adjust the interpolation weight for each pair correspondingly. Moreover, by controlling the interpolation weight, our method can achieve the trade-off between faithfulness and diversity without any additional training, which is required in most of the previous methods. Experiments on WMT’16 en-ro, WMT’14 en-de, and WMT’17 zh-en are conducted to show that our method substantially outperforms all previous diverse machine translation methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_30", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_30.flac", "type": "audio", "duration": 182.044}, {"from": "Assistant", "value": "This paper investigates how to correct Chinese text errors with types of mistaken, missing and redundant characters, which are common for Chinese native speakers. Most existing models based on detect-correct framework can correct mistaken characters, but cannot handle missing or redundant characters due to inconsistency between model inputs and outputs. Although Seq2Seq-based or sequence tagging methods provide solutions to the three error types and achieved relatively good results in English context, they do not perform well in Chinese context according to our experiments. In our work, we propose a novel alignment-agnostic detect-correct framework that can handle both text aligned and non-aligned situations and can serve as a cold start model when no annotation data are provided. Experimental results on three datasets demonstrate that our method is effective and achieves a better performance than most recent published models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_31", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_31.flac", "type": "audio", "duration": 302.022}, {"from": "Assistant", "value": "Visual dialog is a task of answering a sequence of questions grounded in an image using the previous dialog history as context. In this paper, we study how to address two fundamental challenges for this task: (1) reasoning over underlying semantic structures among dialog rounds and (2) identifying several appropriate answers to the given question. To address these challenges, we propose a Sparse Graph Learning (SGL) method to formulate visual dialog as a graph structure learning task. SGL infers inherently sparse dialog structures by incorporating binary and score edges and leveraging a new structural loss function. Next, we introduce a Knowledge Transfer (KT) method that extracts the answer predictions from the teacher model and uses them as pseudo labels. We propose KT to remedy the shortcomings of single ground-truth labels, which severely limit the ability of a model to obtain multiple reasonable answers. As a result, our proposed model significantly improves reasoning capability compared to baseline methods and outperforms the state-of-the-art approaches on the VisDial v1.0 dataset. The source code is available athttps://github.com/gicheonkang/SGLKT-VisDial.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_32", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_32.flac", "type": "audio", "duration": 360.002}, {"from": "Assistant", "value": "Document-level event extraction is critical to various natural language processing tasks for providing structured information. Existing approaches by sequential modeling neglect the complex logic structures for long texts. In this paper, we leverage the entity interactions and sentence interactions within long documents and transform each document into an undirected unweighted graph by exploiting the relationship between sentences. We introduce the Sentence Community to represent each event as a subgraph. Furthermore, our framework SCDEE maintains the ability to extract multiple events by sentence community detection using graph attention networks and alleviate the role overlapping issue by predicting arguments in terms of roles. Experiments demonstrate that our framework achieves competitive results over state-of-the-art methods on the large-scale document-level event extraction dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_33", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_33.flac", "type": "audio", "duration": 328.702}, {"from": "Assistant", "value": "Research on open-domain dialogue systems that allow free topics is challenging in the field of natural language processing (NLP). The performance of the dialogue system has been improved recently by the method utilizing dialogue-related knowledge; however, non-English dialogue systems suffer from reproducing the performance of English dialogue systems because securing knowledge in the same language with the dialogue system is relatively difficult. Through experiments with a Korean dialogue system, this paper proves that the performance of a non-English dialogue system can be improved by utilizing English knowledge, highlighting the system uses cross-lingual knowledge. For the experiments, we 1) constructed a Korean version of the Wizard of Wikipedia dataset, 2) built Korean-English T5 (KE-T5), a language model pre-trained with Korean and English corpus, and 3) developed a knowledge-grounded Korean dialogue model based on KE-T5. We observed the performance improvement in the open-domain Korean dialogue model even only English knowledge was given. The experimental results showed that the knowledge inherent in cross-lingual language models can be helpful for generating responses in open dialogue systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_34", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_34.flac", "type": "audio", "duration": 360.258}, {"from": "Assistant", "value": "The UNESCO World Heritage List (WHL) includes the exceptionally valuable cultural and natural heritage to be preserved for mankind. Evaluating and justifying the Outstanding Universal Value (OUV) is essential for each site inscribed in the WHL, and yet a complex task, even for experts, since the selection criteria of OUV are not mutually exclusive. Furthermore, manual annotation of heritage values and attributes from multi-source textual data, which is currently dominant in heritage studies, is knowledge-demanding and time-consuming, impeding systematic analysis of such authoritative documents in terms of their implications on heritage management. This study applies state-of-the-art NLP models to build a classifier on a new dataset containing Statements of OUV, seeking an explainable and scalable automation tool to facilitate the nomination, evaluation, research, and monitoring processes of World Heritage sites. Label smoothing is innovatively adapted to improve the model performance by adding prior inter-class relationship knowledge to generate soft labels. The study shows that the best models fine-tuned from BERT and ULMFiT can reach 94.3% top-3 accuracy. A human study with expert evaluation on the model prediction shows that the models are sufficiently generalizable. The study is promising to be further developed and applied in heritage research and practice.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_35", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_35.flac", "type": "audio", "duration": 315.699}, {"from": "Assistant", "value": "Few-shot knowledge graph completion is to infer the unknown facts (i.e., query head-tail entity pairs) of a given relation with only a few observed reference entity pairs. Its general process is to first encode the implicit relation of an entity pair and then match the relation of a query entity pair with the relations of the reference entity pairs. Most existing methods have thus far encoded an entity pair and matched entity pairs by using the direct neighbors of concerned entities. In this paper, we propose the P-INT model for effective few-shot knowledge graph completion. First, P-INT infers and leverages the paths that can expressively encode the relation of two entities. Second, to capture the fine grained matches, P-INT calculates the interactions of paths instead of mix- ing them for each entity pair. Extensive experimental results demonstrate that P-INT out- performs the state-of-the-art baselines by 11.2– 14.2% in terms of Hits@1. Our codes and datasets are online now.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_36", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_36.flac", "type": "audio", "duration": 300.931}, {"from": "Assistant", "value": "We propose Cartography Active Learning (CAL), a novel Active Learning (AL) algorithm that exploits the behavior of the model on individual instances during training as a proxy to find the most informative instances for labeling. CAL is inspired by data maps, which were recently proposed to derive insights into dataset quality (Swayamdipta et al., 2020). We compare our method on popular text classification tasks to commonly used AL strategies, which instead rely on post-training behavior. We demonstrate that CAL is competitive to other common AL methods, showing that training dynamics derived from small seed data can be successfully used for AL. We provide insights into our new AL method by analyzing batch-level statistics utilizing the data maps. Our results further show that CAL results in a more data-efficient learning strategy, achieving comparable or better results with considerably less training data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_38", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_38.flac", "type": "audio", "duration": 346.964}, {"from": "Assistant", "value": "While day-to-day questions come with a variety of answer types, the current question-answering (QA) literature has failed to adequately address the answer diversity of questions. To this end, we present GooAQ, a large-scale dataset with a variety of answer types. This dataset contains over 5 million questions and 3 million answers collected from Google. GooAQquestionsare collected semi-automatically from the Google search engine using its autocomplete feature. This results in naturalistic questions of practical interest that are nonetheless short and expressed using simple language. GooAQanswersare mined from Google’s responses to our collected questions, specifically from the answer boxes in the search results. This yields a rich space of answer types, containing both textual answers (short and long) as well as more structured ones such as collections. We benchmark T5 models on GooAQ and observe that: (a) in line with recent work, LM’s strong performance on GooAQ’s short-answer questions heavily benefit from annotated data; however, (b) their quality in generating coherent and accurate responses for questions requiring long responses (such as ‘how’ and ‘why’ questions) is less reliant on observing annotated data and mainly supported by their pre-training. We release GooAQ to facilitate further research on improving QA with diverse response types.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_39", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_39.flac", "type": "audio", "duration": 345.977}, {"from": "Assistant", "value": "This work proposes an extensive analysis of the Transformer architecture in the Neural Machine Translation (NMT) setting. Focusing on the encoder-decoder attention mechanism, we prove that attention weights systematically make alignment errors by relying mainly on uninformative tokens from the source sequence. However, we observe that NMT models assign attention to these tokens to regulate the contribution in the prediction of the two contexts, the source and the prefix of the target sequence. We provide evidence about the influence of wrong alignments on the model behavior, demonstrating that the encoder-decoder attention mechanism is well suited as an interpretability method for NMT. Finally, based on our analysis, we propose methods that largely reduce the word alignment error rate compared to standard induced alignments from attention weights.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_40", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_40.flac", "type": "audio", "duration": 403.934}, {"from": "Assistant", "value": "Backdoor attack introduces artificial vulnerabilities into the model by poisoning a subset of the training data via injecting triggers and modifying labels. Various trigger design strategies have been explored to attack text classifiers, however, defending such attacks remains an open problem. In this work, we propose BFClass, a novel efficient backdoor-free training framework for text classification. The backbone of BFClass is a pre-trained discriminator that predicts whether each token in the corrupted input was replaced by a masked language model. To identify triggers, we utilize this discriminator to locate the most suspicious token from each training sample and then distill a concise set by considering their association strengths with particular labels. To recognize the poisoned subset, we examine the training samples with these identified triggers as the most suspicious token, and check if removing the trigger will change the poisoned model’s prediction. Extensive experiments demonstrate that BFClass can identify all the triggers, remove 95% poisoned training samples with very limited false alarms, and achieve almost the same performance as the models trained on the benign training data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_41", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_41.flac", "type": "audio", "duration": 465.978}, {"from": "Assistant", "value": "As it has been unveiled that pre-trained language models (PLMs) are to some extent capable of recognizing syntactic concepts in natural language, much effort has been made to develop a method for extracting complete (binary) parses from PLMs without training separate parsers. We improve upon this paradigm by proposing a novel chart-based method and an effective top-K ensemble technique. Moreover, we demonstrate that we can broaden the scope of application of the approach into multilingual settings. Specifically, we show that by applying our method on multilingual PLMs, it becomes possible to induce non-trivial parses for sentences from nine languages in an integrated and language-agnostic manner, attaining performance superior or comparable to that of unsupervised PCFGs. We also verify that our approach is robust to cross-lingual transfer. Finally, we provide analyses on the inner workings of our method. For instance, we discover universal attention heads which are consistently sensitive to syntactic information irrespective of the input language.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_42", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_42.flac", "type": "audio", "duration": 306.016}, {"from": "Assistant", "value": "Recent knowledge graph embedding (KGE) models based on hyperbolic geometry have shown great potential in a low-dimensional embedding space. However, the necessity of hyperbolic space in KGE is still questionable, because the calculation based on hyperbolic geometry is much more complicated than Euclidean operations. In this paper, based on the state-of-the-art hyperbolic-based model RotH, we develop two lightweight Euclidean-based models, called RotL and Rot2L. The RotL model simplifies the hyperbolic operations while keeping the flexible normalization effect. Utilizing a novel two-layer stacked transformation and based on RotL, the Rot2L model obtains an improved representation capability, yet costs fewer parameters and calculations than RotH. The experiments on link prediction show that Rot2L achieves the state-of-the-art performance on two widely-used datasets in low-dimensional knowledge graph embeddings. Furthermore, RotL achieves similar performance as RotH but only requires half of the training time.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_43", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_43.flac", "type": "audio", "duration": 347.022}, {"from": "Assistant", "value": "Dynamic early exiting aims to accelerate the inference of pre-trained language models (PLMs) by emitting predictions in internal layers without passing through the entire model. In this paper, we empirically analyze the working mechanism of dynamic early exiting and find that it faces a performance bottleneck under high speed-up ratios. On one hand, the PLMs’ representations in shallow layers lack high-level semantic information and thus are not sufficient for accurate predictions. On the other hand, the exiting decisions made by internal classifiers are unreliable, leading to wrongly emitted early predictions. We instead propose a new framework for accelerating the inference of PLMs, CascadeBERT, which dynamically selects proper-sized and complete models in a cascading manner, providing comprehensive representations for predictions. We further devise a difficulty-aware objective, encouraging the model to output the class probability that reflects the real difficulty of each instance for a more reliable cascading mechanism. Experimental results show that CascadeBERT can achieve an overall 15% improvement under 4x speed-up compared with existing dynamic early exiting methods on six classification tasks, yielding more calibrated and accurate predictions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_44", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_44.flac", "type": "audio", "duration": 360.001}, {"from": "Assistant", "value": "To alleviate human efforts from obtaining large-scale annotations, Semi-Supervised Relation Extraction methods aim to leverage unlabeled data in addition to learning from limited samples. Existing self-training methods suffer from the gradual drift problem, where noisy pseudo labels on unlabeled data are incorporated during training. To alleviate the noise in pseudo labels, we propose a method called MetaSRE, where a Relation Label Generation Network generates accurate quality assessment on pseudo labels by (meta) learning from the successful and failed attempts on Relation Classification Network as an additional meta-objective. To reduce the influence of noisy pseudo labels, MetaSRE adopts a pseudo label selection and exploitation scheme which assesses pseudo label quality on unlabeled samples and only exploits high-quality pseudo labels in a self-training fashion to incrementally augment labeled samples for both robustness and accuracy. Experimental results on two public datasets demonstrate the effectiveness of the proposed approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_45", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_45.flac", "type": "audio", "duration": 350.018}, {"from": "Assistant", "value": "Aiming to generate a set of keyphrases, Keyphrase Generation (KG) is a classical task for capturing the central idea from a given document. Based on Seq2Seq models, the previous reinforcement learning framework on KG tasks utilizes the evaluation metrics to further improve the well-trained neural models. However, these KG evaluation metrics such asF1@5andF1@Mare only aware of the exact correctness of predictions on phrase-level and ignore the semantic similarities between similar predictions and targets, which inhibits the model from learning deep linguistic patterns. In response to this problem, we propose a new fine-grained evaluation metric to improve the RL framework, which considers different granularities: token-levelF1score, edit distance, duplication, and prediction quantities. On the whole, the new framework includes two reward functions: the fine-grained evaluation score and the vanillaF1score. This framework helps the model identifying some partial match phrases which can be further optimized as the exact match ones. Experiments on KG benchmarks show that our proposed training framework outperforms the previous RL training frameworks among all evaluation scores. In addition, our method can effectively ease the synonym problem and generate a higher quality prediction. The source code is available athttps://github.com/xuyige/FGRL4KG.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_46", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_46.flac", "type": "audio", "duration": 356.891}, {"from": "Assistant", "value": "To find a suitable embedding for a knowledge graph remains a big challenge nowadays. By using previous knowledge graph embedding methods, every entity in a knowledge graph is usually represented as a k-dimensional vector. As we know, an affine transformation can be expressed in the form of a matrix multiplication followed by a translation vector. In this paper, we firstly utilize a set of affine transformations related to each relation to operate on entity vectors, and then these transformed vectors are used for performing embedding with previous methods. The main advantage of using affine transformations is their good geometry properties with interpretability. Our experimental results demonstrate that the proposed intuitive design with affine transformations provides a statistically significant increase in performance with adding a few extra processing steps or adding a limited number of additional variables. Taking TransE as an example, we employ the scale transformation (the special case of an affine transformation), and only introduce k additional variables for each relation. Surprisingly, it even outperforms RotatE to some extent on various data sets. We also introduce affine transformations into RotatE, Distmult and ComplEx, respectively, and each one outperforms its original method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_47", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_47.flac", "type": "audio", "duration": 229.97}, {"from": "Assistant", "value": "Neural abstractive summarization models have drastically improved in the recent years. However, the summaries generated by these models generally suffer from issues such as: not capturing the critical facts in source documents, and containing facts that are inconsistent with the source documents. In this work, we present a general framework to train abstractive summarization models to alleviate such issues. We first train a sequence-to-sequence model to summarize documents, and then further train this model in a Reinforcement Learning setting with question-answering based rewards. We evaluate the summaries generated by the this framework using multiple automatic measures and human judgements. The experimental results show that the question-answering rewards can be used as a general framework to improve neural abstractive summarization. Particularly, the results from human evaluations show that the summaries generated by our approach is preferred over 30% of the time over the summaries generated by general abstractive summarization models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_49", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_49.flac", "type": "audio", "duration": 422.046}, {"from": "Assistant", "value": "In this study, we propose a self-supervised learning method that distils representations of word meaning in context from a pre-trained masked language model. Word representations are the basis for context-aware lexical semantics and unsupervised semantic textual similarity (STS) estimation. A previous study transforms contextualised representations employing static word embeddings to weaken excessive effects of contextual information. In contrast, the proposed method derives representations of word meaning in context while preserving useful context information intact. Specifically, our method learns to combine outputs of different hidden layers using self-attention through self-supervised learning with an automatically generated training corpus. To evaluate the performance of the proposed approach, we performed comparative experiments using a range of benchmark tasks. The results confirm that our representations exhibited a competitive performance compared to that of the state-of-the-art method transforming contextualised representations for the context-aware lexical semantic tasks and outperformed it for STS estimation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_50", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_50.flac", "type": "audio", "duration": 174.01}, {"from": "Assistant", "value": "Complex question answering over knowledge base remains as a challenging task because it involves reasoning over multiple pieces of information, including intermediate entities/relations and other constraints. Previous methods simplify the SPARQL query of a question into such forms as a list or a graph, missing such constraints as “filter” and “order_by”, and present models specialized for generating those simplified forms from a given question. We instead introduce a novel approach that directly generates an executable SPARQL query without simplification, addressing the issue of generating unseen entities. We adapt large scale pre-trained encoder-decoder models and show that our method significantly outperforms the previous methods and also that our method has higher interpretability and computational efficiency than the previous methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_51", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_51.flac", "type": "audio", "duration": 343.98}, {"from": "Assistant", "value": "Emotion cause extraction (ECE) aims to extract the causes behind the certain emotion in text. Some works related to the ECE task have been published and attracted lots of attention in recent years. However, these methods neglect two major issues: 1) pay few attentions to the effect of document-level context information on ECE, and 2) lack of sufficient exploration for how to effectively use the annotated emotion clause. For the first issue, we propose a bidirectional hierarchical attention network (BHA) corresponding to the specified candidate cause clause to capture the document-level context in a structured and dynamic manner. For the second issue, we design an emotional filtering module (EF) for each layer of the graph attention network, which calculates a gate score based on the emotion clause to filter the irrelevant information. Combining the BHA and EF, the EF-BHA can dynamically aggregate the contextual information from two directions and filters irrelevant information. The experimental results demonstrate that EF-BHA achieves the competitive performances on two public datasets in different languages (Chinese and English). Moreover, we quantify the effect of context on emotion cause extraction and provide the visualization of the interactions between candidate cause clauses and contexts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_53", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_53.flac", "type": "audio", "duration": 180.001}, {"from": "Assistant", "value": "We introduce and study a problem variant of sentiment analysis, namely the “same sentiment classification problem”, where, given a pair of texts, the task is to determine if they have the same sentiment, disregarding the actual sentiment polarity. Among other things, our goal is to enable a more topic-agnostic sentiment classification. We study the problem using the Yelp business review dataset, demonstrating how sentiment data needs to be prepared for this task, and then carry out sequence pair classification using the BERT language model. In a series of experiments, we achieve an accuracy above 83% for category subsets across topics, and 89% on average.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_54", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_54.flac", "type": "audio", "duration": 184.018}, {"from": "Assistant", "value": "While neural networks are ubiquitous in state-of-the-art semantic parsers, it has been shown that most standard models suffer from dramatic performance losses when faced with compositionally out-of-distribution (OOD) data. Recently several methods have been proposed to improve compositional generalization in semantic parsing. In this work we instead focus on the problem of detecting compositionally OOD examples with neural semantic parsers, which, to the best of our knowledge, has not been investigated before. We investigate several strong yet simple methods for OOD detection based on predictive uncertainty. The experimental results demonstrate that these techniques perform well on the standard SCAN and CFQ datasets. Moreover, we show that OOD detection can be further improved by using a heterogeneous ensemble.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_55", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_55.flac", "type": "audio", "duration": 347.022}, {"from": "Assistant", "value": "Recent multilingual pre-trained models, like XLM-RoBERTa (XLM-R), have been demonstrated effective in many cross-lingual tasks. However, there are still gaps between the contextualized representations of similar words in different languages. To solve this problem, we propose a novel framework named Multi-View Mixed Language Training (MVMLT), which leverages code-switched data with multi-view learning to fine-tune XLM-R. MVMLT uses gradient-based saliency to extract keywords which are the most relevant to downstream tasks and replaces them with the corresponding words in the target language dynamically. Furthermore, MVMLT utilizes multi-view learning to encourage contextualized embeddings to align into a more refined language-invariant space. Extensive experiments with four languages show that our model achieves state-of-the-art results on zero-shot cross-lingual sentiment classification and dialogue state tracking tasks, demonstrating the effectiveness of our proposed model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_56", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_56.flac", "type": "audio", "duration": 422.023}, {"from": "Assistant", "value": "With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic. Fighting this infodemic has been declared one of the most important focus areas of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic. Addressing the issue requires solving a number of challenging problems such as identifying messages containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few. To address this gap, we release a large dataset of 16K manually annotated tweets for fine-grained disinformation analysis that (i) focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society, and (iii) covers Arabic, Bulgarian, Dutch, and English. Finally, we show strong evaluation results using pretrained Transformers, thus confirming the practical utility of the dataset in monolingual vs. multilingual, and single task vs. multitask settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_57", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_57.flac", "type": "audio", "duration": 348.021}, {"from": "Assistant", "value": "Extracting salient topics from a collection of documents can be a challenging task when a) the amount of data is large, b) the number of topics is not known a priori, and/or c) “topic noise” is present. We define “topic noise” as the collection of documents that are irrelevant to any coherent topic and should be filtered out. By design, most clustering algorithms (e.g. k-means, hierarchical clustering) assign all input documents to one of the available clusters, guaranteeing any topic noise to propagate into the result. To address these challenges, we present a novel algorithm, FANATIC, that efficiently distinguishes documents from genuine topics and those that are topic noise. We also introduce a new Reddit dataset to showcase FANATIC as it contains short, noisy data that is difficult to cluster using most clustering algorithms. We find that FANATIC clusters 500k Reddit titles (of which 20% are topic noise) in 2 minutes and achieves an AMI score of 0.59, in contrast with hdbscan (McInnes et al., 2017), a popular algorithm suited for this type of task, which requires over 7 hours and achieves an AMI of 0.03. Finally, we test FANATIC against a Twitter dataset and find again that it outperforms the other algorithms with an AMI score of 0.60. We make our code and data publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_58", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_58.flac", "type": "audio", "duration": 177.099}, {"from": "Assistant", "value": "Simultaneous machine translation has recently gained traction thanks to significant quality improvements and the advent of streaming applications. Simultaneous translation systems need to find a trade-off between translation quality and response time, and with this purpose multiple latency measures have been proposed. However, latency evaluations for simultaneous translation are estimated at the sentence level, not taking into account the sequential nature of a streaming scenario. Indeed, these sentence-level latency measures are not well suited for continuous stream translation, resulting in figures that are not coherent with the simultaneous translation policy of the system being assessed. This work proposes a stream level adaptation of the current latency measures based on a re-segmentation approach applied to the output translation, that is successfully evaluated on streaming conditions for a reference IWSLT task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_59", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_59.flac", "type": "audio", "duration": 359.05}, {"from": "Assistant", "value": "Learning sentence embeddings often requires a large amount of labeled data. However, for most tasks and domains, labeled data is seldom available and creating it is expensive. In this work, we present a new state-of-the-art unsupervised method based on pre-trained Transformers and Sequential Denoising Auto-Encoder (TSDAE) which outperforms previous approaches by up to 6.4 points. It can achieve up to 93.1% of the performance of in-domain supervised approaches. Further, we show that TSDAE is a strong domain adaptation and pre-training method for sentence embeddings, significantly outperforming other approaches like Masked Language Model. A crucial shortcoming of previous studies is the narrow evaluation: Most work mainly evaluates on the single task of Semantic Textual Similarity (STS), which does not require any domain knowledge. It is unclear if these proposed methods generalize to other domains and tasks. We fill this gap and evaluate TSDAE and other recent approaches on four different datasets from heterogeneous domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_60", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_60.flac", "type": "audio", "duration": 358.005}, {"from": "Assistant", "value": "Data-driven subword segmentation has become the default strategy for open-vocabulary machine translation and other NLP tasks, but may not be sufficiently generic for optimal learning of non-concatenative morphology. We design a test suite to evaluate segmentation strategies on different types of morphological phenomena in a controlled, semi-synthetic setting. In our experiments, we compare how well machine translation models trained on subword- and character-level can translate these morphological phenomena. We find that learning to analyse and generate morphologically complex surface representations is still challenging, especially for non-concatenative morphological phenomena like reduplication or vowel harmony and for rare word stems. Based on our results, we recommend that novel text representation strategies be tested on a range of typologically diverse languages to minimise the risk of adopting a strategy that inadvertently disadvantages certain languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_61", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_61.flac", "type": "audio", "duration": 175.009}, {"from": "Assistant", "value": "Supplementary Training on Intermediate Labeled-data Tasks (STILT) is a widely applied technique, which first fine-tunes the pretrained language models on an intermediate task before on the target task of interest. While STILT is able to further improve the performance of pretrained language models, it is still unclear why and when it works. Previous research shows that those intermediate tasks involving complex inference, such as commonsense reasoning, work especially well for RoBERTa-large. In this paper, we discover that the improvement from an intermediate task could be orthogonal to it containing reasoning or other complex skills — a simple real-fake discrimination task synthesized by GPT2 can benefit diverse target tasks. We conduct extensive experiments to study the impact of different factors on STILT. These findings suggest rethinking the role of intermediate fine-tuning in the STILT pipeline.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_62", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_62.flac", "type": "audio", "duration": 347.045}, {"from": "Assistant", "value": "The ability to continuously expand knowledge over time and utilize it to rapidly generalize to new tasks is a key feature of human linguistic intelligence. Existing models that pursue rapid generalization to new tasks (e.g., few-shot learning methods), however, are mostly trained in a single shot on fixed datasets, unable to dynamically expand their knowledge; while continual learning algorithms are not specifically designed for rapid generalization. We present a new learning setup, Continual Learning of Few-Shot Learners (CLIF), to address challenges of both learning settings in a unified setup. CLIF assumes a model learns from a sequence of diverse NLP tasks arriving sequentially, accumulating knowledge for improved generalization to new tasks, while also retaining performance on the tasks learned earlier. We examine how the generalization ability is affected in the continual learning setup, evaluate a number of continual learning algorithms, and propose a novel regularized adapter generation approach. We find that catastrophic forgetting affects generalization ability to a lesser degree than performance on seen tasks; while continual learning algorithms can still bring considerable benefit to the generalization ability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_63", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_63.flac", "type": "audio", "duration": 298.005}, {"from": "Assistant", "value": "Adapters are light-weight modules that allow parameter-efficient fine-tuning of pretrained models. Specialized language and task adapters have recently been proposed to facilitate cross-lingual transfer of multilingual pretrained models (Pfeiffer et al., 2020b). However, this approach requires training a separate language adapter for every language one wishes to support, which can be impractical for languages with limited data. An intuitive solution is to use a related language adapter for the new language variety, but we observe that this solution can lead to sub-optimal performance. In this paper, we aim to improve the robustness of language adapters to uncovered languages without training new adapters. We find that ensembling multiple existing language adapters makes the fine-tuned model significantly more robust to other language varieties not included in these adapters. Building upon this observation, we propose Entropy Minimized Ensemble of Adapters (EMEA), a method that optimizes the ensemble weights of the pretrained language adapters for each test sentence by minimizing the entropy of its predictions. Experiments on three diverse groups of language varieties show that our method leads to significant improvements on both named entity recognition and part-of-speech tagging across all languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_64", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_64.flac", "type": "audio", "duration": 456.162}, {"from": "Assistant", "value": "Much recent work in bilingual lexicon induction (BLI) views word embeddings as vectors in Euclidean space. As such, BLI is typically solved by finding a linear transformation that maps embeddings to a common space. Alternatively, word embeddings may be understood as nodes in a weighted graph. This framing allows us to examine a node’s graph neighborhood without assuming a linear transform, and exploits new techniques from the graph matching optimization literature. These contrasting approaches have not been compared in BLI so far. In this work, we study the behavior of Euclidean versus graph-based approaches to BLI under differing data conditions and show that they complement each other when combined. We release our code athttps://github.com/kellymarchisio/euc-v-graph-bli.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_65", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_65.flac", "type": "audio", "duration": 275.051}, {"from": "Assistant", "value": "Knowledge Distillation (KD) is a model compression algorithm that helps transfer the knowledge in a large neural network into a smaller one. Even though KD has shown promise on a wide range of Natural Language Processing (NLP) applications, little is understood about how one KD algorithm compares to another and whether these approaches can be complimentary to each other. In this work, we evaluate various KD algorithms on in-domain, out-of-domain and adversarial testing. We propose a framework to assess adversarial robustness of multiple KD algorithms. Moreover, we introduce a new KD algorithm, Combined-KD, which takes advantage of two promising approaches (better training scheme and more efficient data augmentation). Our extensive experimental results show that Combined-KD achieves state-of-the-art results on the GLUE benchmark, out-of-domain generalization, and adversarial robustness compared to competitive methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_66", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_66.flac", "type": "audio", "duration": 359.04}, {"from": "Assistant", "value": "Compliments and concerns in reviews are valuable for understanding users’ shopping interests and their opinions with respect to specific aspects of certain items. Existing review-based recommenders favor large and complex language encoders that can only learn latent and uninterpretable text representations. They lack explicit user-attention and item-property modeling, which however could provide valuable information beyond the ability to recommend items. Therefore, we propose a tightly coupled two-stage approach, including an Aspect-Sentiment Pair Extractor (ASPE) and an Attention-Property-aware Rating Estimator (APRE). Unsupervised ASPE mines Aspect-Sentiment pairs (AS-pairs) and APRE predicts ratings using AS-pairs as concrete aspect-level evidences. Extensive experiments on seven real-world Amazon Review Datasets demonstrate that ASPE can effectively extract AS-pairs which enable APRE to deliver superior accuracy over the leading baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_67", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_67.flac", "type": "audio", "duration": 137.536}, {"from": "Assistant", "value": "The Transformer translation model is based on the multi-head attention mechanism, which can be parallelized easily. The multi-head attention network performs the scaled dot-product attention function in parallel, empowering the model by jointly attending to information from different representation subspaces at different positions. In this paper, we present an approach to learning a hard retrieval attention where an attention head only attends to one token in the sentence rather than all tokens. The matrix multiplication between attention probabilities and the value sequence in the standard scaled dot-product attention can thus be replaced by a simple and efficient retrieval operation. We show that our hard retrieval attention mechanism is 1.43 times faster in decoding, while preserving translation quality on a wide range of machine translation tasks when used in the decoder self- and cross-attention networks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_68", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_68.flac", "type": "audio", "duration": 307.456}, {"from": "Assistant", "value": "In this article, we tackle the math word problem, namely, automatically answering a mathematical problem according to its textual description. Although recent methods have demonstrated their promising results, most of these methods are based on template-based generation scheme which results in limited generalization capability. To this end, we propose a novel human-like analogical learning method in a recall and learn manner. Our proposed framework is composed of modules of memory, representation, analogy, and reasoning, which are designed to make a new exercise by referring to the exercises learned in the past. Specifically, given a math word problem, the model first retrieves similar questions by a memory module and then encodes the unsolved problem and each retrieved question using a representation module. Moreover, to solve the problem in a way of analogy, an analogy module and a reasoning module with a copy mechanism are proposed to model the interrelationship between the problem and each retrieved question. Extensive experiments on two well-known datasets show the superiority of our proposed algorithm as compared to other state-of-the-art competitors from both overall performance comparison and micro-scope studies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_69", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_69.flac", "type": "audio", "duration": 359.616}, {"from": "Assistant", "value": "Aspect detection is a fundamental task in opinion mining. Previous works use seed words either as priors of topic models, as anchors to guide the learning of aspects, or as features of aspect classifiers. This paper presents a novel weakly-supervised method to exploit seed words for aspect detection based on an encoder architecture. The encoder maps segments and aspects into a low-dimensional embedding space. The goal is approximating similarity between segments and aspects in the embedding space and their ground-truth similarity generated from seed words. An objective function is proposed to capture the uncertainty of ground-truth similarity. Our method outperforms previous works on several benchmarks in various domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_70", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_70.flac", "type": "audio", "duration": 341.055}, {"from": "Assistant", "value": "Current approaches to empathetic response generation focus on learning a model to predict an emotion label and generate a response based on this label and have achieved promising results. However, the emotion cause, an essential factor for empathetic responding, is ignored. The emotion cause is a stimulus for human emotions. Recognizing the emotion cause is helpful to better understand human emotions so as to generate more empathetic responses. To this end, we propose a novel framework that improves empathetic response generation by recognizing emotion cause in conversations. Specifically, an emotion reasoner is designed to predict a context emotion label and a sequence of emotion cause-oriented labels, which indicate whether the word is related to the emotion cause. Then we devise both hard and soft gated attention mechanisms to incorporate the emotion cause into response generation. Experiments show that incorporating emotion cause information improves the performance of the model on both emotion recognition and response generation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_71", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_71.flac", "type": "audio", "duration": 339.051}, {"from": "Assistant", "value": "Models of language trained on very large corpora have been demonstrated useful for natural language processing. As fixed artifacts, they have become the object of intense study, with many researchers “probing” the extent to which they acquire and readily demonstrate linguistic abstractions, factual and commonsense knowledge, and reasoning abilities. Recent work applied several probes to intermediate training stages to observe the developmental process of a large-scale model (Chiang et al., 2020). Following this effort, we systematically answer a question: for various types of knowledge a language model learns, when during (pre)training are they acquired? Using RoBERTa as a case study, we find: linguistic knowledge is acquired fast, stably, and robustly across domains. Facts and commonsense are slower and more domain-sensitive. Reasoning abilities are, in general, not stably acquired. As new datasets, pretraining protocols, and probes emerge, we believe that probing-across-time analyses can help researchers understand the complex, intermingled learning that these models undergo and guide us toward more efficient approaches that accomplish necessary learning faster.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_72", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_72.flac", "type": "audio", "duration": 400.213}, {"from": "Assistant", "value": "Paraphrase identification (PI), a fundamental task in natural language processing, is to identify whether two sentences express the same or similar meaning, which is a binary classification problem. Recently, BERT-like pre-trained language models have been a popular choice for the frameworks of various PI models, but almost all existing methods consider general domain text. When these approaches are applied to a specific domain, existing models cannot make accurate predictions due to the lack of professional knowledge. In light of this challenge, we propose a novel framework, namely , which can leverage the external unstructured Wikipedia knowledge to accurately identify paraphrases. We propose to mine outline knowledge of concepts related to given sentences from Wikipedia via BM25 model. After retrieving related outline knowledge, makes predictions based on both the semantic information of two sentences and the outline knowledge. Besides, we propose a gating mechanism to aggregate the semantic information-based prediction and the knowledge-based prediction. Extensive experiments are conducted on two public datasets: PARADE (a computer science domain dataset) and clinicalSTS2019 (a biomedical domain dataset). The results show that the proposed outperforms state-of-the-art methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_73", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_73.flac", "type": "audio", "duration": 354.219}, {"from": "Assistant", "value": "This work presents a novel four-stage open-domain QA pipeline R2-D2 (Rank twice, reaD twice). The pipeline is composed of a retriever, passage reranker, extractive reader, generative reader and a mechanism that aggregates the final prediction from all system’s components. We demonstrate its strength across three open-domain QA datasets: NaturalQuestions, TriviaQA and EfficientQA, surpassing state-of-the-art on the first two. Our analysis demonstrates that: (i) combining extractive and generative reader yields absolute improvements up to 5 exact match and it is at least twice as effective as the posterior averaging ensemble of the same models with different parameters, (ii) the extractive reader with fewer parameters can match the performance of the generative reader on extractive QA datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_74", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_74.flac", "type": "audio", "duration": 272.725}, {"from": "Assistant", "value": "Sarcasm and sentiment embody intrinsic uncertainty of human cognition, making joint detection of multi-modal sarcasm and sentiment a challenging task. In view of the advantages of quantum probability (QP) in modeling such uncertainty, this paper explores the potential of QP as a mathematical framework and proposes a QP driven multi-task (QPM) learning framework. The QPM framework involves a complex-valued multi-modal representation encoder, a quantum-like fusion subnetwork and a quantum measurement mechanism. Each multi-modal (e.g., textual, visual) utterance is first encoded as a quantum superposition of a set of basis terms using a complex-valued representation. Then, the quantum-like fusion subnetwork leverages quantum state composition and quantum interference to model the contextual interaction between adjacent utterances and the correlations across modalities respectively. Finally, quantum incompatible measurements are performed on the multi-modal representation of each utterance to yield the probabilistic outcomes of sarcasm and sentiment recognition. The experimental results show that our model achieves a state-of-the-art performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_75", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_75.flac", "type": "audio", "duration": 356.217}, {"from": "Assistant", "value": "Multilingual pre-trained models have demonstrated their effectiveness in many multilingual NLP tasks and enabled zero-shot or few-shot transfer from high-resource languages to low-resource ones. However, due to significant typological differences and contradictions between some languages, such models usually perform poorly on many languages and cross-lingual settings, which shows the difficulty of learning a single model to handle massive diverse languages well at the same time. To alleviate this issue, we present a new multilingual pre-training pipeline. We propose to generate language representation from multilingual pre-trained model and conduct linguistic analysis to show that language representation similarity reflects linguistic similarity from multiple perspectives, including language family, geographical sprachbund, lexicostatistics, and syntax. Then we cluster all the target languages into multiple groups and name each group as a representation sprachbund. Thus, languages in the same representation sprachbund are supposed to boost each other in both pre-training and fine-tuning as they share rich linguistic similarity. We pre-train one multilingual model for each representation sprachbund. Experiments are conducted on cross-lingual benchmarks and significant improvements are achieved compared to strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_76", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_76.flac", "type": "audio", "duration": 352.043}, {"from": "Assistant", "value": "Recent developments in neural networks have led to the advance in data-to-text generation. However, the lack of ability of neural models to control the structure of generated output can be limiting in certain real-world applications. In this study, we propose a novel Plan-then-Generate (PlanGen) framework to improve the controllability of neural data-to-text models. Extensive experiments and analyses are conducted on two benchmark datasets, ToTTo and WebNLG. The results show that our model is able to control both the intra-sentence and inter-sentence structure of the generated output. Furthermore, empirical comparisons against previous state-of-the-art methods show that our model improves the generation quality as well as the output diversity as judged by human and automatic evaluations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_77", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_77.flac", "type": "audio", "duration": 177.045}, {"from": "Assistant", "value": "Neural table-to-text generation models have achieved remarkable progress on an array of tasks. However, due to the data-hungry nature of neural models, their performances strongly rely on large-scale training examples, limiting their applicability in real-world applications. To address this, we propose a new framework: Prototype-to-Generate (P2G), for table-to-text generation under the few-shot scenario. The proposed framework utilizes the retrieved prototypes, which are jointly selected by an IR system and a novel prototype selector to help the model bridging the structural gap between tables and texts. Experimental results on three benchmark datasets with three state-of-the-art models demonstrate that the proposed framework significantly improves the model performance across various evaluation metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_78", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_78.flac", "type": "audio", "duration": 189.056}, {"from": "Assistant", "value": "In parataxis languages like Chinese, word meanings are constructed using specific word-formations, which can help to disambiguate word senses. However, such knowledge is rarely explored in previous word sense disambiguation (WSD) methods. In this paper, we propose to leverage word-formation knowledge to enhance Chinese WSD. We first construct a large-scale Chinese lexical sample WSD dataset with word-formations. Then, we propose a model FormBERT to explicitly incorporate word-formations into sense disambiguation. To further enhance generalizability, we design a word-formation predictor module in case word-formation annotations are unavailable. Experimental results show that our method brings substantial performance improvement over strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_79", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_79.flac", "type": "audio", "duration": 360.64}, {"from": "Assistant", "value": "Back-translation (BT) has become one of the de facto components in unsupervised neural machine translation (UNMT), and it explicitly makes UNMT have translation ability. However, all the pseudo bi-texts generated by BT are treated equally as clean data during optimization without considering the quality diversity, leading to slow convergence and limited translation performance. To address this problem, we propose a curriculum learning method to gradually utilize pseudo bi-texts based on their quality from multiple granularities. Specifically, we first apply crosslingual word embedding to calculate the potential translation difficulty (quality) for the monolingual sentences. Then, the sentences are fed into UNMT from easy to hard batch by batch. Furthermore, considering the quality of sentences/tokens in a particular batch are also diverse, we further adopt the model itself to calculate the fine-grained quality scores, which are served as learning factors to balance the contributions of different parts when computing loss and encourage the UNMT model to focus on pseudo data with higher quality. Experimental results on WMT 14 En-Fr, WMT 14 En-De, WMT 16 En-Ro, and LDC En-Zh translation tasks demonstrate that the proposed method achieves consistent improvements with faster convergence speed.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_80", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_80.flac", "type": "audio", "duration": 166.059}, {"from": "Assistant", "value": "Cross-lingual Sentence Retrieval (CLSR) aims at retrieving parallel sentence pairs that are translations of each other from a multilingual set of comparable documents. The retrieved parallel sentence pairs can be used in other downstream NLP tasks such as machine translation and cross-lingual word sense disambiguation. We propose a CLSR framework called Robust Fragment-level Representation (RFR) CLSR framework to address Out-of-Domain (OOD) CLSR problems. In particular, we improve the sentence retrieval robustness by representing each sentence as a collection of fragments. In this way, we change the retrieval granularity from the sentence to the fragment level. We performed CLSR experiments based on three OOD datasets, four language pairs, and three base well-known sentence encoders: m-USE, LASER, and LaBSE. Experimental results show that RFR significantly improves the base encoders’ performance for more than 85% of the cases.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_81", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_81.flac", "type": "audio", "duration": 351.062}, {"from": "Assistant", "value": "Adversarial training, a method for learning robust deep neural networks, constructs adversarial examples during training. However, recent methods for generating NLP adversarial examples involve combinatorial search and expensive sentence encoders for constraining the generated instances. As a result, it remains challenging to use vanilla adversarial training to improve NLP models’ performance, and the benefits are mainly uninvestigated. This paper proposes a simple and improved vanilla adversarial training process for NLP models, which we name Attacking to Training (A2T). The core part of A2T is a new and cheaper word substitution attack optimized for vanilla adversarial training. We use A2T to train BERT and RoBERTa models on IMDB, Rotten Tomatoes, Yelp, and SNLI datasets. Our results empirically show that it is possible to train robust NLP models using a much cheaper adversary. We demonstrate that vanilla adversarial training with A2T can improve an NLP model’s robustness to the attack it was originally trained with and also defend the model against other types of word substitution attacks. Furthermore, we show that A2T can improve NLP models’ standard accuracy, cross-domain generalization, and interpretability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_82", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_82.flac", "type": "audio", "duration": 359.36}, {"from": "Assistant", "value": "Framing has significant but subtle effects on public opinion and policy. We propose an NLP framework to measure entity-centric frames. We use it to understand media coverage on police violence in the United States in a new Police Violence Frames Corpus of 82k news articles spanning 7k police killings. Our work uncovers more than a dozen framing devices and reveals significant differences in the way liberal and conservative news sources frame both the issue of police violence and the entities involved. Conservative sources emphasize when the victim is armed or attacking an officer and are more likely to mention the victim’s criminal record. Liberal sources focus more on the underlying systemic injustice, highlighting the victim’s race and that they were unarmed. We discover temporary spikes in these injustice frames near high-profile shooting events, and finally, we show protest volume correlates with and precedes media framing decisions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_83", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_83.flac", "type": "audio", "duration": 208.051}, {"from": "Assistant", "value": "To be good conversational partners, natural language processing (NLP) systems should be trained to produce contextually useful utterances. Prior work has investigated training NLP systems with communication-based objectives, where a neural listener stands in as a communication partner. However, these systems commonly suffer from semantic drift where the learned language diverges radically from natural language. We propose a method that uses a population of neural listeners to regularize speaker training. We first show that language drift originates from the poor uncertainty calibration of a neural listener, which makes high-certainty predictions on novel sentences. We explore ensemble- and dropout-based populations of listeners and find that the former results in better uncertainty quantification. We evaluate both population-based objectives on reference games, and show that the ensemble method with better calibration enables the speaker to generate pragmatic utterances while scaling to a large vocabulary and generalizing to new games and listeners.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_84", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_84.flac", "type": "audio", "duration": 335.017}, {"from": "Assistant", "value": "Scenario-based question answering (SQA) requires retrieving and reading paragraphs from a large corpus to answer a question which is contextualized by a long scenario description. Since a scenario contains both keyphrases for retrieval and much noise, retrieval for SQA is extremely difficult. Moreover, it can hardly be supervised due to the lack of relevance labels of paragraphs for SQA. To meet the challenge, in this paper we propose a joint retriever-reader model called JEEVES where the retriever is implicitly supervised only using QA labels via a novel word weighting mechanism. JEEVES significantly outperforms a variety of strong baselines on multiple-choice questions in three SQA datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_86", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_86.flac", "type": "audio", "duration": 342.053}, {"from": "Assistant", "value": "Task-adaptive pre-training (TAPT) and Self-training (ST) have emerged as the major semi-supervised approaches to improve natural language understanding (NLU) tasks with massive amount of unlabeled data. However, it’s unclear whether they learn similar representations or they can be effectively combined. In this paper, we show that TAPT and ST can be complementary with simple TFS protocol by following TAPT -> Finetuning -> Self-training (TFS) process. Experimental results show that TFS protocol can effectively utilize unlabeled data to achieve strong combined gains consistently across six datasets covering sentiment classification, paraphrase identification, natural language inference, named entity recognition and dialogue slot classification. We investigate various semi-supervised settings and consistently show that gains from TAPT and ST can be strongly additive by following TFS procedure. We hope that TFS could serve as an important semi-supervised baseline for future NLP studies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_87", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_87.flac", "type": "audio", "duration": 181.653}, {"from": "Assistant", "value": "Transformer models fine-tuned with a sequence labeling objective have become the dominant choice for named entity recognition tasks. However, a self-attention mechanism with unconstrained length can fail to fully capture local dependencies, particularly when training data is limited. In this paper, we propose a novel joint training objective which better captures the semantics of words corresponding to the same entity. By augmenting the training objective with a group-consistency loss component we enhance our ability to capture local dependencies while still enjoying the advantages of the unconstrained self-attention mechanism. On the CoNLL2003 dataset, our method achieves a test F1 of 93.98 with a single transformer model. More importantly our fine-tuned CoNLL2003 model displays significant gains in generalization to out of domain datasets: on the OntoNotes subset we achieve an F1 of 72.67 which is 0.49 points absolute better than the baseline, and on the WNUT16 set an F1 of 68.22 which is a gain of 0.48 points. Furthermore, on the WNUT17 dataset we achieve an F1 of 55.85, yielding a 2.92 point absolute improvement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_88", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_88.flac", "type": "audio", "duration": 354.058}, {"from": "Assistant", "value": "Although neural sequence-to-sequence models have been successfully applied to semantic parsing, they fail at compositional generalization, i.e., they are unable to systematically generalize to unseen compositions of seen components. Motivated by traditional semantic parsing where compositionality is explicitly accounted for by symbolic grammars, we propose a new decoding framework that preserves the expressivity and generality of sequence-to-sequence models while featuring lexicon-style alignments and disentangled information processing. Specifically, we decompose decoding into two phases where an input utterance is first tagged with semantic symbols representing the meaning of individual words, and then a sequence-to-sequence model is used to predict the final meaning representation conditioning on the utterance and the predicted tag sequence. Experimental results on three semantic parsing datasets show that the proposed approach consistently improves compositional generalization across model architectures, domains, and semantic formalisms.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_90", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_90.flac", "type": "audio", "duration": 172.06}, {"from": "Assistant", "value": "Fact verification based on structured data is challenging as it requires models to understand both natural language and symbolic operations performed over tables. Although pre-trained language models have demonstrated a strong capability in verifying simple statements, they struggle with complex statements that involve multiple operations. In this paper, we improve fact verification by decomposing complex statements into simpler subproblems. Leveraging the programs synthesized by a weakly supervised semantic parser, we propose a program-guided approach to constructing a pseudo dataset for decomposition model training. The subproblems, together with their predicted answers, serve as the intermediate evidence to enhance our fact verification model. Experiments show that our proposed approach achieves the new state-of-the-art performance, an 82.7% accuracy, on the TabFact benchmark.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_91", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_91.flac", "type": "audio", "duration": 358.528}, {"from": "Assistant", "value": "Although showing promising values to downstream applications, generating question and answer together is under-explored. In this paper, we introduce a novel task that targets question-answer pair generation from visual images. It requires not only generating diverse question-answer pairs but also keeping the consistency of them. We study different generation paradigms for this task and propose three models: the pipeline model, the joint model, and the sequential model. We integrate variational inference into these models to achieve diversity and consistency. We also propose region representation scaling and attention alignment to improve the consistency further. We finally devise an evaluator as a quantitative metric for consistency. We validate our approach on two benchmarks, VQA2.0 and Visual-7w, by automatically and manually evaluating diversity and consistency. Experimental results show the effectiveness of our models: they can generate diverse or consistent pairs. Moreover, this task can be used to improve visual question generation and visual question answering.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_92", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_92.flac", "type": "audio", "duration": 291.968}, {"from": "Assistant", "value": "Multi-modal machine translation (MMT) aims at improving translation performance by incorporating visual information. Most of the studies leverage the visual information through integrating the global image features as auxiliary input or decoding by attending to relevant local regions of the image. However, this kind of usage of visual information makes it difficult to figure out how the visual modality helps and why it works. Inspired by the findings of (CITATION) that entities are most informative in the image, we propose an explicit entity-level cross-modal learning approach that aims to augment the entity representation. Specifically, the approach is framed as a reconstruction task that reconstructs the original textural input from multi-modal input in which entities are replaced with visual features. Then, a multi-task framework is employed to combine the translation task and the reconstruction task to make full use of cross-modal entity representation learning. The extensive experiments demonstrate that our approach can achieve comparable or even better performance than state-of-the-art models. Furthermore, our in-depth analysis shows how visual information improves translation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_94", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_94.flac", "type": "audio", "duration": 342.08}, {"from": "Assistant", "value": "Recommendation dialogs require the system to build a social bond with users to gain trust and develop affinity in order to increase the chance of a successful recommendation. It is beneficial to divide up, such conversations with multiple subgoals (such as social chat, question answering, recommendation, etc.), so that the system can retrieve appropriate knowledge with better accuracy under different subgoals. In this paper, we propose a unified framework for common knowledge-based multi-subgoal dialog: knowledge-enhanced multi-subgoal driven recommender system (KERS). We first predict a sequence of subgoals and use them to guide the dialog model to select knowledge from a sub-set of existing knowledge graph. We then propose three new mechanisms to filter noisy knowledge and to enhance the inclusion of cleaned knowledge in the dialog response generation process. Experiments show that our method obtains state-of-the-art results on DuRecDial dataset in both automatic and human evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_95", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_95.flac", "type": "audio", "duration": 359.765}, {"from": "Assistant", "value": "In this paper, we propose a simple few-shot domain adaptation paradigm for reading comprehension. We first identify the lottery subnetwork structure within the Transformer-based source domain model via gradual magnitude pruning. Then, we only fine-tune the lottery subnetwork, a small fraction of the whole parameters, on the annotated target domain data for adaptation. To obtain more adaptable subnetworks, we introduce self-attention attribution to weigh parameters, beyond simply pruning the smallest magnitude parameters, which can be seen as combining structured pruning and unstructured magnitude pruning softly. Experimental results show that our method outperforms the full model fine-tuning adaptation on four out of five domains when only a small amount of annotated data available for adaptation. Moreover, introducing self-attention attribution reserves more parameters for important attention heads in the lottery subnetwork and improves the target domain model performance. Our further analyses reveal that, besides exploiting fewer parameters, the choice of subnetworks is critical to the effectiveness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_96", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_96.flac", "type": "audio", "duration": 178.048}, {"from": "Assistant", "value": "This paper investigates the effectiveness of pre-training for few-shot intent classification. While existing paradigms commonly further pre-train language models such as BERT on a vast amount of unlabeled corpus, we find it highly effective and efficient to simply fine-tune BERT with a small set of labeled utterances from public datasets. Specifically, fine-tuning BERT with roughly 1,000 labeled data yields a pre-trained model – IntentBERT, which can easily surpass the performance of existing pre-trained models for few-shot intent classification on novel domains with very different semantics. The high effectiveness of IntentBERT confirms the feasibility and practicality of few-shot intent detection, and its high generalization ability across different domains suggests that intent classification tasks may share a similar underlying structure, which can be efficiently learned from a small set of labeled data. The source code can be found athttps://github.com/hdzhang-code/IntentBERT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_97", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_97.flac", "type": "audio", "duration": 291.648}, {"from": "Assistant", "value": "With the increasing abundance of meeting transcripts, meeting summary has attracted more and more attention from researchers. The unsupervised pre-training method based on transformer structure combined with fine-tuning of downstream tasks has achieved great success in the field of text summarization. However, the semantic structure and style of meeting transcripts are quite different from that of articles. In this work, we propose a hierarchical transformer encoder-decoder network with multi-task pre-training. Specifically, we mask key sentences at the word-level encoder and generate them at the decoder. Besides, we randomly mask some of the role alignments in the input text and force the model to recover the original role tags to complete the alignments. In addition, we introduce a topic segmentation mechanism to further improve the quality of the generated summaries. The experimental results show that our model is superior to the previous methods in meeting summary datasets AMI and ICSI.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_99", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_99.flac", "type": "audio", "duration": 339.058}, {"from": "Assistant", "value": "We propose a novel Chain Guided Retriever-reader (CGR) framework to model the reasoning chain for multi-hop Science Question Answering. Our framework is capable of performing explainable reasoning without the need of any corpus-specific annotations, such as the ground-truth reasoning chain, or human-annotated entity mentions. Specifically, we first generate reasoning chains from a semantic graph constructed by Abstract Meaning Representation of retrieved evidence facts. AChain-aware loss, concerning both local and global chain information, is also designed to enable the generated chains to serve as distant supervision signals for training the retriever, where reinforcement learning is also adopted to maximize the utility of the reasoning chains. Our framework allows the retriever to capture step-by-step clues of the entire reasoning process, which is not only shown to be effective on two challenging multi-hop Science QA tasks, namely OpenBookQA and ARC-Challenge, but also favors explainability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_100", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_100.flac", "type": "audio", "duration": 160.491}, {"from": "Assistant", "value": "We tackle multi-choice question answering. Acquiring related commonsense knowledge to the question and options facilitates the recognition of the correct answer. However, the current reasoning models suffer from the noises in the retrieved knowledge. In this paper, we propose a novel encoding method which is able to conduct interception and soft filtering. This contributes to the harvesting and absorption of representative information with less interference from noises. We experiment on CommonsenseQA. Experimental results illustrate that our method yields substantial and consistent improvements compared to the strong Bert, RoBERTa and Albert-based baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_101", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_101.flac", "type": "audio", "duration": 373.423}, {"from": "Assistant", "value": "Media coverage has a substantial effect on the public perception of events. Nevertheless, media outlets are often biased. One way to bias news articles is by altering the word choice. The automatic identification of bias by word choice is challenging, primarily due to the lack of a gold standard data set and high context dependencies. This paper presents BABE, a robust and diverse data set created by trained experts, for media bias research. We also analyze why expert labeling is essential within this domain. Our data set offers better annotation quality and higher inter-annotator agreement than existing work. It consists of 3,700 sentences balanced among topics and outlets, containing media bias labels on the word and sentence level. Based on our data, we also introduce a way to detect bias-inducing sentences in news articles automatically. Our best performing BERT-based model is pre-trained on a larger corpus consisting of distant labels. Fine-tuning and evaluating the model on our proposed supervised data set, we achieve a macro F1-score of 0.804, outperforming existing methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_103", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_103.flac", "type": "audio", "duration": 353.045}, {"from": "Assistant", "value": "Popular dialog datasets such as MultiWOZ are created by providing crowd workers an instruction, expressed in natural language, that describes the task to be accomplished. Crowd workers play the role of a user and an agent to generate dialogs to accomplish tasks involving booking restaurant tables, calling a taxi etc. In this paper, we present a data creation strategy that uses the pre-trained language model, GPT2, to simulate the interaction between crowd workers by creating a user bot and an agent bot. We train the simulators using a smaller percentage of actual crowd-generated conversations and their corresponding instructions. We demonstrate that by using the simulated data, we achieve significant improvements in low-resource settings on two publicly available datasets - MultiWOZ dataset and the Persona chat dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_104", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_104.flac", "type": "audio", "duration": 344.043}, {"from": "Assistant", "value": "Conversational Emotion Recognition (CER) is a task to predict the emotion of an utterance in the context of a conversation. Although modeling the conversational context and interactions between speakers has been studied broadly, it is important to consider the speaker’s psychological state, which controls the action and intention of the speaker. The state-of-the-art method introduces CommonSense Knowledge (CSK) to model psychological states in a sequential way (forwards and backwards). However, it ignores the structural psychological interactions between utterances. In this paper, we propose a pSychological-Knowledge-Aware Interaction Graph (SKAIG). In the locally connected graph, the targeted utterance will be enhanced with the information of action inferred from the past context and intention implied by the future context. The utterance is self-connected to consider the present effect from itself. Furthermore, we utilize CSK to enrich edges with knowledge representations and process the SKAIG with a graph transformer. Our method achieves state-of-the-art and competitive performance on four popular CER datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_105", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_105.flac", "type": "audio", "duration": 314.048}, {"from": "Assistant", "value": "Morality plays an important role in social well-being, but people’s moral perception is not stable and changes over time. Recent advances in natural language processing have shown that text is an effective medium for informing moral change, but no attempt has been made to quantify the origins of these changes. We present a novel unsupervised framework for tracing textual sources of moral change toward entities through time. We characterize moral change with probabilistic topical distributions and infer the source text that exerts prominent influence on the moral time course. We evaluate our framework on a diverse set of data ranging from social media to news articles. We show that our framework not only captures fine-grained human moral judgments, but also identifies coherent source topics of moral change triggered by historical events. We apply our methodology to analyze the news in the COVID-19 pandemic and demonstrate its utility in identifying sources of moral change in high-impact and real-time social events.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_106", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_106.flac", "type": "audio", "duration": 342.059}, {"from": "Assistant", "value": "Unlike well-structured text, such as news reports and encyclopedia articles, dialogue content often comes from two or more interlocutors, exchanging information with each other. In such a scenario, the topic of a conversation can vary upon progression and the key information for a certain topic is often scattered across multiple utterances of different speakers, which poses challenges to abstractly summarize dialogues. To capture the various topic information of a conversation and outline salient facts for the captured topics, this work proposes two topic-aware contrastive learning objectives, namely coherence detection and sub-summary generation objectives, which are expected to implicitly model the topic change and handle information scattering challenges for the dialogue summarization task. The proposed contrastive objectives are framed as auxiliary tasks for the primary dialogue summarization task, united via an alternative parameter updating strategy. Extensive experiments on benchmark datasets demonstrate that the proposed simple method significantly outperforms strong baselines and achieves new state-of-the-art performance. The code and trained models are publicly available via .", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_107", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_107.flac", "type": "audio", "duration": 361.047}, {"from": "Assistant", "value": "Large pre-trained neural models have recently shown remarkable progress in text generation. In this paper, we propose to generate text conditioned on the structured data (table) and a prefix (the written text) by leveraging the pre-trained models. We present a new data-to-text dataset, Table with Written Text (TWT), by repurposing two existing datasets: ToTTo and TabFact. TWT contains both factual and logical statements that are faithful to the structured data, aiming to serve as a useful benchmark for controlled text generation. Compared with existing data-to-text task settings, TWT is more intuitive, the prefix (usually provided by the user) controls the topic of the generated text. Existing methods usually output hallucinated text that is not faithful on TWT. Therefore, we design a novel approach with table-aware attention visibility and copy mechanism over the table. Experimental results show that our approach outperforms state-of-the-art methods under both automatic and human evaluation metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_108", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_108.flac", "type": "audio", "duration": 209.216}, {"from": "Assistant", "value": "Pre-training Transformer-based models such as BERT and ELECTRA on a collection of Arabic corpora, demonstrated by both AraBERT and AraELECTRA, shows an impressive result on downstream tasks. However, pre-training Transformer-based language models is computationally expensive, especially for large-scale models. Recently, Funnel Transformer has addressed the sequential redundancy inside Transformer architecture by compressing the sequence of hidden states, leading to a significant reduction in the pre-training cost. This paper empirically studies the performance and efficiency of building an Arabic language model with Funnel Transformer and ELECTRA objective. We find that our model achieves state-of-the-art results on several Arabic downstream tasks despite using less computational resources compared to other BERT-based models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_109", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_109.flac", "type": "audio", "duration": 325.822}, {"from": "Assistant", "value": "Multimodal sentiment analysis (MSA) draws increasing attention with the availability of multimodal data. The boost in performance of MSA models is mainly hindered by two problems. On the one hand, recent MSA works mostly focus on learning cross-modal dynamics, but neglect to explore an optimal solution for unimodal networks, which determines the lower limit of MSA models. On the other hand, noisy information hidden in each modality interferes the learning of correct cross-modal dynamics. To address the above-mentioned problems, we propose a novel MSA framework Modulation Model for Multimodal Sentiment Analysis (M3SA) to identify the contribution of modalities and reduce the impact of noisy information, so as to better learn unimodal and cross-modal dynamics. Specifically, modulation loss is designed to modulate the loss contribution based on the confidence of individual modalities in each utterance, so as to explore an optimal update solution for each unimodal network. Besides, contrary to most existing works which fail to explicitly filter out noisy information, we devise a modality filter module to identify and filter out modality noise for the learning of correct cross-modal embedding. Extensive experiments on publicly datasets demonstrate that our approach achieves state-of-the-art performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_110", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_110.flac", "type": "audio", "duration": 169.045}, {"from": "Assistant", "value": "Training implicit discourse relation classifiers suffers from data sparsity. Variational AutoEncoder (VAE) appears to be the proper solution. It is because ideally VAE is capable of generating inexhaustible varying samples, and this facilitates selective data augmentation. However, our experiments show that coupling VAE with the RoBERTa-based classifier results in severe performance degradation. We ascribe the unusual phenomenon to erroneous sampling that would happen when VAE pursued variations. To overcome the problem, we develop a re-anchoring strategy, where Conditional VAE (CVAE) is used for estimating the risk of erroneous sampling, and meanwhile migrating the anchor to reduce the risk. The test results on PDTB v2.0 illustrate that, compared to the RoBERTa-based baseline, re-anchoring yields substantial improvements. Besides, we observe that re-anchoring can cooperate with other auxiliary strategies (transfer learning and interactive attention mechanism) to further improve the baseline, obtaining the F-scores of about 55%, 63%, 80% and 44% for the four main relation types (Comparison, Contingency, Expansion, Temporality) in the binary classification (Yes/No) scenario.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_111", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_111.flac", "type": "audio", "duration": 358.059}, {"from": "Assistant", "value": "Curriculum learning, a machine training strategy that feeds training instances to the model from easy to hard, has been proven to facilitate the dialogue generation task. Meanwhile, knowledge distillation, a knowledge transformation methodology among teachers and students networks can yield significant performance boost for student models. Hence, in this paper, we introduce a combination of curriculum learning and knowledge distillation for efficient dialogue generation models, where curriculum learning can help knowledge distillation from data and model aspects. To start with, from the data aspect, we cluster the training cases according to their complexity, which is calculated by various types of features such as sentence length and coherence between dialog pairs. Furthermore, we employ an adversarial training strategy to identify the complexity of cases from model level. The intuition is that, if a discriminator can tell the generated response is from the teacher or the student, then the case is difficult that the student model has not adapted to yet. Finally, we use self-paced learning, which is an extension to curriculum learning to assign weights for distillation. In conclusion, we arrange a hierarchical curriculum based on the above two aspects for the student model under the guidance from the teacher model. Experimental results demonstrate that our methods achieve improvements compared with competitive baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_112", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_112.flac", "type": "audio", "duration": 173.056}, {"from": "Assistant", "value": "The paradigm of leveraging large pre-trained language models has made significant progress on benchmarks on task-oriented dialogue (TOD) systems. In this paper, we combine this paradigm with multi-task learning framework for end-to-end TOD modeling by adopting span prediction as an auxiliary task. In end-to-end setting, our model achieves new state-of-the-art results with combined scores of 108.3 and 107.5 on MultiWOZ 2.0 and MultiWOZ 2.1, respectively. Furthermore, we demonstrate that multi-task learning improves not only the performance of model but its generalization capability through domain adaptation experiments in the few-shot setting. The code is available at github.com/bepoetree/MTTOD.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_113", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_113.flac", "type": "audio", "duration": 169.045}, {"from": "Assistant", "value": "Discourse analysis has long been known to be fundamental in natural language processing. In this research, we present our insight on discourse-level topic chain (DTC) parsing which aims at discovering new topics and investigating how these topics evolve over time within an article. To address the lack of data, we contribute a new discourse corpus with DTC-style dependency graphs annotated upon news articles. In particular, we ensure the high reliability of the corpus by utilizing a two-step annotation strategy to build the data and filtering out the annotations with low confidence scores. Based on the annotated corpus, we introduce a simple yet robust system for automatic discourse-level topic chain parsing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_114", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_114.flac", "type": "audio", "duration": 322.859}, {"from": "Assistant", "value": "Multilingual Neural Machine Translation (MNMT) trains a single NMT model that supports translation between multiple languages, rather than training separate models for different languages. Learning a single model can enhance the low-resource translation by leveraging data from multiple languages. However, the performance of an MNMT model is highly dependent on the type of languages used in training, as transferring knowledge from a diverse set of languages degrades the translation performance due to negative transfer. In this paper, we propose a Hierarchical Knowledge Distillation (HKD) approach for MNMT which capitalises on language groups generated according to typological features and phylogeny of languages to overcome the issue of negative transfer. HKD generates a set of multilingual teacher-assistant models via a selective knowledge distillation mechanism based on the language groups, and then distills the ultimate multilingual model from those assistants in an adaptive way. Experimental results derived from the TED dataset with 53 languages demonstrate the effectiveness of our approach in avoiding the negative transfer effect in MNMT, leading to an improved translation performance (about 1 BLEU score in average) compared to strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_115", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_115.flac", "type": "audio", "duration": 251.691}, {"from": "Assistant", "value": "The pivot for the unified Aspect-based Sentiment Analysis (ABSA) is to couple aspect terms with their corresponding opinion terms, which might further derive easier sentiment predictions. In this paper, we investigate the unified ABSA task from the perspective of Machine Reading Comprehension (MRC) by observing that the aspect and the opinion terms can serve as the query and answer in MRC interchangeably. We propose a new paradigm named Role Flipped Machine Reading Comprehension (RF-MRC) to resolve. At its heart, the predicted results of either the Aspect Term Extraction (ATE) or the Opinion Terms Extraction (OTE) are regarded as the queries, respectively, and the matched opinion or aspect terms are considered as answers. The queries and answers can be flipped for multi-hop detection. Finally, every matched aspect-opinion pair is predicted by the sentiment classifier. RF-MRC can solve the ABSA task without any additional data annotation or transformation. Experiments on three widely used benchmarks and a challenging dataset demonstrate the superiority of the proposed framework.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_116", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_116.flac", "type": "audio", "duration": 292.416}, {"from": "Assistant", "value": "Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents. However, the generalization still remains a big challenge as the agents depend critically on the complexity and variety of training tasks. In this paper, we address this problem by introducing a hierarchical framework built upon the knowledge graph-based RL agent. In the high level, a meta-policy is executed to decompose the whole game into a set of subtasks specified by textual goals, and select one of them based on the KG. Then a sub-policy in the low level is executed to conduct goal-conditioned reinforcement learning. We carry out experiments on games with various difficulty levels and show that the proposed method enjoys favorable generalizability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_117", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_117.flac", "type": "audio", "duration": 338.368}, {"from": "Assistant", "value": "Although abstractive summarization models have achieved impressive results on document summarization tasks, their performance on dialogue modeling is much less satisfactory due to the crude and straight methods for dialogue encoding. To address this question, we propose a novel end-to-end Transformer-based model FinDS for abstractive dialogue summarization that leverages Finer-grain universal Dialogue semantic Structures to model dialogue and generates better summaries. Experiments on the SAMsum dataset show that FinDS outperforms various dialogue summarization approaches and achieves new state-of-the-art (SOTA) ROUGE results. Finally, we apply FinDS to a more complex scenario, showing the robustness of our model. We also release our source code.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_118", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_118.flac", "type": "audio", "duration": 360.043}, {"from": "Assistant", "value": "Contrastive Learning has emerged as a powerful representation learning method and facilitates various downstream tasks especially when supervised data is limited. How to construct efficient contrastive samples through data augmentation is key to its success. Unlike vision tasks, the data augmentation method for contrastive learning has not been investigated sufficiently in language tasks. In this paper, we propose a novel approach to construct contrastive samples for language tasks using text summarization. We use these samples for supervised contrastive learning to gain better text representations which greatly benefit text classification tasks with limited annotations. To further improve the method, we mix up samples from different classes and add an extra regularization, named Mixsum, in addition to the cross-entropy-loss. Experiments on real-world text classification datasets (Amazon-5, Yelp-5, AG News, and IMDb) demonstrate the effectiveness of the proposed contrastive learning framework with summarization-based data augmentation and Mixsum regularization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_119", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_119.flac", "type": "audio", "duration": 506.219}, {"from": "Assistant", "value": "Most previous studies on information status (IS) classification and bridging anaphora recognition assume that the gold mention or syntactic tree information is given (Hou et al., 2013; Roesiger et al., 2018; Hou, 2020; Yu and Poesio, 2020). In this paper, we propose an end-to-end neural approach for information status classification. Our approach consists of a mention extraction component and an information status assignment component. During the inference time, our system takes a raw text as the input and generates mentions together with their information status. On the ISNotes corpus (Markert et al., 2012), we show that our information status assignment component achieves new state-of-the-art results on fine-grained IS classification based on gold mentions. Furthermore, our system performs significantly better than other baselines for both mention extraction and fine-grained IS classification in the end-to-end setting. Finally, we apply our system on BASHI (Roesiger, 2018) and SciCorp (Roesiger, 2016) to recognize referential bridging anaphora. We find that our end-to-end system trained on ISNotes achieves competitive results on bridging anaphora recognition compared to the previous state-of-the-art system that relies on syntactic information and is trained on the in-domain datasets (Yu and Poesio, 2020).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_120", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_120.flac", "type": "audio", "duration": 352.619}, {"from": "Assistant", "value": "Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities. In this paper, we emphasize the importance of incorporating events in KG representation learning, and propose an event-enhanced KG embedding model EventKE. Specifically, given the original KG, we first incorporate event nodes by building a heterogeneous network, where entity nodes and event nodes are distributed on the two sides of the network inter-connected by event argument links. We then use entity-entity relations from the original KG and event-event temporal links to inner-connect entity and event nodes respectively. We design a novel and effective attention-based message passing method, which is conducted on entity-entity, event-entity, and event-event relations to fuse the event information into KG embeddings. Experimental results on real-world datasets demonstrate that events can greatly improve the quality of the KG embeddings on multiple downstream tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_121", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_121.flac", "type": "audio", "duration": 314.88}, {"from": "Assistant", "value": "Cross-attention is an important component of neural machine translation (NMT), which is always realized by dot-product attention in previous methods. However, dot-product attention only considers the pair-wise correlation between words, resulting in dispersion when dealing with long sentences and neglect of source neighboring relationships. Inspired by linguistics, the above issues are caused by ignoring a type of cross-attention, called concentrated attention, which focuses on several central words and then spreads around them. In this work, we apply Gaussian Mixture Model (GMM) to model the concentrated attention in cross-attention. Experiments and analyses we conducted on three datasets show that the proposed method outperforms the baseline and has significant improvement on alignment quality, N-gram accuracy, and long sentence translation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_122", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_122.flac", "type": "audio", "duration": 327.04}, {"from": "Assistant", "value": "Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers. Though a set of rumor detection models have exploited the multi-modal data, they seldom consider the inconsistent relationships among images and texts. Moreover, they also fail to find a powerful way to spot the inconsistency information among the post contents and background knowledge. Motivated by the intuition that rumors are more likely to have inconsistency information in semantics, a novel Knowledge-guided Dual-inconsistency network is proposed to detect rumors with multimedia contents. It can capture the inconsistent semantics at the cross-modal level and the content-knowledge level in one unified framework. Extensive experiments on two public real-world datasets demonstrate that our proposal can outperform the state-of-the-art baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_123", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_123.flac", "type": "audio", "duration": 359.05}, {"from": "Assistant", "value": "Pre-trained language models have shown remarkable results on various NLP tasks. Nevertheless, due to their bulky size and slow inference speed, it is hard to deploy them on edge devices. In this paper, we have a critical insight that improving the feed-forward network (FFN) in BERT has a higher gain than improving the multi-head attention (MHA) since the computational cost of FFN is 2~3 times larger than MHA. Hence, to compact BERT, we are devoted to designing efficient FFN as opposed to previous works that pay attention to MHA. Since FFN comprises a multilayer perceptron (MLP) that is essential in BERT optimization, we further design a thorough search space towards an advanced MLP and perform a coarse-to-fine mechanism to search for an efficient BERT architecture. Moreover, to accelerate searching and enhance model transferability, we employ a novel warm-up knowledge distillation strategy at each search stage. Extensive experiments show our searched EfficientBERT is 6.9×smaller and 4.4×faster than BERTBASE, and has competitive performances on GLUE and SQuAD Benchmarks. Concretely, EfficientBERT attains a 77.7 average score on GLUEtest, 0.7 higher than MobileBERTTINY, and achieves an 85.3/74.5 F1 score on SQuAD v1.1/v2.0dev, 3.2/2.7 higher than TinyBERT4even without data augmentation. The code is released athttps://github.com/cheneydon/efficient-bert.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_124", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_124.flac", "type": "audio", "duration": 297.451}, {"from": "Assistant", "value": "News recommendation techniques can help users on news platforms obtain their preferred news information. Most existing news recommendation methods rely on centrally stored user behavior data to train models and serve users. However, user data is usually highly privacy-sensitive, and centrally storing them in the news platform may raise privacy concerns and risks. In this paper, we propose a unified news recommendation framework, which can utilize user data locally stored in user clients to train models and serve users in a privacy-preserving way. Following a widely used paradigm in real-world recommender systems, our framework contains a stage for candidate news generation (i.e., recall) and a stage for candidate news ranking (i.e., ranking). At the recall stage, each client locally learns multiple interest representations from clicked news to comprehensively model user interests. These representations are uploaded to the server to recall candidate news from a large news pool, which are further distributed to the user client at the ranking stage for personalized news display. In addition, we propose an interest decomposer-aggregator method with perturbation noise to better protect private user information encoded in user interest representations. Besides, we collaboratively train both recall and ranking models on the data decentralized in a large number of user clients in a privacy-preserving way. Experiments on two real-world news datasets show that our method can outperform baseline methods and effectively protect user privacy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_125", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_125.flac", "type": "audio", "duration": 330.002}, {"from": "Assistant", "value": "Mapping natural language instructions to programs that computers can process is a fundamental challenge. Existing approaches focus on likelihood-based training or using reinforcement learning to fine-tune models based on a single reward. In this paper, we pose program generation from language as Inverse Reinforcement Learning. We introduce several interpretable reward components and jointly learn (1) a reward function that linearly combines them, and (2) a policy for program generation. Fine-tuning with our approach achieves significantly better performance than competitive methods using Reinforcement Learning (RL). On the VirtualHome framework, we get improvements of up to 9.0% on the Longest Common Subsequence metric and 14.7% on recall-based metrics over previous work on this framework (Puig et al., 2018). The approach is data-efficient, showing larger gains in performance in the low-data regime. Generated programs are also preferred by human evaluators over an RL-based approach, and rated higher on relevance, completeness, and human-likeness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_127", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_127.flac", "type": "audio", "duration": 311.851}, {"from": "Assistant", "value": "Math word problem solving has attracted considerable research interest in recent years. Previous works have shown the effectiveness of utilizing graph neural networks to capture the relationships in the problem. However, these works did not carefully take the edge label information and the long-range word relationship across sentences into consideration. In addition, during generation, they focus on the most relevant areas of the currently generated word, while neglecting the rest of the problem. In this paper, we propose a novel Edge-Enhanced Hierarchical Graph-to-Tree model (EEH-G2T), in which the math word problems are represented as edge-labeled graphs. Specifically, an edge-enhanced hierarchical graph encoder is used to incorporate edge label information. This encoder updates the graph nodes hierarchically in two steps: sentence-level aggregation and problem-level aggregation. Furthermore, a tree-structured decoder with a split attention mechanism is applied to guide the model to pay attention to different parts of the input problem. Experimental results on the MAWPS and Math23K dataset showed that our EEH-G2T can effectively improve performance compared with state-of-the-art methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_128", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_128.flac", "type": "audio", "duration": 313.685}, {"from": "Assistant", "value": "Generating texts in scientific papers requires not only capturing the content contained within the given input but also frequently acquiring the external information called context. We push forward the scientific text generation by proposing a new task, namely context-aware text generation in the scientific domain, aiming at exploiting the contributions of context in generated texts. To this end, we present a novel challenging large-scale Scientific Paper Dataset for ConteXt-Aware Text Generation (SciXGen), consisting of well-annotated 205,304 papers with full references to widely-used objects (e.g., tables, figures, algorithms) in a paper. We comprehensively benchmark, using state-of-the-arts, the efficacy of our newly constructed SciXGen dataset in generating description and paragraph. Our dataset and benchmarks will be made publicly available to hopefully facilitate the scientific text generation research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_129", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_129.flac", "type": "audio", "duration": 358.293}, {"from": "Assistant", "value": "User targeting is an essential task in the modern advertising industry: given a package of ads for a particular category of products (e.g., green tea), identify the online users to whom the ad package should be targeted. A (ad package specific) user targeting model is typically trained using historical clickthrough data: positive instances correspond to users who have clicked on an ad in the package before, whereas negative instances correspond to users who have not clicked on any ads in the package that were displayed to them. Collecting a sufficient amount of positive training data for training an accurate user targeting model, however, is by no means trivial. This paper focuses on the development of a method for automatic augmentation of the set of positive training instances. Experimental results on two datasets, including a real-world company dataset, demonstrate the effectiveness of our proposed method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_130", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_130.flac", "type": "audio", "duration": 310.016}, {"from": "Assistant", "value": "In cross-lingual text classification, it is required that task-specific training data in high-resource source languages are available, where the task is identical to that of a low-resource target language. However, collecting such training data can be infeasible because of the labeling cost, task characteristics, and privacy concerns. This paper proposes an alternative solution that uses only task-independent word embeddings of high-resource languages and bilingual dictionaries. First, we construct a dictionary-based heterogeneous graph (DHG) from bilingual dictionaries. This opens the possibility to use graph neural networks for cross-lingual transfer. The remaining challenge is the heterogeneity of DHG because multiple languages are considered. To address this challenge, we propose dictionary-based heterogeneous graph neural network (DHGNet) that effectively handles the heterogeneity of DHG by two-step aggregations, which are word-level and language-level aggregations. Experimental results demonstrate that our method outperforms pretrained models even though it does not access to large corpora. Furthermore, it can perform well even though dictionaries contain many incorrect translations. Its robustness allows the usage of a wider range of dictionaries such as an automatically constructed dictionary and crowdsourced dictionary, which are convenient for real-world applications.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_131", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_131.flac", "type": "audio", "duration": 360.043}, {"from": "Assistant", "value": "Distantly supervised named entity recognition (DS-NER) efficiently reduces labor costs but meanwhile intrinsically suffers from the label noise due to the strong assumption of distant supervision. Typically, the wrongly labeled instances comprise numbers of incomplete and inaccurate annotations, while most prior denoising works are only concerned with one kind of noise and fail to fully explore useful information in the training set. To address this issue, we propose a robust learning paradigm named Self-Collaborative Denoising Learning (SCDL), which jointly trains two teacher-student networks in a mutually-beneficial manner to iteratively perform noisy label refinery. Each network is designed to exploit reliable labels via self denoising, and two networks communicate with each other to explore unreliable annotations by collaborative denoising. Extensive experimental results on five real-world datasets demonstrate that SCDL is superior to state-of-the-art DS-NER denoising methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_132", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_132.flac", "type": "audio", "duration": 330.002}, {"from": "Assistant", "value": "While powerful pre-trained language models have improved the fluency of text generation models, semantic adequacy -the ability to generate text that is semantically faithful to the input- remains an unsolved issue. In this paper, we introduce a novel automatic evaluation metric, Entity-Based Semantic Adequacy, which can be used to assess to what extent generation models that verbalise RDF (Resource Description Framework) graphs produce text that contains mentions of the entities occurring in the RDF input. This is important as RDF subject and object entities make up 2/3 of the input. We use our metric to compare 25 models from the WebNLG Shared Tasks and we examine correlation with results from human evaluations of semantic adequacy. We show that while our metric correlates with human evaluation scores, this correlation varies with the specifics of the human evaluation setup. This suggests that in order to measure the entity-based adequacy of generated texts, an automatic metric such as the one proposed here might be more reliable, as less subjective and more focused on correct verbalisation of the input, than human evaluation measures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_133", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_133.flac", "type": "audio", "duration": 374.251}, {"from": "Assistant", "value": "One of the most challenging aspects of current single-document news summarization is that the summary often contains ‘extrinsic hallucinations’, i.e., facts that are not present in the source document, which are often derived via world knowledge. This causes summarisation systems to act more like open-ended language models tending to hallucinate facts that are erroneous. In this paper, we mitigate this problem with the help of multiple supplementary resource documents assisting the task. We present a new dataset MiraNews and benchmark existing summarisation models. In contrast to multi-document summarization, which addresses multiple events from several source documents, we still aim at generating a summary for a single document. We show via data analysis that it’s not only the models which are to blame: more than 27% of facts mentioned in the gold summaries of MiraNews are better grounded on assisting documents than in the main source articles. An error analysis of generated summaries from pretrained models fine-tuned on MIRANEWS reveals that this has an even bigger effects on models: assisted summarisation reduces 55% of hallucinations when compared to single-document summarisation models trained on the main article only.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_134", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_134.flac", "type": "audio", "duration": 372.007}, {"from": "Assistant", "value": "We study the problem of multilingual automated reply suggestions (RS) model serving many languages simultaneously. Multilingual models are often challenged by model capacity and severe data distribution skew across languages. While prior works largely focus on monolingual models, we propose Conditional Generative Matching models (CGM), optimized within a Variational Autoencoder framework to address challenges arising from multilingual RS. CGM does so with expressive message conditional priors, mixture densities to enhance multilingual data representation, latent alignment for language discrimination, and effective variational optimization techniques for training multilingual RS. The enhancements result in performance that exceed competitive baselines in relevance (ROUGE score) by more than 10% on average, and 16%for low resource languages. CGM also shows remarkable improvements in diversity (80%) illustrating its expressiveness in representation of multi-lingual data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_136", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_136.flac", "type": "audio", "duration": 360.049}, {"from": "Assistant", "value": "A hyperbole is an intentional and creative exaggeration not to be taken literally. Despite its ubiquity in daily life, the computational explorations of hyperboles are scarce. In this paper, we tackle the under-explored and challenging task: sentence-level hyperbole generation. We start with a representative syntactic pattern for intensification and systematically study the semantic (commonsense and counterfactual) relationships between each component in such hyperboles. We then leverage commonsense and counterfactual inference to generate hyperbole candidates based on our findings from the pattern, and train neural classifiers to rank and select high-quality hyperboles. Automatic and human evaluations show that our generation method is able to generate hyperboles with high success rate, intensity, funniness, and creativity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_137", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_137.flac", "type": "audio", "duration": 322.014}, {"from": "Assistant", "value": "We present an actor-critic framework to induce subtopical structures in a news article for news discourse profiling. The model uses multiple critics that act according to known subtopic structures while the actor aims to outperform them. The content structures constitute sentences that represent latent subtopic boundaries. Then, we introduce a hierarchical neural network that uses the identified subtopic boundary sentences to model multi-level interaction between sentences, subtopics, and the document. Experimental results and analyses on the NewsDiscourse corpus show that the actor model learns to effectively segment a document into subtopics and improves the performance of the hierarchical model on the news discourse profiling task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_138", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_138.flac", "type": "audio", "duration": 347.022}, {"from": "Assistant", "value": "The ability to detect Out-of-Domain (OOD) inputs has been a critical requirement in many real-world NLP applications. For example, intent classification in dialogue systems. The reason is that the inclusion of unsupported OOD inputs may lead to catastrophic failure of systems. However, it remains an empirical question whether current methods can tackle such problems reliably in a realistic scenario where zero OOD training data is available. In this study, we propose ProtoInfoMax, a new architecture that extends Prototypical Networks to simultaneously process in-domain and OOD sentences via Mutual Information Maximization (InfoMax) objective. Experimental results show that our proposed method can substantially improve performance up to 20% for OOD detection in low resource settings of text classification. We also show that ProtoInfoMax is less prone to typical overconfidence errors of Neural Networks, leading to more reliable prediction results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_139", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_139.flac", "type": "audio", "duration": 312.043}, {"from": "Assistant", "value": "In this work, we study the problem of named entity recognition (NER) in a low resource scenario, focusing on few-shot and zero-shot settings. Built upon large-scale pre-trained language models, we propose a novel NER framework, namely SpanNER, which learns from natural language supervision and enables the identification of never-seen entity classes without using in-domain labeled data. We perform extensive experiments on 5 benchmark datasets and evaluate the proposed method in the few-shot learning, domain transfer and zero-shot learning settings. The experimental results show that the proposed method can bring 10%, 23% and 26% improvements in average over the best baselines in few-shot learning, domain transfer and zero-shot learning settings respectively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_140", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_140.flac", "type": "audio", "duration": 176.533}, {"from": "Assistant", "value": "Biomedical entity linking is the task of linking entity mentions in a biomedical document to referent entities in a knowledge base. Recently, many BERT-based models have been introduced for the task. While these models achieve competitive results on many datasets, they are computationally expensive and contain about 110M parameters. Little is known about the factors contributing to their impressive performance and whether the over-parameterization is needed. In this work, we shed some light on the inner workings of these large BERT-based models. Through a set of probing experiments, we have found that the entity linking performance only changes slightly when the input word order is shuffled or when the attention scope is limited to a fixed window size. From these observations, we propose an efficient convolutional neural network with residual connections for biomedical entity linking. Because of the sparse connectivity and weight sharing properties, our model has a small number of parameters and is highly efficient. On five public datasets, our model achieves comparable or even better linking accuracy than the state-of-the-art BERT-based models while having about 60 times fewer parameters.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_141", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_141.flac", "type": "audio", "duration": 467.023}, {"from": "Assistant", "value": "Byte-pair encoding (BPE) is a ubiquitous algorithm in the subword tokenization process of language models as it provides multiple benefits. However, this process is solely based on pre-training data statistics, making it hard for the tokenizer to handle infrequent spellings. On the other hand, though robust to misspellings, pure character-level models often lead to unreasonably long sequences and make it harder for the model to learn meaningful words. To alleviate these challenges, we propose a character-based subword module (char2subword) that learns the subword embedding table in pre-trained models like BERT. Our char2subword module builds representations from characters out of the subword vocabulary, and it can be used as a drop-in replacement of the subword embedding table. The module is robust to character-level alterations such as misspellings, word inflection, casing, and punctuation. We integrate it further with BERT through pre-training while keeping BERT transformer parameters fixed–and thus, providing a practical method. Finally, we show that incorporating our module to mBERT significantly improves the performance on the social media linguistic code-switching evaluation (LinCE) benchmark.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_142", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_142.flac", "type": "audio", "duration": 181.023}, {"from": "Assistant", "value": "This paper explores the effect of using multitask learning for abstractive summarization in the context of small training corpora. In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and paraphrase detection) both individually and in combination, with the goal of enhancing the target task of abstractive summarization via multitask learning. We show that for many task combinations, a model trained in a multitask setting outperforms a model trained only for abstractive summarization, with no additional summarization data introduced. Additionally, we do a comprehensive search and find that certain tasks (e.g. paraphrase detection) consistently benefit abstractive summarization, not only when combined with other tasks but also when using different architectures and training corpora.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_143", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_143.flac", "type": "audio", "duration": 367.04}, {"from": "Assistant", "value": "As the Internet grows in size, so does the amount of text based information that exists. For many application spaces it is paramount to isolate and identify texts that relate to a particular topic. While one-class classification would be ideal for such analysis, there is a relative lack of research regarding efficient approaches with high predictive power. By noting that the range of documents we wish to identify can be represented as positive linear combinations of the Vector Space Model representing our text, we propose Conical classification, an approach that allows us to identify if a document is of a particular topic in a computationally efficient manner. We also propose Normal Exclusion, a modified version of Bi-Normal Separation that makes it more suitable within the one-class classification context. We show in our analysis that our approach not only has higher predictive power on our datasets, but is also faster to compute.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_144", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_144.flac", "type": "audio", "duration": 345.044}, {"from": "Assistant", "value": "While state-of-the-art Dialogue State Tracking (DST) models show promising results, all of them rely on a traditional cross-entropy loss function during the training process, which may not be optimal for improving the joint goal accuracy. Although several approaches recently propose augmenting the training set by copying user utterances and replacing the real slot values with other possible or even similar values, they are not effective at improving the performance of existing DST models. To address these challenges, we propose a Turn-based Loss Function (TLF) that penalises the model if it inaccurately predicts a slot value at the early turns more so than in later turns in order to improve joint goal accuracy. We also propose a simple but effective Sequential Data Augmentation (SDA) algorithm to generate more complex user utterances and system responses to effectively train existing DST models. Experimental results on two standard DST benchmark collections demonstrate that our proposed TLF and SDA techniques significantly improve the effectiveness of the state-of-the-art DST model by approximately 7-8% relative reduction in error and achieves a new state-of-the-art joint goal accuracy with 59.50 and 54.90 on MultiWOZ2.1 and MultiWOZ2.2, respectively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_145", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_145.flac", "type": "audio", "duration": 181.056}, {"from": "Assistant", "value": "Human conversations naturally evolve around different topics and fluently move between them. In research on dialog systems, the ability to actively and smoothly transition to new topics is often ignored. In this paper we introduce TIAGE, a new topic-shift aware dialog benchmark constructed utilizing human annotations on topic shifts. Based on TIAGE, we introduce three tasks to investigate different scenarios of topic-shift modeling in dialog settings: topic-shift detection, topic-shift triggered response generation and topic-aware dialog generation. Experiments on these tasks show that the topic-shift signals in TIAGE are useful for topic-shift response generation. On the other hand, dialog systems still struggle to decide when to change topic. This indicates further research is needed in topic-shift aware dialog modeling.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_146", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_146.flac", "type": "audio", "duration": 354.005}, {"from": "Assistant", "value": "Multimodal program synthesis, which leverages different types of user input to synthesize a desired program, is an attractive way to scale program synthesis to challenging settings; however, it requires integrating noisy signals from the user, like natural language, with hard constraints on the program’s behavior. This paper proposes anoptimal neural synthesisapproach where the goal is to find a program that satisfies user-provided constraints while also maximizing the program’s score with respect to a neural model. Specifically, we focus on multimodal synthesis tasks in which the user intent is expressed using a combination of natural language (NL) and input-output examples. At the core of our method is a top-down recurrent neural model that places distributions over abstract syntax trees conditioned on the NL input. This model not only allows for efficient search over the space of syntactically valid programs, but it allows us to leverage automated program analysis techniques for pruning the search space based on infeasibility of partial programs with respect to the user’s constraints. The experimental results on a multimodal synthesis dataset (StructuredRegex) show that our method substantially outperforms prior state-of-the-art techniques in terms of accuracy and efficiency, and finds model-optimal programs more frequently.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_147", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_147.flac", "type": "audio", "duration": 333.013}, {"from": "Assistant", "value": "The rapid growth in published clinical trials makes it difficult to maintain up-to-date systematic reviews, which require finding all relevant trials. This leads to policy and practice decisions based on out-of-date, incomplete, and biased subsets of available clinical evidence. Extracting and then normalising Population, Intervention, Comparator, and Outcome (PICO) information from clinical trial articles may be an effective way to automatically assign trials to systematic reviews and avoid searching and screening—the two most time-consuming systematic review processes. We propose and test a novel approach to PICO span detection. The major difference between our proposed method and previous approaches comes from detecting spans without needing annotated span data and using only crowdsourced sentence-level annotations. Experiments on two datasets show that PICO span detection results achieve much higher results for recall when compared to fully supervised methods with PICO sentence detection at least as good as human annotations. By removing the reliance on expert annotations for span detection, this work could be used in a human-machine pipeline for turning low-quality, crowdsourced, and sentence-level PICO annotations into structured information that can be used to quickly assign trials to relevant systematic reviews.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_148", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_148.flac", "type": "audio", "duration": 168.576}, {"from": "Assistant", "value": "We introduce Classification with Alternating Normalization (CAN), a non-parametric post-processing step for classification. CAN improves classification accuracy for challenging examples by re-adjusting their predicted class probability distribution using the predicted class distributions of high-confidence validation examples. CAN is easily applicable to any probabilistic classifier, with minimal computation overhead. We analyze the properties of CAN using simulated experiments, and empirically demonstrate its effectiveness across a diverse set of classification tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_149", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_149.flac", "type": "audio", "duration": 308.053}, {"from": "Assistant", "value": "Thanks to the strong representation learning capability of deep learning, especially pre-training techniques with language model loss, dependency parsing has achieved great performance boost in the in-domain scenario with abundant labeled training data for target domains. However, the parsing community has to face the more realistic setting where the parsing performance drops drastically when labeled data only exists for several fixed out-domains. In this work, we propose a novel model for multi-source cross-domain dependency parsing. The model consists of two components, i.e., a parameter generation network for distinguishing domain-specific features, and an adversarial network for learning domain-invariant representations. Experiments on a recently released NLPCC-2019 dataset for multi-domain dependency parsing show that our model can consistently improve cross-domain parsing performance by about 2 points in averaged labeled attachment accuracy (LAS) over strong BERT-enhanced baselines. Detailed analysis is conducted to gain more insights on contributions of the two components.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_150", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_150.flac", "type": "audio", "duration": 372.523}, {"from": "Assistant", "value": "When reading a literary piece, readers often make inferences about various characters’ roles, personalities, relationships, intents, actions, etc. While humans can readily draw upon their past experiences to build such a character-centric view of the narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, we present LiSCU – a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_151", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_151.flac", "type": "audio", "duration": 446.272}, {"from": "Assistant", "value": "Pre-trained language-vision models have shown remarkable performance on the visual question answering (VQA) task. However, most pre-trained models are trained by only considering monolingual learning, especially the resource-rich language like English. Training such models for multilingual setups demand high computing resources and multilingual language-vision dataset which hinders their application in practice. To alleviate these challenges, we propose a knowledge distillation approach to extend an English language-vision model (teacher) into an equally effective multilingual and code-mixed model (student). Unlike the existing knowledge distillation methods, which only use the output from the last layer of the teacher network for distillation, our student model learns and imitates the teacher from multiple intermediate layers (language and vision encoders) with appropriately designed distillation objectives for incremental knowledge extraction. We also create the large-scale multilingual and code-mixed VQA dataset in eleven different language setups considering the multiple Indian and European languages. Experimental results and in-depth analysis show the effectiveness of the proposed VQA model over the pre-trained language-vision models on eleven diverse language setups.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_152", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_152.flac", "type": "audio", "duration": 339.058}, {"from": "Assistant", "value": "Aspect-based sentiment analysis (ABSA) mainly involves three subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification, which are typically handled in a separate or joint manner. However, previous approaches do not well exploit the interactive relations among three subtasks and do not pertinently leverage the easily available document-level labeled domain/sentiment knowledge, which restricts their performances. To address these issues, we propose a novel Iterative Multi-Knowledge Transfer Network (IMKTN) for end-to-end ABSA. For one thing, through the interactive correlations between the ABSA subtasks, our IMKTN transfers the task-specific knowledge from any two of the three subtasks to another one at the token level by utilizing a well-designed routing algorithm, that is, any two of the three subtasks will help the third one. For another, our IMKTN pertinently transfers the document-level knowledge, i.e., domain-specific and sentiment-related knowledge, to the aspect-level subtasks to further enhance the corresponding performance. Experimental results on three benchmark datasets demonstrate the effectiveness and superiority of our approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_153", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_153.flac", "type": "audio", "duration": 360.043}, {"from": "Assistant", "value": "Measuring the similarity score between a pair of sentences in different languages is the essential requisite for multilingual sentence embedding methods. Predicting the similarity score consists of two sub-tasks, which are monolingual similarity evaluation and multilingual sentence retrieval. However, conventional methods have mainly tackled only one of the sub-tasks and therefore showed biased performances. In this paper, we suggest a novel and strong method for multilingual sentence embedding, which shows performance improvement on both sub-tasks, consequently resulting in robust predictions of multilingual similarity scores. The suggested method consists of two parts: to learn semantic similarity of sentences in the pivot language and then to extend the learned semantic structure to different languages. To align semantic structures across different languages, we introduce a teacher-student network. The teacher network distills the knowledge of the pivot language to different languages of the student network. During the distillation, the parameters of the teacher network are updated with the slow-moving average. Together with the distillation and the parameter updating, the semantic structure of the student network can be directly aligned across different languages while preserving the ability to measure the semantic similarity. Thus, the multilingual training method drives performance improvement on multilingual similarity evaluation. The suggested model achieves the state-of-the-art performance on extended STS 2017 multilingual similarity evaluation as well as two sub-tasks, which are extended STS 2017 monolingual similarity evaluation and Tatoeba multilingual retrieval in 14 languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_155", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_155.flac", "type": "audio", "duration": 349.056}, {"from": "Assistant", "value": "Biases continue to be prevalent in modern text and media, especially subjective bias – a special type of bias that introduces improper attitudes or presents a statement with the presupposition of truth. To tackle the problem of detecting and further mitigating subjective bias, we introduce a manually annotated parallel corpus WIKIBIAS with more than 4,000 sentence pairs from Wikipedia edits. This corpus contains annotations towards both sentence-level bias types and token-level biased segments. We present systematic analyses of our dataset and results achieved by a set of state-of-the-art baselines in terms of three tasks: bias classification, tagging biased segments, and neutralizing biased text. We find that current models still struggle with detecting multi-span biases despite their reasonable performances, suggesting that our dataset can serve as a useful research benchmark. We also demonstrate that models trained on our dataset can generalize well to multiple domains such as news and political speeches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_156", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_156.flac", "type": "audio", "duration": 353.731}, {"from": "Assistant", "value": "Natural language processing (NLP) models often require a massive number of parameters for word embeddings, which limits their application on mobile devices. Researchers have employed many approaches, e.g. adaptive inputs, to reduce the parameters of word embeddings. However, existing methods rarely pay attention to semantic information. In this paper, we propose a novel method called Unique and Class Embeddings (UnClE), which explicitly leverages semantic similarity with weight sharing to reduce the dimensionality of word embeddings. Inspired by the fact that words with similar semantic can share a part of weights, we divide the embeddings of words into two parts: unique embedding and class embedding. The former is one-to-one mapping like traditional embedding, while the latter is many-to-one mapping and learn the representation of class information. Our method is suitable for both word-level and sub-word level models and can be used to reduce both input and output embeddings. Experimental results on the standard WMT 2014 English-German dataset show that our method is able to reduce the parameters of word embeddings by more than 11x, with about 93% performance retaining in BLEU metrics. For language modeling task, our model can reduce word embeddings by 6x or 11x on PTB/WT2 dataset at the cost of a certain degree of performance degradation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_157", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_157.flac", "type": "audio", "duration": 349.888}, {"from": "Assistant", "value": "Question answering models struggle to generalize to novel compositions of training patterns. Current end-to-end models learn a flat input embedding which can lose input syntax context. Prior approaches improve generalization by learning permutation invariant models, but these methods do not scale to more complex train-test splits. We propose Grounded Graph Decoding, a method to improve compositional generalization of language representations by grounding structured predictions with an attention mechanism. Grounding enables the model to retain syntax information from the input that significantly improves generalization to complex inputs. By predicting a structured graph containing conjunctions of query clauses, we learn a group invariant representation without making assumptions on the target domain. Our model performs competitively on the Compositional Freebase Questions (CFQ) dataset, a challenging benchmark for compositional generalization in question answering. Especially, our model effectively solves the MCD1 split with 98% accuracy. All source is available athttps://github.com/gaiyu0/cfq.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_158", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_158.flac", "type": "audio", "duration": 250.056}, {"from": "Assistant", "value": "Considering the importance of building a good Visual Dialog (VD) Questioner, many researchers study the topic under a Q-Bot-A-Bot image-guessing game setting, where the Questioner needs to raise a series of questions to collect information of an undisclosed image. Despite progress has been made in Supervised Learning (SL) and Reinforcement Learning (RL), issues still exist. Firstly, previous methods do not provide explicit and effective guidance for Questioner to generate visually related and informative questions. Secondly, the effect of RL is hampered by an incompetent component, i.e., the Guesser, who makes image predictions based on the generated dialogs and assigns rewards accordingly. To enhance VD Questioner: 1) we propose a Related entity enhanced Questioner (ReeQ) that generates questions under the guidance of related entities and learns entity-based questioning strategy from human dialogs; 2) we propose an Augmented Guesser that is strong and is optimized for VD especially. Experimental results on the VisDial v1.0 dataset show that our approach achieves state-of-the-art performance on both image-guessing task and question diversity. Human study further verifies that our model generates more visually related, informative and coherent questions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_159", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_159.flac", "type": "audio", "duration": 303.04}, {"from": "Assistant", "value": "Knowledge Base Question Answering (KBQA) is to answer natural language questions posed over knowledge bases (KBs). This paper targets at empowering the IR-based KBQA models with the ability of numerical reasoning for answering ordinal constrained questions. A major challenge is the lack of explicit annotations about numerical properties. To address this challenge, we propose a pretraining numerical reasoning model consisting of NumGNN and NumTransformer, guided by explicit self-supervision signals. The two modules are pretrained to encode the magnitude and ordinal properties of numbers respectively and can serve as model-agnostic plugins for any IR-based KBQA model to enhance its numerical reasoning ability. Extensive experiments on two KBQA benchmarks verify the effectiveness of our method to enhance the numerical reasoning ability for IR-based KBQA models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_160", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_160.flac", "type": "audio", "duration": 330.048}, {"from": "Assistant", "value": "Transformer-based pre-trained models, such as BERT, have achieved remarkable results on machine reading comprehension. However, due to the constraint of encoding length (e.g., 512 WordPiece tokens), a long document is usually split into multiple chunks that are independently read. It results in the reading field being limited to individual chunks without information collaboration for long document machine reading comprehension. To address this problem, we propose RoR, a read-over-read method, which expands the reading field from chunk to document. Specifically, RoR includes a chunk reader and a document reader. The former first predicts a set of regional answers for each chunk, which are then compacted into a highly-condensed version of the original document, guaranteeing to be encoded once. The latter further predicts the global answers from this condensed document. Eventually, a voting strategy is utilized to aggregate and rerank the regional and global answers for final prediction. Extensive experiments on two benchmarks QuAC and TriviaQA demonstrate the effectiveness of RoR for long document reading. Notably, RoR ranks 1st place on the QuAC leaderboard (https://quac.ai/) at the time of submission (May 17th, 2021).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_161", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_161.flac", "type": "audio", "duration": 327.615}, {"from": "Assistant", "value": "An effective recipe for building seq2seq, non-autoregressive, task-oriented parsers to map utterances to semantic frames proceeds in three steps: encoding an utterance x, predicting a frame’s length |y|, and decoding a |y|-sized frame with utterance and ontology tokens. Though empirically strong, these models are typically bottlenecked by length prediction, as even small inaccuracies change the syntactic and semantic characteristics of resulting frames. In our work, we propose span pointer networks, non-autoregressive parsers which shift the decoding task from text generation to span prediction; that is, when imputing utterance spans into frame slots, our model produces endpoints (e.g., [i, j]) as opposed to text (e.g., “6pm”). This natural quantization of the output space reduces the variability of gold frames, therefore improving length prediction and, ultimately, exact match. Furthermore, length prediction is now responsible for frame syntax and the decoder is responsible for frame semantics, resulting in a coarse-to-fine model. We evaluate our approach on several task-oriented semantic parsing datasets. Notably, we bridge the quality gap between non-autogressive and autoregressive parsers, achieving 87 EM on TOPv2 (Chen et al. 2020). Furthermore, due to our more consistent gold frames, we show strong improvements in model generalization in both cross-domain and cross-lingual transfer in low-resource settings. Finally, due to our diminished output vocabulary, we observe 70% reduction in latency and 83% reduction in memory at beam size 5 compared to prior non-autoregressive parsers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_106", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_106.flac", "type": "audio", "duration": 689.77}, {"from": "Assistant", "value": "Performing event and entity coreference resolution across documents vastly increases the number of candidate mentions, making it intractable to do the fulln2pairwise comparisons. Existing approaches simplify by considering coreference only within document clusters, but this fails to handle inter-cluster coreference, common in many applications. As a result cross-document coreference algorithms are rarely applied to downstream tasks. We draw on an insight from discourse coherence theory: potential coreferences are constrained by the reader’s discourse focus. We model the entities/events in a reader’s focus as a neighborhood within a learned latent embedding space which minimizes the distance between mentions and the centroids of their gold coreference clusters. We then use these neighborhoods to sample only hard negatives to train a fine-grained classifier on mention pairs and their local discourse features. Our approach achieves state-of-the-art results for both events and entities on the ECB+, Gun Violence, Football Coreference, and Cross-Domain Cross-Document Coreference corpora. Furthermore, training on multiple corpora improves average performance across all datasets by 17.2 F1 points, leading to a robust coreference resolution model that is now feasible to apply to downstream tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_107", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_107.flac", "type": "audio", "duration": 730.059}, {"from": "Assistant", "value": "Storytelling, whether via fables, news reports, documentaries, or memoirs, can be thought of as the communication of interesting and related events that, taken together, form a concrete process. It is desirable to extract the event chains that represent such processes. However, this extraction remains a challenging problem. We posit that this is due to the nature of the texts from which chains are discovered. Natural language text interleaves a narrative of concrete, salient events with background information, contextualization, opinion, and other elements that are important for a variety of necessary discourse and pragmatics acts but are not part of the principal chain of events being communicated. We introduce methods for extracting this principal chain from natural language text, by filtering away non-salient events and supportive sentences. We demonstrate the effectiveness of our methods at isolating critical event chains by comparing their effect on downstream tasks. We show that by pre-training large language models on our extracted chains, we obtain improvements in two tasks that benefit from a clear understanding of event chains: narrative prediction and event-based temporal question answering. The demonstrated improvements and ablative studies confirm that our extraction method isolates critical event chains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_108", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_108.flac", "type": "audio", "duration": 753.766}, {"from": "Assistant", "value": "Asking questions about a situation is an inherent step towards understanding it. To this end, we introduce the task of role question generation, which, given a predicate mention and a passage, requires producing a set of questions asking about all possible semantic roles of the predicate. We develop a two-stage model for this task, which first produces a context-independent question prototype for each role and then revises it to be contextually appropriate for the passage. Unlike most existing approaches to question generation, our approach does not require conditioning on existing answers in the text. Instead, we condition on the type of information to inquire about, regardless of whether the answer appears explicitly in the text, could be inferred from it, or should be sought elsewhere. Our evaluation demonstrates that we generate diverse and well-formed questions for a large, broad-coverage ontology of predicates and roles.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_109", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_109.flac", "type": "audio", "duration": 702.699}, {"from": "Assistant", "value": "Previous work has indicated that pretrained Masked Language Models (MLMs) are not effective as universal lexical and sentence encoders off-the-shelf, i.e., without further task-specific fine-tuning on NLI, sentence similarity, or paraphrasing tasks using annotated task data. In this work, we demonstrate that it is possible to turn MLMs into effective lexical and sentence encoders even without any additional data, relying simply on self-supervision. We propose an extremely simple, fast, and effective contrastive learning technique, termed Mirror-BERT, which converts MLMs (e.g., BERT and RoBERTa) into such encoders in 20-30 seconds with no access to additional external knowledge. Mirror-BERT relies on identical and slightly modified string pairs as positive (i.e., synonymous) fine-tuning examples, and aims to maximise their similarity during “identity fine-tuning”. We report huge gains over off-the-shelf MLMs with Mirror-BERT both in lexical-level and in sentence-level tasks, across different domains and different languages. Notably, in sentence similarity (STS) and question-answer entailment (QNLI) tasks, our self-supervised Mirror-BERT model even matches the performance of the Sentence-BERT models from prior work which rely on annotated task data. Finally, we delve deeper into the inner workings of MLMs, and suggest some evidence on why this simple Mirror-BERT fine-tuning approach can yield effective universal lexical and sentence encoders.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_110", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_110.flac", "type": "audio", "duration": 713.259}, {"from": "Assistant", "value": "While pre-trained language models (PLMs) are the go-to solution to tackle many natural language processing problems, they are still very limited in their ability to capture and to use common-sense knowledge. In fact, even if information is available in the form of approximate (soft) logical rules, it is not clear how to transfer it to a PLM in order to improve its performance for deductive reasoning tasks. Here, we aim to bridge this gap by teaching PLMs how to reason with soft Horn rules. We introduce a classification task where, given facts and soft rules, the PLM should return a prediction with a probability for a given hypothesis. We release the first dataset for this task, and we propose a revised loss function that enables the PLM to learn how to predict precise probabilities for the task. Our evaluation results show that the resulting fine-tuned models achieve very high performance, even on logical rules that were unseen at training. Moreover, we demonstrate that logical notions expressed by the rules are transferred to the fine-tuned model, yielding state-of-the-art results on external datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_111", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_111.flac", "type": "audio", "duration": 761.057}, {"from": "Assistant", "value": "In this paper, we investigate what types of stereotypical information are captured by pretrained language models. We present the first dataset comprising stereotypical attributes of a range of social groups and propose a method to elicit stereotypes encoded by pretrained language models in an unsupervised fashion. Moreover, we link the emergent stereotypes to their manifestation as basic emotions as a means to study their emotional effects in a more generalized manner. To demonstrate how our methods can be used to analyze emotion and stereotype shifts due to linguistic experience, we use fine-tuning on news sources as a case study. Our experiments expose how attitudes towards different social groups vary across models and how quickly emotions and stereotypes can shift at the fine-tuning stage.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_112", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_112.flac", "type": "audio", "duration": 712.063}, {"from": "Assistant", "value": "Supervised systems have nowadays become the standard recipe for Word Sense Disambiguation (WSD), with Transformer-based language models as their primary ingredient. However, while these systems have certainly attained unprecedented performances, virtually all of them operate under the constraining assumption that, given a context, each word can be disambiguated individually with no account of the other sense choices. To address this limitation and drop this assumption, we propose CONtinuous SEnse Comprehension (ConSeC), a novel approach to WSD: leveraging a recent re-framing of this task as a text extraction problem, we adapt it to our formulation and introduce a feedback loop strategy that allows the disambiguation of a target word to be conditioned not only on its context but also on the explicit senses assigned to nearby words. We evaluate ConSeC and examine how its components lead it to surpass all its competitors and set a new state of the art on English WSD. We also explore how ConSeC fares in the cross-lingual setting, focusing on 8 languages with various degrees of resource availability, and report significant improvements over prior systems. We release our code athttps://github.com/SapienzaNLP/consec.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_113", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_113.flac", "type": "audio", "duration": 679.044}, {"from": "Assistant", "value": "Commonsense is a quintessential human capacity that has been a core challenge to Artificial Intelligence since its inception. Impressive results in Natural Language Processing tasks, including in commonsense reasoning, have consistently been achieved with Transformer neural language models, even matching or surpassing human performance in some benchmarks. Recently, some of these advances have been called into question: so called data artifacts in the training data have been made evident as spurious correlations and shallow shortcuts that in some cases are leveraging these outstanding results. In this paper we seek to further pursue this analysis into the realm of commonsense related language processing tasks. We undertake a study on different prominent benchmarks that involve commonsense reasoning, along a number of key stress experiments, thus seeking to gain insight on whether the models are learning transferable generalizations intrinsic to the problem at stake or just taking advantage of incidental shortcuts in the data items. The results obtained indicate that most datasets experimented with are problematic, with models resorting to non-robust features and appearing not to be learning and generalizing towards the overall tasks intended to be conveyed or exemplified by the datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_114", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_114.flac", "type": "audio", "duration": 487.967}, {"from": "Assistant", "value": "Differential privacy provides a formal approach to privacy of individuals. Applications of differential privacy in various scenarios, such as protecting users’ original utterances, must satisfy certain mathematical properties. Our contribution is a formal analysis of ADePT, a differentially private auto-encoder for text rewriting (Krishna et al, 2021). ADePT achieves promising results on downstream tasks while providing tight privacy guarantees. Our proof reveals that ADePT is not differentially private, thus rendering the experimental results unsubstantiated. We also quantify the impact of the error in its private mechanism, showing that the true sensitivity is higher by at least factor 6 in an optimistic case of a very small encoder’s dimension and that the amount of utterances that are not privatized could easily reach 100% of the entire dataset. Our intention is neither to criticize the authors, nor the peer-reviewing process, but rather point out that if differential privacy applications in NLP rely on formal guarantees, these should be outlined in full and put under detailed scrutiny.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_115", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_115.flac", "type": "audio", "duration": 774.13}, {"from": "Assistant", "value": "Discrete adversarial attacks are symbolic perturbations to a language input that preserve the output label but lead to a prediction error. While such attacks have been extensively explored for the purpose of evaluating model robustness, their utility for improving robustness has been limited to offline augmentation only. Concretely, given a trained model, attacks are used to generate perturbed (adversarial) examples, and the model is re-trained exactly once. In this work, we address this gap and leverage discrete attacks for online augmentation, where adversarial examples are generated at every training step, adapting to the changing nature of the model. We propose (i) a new discrete attack, based on best-first search, and (ii) random sampling attacks that unlike prior work are not based on expensive search-based procedures. Surprisingly, we find that random sampling leads to impressive gains in robustness, outperforming the commonly-used offline augmentation, while leading to a speedup at training time of ~10x. Furthermore, online augmentation with search-based attacks justifies the higher training cost, significantly improving robustness on three datasets. Last, we show that our new attack substantially improves robustness compared to prior methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_116", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_116.flac", "type": "audio", "duration": 391.04}, {"from": "Assistant", "value": "Model robustness to bias is often determined by the generalization on carefully designed out-of-distribution datasets. Recent debiasing methods in natural language understanding (NLU) improve performance on such datasets by pressuring models into making unbiased predictions. An underlying assumption behind such methods is that this also leads to the discovery of more robust features in the model’s inner representations. We propose a general probing-based framework that allows for post-hoc interpretation of biases in language models, and use an information-theoretic approach to measure the extractability of certain biases from the model’s representations. We experiment with several NLU datasets and known biases, and show that, counter-intuitively, the more a language model is pushed towards a debiased regime, the more bias is actually encoded in its inner representations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_117", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_117.flac", "type": "audio", "duration": 721.045}, {"from": "Assistant", "value": "High-performance neural language models have obtained state-of-the-art results on a wide range of Natural Language Processing (NLP) tasks. However, results for common benchmark datasets often do not reflect model reliability and robustness when applied to noisy, real-world data. In this study, we design and implement various types of character-level and word-level perturbation methods to simulate realistic scenarios in which input texts may be slightly noisy or different from the data distribution on which NLP systems were trained. Conducting comprehensive experiments on different NLP tasks, we investigate the ability of high-performance language models such as BERT, XLNet, RoBERTa, and ELMo in handling different types of input perturbations. The results suggest that language models are sensitive to input perturbations and their performance can decrease even when small changes are introduced. We highlight that models need to be further improved and that current benchmarks are not reflecting model robustness well. We argue that evaluations on perturbed inputs should routinely complement widely-used benchmarks in order to yield a more realistic understanding of NLP systems’ robustness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_118", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_118.flac", "type": "audio", "duration": 744.0}, {"from": "Assistant", "value": "Transformers-based pretrained language models achieve outstanding results in many well-known NLU benchmarks. However, while pretraining methods are very convenient, they are expensive in terms of time and resources. This calls for a study of the impact of pretraining data size on the knowledge of the models. We explore this impact on the syntactic capabilities of RoBERTa, using models trained on incremental sizes of raw text data. First, we use syntactic structural probes to determine whether models pretrained on more data encode a higher amount of syntactic information. Second, we perform a targeted syntactic evaluation to analyze the impact of pretraining data size on the syntactic generalization performance of the models. Third, we compare the performance of the different models on three downstream applications: part-of-speech tagging, dependency parsing and paraphrase identification. We complement our study with an analysis of the cost-benefit trade-off of training such models. Our experiments show that while models pretrained on more data encode more syntactic knowledge and perform better on downstream applications, they do not always offer a better performance across the different syntactic phenomena and come at a higher financial and environmental cost.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_119", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_119.flac", "type": "audio", "duration": 710.043}, {"from": "Assistant", "value": "Pre-trained LMs have shown impressive performance on downstream NLP tasks, but we have yet to establish a clear understanding of their sophistication when it comes to processing, retaining, and applying information presented in their input. In this paper we tackle a component of this question by examining robustness of models’ ability to deploy relevant context information in the face of distracting content. We present models with cloze tasks requiring use of critical context information, and introduce distracting content to test how robustly the models retain and use that critical information for prediction. We also systematically manipulate the nature of these distractors, to shed light on dynamics of models’ use of contextual cues. We find that although models appear in simple contexts to make predictions based on understanding and applying relevant facts from prior context, the presence of distracting but irrelevant content has clear impact in confusing model predictions. In particular, models appear particularly susceptible to factors of semantic similarity and word position. The findings are consistent with the conclusion that LM predictions are driven in large part by superficial contextual cues, rather than by robust representations of context meaning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_120", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_120.flac", "type": "audio", "duration": 720.448}, {"from": "Assistant", "value": "Contrastive explanations clarify why an event occurred in contrast to another. They are inherently intuitive to humans to both produce and comprehend. We propose a method to produce contrastive explanations in the latent space, via a projection of the input representation, such that only the features that differentiate two potential decisions are captured. Our modification allows model behavior to consider only contrastive reasoning, and uncover which aspects of the input are useful for and against particular decisions. Our contrastive explanations can additionally answer for which label, and against which alternative label, is a given input feature useful. We produce contrastive explanations via both high-level abstract concept attribution and low-level input token/span attribution for two NLP classification benchmarks. Our findings demonstrate the ability of label-contrastive explanations to provide fine-grained interpretability of model decisions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_121", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_121.flac", "type": "audio", "duration": 717.055}, {"from": "Assistant", "value": "Deep neural networks are vulnerable to adversarial attacks, where a small perturbation to an input alters the model prediction. In many cases, malicious inputs intentionally crafted for one model can fool another model. In this paper, we present the first study to systematically investigate the transferability of adversarial examples for text classification models and explore how various factors, including network architecture, tokenization scheme, word embedding, and model capacity, affect the transferability of adversarial examples. Based on these studies, we propose a genetic algorithm to find an ensemble of models that can be used to induce adversarial examples to fool almost all existing models. Such adversarial examples reflect the defects of the learning process and the data bias in the training set. Finally, we derive word replacement rules that can be used for model diagnostics from these adversarial examples.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_122", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_122.flac", "type": "audio", "duration": 496.043}, {"from": "Assistant", "value": "Probing experiments investigate the extent to which neural representations make properties—like part-of-speech—predictable. One suggests that a representation encodes a property if probing that representation produces higher accuracy than probing a baseline representation like non-contextual word embeddings. Instead of using baselines as a point of comparison, we’re interested in measuring information that is contained in the representation but not in the baseline. For example, current methods can detect when a representation is more useful than the word identity (a baseline) for predicting part-of-speech; however, they cannot detect when the representation is predictive of just the aspects of part-of-speech not explainable by the word identity. In this work, we extend a theory of usable information called V-information and propose conditional probing, which explicitly conditions on the information in the baseline. In a case study, we find that after conditioning on non-contextual word embeddings, properties like part-of-speech are accessible at deeper layers of a network than previously thought.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_123", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_123.flac", "type": "audio", "duration": 717.102}, {"from": "Assistant", "value": "Targeted evaluations have found that machine translation systems often output incorrect gender in translations, even when the gender is clear from context. Furthermore, these incorrectly gendered translations have the potential to reflect or amplify social biases. We propose gender-filtered self-training (GFST) to improve gender translation accuracy on unambiguously gendered inputs. Our GFST approach uses a source monolingual corpus and an initial model to generate gender-specific pseudo-parallel corpora which are then filtered and added to the training data. We evaluate GFST on translation from English into five languages, finding that it improves gender accuracy without damaging generic quality. We also show the viability of GFST on several experimental settings, including re-training from scratch, fine-tuning, controlling the gender balance of the data, forward translation, and back-translation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_124", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_124.flac", "type": "audio", "duration": 721.862}, {"from": "Assistant", "value": "We present a simple but effective approach for leveraging Wikipedia for neural machine translation as well as cross-lingual tasks of image captioning and dependency parsing without using any direct supervision from external parallel data or supervised models in the target language. We show that first sentences and titles of linked Wikipedia pages, as well as cross-lingual image captions, are strong signals for a seed parallel data to extract bilingual dictionaries and cross-lingual word embeddings for mining parallel text from Wikipedia. Our final model achieves high BLEU scores that are close to or sometimes higher than strongsupervisedbaselines in low-resource languages; e.g. supervised BLEU of 4.0 versus 12.1 from our model in English-to-Kazakh. Moreover, we tailor ourwikilytranslation models to unsupervised image captioning, and cross-lingual dependency parser transfer. In image captioning, we train a multi-tasking machine translation and image captioning pipeline for Arabic and English from which the Arabic training data is awikilytranslation of the English captioning data. Our captioning results on Arabic are slightlybetterthan that of its supervised model. In dependency parsing, we translate a large amount of monolingual text, and use it as an artificial training data in anannotation projectionframework. We show that our model outperforms recent work on cross-lingual transfer of dependency parsers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_125", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_125.flac", "type": "audio", "duration": 619.415}, {"from": "Assistant", "value": "Multilingual T5 pretrains a sequence-to-sequence model on massive monolingual texts, which has shown promising results on many cross-lingual tasks. In this paper, we improve multilingual text-to-text transfer Transformer with translation pairs (mT6). Specifically, we explore three cross-lingual text-to-text pre-training tasks, namely, machine translation, translation pair span corruption, and translation span corruption. In addition, we propose a partially non-autoregressive objective for text-to-text pre-training. We evaluate the methods on seven multilingual benchmark datasets, including sentence classification, named entity recognition, question answering, and abstractive summarization. Experimental results show that the proposed mT6 improves cross-lingual transferability over mT5.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_126", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_126.flac", "type": "audio", "duration": 703.04}, {"from": "Assistant", "value": "Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potential for zero-shot cross-lingual transfer. However, these multilingual encoders do not precisely align words and phrases across languages. Especially, learning alignments in the multilingual embedding space usually requires sentence-level or word-level parallel corpora, which are expensive to be obtained for low-resource languages. An alternative is to make the multilingual encoders more robust; when fine-tuning the encoder using downstream task, we train the encoder to tolerate noise in the contextual embedding spaces such that even if the representations of different languages are not aligned well, the model can still achieve good performance on zero-shot cross-lingual transfer. In this work, we propose a learning strategy for training robust models by drawing connections between adversarial examples and the failure cases of zero-shot cross-lingual transfer. We adopt two widely used robust training methods, adversarial training and randomized smoothing, to train the desired robust model. The experimental results demonstrate that robust training improves zero-shot cross-lingual transfer on text classification tasks. The improvement is more significant in the generalized cross-lingual transfer setting, where the pair of input sentences belong to two different languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_127", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_127.flac", "type": "audio", "duration": 459.051}, {"from": "Assistant", "value": "Transformer-based models have gained increasing popularity achieving state-of-the-art performance in many research fields including speech translation. However, Transformer’s quadratic complexity with respect to the input sequence length prevents its adoption as is with audio signals, which are typically represented by long sequences. Current solutions resort to an initial sub-optimal compression based on a fixed sampling of raw audio features. Therefore, potentially useful linguistic information is not accessible to higher-level layers in the architecture. To solve this issue, we propose Speechformer, an architecture that, thanks to reduced memory usage in the attention layers, avoids the initial lossy compression and aggregates information only at a higher level according to more informed linguistic criteria. Experiments on three language pairs (en→de/es/nl) show the efficacy of our solution, with gains of up to 0.8 BLEU on the standard MuST-C corpus and of up to 4.0 BLEU in a low resource scenario.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_128", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_128.flac", "type": "audio", "duration": 465.045}, {"from": "Assistant", "value": "Automatic translation systems are known to struggle with rare words. Among these, named entities (NEs) and domain-specific terms are crucial, since errors in their translation can lead to severe meaning distortions. Despite their importance, previous speech translation (ST) studies have neglected them, also due to the dearth of publicly available resources tailored to their specific evaluation. To fill this gap, we i) present the first systematic analysis of the behavior of state-of-the-art ST systems in translating NEs and terminology, and ii) release NEuRoparl-ST, a novel benchmark built from European Parliament speeches annotated with NEs and terminology. Our experiments on the three language directions covered by our benchmark (en→es/fr/it) show that ST systems correctly translate 75–80% of terms and 65–70% of NEs, with very low performance (37–40%) on person names.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_129", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_129.flac", "type": "audio", "duration": 719.052}, {"from": "Assistant", "value": "Back-translation (BT) of target monolingual corpora is a widely used data augmentation strategy for neural machine translation (NMT), especially for low-resource language pairs. To improve effectiveness of the available BT data, we introduce HintedBT—a family of techniques which provides hints (through tags) to the encoder and decoder. First, we propose a novel method of using both high and low quality BT data by providing hints (as source tags on the encoder) to the model about the quality of each source-target pair. We don’t filter out low quality data but instead show that these hints enable the model to learn effectively from noisy data. Second, we address the problem of predicting whether a source token needs to be translated or transliterated to the target language, which is common in cross-script translation tasks (i.e., where source and target do not share the written script). For such cases, we propose training the model with additional hints (as target tags on the decoder) that provide information about the operation required on the source (translation or both translation and transliteration). We conduct experiments and detailed analyses on standard WMT benchmarks for three cross-script low/medium-resource language pairs: Hindi,Gujarati,Tamil-to-English. Our methods compare favorably with five strong and well established baselines. We show that using these hints, both separately and together, significantly improves translation quality and leads to state-of-the-art performance in all three language pairs in corresponding bilingual settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_130", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_130.flac", "type": "audio", "duration": 630.059}, {"from": "Assistant", "value": "In simultaneous machine translation, finding an agent with the optimal action sequence of reads and writes that maintain a high level of translation quality while minimizing the average lag in producing target tokens remains an extremely challenging problem. We propose a novel supervised learning approach for training an agent that can detect the minimum number of reads required for generating each target token by comparing simultaneous translations against full-sentence translations during training to generate oracle action sequences. These oracle sequences can then be used to train a supervised model for action generation at inference time. Our approach provides an alternative to current heuristic methods in simultaneous translation by introducing a new training objective, which is easier to train than previous attempts at training the agent using reinforcement learning techniques for this task. Our experimental results show that our novel training method for action generation produces much higher quality translations while minimizing the average lag in simultaneous translation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_131", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_131.flac", "type": "audio", "duration": 455.979}, {"from": "Assistant", "value": "Even though large pre-trained multilingual models (e.g. mBERT, XLM-R) have led to significant performance gains on a wide range of cross-lingual NLP tasks, success on many downstream tasks still relies on the availability of sufficient annotated data. Traditional fine-tuning of pre-trained models using only a few target samples can cause over-fitting. This can be quite limiting as most languages in the world are under-resourced. In this work, we investigate cross-lingual adaptation using a simple nearest-neighbor few-shot (<15samples) inference technique for classification tasks. We experiment using a total of 16 distinct languages across two NLP tasks- XNLI and PAWS-X. Our approach consistently improves traditional fine-tuning using only a handful of labeled samples in target locales. We also demonstrate its generalization capability across tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_132", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_132.flac", "type": "audio", "duration": 628.053}, {"from": "Assistant", "value": "We study the power of cross-attention in the Transformer architecture within the context of transfer learning for machine translation, and extend the findings of studies into cross-attention when training from scratch. We conduct a series of experiments through fine-tuning a translation model on data where either the source or target language has changed. These experiments reveal that fine-tuning only the cross-attention parameters is nearly as effective as fine-tuning all parameters (i.e., the entire translation model). We provide insights into why this is the case and observe that limiting fine-tuning in this manner yields cross-lingually aligned embeddings. The implications of this finding for researchers and practitioners include a mitigation of catastrophic forgetting, the potential for zero-shot translation, and the ability to extend machine translation models to several new language pairs with reduced parameter storage overhead.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_133", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_133.flac", "type": "audio", "duration": 638.336}, {"from": "Assistant", "value": "The capacity of neural networks like the widely adopted transformer is known to be very high. Evidence is emerging that they learn successfully due to inductive bias in the training routine, typically a variant of gradient descent (GD). To better understand this bias, we study the tendency for transformer parameters to grow in magnitude (ℓ2norm) during training, and its implications for the emergent representations within self attention layers. Empirically, we document norm growth in the training of transformer language models, including T5 during its pretraining. As the parameters grow in magnitude, we prove that the network approximates a discretized network with saturated activation functions. Such “saturated” networks are known to have a reduced capacity compared to the full network family that can be described in terms of formal languages and automata. Our results suggest saturation is a new characterization of an inductive bias implicit in GD of particular interest for NLP. We leverage the emergent discrete structure in a saturated transformer to analyze the role of different attention heads, finding that some focus locally on a small number of positions, while other heads compute global averages, allowing counting. We believe understanding the interplay between these two capabilities may shed further light on the structure of computation within large transformers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_134", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_134.flac", "type": "audio", "duration": 717.055}, {"from": "Assistant", "value": "Real-world applications often require improved models by leveraging *a range of cheap incidental supervision signals*. These could include partial labels, noisy labels, knowledge-based constraints, and cross-domain or cross-task annotations – all having statistical associations with gold annotations but not exactly the same. However, we currently lack a principled way to measure the benefits of these signals to a given target task, and the common practice of evaluating these benefits is through exhaustive experiments with various models and hyperparameters. This paper studies whether we can, *in a single framework, quantify the benefits of various types of incidental signals for a given target task without going through combinatorial experiments*. We propose a unified PAC-Bayesian motivated informativeness measure, PABI, that characterizes the uncertainty reduction provided by incidental supervision signals. We demonstrate PABI’s effectiveness by quantifying the value added by various types of incidental signals to sequence tagging tasks. Experiments on named entity recognition (NER) and question answering (QA) show that PABI’s predictions correlate well with learning performance, providing a promising way to determine, ahead of learning, which supervision signals would be beneficial.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_135", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_135.flac", "type": "audio", "duration": 1145.899}, {"from": "Assistant", "value": "Much recent work in NLP has documented dataset artifacts, bias, and spurious correlations between input features and output labels. However, how to tell which features have “spurious” instead of legitimate correlations is typically left unspecified. In this work we argue that for complex language understanding tasks, all simple feature correlations are spurious, and we formalize this notion into a class of problems which we call competency problems. For example, the word “amazing” on its own should not give information about a sentiment label independent of the context in which it appears, which could include negation, metaphor, sarcasm, etc. We theoretically analyze the difficulty of creating data for competency problems when human bias is taken into account, showing that realistic datasets will increasingly deviate from competency problems as dataset size increases. This analysis gives us a simple statistical test for dataset artifacts, which we use to show more subtle biases than were described in prior work, including demonstrating that models are inappropriately affected by these less extreme biases. Our theoretical treatment of this problem also allows us to analyze proposed solutions, such as making local edits to dataset instances, and to give recommendations for future data collection and model design efforts that target competency problems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_136", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_136.flac", "type": "audio", "duration": 506.009}, {"from": "Assistant", "value": "Meta-learning has achieved great success in leveraging the historical learned knowledge to facilitate the learning process of the new task. However, merely learning the knowledge from the historical tasks, adopted by current meta-learning algorithms, may not generalize well to testing tasks when they are not well-supported by training tasks. This paper studies a low-resource text classification problem and bridges the gap between meta-training and meta-testing tasks by leveraging the external knowledge bases. Specifically, we propose KGML to introduce additional representation for each sentence learned from the extracted sentence-specific knowledge graph. The extensive experiments on three datasets demonstrate the effectiveness of KGML under both supervised adaptation and unsupervised adaptation settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_137", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_137.flac", "type": "audio", "duration": 447.04}, {"from": "Assistant", "value": "Representation learning for text via pretraining a language model on a large corpus has become a standard starting point for building NLP systems. This approach stands in contrast to autoencoders, also trained on raw text, but with the objective of learning to encode each input as a vector that allows full reconstruction. Autoencoders are attractive because of their latent space structure and generative properties. We therefore explore the construction of a sentence-level autoencoder from a pretrained, frozen transformer language model. We adapt the masked language modeling objective as a generative, denoising one, while only training a sentence bottleneck and a single-layer modified transformer decoder. We demonstrate that the sentence representations discovered by our model achieve better quality than previous methods that extract representations from pretrained transformers on text similarity tasks, style transfer (an example of controlled generation), and single-sentence classification tasks in the GLUE benchmark, while using fewer parameters than large pretrained models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_138", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_138.flac", "type": "audio", "duration": 449.045}, {"from": "Assistant", "value": "We introduce EfficientCL, a memory-efficient continual pretraining method that applies contrastive learning with novel data augmentation and curriculum learning. For data augmentation, we stack two types of operation sequentially: cutoff and PCA jittering. While pretraining steps proceed, we apply curriculum learning by incrementing the augmentation degree for each difficulty step. After data augmentation is finished, contrastive learning is applied on projected embeddings of original and augmented examples. When finetuned on GLUE benchmark, our model outperforms baseline models, especially for sentence-level tasks. Additionally, this improvement is capable with only 70% of computational memory compared to the baseline model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_139", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_139.flac", "type": "audio", "duration": 713.062}, {"from": "Assistant", "value": "Growing interests have been attracted in Conversational Recommender Systems (CRS), which explore user preference through conversational interactions in order to make appropriate recommendation. However, there is still a lack of ability in existing CRS to (1) traverse multiple reasoning paths over background knowledge to introduce relevant items and attributes, and (2) arrange selected entities appropriately under current system intents to control response generation. To address these issues, we propose CR-Walker in this paper, a model that performs tree-structured reasoning on a knowledge graph, and generates informative dialog acts to guide language generation. The unique scheme of tree-structured reasoning views the traversed entity at each hop as part of dialog acts to facilitate language generation, which links how entities are selected and expressed. Automatic and human evaluations show that CR-Walker can arrive at more accurate recommendation, and generate more informative and engaging responses.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_140", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_140.flac", "type": "audio", "duration": 654.251}, {"from": "Assistant", "value": "Identifying relevant knowledge to be used in conversational systems that are grounded in long documents is critical to effective response generation. We introduce a knowledge identification model that leverages the document structure to provide dialogue-contextualized passage encodings and better locate knowledge relevant to the conversation. An auxiliary loss captures the history of dialogue-document connections. We demonstrate the effectiveness of our model on two document-grounded conversational datasets and provide analyses showing generalization to unseen documents and long dialogue contexts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_141", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_141.flac", "type": "audio", "duration": 719.04}, {"from": "Assistant", "value": "Communicating with humans is challenging for AIs because it requires a shared understanding of the world, complex semantics (e.g., metaphors or analogies), and at times multi-modal gestures (e.g., pointing with a finger, or an arrow in a diagram). We investigate these challenges in the context of Iconary, a collaborative game of drawing and guessing based on Pictionary, that poses a novel challenge for the research community. In Iconary, a Guesser tries to identify a phrase that a Drawer is drawing by composing icons, and the Drawer iteratively revises the drawing to help the Guesser in response. This back-and-forth often uses canonical scenes, visual metaphor, or icon compositions to express challenging words, making it an ideal test for mixing language and visual/symbolic communication in AI. We propose models to play Iconary and train them on over 55,000 games between human players. Our models are skillful players and are able to employ world knowledge in language models to play with words unseen during training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_142", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_142.flac", "type": "audio", "duration": 725.76}, {"from": "Assistant", "value": "As the labeling cost for different modules in task-oriented dialog (ToD) systems is expensive, a major challenge is to train different modules with the least amount of labeled data. Recently, large-scale pre-trained language models, have shown promising results for few-shot learning in ToD. In this paper, we devise a self-training approach to utilize the abundant unlabeled dialog data to further improve state-of-the-art pre-trained models in few-shot learning scenarios for ToD systems. Specifically, we propose a self-training approach that iteratively labels the most confident unlabeled data to train a stronger Student model. Moreover, a new text augmentation technique (GradAug) is proposed to better train the Student by replacing non-crucial tokens using a masked language model. We conduct extensive experiments and present analyses on four downstream tasks in ToD, including intent classification, dialog state tracking, dialog act prediction, and response selection. Empirical results demonstrate that the proposed self-training approach consistently improves state-of-the-art pre-trained models (BERT, ToD-BERT) when only a small number of labeled data are available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_143", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_143.flac", "type": "audio", "duration": 477.056}, {"from": "Assistant", "value": "For voice assistants like Alexa, Google Assistant, and Siri, correctly interpreting users’ intentions is of utmost importance. However, users sometimes experience friction with these assistants, caused by errors from different system components or user errors such as slips of the tongue. Users tend to rephrase their queries until they get a satisfactory response. Rephrase detection is used to identify the rephrases and has long been treated as a task with pairwise input, which does not fully utilize the contextual information (e.g. users’ implicit feedback). To this end, we propose a contextual rephrase detection model ContReph to automatically identify rephrases from multi-turn dialogues. We showcase how to leverage the dialogue context and user-agent interaction signals, including the user’s implicit feedback and the time gap between different turns, which can help significantly outperform the pairwise rephrase detection models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_144", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_144.flac", "type": "audio", "duration": 457.685}, {"from": "Assistant", "value": "In this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar. We present a simple yet effective few-shot intent detection schema via contrastive pre-training and fine-tuning. Specifically, we first conduct self-supervised contrastive pre-training on collected intent datasets, which implicitly learns to discriminate semantically similar utterances without using any labels. We then perform few-shot intent detection together with supervised contrastive learning, which explicitly pulls utterances from the same intent closer and pushes utterances across different intents farther. Experimental results show that our proposed method achieves state-of-the-art performance on three challenging intent detection datasets under 5-shot and 10-shot settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_146", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_146.flac", "type": "audio", "duration": 531.04}, {"from": "Assistant", "value": "Keyword or keyphrase extraction is to identify words or phrases presenting the main topics of a document. This paper proposes the AttentionRank, a hybrid attention model, to identify keyphrases from a document in an unsupervised manner. AttentionRank calculates self-attention and cross-attention using a pre-trained language model. The self-attention is designed to determine the importance of a candidate within the context of a sentence. The cross-attention is calculated to identify the semantic relevance between a candidate and sentences within a document. We evaluate the AttentionRank on three publicly available datasets against seven baselines. The results show that the AttentionRank is an effective and robust unsupervised keyphrase extraction model on both long and short documents. Source code is available on Github.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_147", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_147.flac", "type": "audio", "duration": 614.052}, {"from": "Assistant", "value": "Unsupervised relation extraction works by clustering entity pairs that have the same relations in the text. Some existing variational autoencoder (VAE)-based approaches train the relation extraction model as an encoder that generates relation classifications. A decoder is trained along with the encoder to reconstruct the encoder input based on the encoder-generated relation classifications. These classifications are a latent variable so they are required to follow a pre-defined prior distribution which results in unstable training. We propose a VAE-based unsupervised relation extraction technique that overcomes this limitation by using the classifications as an intermediate variable instead of a latent variable. Specifically, classifications are conditioned on sentence input, while the latent variable is conditioned on both the classifications and the sentence input. This allows our model to connect the decoder with the encoder without putting restrictions on the classification distribution; which improves training stability. Our approach is evaluated on the NYT dataset and outperforms state-of-the-art methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_148", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_148.flac", "type": "audio", "duration": 718.059}, {"from": "Assistant", "value": "Automatically inducing high quality knowledge graphs from a given collection of documents still remains a challenging problem in AI. One way to make headway for this problem is through advancements in a related task known as slot filling. In this task, given an entity query in form of [Entity, Slot, ?], a system is asked to ‘fill’ the slot by generating or extracting the missing value exploiting evidence extracted from relevant passage(s) in the given document collection. The recent works in the field try to solve this task in an end-to-end fashion using retrieval-based language models. In this paper, we present a novel approach to zero-shot slot filling that extends dense passage retrieval with hard negatives and robust training procedures for retrieval augmented generation models. Our model reports large improvements on both T-REx and zsRE slot filling datasets, improving both passage retrieval and slot value generation, and ranking at the top-1 position in the KILT leaderboard. Moreover, we demonstrate the robustness of our system showing its domain adaptation capability on a new variant of the TACRED dataset for slot filling, through a combination of zero/few-shot learning. We release the source code and pre-trained models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_149", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_149.flac", "type": "audio", "duration": 706.048}, {"from": "Assistant", "value": "Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English. While the advance of pretrained multilingual encoders suggests an easy optimism of “train on English, run on any language”, we find through a thorough exploration and extension of techniques that a combination of approaches, both new and old, leads to better performance than any one cross-lingual strategy in particular. We explore techniques including data projection and self-training, and how different pretrained encoders impact them. We use English-to-Arabic IE as our initial example, demonstrating strong performance in this setting for event extraction, named entity recognition, part-of-speech tagging, and dependency parsing. We then apply data projection and self-training to three tasks across eight target languages. Because no single set of techniques performs the best across all tasks, we encourage practitioners to explore various configurations of the techniques described in this work when seeking to improve on zero-shot training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_150", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_150.flac", "type": "audio", "duration": 719.819}, {"from": "Assistant", "value": "Gender is widely discussed in the context of language tasks and when examining the stereotypes propagated by language models. However, current discussions primarily treat gender as binary, which can perpetuate harms such as the cyclical erasure of non-binary gender identities. These harms are driven by model and dataset biases, which are consequences of the non-recognition and lack of understanding of non-binary genders in society. In this paper, we explain the complexity of gender and language around it, and survey non-binary persons to understand harms associated with the treatment of gender as binary in English language technologies. We also detail how current language representations (e.g., GloVe, BERT) capture and perpetuate these harms and related challenges that need to be acknowledged and addressed for representations to equitably encode gender information.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_151", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_151.flac", "type": "audio", "duration": 703.007}, {"from": "Assistant", "value": "Internet search affects people’s cognition of the world, so mitigating biases in search results and learning fair models is imperative for social good. We study a unique gender bias in image search in this work: the search images are often gender-imbalanced for gender-neutral natural language queries. We diagnose two typical image search models, the specialized model trained on in-domain datasets and the generalized representation model pre-trained on massive image and text data across the internet. Both models suffer from severe gender bias. Therefore, we introduce two novel debiasing approaches: an in-processing fair sampling method to address the gender imbalance issue for training models, and a post-processing feature clipping method base on mutual information to debias multimodal representations of pre-trained models. Extensive experiments on MS-COCO and Flickr30K benchmarks show that our methods significantly reduce the gender bias in image search models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_152", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_152.flac", "type": "audio", "duration": 713.456}, {"from": "Assistant", "value": "Text style can reveal sensitive attributes of the author (e.g. age and race) to the reader, which can, in turn, lead to privacy violations and bias in both human and algorithmic decisions based on text. For example, the style of writing in job applications might reveal protected attributes of the candidate which could lead to bias in hiring decisions, regardless of whether hiring decisions are made algorithmically or by humans. We propose a VAE-based framework that obfuscates stylistic features of human-generated text through style transfer, by automatically re-writing the text itself. Critically, our framework operationalizes the notion of obfuscated style in a flexible way that enables two distinct notions of obfuscated style: (1) a minimal notion that effectively intersects the various styles seen in training, and (2) a maximal notion that seeks to obfuscate by adding stylistic features of all sensitive attributes to text, in effect, computing a union of styles. Our style-obfuscation framework can be used for multiple purposes, however, we demonstrate its effectiveness in improving the fairness of downstream classifiers. We also conduct a comprehensive study on style-pooling’s effect on fluency, semantic consistency, and attribute removal from text, in two and three domain style transfer.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_153", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_153.flac", "type": "audio", "duration": 664.044}, {"from": "Assistant", "value": "Broader disclosive transparency—truth and clarity in communication regarding the function of AI systems—is widely considered desirable. Unfortunately, it is a nebulous concept, difficult to both define and quantify. This is problematic, as previous work has demonstrated possible trade-offs and negative consequences to disclosive transparency, such as a confusion effect, where “too much information” clouds a reader’s understanding of what a system description means. Disclosive transparency’s subjective nature has rendered deep study into these problems and their remedies difficult. To improve this state of affairs, We introduce neural language model-based probabilistic metrics to directly model disclosive transparency, and demonstrate that they correlate with user and expert opinions of system transparency, making them a valid objective proxy. Finally, we demonstrate the use of these metrics in a pilot study quantifying the relationships between transparency, confusion, and user perceptions in a corpus of real NLP system descriptions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_154", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_154.flac", "type": "audio", "duration": 411.341}, {"from": "Assistant", "value": "A private learning scheme TextHide was recently proposed to protect the private text data during the training phase via so-called instance encoding. We propose a novel reconstruction attack to break TextHide by recovering the private training data, and thus unveil the privacy risks of instance encoding. We have experimentally validated the effectiveness of the reconstruction attack with two commonly-used datasets for sentence classification. Our attack would advance the development of privacy preserving machine learning in the context of natural language processing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_155", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_155.flac", "type": "audio", "duration": 493.935}, {"from": "Assistant", "value": "Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the expense of minority groups. However there has traditionally been a disconnect between research on class-imbalanced learning and mitigating bias, and only recently have the two been looked at through a common lens. In this work we evaluate long-tail learning methods for tweet sentiment and occupation classification, and extend a margin-loss based approach with methods to enforce fairness. We empirically show through controlled experiments that the proposed approaches help mitigate both class imbalance and demographic biases.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_156", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_156.flac", "type": "audio", "duration": 520.847}, {"from": "Assistant", "value": "Homomorphic encryption (HE) and garbled circuit (GC) provide the protection for users’ privacy. However, simply mixing the HE and GC in RNN models suffer from long inference latency due to slow activation functions. In this paper, we present a novel hybrid structure of HE and GC gated recurrent unit (GRU) network, , for low-latency secure inferences. replaces computationally expensive GC-basedtanhwith fast GC-basedReLU, and then quantizessigmoidandReLUto smaller bit-length to accelerate activations in a GRU. We evaluate with multiple GRU models trained on 4 public datasets. Experimental results show achieves top-notch accuracy and improves the secure inference latency by up to138×over one of the state-of-the-art secure networks on the Penn Treebank dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_162", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_162.flac", "type": "audio", "duration": 356.053}, {"from": "Assistant", "value": "Due to complex cognitive and inferential efforts involved in the manual generation of one caption per image/video input, the human annotation resources are very limited for captioning tasks. We define language resource efficient as reaching the same performance with fewer annotated captions per input. We first study the performance degradation of caption models in different language resource settings. Our analysis of caption models with SC loss shows that the performance degradation is caused by the increasingly noisy estimation of reward and baseline with fewer language resources. To mitigate this issue, we propose to reduce the variance of noise in the baseline by generalizing the single pairwise comparison in SC loss and using multiple generalized pairwise comparisons. The generalized pairwise comparison (GPC) measures the difference between the evaluation scores of two captions with respect to an input. Empirically, we show that the model trained with the proposed GPC loss is efficient on language resource and achieves similar performance with the state-of-the-art models on MSCOCO by using only half of the language resources. Furthermore, our model significantly outperforms the state-of-the-art models on a video caption dataset that has only one labeled caption per input in the training set.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_164", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_164.flac", "type": "audio", "duration": 358.005}, {"from": "Assistant", "value": "Reviewing contracts is a time-consuming procedure that incurs large expenses to companies and social inequality to those who cannot afford it. In this work, we propose “document-level natural language inference (NLI) for contracts”, a novel, real-world application of NLI that addresses such problems. In this task, a system is given a set of hypotheses (such as “Some obligations of Agreement may survive termination.”) and a contract, and it is asked to classify whether each hypothesis is “entailed by”, “contradicting to” or “not mentioned by” (neutral to) the contract as well as identifying “evidence” for the decision as spans in the contract. We annotated and release the largest corpus to date consisting of 607 annotated contracts. We then show that existing models fail badly on our task and introduce a strong baseline, which (a) models evidence identification as multi-label classification over spans instead of trying to predict start and end tokens, and (b) employs more sophisticated context segmentation for dealing with long documents. We also show that linguistic characteristics of contracts, such as negations by exceptions, are contributing to the difficulty of this task and that there is much room for improvement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_165", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_165.flac", "type": "audio", "duration": 316.047}, {"from": "Assistant", "value": "Parallel texts of Japanese and a non-pro-drop language have the potential of improving the performance of Japanese zero anaphora resolution (ZAR) because pronouns dropped in the former are usually mentioned explicitly in the latter. However, rule-based cross-lingual transfer is hampered by error propagation in an NLP pipeline and the frequent lack of transparency in translation correspondences. In this paper, we propose implicit transfer by injecting machine translation (MT) as an intermediate task between pretraining and ZAR. We employ a pretrained BERT model to initialize the encoder part of the encoder-decoder model for MT, and eject the encoder part for fine-tuning on ZAR. The proposed framework empirically demonstrates that ZAR performance can be improved by transfer learning from MT. In addition, we find that the incorporation of the masked language model training into MT leads to further gains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_166", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_166.flac", "type": "audio", "duration": 378.24}, {"from": "Assistant", "value": "Content-planning is an essential part of data-to-text generation to determine the order of data mentioned in generated texts. Recent neural data-to-text generation models employ Pointer Networks to explicitly learn content-plan given a set of attributes as input. They use LSTM to encode the input, which assumes a sequential relationship in the input. This may be sub-optimal to encode a set of attributes, where the attributes have a composite structure: the attributes are disordered while each attribute value is an ordered list of tokens. We handle this problem by proposing a neural content-planner that can capture both local and global contexts of such a structure. Specifically, we propose a novel attention mechanism called GSC-attention. A key component of the GSC-attention is grouped-attention, which is token-level attention constrained within each input attribute that enables our proposed model captures both local and global context. Moreover, our content-planner explicitly learns content-selection, which is integrated into the content-planner to select the most important data to be included in the generated text via an attention masking procedure. Experimental results show that our model outperforms the competitors by 4.92%, 4.70%, and 16.56% in terms of Damerau-Levenshtein Distance scores on three real-world datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_167", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_167.flac", "type": "audio", "duration": 310.059}, {"from": "Assistant", "value": "Intent classification (IC) and slot filling (SF) are critical building blocks in task-oriented dialogue systems. These two tasks are closely-related and can flourish each other. Since only a few utterances can be utilized for identifying fast-emerging new intents and slots, data scarcity issue often occurs when implementing IC and SF. However, few IC/SF models perform well when the number of training samples per class is quite small. In this paper, we propose a novel explicit-joint and supervised-contrastive learning framework for few-shot intent classification and slot filling. Its highlights are as follows. (i) The model extracts intent and slot representations via bidirectional interactions, and extends prototypical network to achieve explicit-joint learning, which guarantees that IC and SF tasks can mutually reinforce each other. (ii) The model integrates with supervised contrastive learning, which ensures that samples from same class are pulled together and samples from different classes are pushed apart. In addition, the model follows a not common but practical way to construct the episode, which gets rid of the traditional setting with fixed way and shot, and allows for unbalanced datasets. Extensive experiments on three public datasets show that our model can achieve promising performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_168", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_168.flac", "type": "audio", "duration": 356.288}, {"from": "Assistant", "value": "Obtaining affective response is a key step in building empathetic dialogue systems. This task has been studied a lot in generation-based chatbots, but the related research in retrieval-based chatbots is still in the early stage. Existing works in retrieval-based chatbots are based on Retrieve-and-Rerank framework, which have a common problem of satisfying affect label at the expense of response quality. To address this problem, we propose a simple and effective Retrieve-Discriminate-Rewrite framework. The framework replaces the reranking mechanism with a new discriminate-and-rewrite mechanism, which predicts the affect label of the retrieved high-quality response via discrimination module and further rewrites the affect unsatisfied response via rewriting module. This can not only guarantee the quality of the response, but also satisfy the given affect label. In addition, another challenge for this line of research is the lack of an off-the-shelf affective response dataset. To address this problem and test our proposed framework, we annotate a Sentimental Douban Conversation Corpus based on the original Douban Conversation Corpus. Experimental results show that our proposed framework is effective and outperforms competitive baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_169", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_169.flac", "type": "audio", "duration": 438.933}, {"from": "Assistant", "value": "Pre-trained language models (PrLM) have to carefully manage input units when training on a very large text with a vocabulary consisting of millions of words. Previous works have shown that incorporating span-level information over consecutive words in pre-training could further improve the performance of PrLMs. However, given that span-level clues are introduced and fixed in pre-training, previous methods are time-consuming and lack of flexibility. To alleviate the inconvenience, this paper presents a novel span fine-tuning method for PrLMs, which facilitates the span setting to be adaptively determined by specific downstream tasks during the fine-tuning phase. In detail, any sentences processed by the PrLM will be segmented into multiple spans according to a pre-sampled dictionary. Then the segmentation information will be sent through a hierarchical CNN module together with the representation outputs of the PrLM and ultimately generate a span-enhanced representation. Experiments on GLUE benchmark show that the proposed span fine-tuning method significantly enhances the PrLM, and at the same time, offer more flexibility in an efficient way.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_170", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_170.flac", "type": "audio", "duration": 373.056}, {"from": "Assistant", "value": "We create a large-scale dialogue corpus that provides pragmatic paraphrases to advance technology for understanding the underlying intentions of users. While neural conversation models acquire the ability to generate fluent responses through training on a dialogue corpus, previous corpora have mainly focused on the literal meanings of utterances. However, in reality, people do not always present their intentions directly. For example, if a person said to the operator of a reservation service “I don’t have enough budget.”, they, in fact, mean “please find a cheaper option for me.” Our corpus provides a total of 71,498 indirect–direct utterance pairs accompanied by a multi-turn dialogue history extracted from the MultiWoZ dataset. In addition, we propose three tasks to benchmark the ability of models to recognize and generate indirect and direct utterances. We also investigated the performance of state-of-the-art pre-trained models as baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_171", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_171.flac", "type": "audio", "duration": 365.041}, {"from": "Assistant", "value": "Image captioning systems are expected to have the ability to combine individual concepts when describing scenes with concept combinations that are not observed during training. In spite of significant progress in image captioning with the help of the autoregressive generation framework, current approaches fail to generalize well to novel concept combinations. We propose a new framework that revolves around probing several similar image caption training instances (retrieval), performing analogical reasoning over relevant entities in retrieved prototypes (analogy), and enhancing the generation process with reasoning outcomes (composition). Our method augments the generation model by referring to the neighboring instances in the training set to produce novel concept combinations in generated captions. We perform experiments on the widely used image captioning benchmarks. The proposed models achieve substantial improvement over the compared baselines on both composition-related evaluation metrics and conventional image captioning metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_172", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_172.flac", "type": "audio", "duration": 357.053}, {"from": "Assistant", "value": "Recent progress in generative language models has enabled machines to generate astonishingly realistic texts. While there are many legitimate applications of such models, there is also a rising need to distinguish machine-generated texts from human-written ones (e.g., fake news detection). However, to our best knowledge, there is currently no benchmark environment with datasets and tasks to systematically study the so-called ”Turing Test” problem for neural text generation methods. In this work, we present the TURINGBENCH benchmark environment, which is comprised of (1) a dataset with 200K human- or machine-generated samples across 20 labels Human, GPT-1, GPT-2_small, GPT-2_medium, GPT-2_large,GPT-2_xl, GPT-2_PyTorch, GPT-3, GROVER_base, GROVER_large, GROVER_mega, CTRL, XLM, XLNET_base, XLNET_large, FAIR_wmt19, FAIR_wmt20, TRANSFORMER_XL, PPLM_distil, PPLM_gpt2, (2) two benchmark tasks–i.e., Turing Test (TT) and Authorship Attribution (AA), and (3) a website with leaderboards. Our preliminary experimental results using TURINGBENCH show that GPT-3 and FAIR_wmt20 are the current winners, among all language models tested, in generating the most human-like indistinguishable texts with the lowest F1 score by five state-of-the-art TT detection models. The TURINGBENCH is available at:https://turingbench.ist.psu.edu/", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_173", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_173.flac", "type": "audio", "duration": 350.041}, {"from": "Assistant", "value": "Workplace communication (e.g. email, chat, etc.) is a central part of enterprise productivity. Healthy conversations are crucial for creating an inclusive environment and maintaining harmony in an organization. Toxic communications at the workplace can negatively impact overall job satisfaction and are often subtle, hidden, or demonstrate human biases. The linguistic subtlety of mild yet hurtful conversations has made it difficult for researchers to quantify and extract toxic conversations automatically. While offensive language or hate speech has been extensively studied in social communities, there has been little work studying toxic communication in emails. Specifically, the lack of corpus, sparsity of toxicity in enterprise emails, and well-defined criteria for annotating toxic conversations have prevented researchers from addressing the problem at scale. We take the first step towards studying toxicity in workplace emails by providing (1) a general and computationally viable taxonomy to study toxic language at the workplace (2) a dataset to study toxic language at the workplace based on the taxonomy and (3) analysis on why offensive language and hate-speech datasets are not suitable to detect workplace toxicity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_174", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_174.flac", "type": "audio", "duration": 347.925}, {"from": "Assistant", "value": "Addressing the mismatch between natural language descriptions and the corresponding SQL queries is a key challenge for text-to-SQL translation. To bridge this gap, we propose an SQL intermediate representation (IR) called Natural SQL (NatSQL). Specifically, NatSQL preserves the core functionalities of SQL, while it simplifies the queries as follows: (1) dispensing with operators and keywords such as GROUP BY, HAVING, FROM, JOIN ON, which are usually hard to find counterparts in the text descriptions; (2) removing the need of nested subqueries and set operators; and (3) making the schema linking easier by reducing the required number of schema items. On Spider, a challenging text-to-SQL benchmark that contains complex and nested SQL queries, we demonstrate that NatSQL outperforms other IRs, and significantly improves the performance of several previous SOTA models. Furthermore, for existing models that do not support executable SQL generation, NatSQL easily enables them to generate executable SQL queries, and achieves the new state-of-the-art execution accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_175", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_175.flac", "type": "audio", "duration": 175.009}, {"from": "Assistant", "value": "This paper explores three simple data manipulation techniques (synthesis, augmentation, curriculum) for improving abstractive summarization models without the need for any additional data. We introduce a method of data synthesis with paraphrasing, a data augmentation technique with sample mixing, and curriculum learning with two new difficulty metrics based on specificity and abstractiveness. We conduct experiments to show that these three techniques can help improve abstractive summarization across two summarization models and two different small datasets. Furthermore, we show that these techniques can improve performance when applied in isolation and when combined.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_176", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_176.flac", "type": "audio", "duration": 341.056}, {"from": "Assistant", "value": "Multi-party dialogue machine reading comprehension (MRC) brings tremendous challenge since it involves multiple speakers at one dialogue, resulting in intricate speaker information flows and noisy dialogue contexts. To alleviate such difficulties, previous models focus on how to incorporate these information using complex graph-based modules and additional manually labeled data, which is usually rare in real scenarios. In this paper, we design two labour-free self- and pseudo-self-supervised prediction tasks on speaker and key-utterance to implicitly model the speaker information flows, and capture salient clues in a long dialogue. Experimental results on two benchmark datasets have justified the effectiveness of our method over competitive baselines and current state-of-the-art models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_177", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_177.flac", "type": "audio", "duration": 336.939}, {"from": "Assistant", "value": "Humans are capable of learning novel concepts from very few examples; in contrast, state-of-the-art machine learning algorithms typically need thousands of examples to do so. In this paper, we propose an algorithm for learning novel concepts by representing them as programs over existing concepts. This way the concept learning problem is naturally a program synthesis problem and our algorithm learns from a few examples to synthesize a program representing the novel concept. In addition, we perform a theoretical analysis of our approach for the case where the program defining the novel concept over existing ones is context-free. We show that given a learned grammar-based parser and a novel production rule, we can augment the parser with the production rule in a way that provably generalizes. We evaluate our approach by learning concepts in the semantic parsing domain extended to the few-shot novel concept learning setting, showing that our approach significantly outperforms end-to-end neural semantic parsers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_178", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_178.flac", "type": "audio", "duration": 173.174}, {"from": "Assistant", "value": "Executing natural language instructions in a physically grounded domain requires a model that understands both spatial concepts such as “left of” and “above”, and the compositional language used to identify landmarks and articulate instructions relative to them. In this paper, we study instruction understanding in the blocks world domain. Given an initial arrangement of blocks and a natural language instruction, the system executes the instruction by manipulating selected blocks. The highly compositional instructions are composed of atomic components and understanding these components is a necessary step to executing the instruction. We show that while end-to-end training (supervised only by the correct block location) fails to address the challenges of this task and performs poorly on instructions involving a single atomic component, knowledge-free auxiliary signals can be used to significantly improve performance by providing supervision for the instruction’s components. Specifically, we generate signals that aim at helping the model gradually understand components of the compositional instructions, as well as those that help it better understand spatial concepts, and show their benefit to the overall task for two datasets and two state-of-the-art (SOTA) models, especially when the training data is limited—which is usual in such tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_179", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_179.flac", "type": "audio", "duration": 356.96}, {"from": "Assistant", "value": "With the continuous upgrading of the summarization systems driven by deep neural networks, researchers have higher requirements on the quality of the generated summaries, which should be not only fluent and informative but also factually correct. As a result, the field of factual evaluation has developed rapidly recently. Despite its initial progress in evaluating generated summaries, the meta-evaluation methodologies of factuality metrics are limited in their opacity, leading to the insufficient understanding of factuality metrics’ relative advantages and their applicability. In this paper, we present an adversarial meta-evaluation methodology that allows us to (i) diagnose the fine-grained strengths and weaknesses of 6 existing top-performing metrics over 24 diagnostic test datasets, (ii) search for directions for further improvement by data augmentation. Our observations from this work motivate us to propose several calls for future research. We make all codes, diagnostic test datasets, trained factuality models available:https://github.com/zide05/AdvFact.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_180", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_180.flac", "type": "audio", "duration": 174.661}, {"from": "Assistant", "value": "Large, pre-trained transformer language models, which are pervasive in natural language processing tasks, are notoriously expensive to train. To reduce the cost of training such large models, prior work has developed smaller, more compact models which achieves a significant speedup in training time while maintaining competitive accuracy to the original model on downstream tasks. Though these smaller pre-trained models have been widely adopted by the community, it is not known how well are they calibrated compared to their larger counterparts. In this paper, focusing on a wide range of tasks, we thoroughly investigate the calibration properties of pre-trained transformers, as a function of their size. We demonstrate that when evaluated in-domain, smaller models are able to achieve competitive, and often better, calibration compared to larger models, while achieving significant speedup in training time. Post-hoc calibration techniques further reduce calibration error for all models in-domain. However, when evaluated out-of-domain, larger models tend to be better calibrated, and label-smoothing instead is an effective strategy to calibrate models in this setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_181", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_181.flac", "type": "audio", "duration": 360.049}, {"from": "Assistant", "value": "Growing polarization of the news media has been blamed for fanning disagreement, controversy and even violence. Early identification of polarized topics is thus an urgent matter that can help mitigate conflict. However, accurate measurement of topic-wise polarization is still an open research challenge. To address this gap, we propose Partisanship-aware Contextualized Topic Embeddings (PaCTE), a method to automatically detect polarized topics from partisan news sources. Specifically, utilizing a language model that has been finetuned on recognizing partisanship of the news articles, we represent the ideology of a news corpus on a topic by corpus-contextualized topic embedding and measure the polarization using cosine distance. We apply our method to a dataset of news articles about the COVID-19 pandemic. Extensive experiments on different news sources and topics demonstrate the efficacy of our method to capture topical polarization, as indicated by its effectiveness of retrieving the most polarized topics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_182", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_182.flac", "type": "audio", "duration": 175.961}, {"from": "Assistant", "value": "Previous neural Seq2Seq models have shown the effectiveness for jointly extracting relation triplets. However, most of these models suffer from incompletion and disorder problems when they extract multi-token entities from input sentences. To tackle these problems, we propose a generative, multi-task learning framework, named GenerativeRE. We firstly propose a special entity labelling method on both input and output sequences. During the training stage, GenerativeRE fine-tunes the pre-trained generative model and learns the special entity labels simultaneously. During the inference stage, we propose a novel copy mechanism equipped with three mask strategies, to generate the most probable tokens by diminishing the scope of the model decoder. Experimental results show that our model achieves 4.6% and 0.9% F1 score improvements over the current state-of-the-art methods in the NYT24 and NYT29 benchmark datasets respectively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_183", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_183.flac", "type": "audio", "duration": 360.722}, {"from": "Assistant", "value": "In recent years, world business in online discussions and opinion sharing on social media is booming. Re-entry prediction task is thus proposed to help people keep track of the discussions which they wish to continue. Nevertheless, existing works only focus on exploiting chatting history and context information, and ignore the potential useful learning signals underlying conversation data, such as conversation thread patterns and repeated engagement of target users, which help better understand the behavior of target users in conversations. In this paper, we propose three interesting and well-founded auxiliary tasks, namely, Spread Pattern, Repeated Target user, and Turn Authorship, as the self-supervised signals for re-entry prediction. These auxiliary tasks are trained together with the main task in a multi-task manner. Experimental results on two datasets newly collected from Twitter and Reddit show that our method outperforms the previous state-of-the-arts with fewer parameters and faster convergence. Extensive experiments and analysis show the effectiveness of our proposed models and also point out some key ideas in designing self-supervised tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_184", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_184.flac", "type": "audio", "duration": 334.042}, {"from": "Assistant", "value": "Scripts – prototypical event sequences describing everyday activities – have been shown to help understand narratives by providing expectations, resolving ambiguity, and filling in unstated information. However, to date they have proved hard to author or extract from text. In this work, we demonstrate for the first time that pre-trained neural language models can be finetuned to generate high-quality scripts, at varying levels of granularity, for a wide range of everyday scenarios (e.g., bake a cake). To do this, we collect a large (6.4k) crowdsourced partially ordered scripts (named proScript), that is substantially larger than prior datasets, and develop models that generate scripts by combining language generation and graph structure prediction. We define two complementary tasks: (i) edge prediction: given a scenario and unordered events, organize the events into a valid (possibly partial-order) script, and (ii) script generation: given only a scenario, generate events and organize them into a (possibly partial-order) script. Our experiments show that our models perform well (e.g., F1=75.7 on task (i)), illustrating a new approach to overcoming previous barriers to script collection. We also show that there is still significant room for improvement toward human level performance. Together, our tasks, dataset, and models offer a new research direction for learning script knowledge.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_185", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_185.flac", "type": "audio", "duration": 181.046}, {"from": "Assistant", "value": "Dialogue Act (DA) classification is the task of classifying utterances with respect to the function they serve in a dialogue. Existing approaches to DA classification model utterances without incorporating the turn changes among speakers throughout the dialogue, therefore treating it no different than non-interactive written text. In this paper, we propose to integrate the turn changes in conversations among speakers when modeling DAs. Specifically, we learn conversation-invariant speaker turn embeddings to represent the speaker turns in a conversation; the learned speaker turn embeddings are then merged with the utterance embeddings for the downstream task of DA classification. With this simple yet effective mechanism, our model is able to capture the semantics from the dialogue content while accounting for different speaker turns in a conversation. Validation on three benchmark public datasets demonstrates superior performance of our model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_186", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_186.flac", "type": "audio", "duration": 355.01}, {"from": "Assistant", "value": "Unsupervised cross-domain dependency parsing is to accomplish domain adaptation for dependency parsing without using labeled data in target domain. Existing methods are often of the pseudo-annotation type, which generates data through self-annotation of the base model and performing iterative training. However, these methods fail to consider the change of model structure for domain adaptation. In addition, the structural information contained in the text cannot be fully exploited. To remedy these drawbacks, we propose a Semantics-Structure Adaptative Dependency Parser (SSADP), which accomplishes unsupervised cross-domain dependency parsing without relying on pseudo-annotation or data selection. In particular, we design two feature extractors to extract semantic and structural features respectively. For each type of features, a corresponding feature adaptation method is utilized to achieve domain adaptation to align the domain distribution, which effectively enhances the unsupervised cross-domain transfer capability of the model. We validate the effectiveness of our model by conducting experiments on the CODT1 and CTB9 respectively, and the results demonstrate that our model can achieve consistent performance improvement. Besides, we verify the structure transfer ability of the proposed model by introducing Weisfeiler-Lehman Test.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_187", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_187.flac", "type": "audio", "duration": 171.178}, {"from": "Assistant", "value": "We present a new form of ensemble method–Devil’s Advocate, which uses a deliberately dissenting model to force other submodels within the ensemble to better collaborate. Our method consists of two different training settings: one follows the conventional training process (Norm), and the other is trained by artificially generated labels (DevAdv). After training the models, Norm models are fine-tuned through an additional loss function, which uses the DevAdv model as a constraint. In making a final decision, the proposed ensemble model sums the scores of Norm models and then subtracts the score of the DevAdv model. The DevAdv model improves the overall performance of the other models within the ensemble. In addition to our ensemble framework being based on psychological background, it also shows comparable or improved performance on 5 text classification tasks when compared to conventional ensemble methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_188", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_188.flac", "type": "audio", "duration": 354.011}, {"from": "Assistant", "value": "Transformer-based pre-trained language models boost the performance of open-domain dialogue systems. Prior works leverage Transformer-based pre-trained language models to generate texts with desired attributes in two general approaches: (1) gradient-based methods: updating all latent representations of pre-trained models with gradients from attribute models; (2) weighted-decoding methods: re-ranking beam candidates from pre-trained models with attribute functions. However, gradient-based methods lead to high computation cost and can easily get overfitted on small training sets, while weighted-decoding methods are inherently constrained by the low-variance high-bias pre-trained model. In this work, we propose a novel approach to control the generation of Transformer-based pre-trained language models: the SideControl framework, which leverages a novel control attributes loss to incorporate useful control signals, and is shown to perform well with very limited training samples. We evaluate our proposed method on two benchmark open-domain dialogue datasets, and results show that the SideControl framework has better controllability, higher generation quality and better sample-efficiency than existing gradient-based and weighted-decoding baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_189", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_189.flac", "type": "audio", "duration": 363.477}, {"from": "Assistant", "value": "This paper investigates whether the power of the models pre-trained on text data, such as BERT, can be transferred to general token sequence classification applications. To verify pre-trained models’ transferability, we test the pre-trained models on text classification tasks with meanings of tokens mismatches, and real-world non-text token sequence classification data, including amino acid, DNA, and music. We find that even on non-text data, the models pre-trained on text converge faster, perform better than the randomly initialized models, and only slightly worse than the models using task-specific knowledge. We also find that the representations of the text and non-text pre-trained models share non-trivial similarities.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_190", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_190.flac", "type": "audio", "duration": 351.782}, {"from": "Assistant", "value": "Query Rewriting (QR) is proposed to solve the problem of the word mismatch between queries and documents in Web search. Existing approaches usually model QR with an end-to-end sequence-to-sequence (seq2seq) model. The state-of-the-art Transformer-based models can effectively learn textual semantics from user session logs, but they often ignore users’ geographic location information that is crucial for the Point-of-Interest (POI) search of map services. In this paper, we proposed a pre-training model, called Geo-BERT, to integrate semantics and geographic information in the pre-trained representations of POIs. Firstly, we simulate POI distribution in the real world as a graph, in which nodes represent POIs and multiple geographic granularities. Then we use graph representation learning methods to get geographic representations. Finally, we train a BERT-like pre-training model with text and POIs’ graph embeddings to get an integrated representation of both geographic and semantic information, and apply it in the QR of POI search. The proposed model achieves excellent accuracy on a wide range of real-world datasets of map services.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_191", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_191.flac", "type": "audio", "duration": 359.445}, {"from": "Assistant", "value": "Recently, sponsored search has become one of the most lucrative channels for marketing. As the fundamental basis of sponsored search, relevance modeling has attracted increasing attention due to the tremendous practical value. Most existing methods solely rely on the query-keyword pairs. However, keywords are usually short texts with scarce semantic information, which may not precisely reflect the underlying advertising intents. In this paper, we investigate the novel problem of advertiser-aware relevance modeling, which leverages the advertisers’ information to bridge the gap between the search intents and advertising purposes. Our motivation lies in incorporating the unsupervised bidding behaviors as the complementary graphs to learn desirable advertiser representations. We further propose a Bidding-Graph augmented Triple-based Relevance model BGTR with three towers to deeply fuse the bidding graphs and semantic textual data. Empirically, we evaluate the BGTR model over a large industry dataset, and the experimental results consistently demonstrate its superiority.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_193", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_193.flac", "type": "audio", "duration": 344.886}, {"from": "Assistant", "value": "Knowledge graph entity typing aims to infer entities’ missing types in knowledge graphs which is an important but under-explored issue. This paper proposes a novel method for this task by utilizing entities’ contextual information. Specifically, we design two inference mechanisms: i) N2T: independently use each neighbor of an entity to infer its type; ii) Agg2T: aggregate the neighbors of an entity to infer its type. Those mechanisms will produce multiple inference results, and an exponentially weighted pooling method is used to generate the final inference result. Furthermore, we propose a novel loss function to alleviate the false-negative problem during training. Experiments on two real-world KGs demonstrate the effectiveness of our method. The source code and data of this paper can be obtained fromhttps://github.com/CCIIPLab/CET.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_194", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_194.flac", "type": "audio", "duration": 347.022}, {"from": "Assistant", "value": "Large language models benefit from training with a large amount of unlabeled text, which gives them increasingly fluent and diverse generation capabilities. However, using these models for text generation that takes into account target attributes, such as sentiment polarity or specific topics, remains a challenge. We propose a simple and flexible method for controlling text generation by aligning disentangled attribute representations. In contrast to recent efforts on training a discriminator to perturb the token level distribution for an attribute, we use the same data to learn an alignment function to guide the pre-trained, non-controlled language model to generate texts with the target attribute without changing the original language model parameters. We evaluate our method on sentiment- and topic-controlled generation, and show large performance gains over previous methods while retaining fluency and diversity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_195", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_195.flac", "type": "audio", "duration": 353.965}, {"from": "Assistant", "value": "Math word problem (MWP) is a challenging and critical task in natural language processing. Many recent studies formalize MWP as a generation task and have adopted sequence-to-sequence models to transform problem descriptions to mathematical expressions. However, mathematical expressions are prone to minor mistakes while the generation objective does not explicitly handle such mistakes. To address this limitation, we devise a new ranking task for MWP and propose Generate & Rank, a multi-task framework based on a generative pre-trained language model. By joint training with generation and ranking, the model learns from its own mistakes and is able to distinguish between correct and incorrect expressions. Meanwhile, we perform tree-based disturbance specially designed for MWP and an online update to boost the ranker. We demonstrate the effectiveness of our proposed method on the benchmark and the results show that our method consistently outperforms baselines in all datasets. Particularly, in the classical Math23k, our method is 7% (78.4% to 85.4%) higher than the state-of-the-art. Code could be found athttps://github.com/huawei-noah/noah-research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_196", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_196.flac", "type": "audio", "duration": 316.0}, {"from": "Assistant", "value": "In Visual Question Answering (VQA), existing bilinear methods focus on the interaction between images and questions. As a result, the answers are either spliced into the questions or utilized as labels only for classification. On the other hand, trilinear models such as the CTI model efficiently utilize the inter-modality information between answers, questions, and images, while ignoring intra-modality information. Inspired by this observation, we propose a new trilinear interaction framework called MIRTT (Learning Multimodal Interaction Representations from Trilinear Transformers), incorporating the attention mechanisms for capturing inter-modality and intra-modality relationships. Moreover, we design a two-stage workflow where a bilinear model reduces the free-form, open-ended VQA problem into a multiple-choice VQA problem. Furthermore, to obtain accurate and generic multimodal representations, we pre-train MIRTT with masked language prediction. Our method achieves state-of-the-art performance on the Visual7W Telling task and VQA-1.0 Multiple Choice task and outperforms bilinear baselines on the VQA-2.0, TDIUC and GQA datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_197", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_197.flac", "type": "audio", "duration": 354.011}, {"from": "Assistant", "value": "Multilingual and cross-lingual Semantic Role Labeling (SRL) have recently garnered increasing attention as multilingual text representation techniques have become more effective and widely available. While recent work has attained growing success, results on gold multilingual benchmarks are still not easily comparable across languages, making it difficult to grasp where we stand. For example, in CoNLL-2009, the standard benchmark for multilingual SRL, language-to-language comparisons are affected by the fact that each language has its own dataset which differs from the others in size, domains, sets of labels and annotation guidelines. In this paper, we address this issue and propose UniteD-SRL, a new benchmark for multilingual and cross-lingual, span- and dependency-based SRL. UniteD-SRL provides expert-curated parallel annotations using a common predicate-argument structure inventory, allowing direct comparisons across languages and encouraging studies on cross-lingual transfer in SRL. We release UniteD-SRL v1.0 athttps://github.com/SapienzaNLP/united-srl.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_198", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_198.flac", "type": "audio", "duration": 331.163}, {"from": "Assistant", "value": "Dual-Encoders is a promising mechanism for answer retrieval in question answering (QA) systems. Currently most conventional Dual-Encoders learn the semantic representations of questions and answers merely through matching score. Researchers proposed to introduce the QA interaction features in scoring function but at the cost of low efficiency in inference stage. To keep independent encoding of questions and answers during inference stage, variational auto-encoder is further introduced to reconstruct answers (questions) from question (answer) embeddings as an auxiliary task to enhance QA interaction in representation learning in training stage. However, the needs of text generation and answer retrieval are different, which leads to hardness in training. In this work, we propose a framework to enhance the Dual-Encoders model with question answer cross-embeddings and a novel Geometry Alignment Mechanism (GAM) to align the geometry of embeddings from Dual-Encoders with that from Cross-Encoders. Extensive experimental results show that our framework significantly improves Dual-Encoders model and outperforms the state-of-the-art method on multiple answer retrieval datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_199", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_199.flac", "type": "audio", "duration": 270.814}, {"from": "Assistant", "value": "Entity grids and entity graphs are two frameworks for modeling local coherence. These frameworks represent entity relations between sentences and then extract features from such representations to encode coherence. The benefits of convolutional neural models for extracting informative features from entity grids have been recently studied. In this work, we study the benefits of Relational Graph Convolutional Networks (RGCN) to encode entity graphs for measuring local coherence. We evaluate our neural graph-based model for two benchmark coherence evaluation tasks: sentence ordering (SO) and summary coherence rating (SCR). The results show that our neural graph-based model consistently outperforms the neural grid-based model for both tasks. Our model performs competitively with a strong baseline coherence model, while our model uses 50% fewer parameters. Our work defines a new, efficient, and effective baseline for local coherence modeling.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_200", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_200.flac", "type": "audio", "duration": 332.022}, {"from": "Assistant", "value": "Large pre-trained language models such as BERT have been the driving force behind recent improvements across many NLP tasks. However, BERT is only trained to predict missing words – either through masking or next sentence prediction – and has no knowledge of lexical, syntactic or semantic information beyond what it picks up through unsupervised pre-training. We propose a novel method to explicitly inject linguistic information in the form of word embeddings into any layer of a pre-trained BERT. When injecting counter-fitted and dependency-based embeddings, the performance improvements on multiple semantic similarity datasets indicate that such information is beneficial and currently missing from the original model. Our qualitative analysis shows that counter-fitted embedding injection is particularly beneficial, with notable improvements on examples that require synonym resolution.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_201", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_201.flac", "type": "audio", "duration": 360.049}, {"from": "Assistant", "value": "We propose a rolling version of the Latent Dirichlet Allocation, called RollingLDA. By a sequential approach, it enables the construction of LDA-based time series of topics that are consistent with previous states of LDA models. After an initial modeling, updates can be computed efficiently, allowing for real-time monitoring and detection of events or structural breaks. For this purpose, we propose suitable similarity measures for topics and provide simulation evidence of superiority over other commonly used approaches. The adequacy of the resulting method is illustrated by an application to an example corpus. In particular, we compute the similarity of sequentially obtained topic and word distributions over consecutive time periods. For a representative example corpus consisting of The New York Times articles from 1980 to 2020, we analyze the effect of several tuning parameter choices and we run the RollingLDA method on the full dataset of approximately 4 million articles to demonstrate its feasibility.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_202", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_202.flac", "type": "audio", "duration": 309.081}, {"from": "Assistant", "value": "Machine reading comprehension (MRC) is a challenging NLP task for it requires to carefully deal with all linguistic granularities from word, sentence to passage. For extractive MRC, the answer span has been shown mostly determined by key evidence linguistic units, in which it is a sentence in most cases. However, we recently discovered that sentences may not be clearly defined in many languages to different extents, so that this causes so-called location unit ambiguity problem and as a result makes it difficult for the model to determine which sentence exactly contains the answer span when sentence itself has not been clearly defined at all. Taking Chinese language as a case study, we explain and analyze such a linguistic phenomenon and correspondingly propose a reader with Explicit Span-Sentence Predication to alleviate such a problem. Our proposed reader eventually helps achieve a new state-of-the-art on Chinese MRC benchmark and shows great potential in dealing with other languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_203", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_203.flac", "type": "audio", "duration": 358.981}, {"from": "Assistant", "value": "Existing unsupervised document hashing methods are mostly established on generative models. Due to the difficulties of capturing long dependency structures, these methods rarely model the raw documents directly, but instead to model the features extracted from them (e.g.bag-of-words (BOG), TFIDF). In this paper, we propose to learn hash codes from BERT embeddings after observing their tremendous successes on downstream tasks. As a first try, we modify existing generative hashing models to accommodate the BERT embeddings. However, little improvement is observed over the codes learned from the old BOG or TFIDF features. We attribute this to the reconstruction requirement in the generative hashing, which will enforce irrelevant information that is abundant in the BERT embeddings also compressed into the codes. To remedy this issue, a new unsupervised hashing paradigm is further proposed based on the mutual information (MI) maximization principle. Specifically, the method first constructs appropriate global and local codes from the documents and then seeks to maximize their mutual information. Experimental results on three benchmark datasets demonstrate that the proposed method is able to generate hash codes that outperform existing ones learned from BOG features by a substantial margin.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_204", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_204.flac", "type": "audio", "duration": 313.051}, {"from": "Assistant", "value": "Extracting relation triplets from raw text is a crucial task in Information Extraction, enabling multiple applications such as populating or validating knowledge bases, factchecking, and other downstream tasks. However, it usually involves multiple-step pipelines that propagate errors or are limited to a small number of relation types. To overcome these issues, we propose the use of autoregressive seq2seq models. Such models have previously been shown to perform well not only in language generation, but also in NLU tasks such as Entity Linking, thanks to their framing as seq2seq tasks. In this paper, we show how Relation Extraction can be simplified by expressing triplets as a sequence of text and we present REBEL, a seq2seq model based on BART that performs end-to-end relation extraction for more than 200 different relation types. We show our model’s flexibility by fine-tuning it on an array of Relation Extraction and Relation Classification benchmarks, with it attaining state-of-the-art performance in most of them.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_205", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_205.flac", "type": "audio", "duration": 305.923}, {"from": "Assistant", "value": "The size of the vocabulary is a central design choice in large pretrained language models, with respect to both performance and memory requirements. Typically, subword tokenization algorithms such as byte pair encoding and WordPiece are used. In this work, we investigate the compatibility of tokenizations for multilingual static and contextualized embedding spaces and propose a measure that reflects the compatibility of tokenizations across languages. Our goal is to prevent incompatible tokenizations, e.g., “wine” (word-level) in English vs. “v i n” (character-level) in French, which make it hard to learn good multilingual semantic representations. We show that our compatibility measure allows the system designer to create vocabularies across languages that are compatible – a desideratum that so far has been neglected in multilingual models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_206", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_206.flac", "type": "audio", "duration": 348.021}, {"from": "Assistant", "value": "Language use differs between domains and even within a domain, language use changes over time. For pre-trained language models like BERT, domain adaptation through continued pre-training has been shown to improve performance on in-domain downstream tasks. In this article, we investigate whether temporal adaptation can bring additional benefits. For this purpose, we introduce a corpus of social media comments sampled over three years. It contains unlabelled data for adaptation and evaluation on an upstream masked language modelling task as well as labelled data for fine-tuning and evaluation on a downstream document classification task. We find that temporality matters for both tasks: temporal adaptation improves upstream and temporal fine-tuning downstream task performance. Time-specific models generally perform better on past than on future test sets, which matches evidence on the bursty usage of topical words. However, adapting BERT to time and domain does not improve performance on the downstream task over only adapting to domain. Token-level analysis shows that temporal adaptation captures event-driven changes in language use in the downstream task, but not those changes that are actually relevant to task performance. Based on our findings, we discuss when temporal adaptation may be more effective.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_207", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_207.flac", "type": "audio", "duration": 376.001}, {"from": "Assistant", "value": "Transformer-based pre-training techniques of text and layout have proven effective in a number of document understanding tasks. Despite this success, multimodal pre-training models suffer from very high computational and memory costs. Motivated by human reading strategies, this paper presents Skim-Attention, a new attention mechanism that takes advantage of the structure of the document and its layout. Skim-Attention only attends to the 2-dimensional position of the words in a document. Our experiments show that Skim-Attention obtains a lower perplexity than prior works, while being more computationally efficient. Skim-Attention can be further combined with long-range Transformers to efficiently process long documents. We also show how Skim-Attention can be used off-the-shelf as a mask for any Pre-trained Language Model, allowing to improve their performance while restricting attention. Finally, we show the emergence of a document structure representation in Skim-Attention.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_208", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_208.flac", "type": "audio", "duration": 194.003}, {"from": "Assistant", "value": "Self-supervised learning has recently attracted considerable attention in the NLP community for its ability to learn discriminative features using a contrastive objective. This paper investigates whether contrastive learning can be extended to Transfomer attention to tackling the Winograd Schema Challenge. To this end, we propose a novel self-supervised framework, leveraging a contrastive loss directly at the level of self-attention. Experimental analysis of our attention-based models on multiple datasets demonstrates superior commonsense reasoning capabilities. The proposed approach outperforms all comparable unsupervised approaches while occasionally surpassing supervised ones.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_209", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_209.flac", "type": "audio", "duration": 358.934}, {"from": "Assistant", "value": "Abstractive dialogue summarization suffers from a lots of factual errors, which are due to scattered salient elements in the multi-speaker information interaction process. In this work, we design a heterogeneous semantic slot graph with a slot-level mask cross-attention to enhance the slot features for more correct summarization. We also propose a slot-driven beam search algorithm in the decoding process to give priority to generating salient elements in a limited length by “filling-in-the-blanks”. Besides, an adversarial contrastive learning assisting the training process is introduced to alleviate the exposure bias. Experimental performance on different types of factual errors shows the effectiveness of our methods and human evaluation further verifies the results..", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_210", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_210.flac", "type": "audio", "duration": 356.095}, {"from": "Assistant", "value": "Large language models (LM) generate remarkably fluent text and can be efficiently adapted across NLP tasks. Measuring and guaranteeing the quality of generated text in terms of safety is imperative for deploying LMs in the real world; to this end, prior work often relies on automatic evaluation of LM toxicity. We critically discuss this approach, evaluate several toxicity mitigation strategies with respect to both automatic and human evaluation, and analyze consequences of toxicity mitigation in terms of model bias and LM quality. We demonstrate that while basic intervention strategies can effectively optimize previously established automatic metrics on the REALTOXICITYPROMPTS dataset, this comes at the cost of reduced LM coverage for both texts about, and dialects of, marginalized groups. Additionally, we find that human raters often disagree with high automatic toxicity scores after strong toxicity reduction interventions—highlighting further the nuances involved in careful evaluation of LM toxicity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_211", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_211.flac", "type": "audio", "duration": 578.88}, {"from": "Assistant", "value": "Recent works have found evidence of gender bias in models of machine translation and coreference resolution using mostly synthetic diagnostic datasets. While these quantify bias in a controlled experiment, they often do so on a small scale and consist mostly of artificial, out-of-distribution sentences. In this work, we find grammatical patterns indicating stereotypical and non-stereotypical gender-role assignments (e.g., female nurses versus male dancers) in corpora from three domains, resulting in a first large-scale gender bias dataset of 108K diverse real-world English sentences. We manually verify the quality of our corpus and use it to evaluate gender bias in various coreference resolution and machine translation models. We find that all tested models tend to over-rely on gender stereotypes when presented with natural inputs, which may be especially harmful when deployed in commercial systems. Finally, we show that our dataset lends itself to finetuning a coreference resolution model, finding it mitigates bias on a held out set. Our dataset and models are publicly available at github.com/SLAB-NLP/BUG. We hope they will spur future research into gender bias evaluation mitigation techniques in realistic settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_213", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_213.flac", "type": "audio", "duration": 355.01}, {"from": "Assistant", "value": "Deep-learning models for language generation tasks tend to produce repetitive output. Various methods have been proposed to encourage lexical diversity during decoding, but this often comes at a cost to the perceived fluency and adequacy of the output. In this work, we propose to ameliorate this cost by using an Imitation Learning approach to explore the level of diversity that a language generation model can reliably produce. Specifically, we augment the decoding process with a meta-classifier trained to distinguish which words at any given timestep will lead to high-quality output. We focus our experiments on concept-to-text generation where models are sensitive to the inclusion of irrelevant words due to the strict relation between input and output. Our analysis shows that previous methods for diversity underperform in this setting, while human evaluation suggests that our proposed method achieves a high level of diversity with minimal effect on the output’s fluency and adequacy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_214", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_214.flac", "type": "audio", "duration": 342.053}, {"from": "Assistant", "value": "The lack of description of a given program code acts as a big hurdle to those developers new to the code base for its understanding. To tackle this problem, previous work on code summarization, the task of automatically generating code description given a piece of code reported that an auxiliary learning model trained to produce API (Application Programming Interface) embeddings showed promising results when applied to a downstream, code summarization model. However, different codes having different summaries can have the same set of API sequences. If we train a model to generate summaries given an API sequence, the model will not be able to learn effectively. Nevertheless, we note that the API sequence can still be useful and has not been actively utilized. This work proposes a novel multi-task approach that simultaneously trains two similar tasks: 1) summarizing a given code (code to summary), and 2) summarizing a given API sequence (API sequence to summary). We propose a novel code-level encoder based on BERT capable of expressing the semantics of code, and obtain representations for every line of code. Our work is the first code summarization work that utilizes a natural language-based contextual pre-trained language model in its encoder. We evaluate our approach using two common datasets (Java and Python) that have been widely used in previous studies. Our experimental results show that our multi-task approach improves over the baselines and achieves the new state-of-the-art.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_215", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_215.flac", "type": "audio", "duration": 323.965}, {"from": "Assistant", "value": "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_216", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_216.flac", "type": "audio", "duration": 179.049}, {"from": "Assistant", "value": "In this paper, we present a new method for training a writing improvement model adapted to the writer’s first language (L1) that goes beyond grammatical error correction (GEC). Without using annotated training data, we rely solely on pre-trained language models fine-tuned with parallel corpora of reference translation aligned with machine translation. We evaluate our model with corpora of academic papers written in English by L1 Portuguese and L1 Spanish scholars and a reference corpus of expert academic English. We show that our model is able to address specific L1-influenced writing and more complex linguistic phenomena than existing methods, outperforming what a state-of-the-art GEC system can achieve in this regard. Our code and data are open to other researchers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_217", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_217.flac", "type": "audio", "duration": 345.002}, {"from": "Assistant", "value": "Online tenant reviews of multifamily residential properties present a unique source of information for commercial real estate investing and research. Real estate professionals frequently read tenant reviews to uncover property-related issues that are otherwise difficult to detect, a process that is both biased and time-consuming. Using this as motivation, we asked whether a text classification-based approach can automate the detection of four carefully defined, major quality-of-life issues: severe crime, noise nuisance, pest burden, and parking difficulties. We aggregate 5.5 million tenant reviews from five sources and use two-stage crowdsourced labeling on 0.1% of the data to produce high-quality labels for subsequent text classification. Following fine-tuning of pretrained language models on millions of reviews, we train a multi-label reviews classifier that achieves a mean AUROC of 0.965 on these labels. We next use the model to reveal temporal and spatial patterns among tens of thousands of multifamily properties. Collectively, these results highlight the feasibility of automated analysis of housing trends and investment opportunities using tenant-perspective data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_218", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_218.flac", "type": "audio", "duration": 219.011}, {"from": "Assistant", "value": "Pretrained language models (PTLMs) yield state-of-the-art performance on many natural language processing tasks, including syntax, semantics and commonsense. In this paper, we focus on identifying to what extent do PTLMs capture semantic attributes and their values, e.g., the correlation between rich and high net worth. We use PTLMs to predict masked tokens using patterns and lists of items from Wikidata in order to verify how likely PTLMs encode semantic attributes along with their values. Such inferences based on semantics are intuitive for humans as part of our language understanding. Since PTLMs are trained on large amount of Wikipedia data we would assume that they can generate similar predictions, yet our findings reveal that PTLMs are still much worse than humans on this task. We show evidence and analysis explaining how to exploit our methodology to integrate better context and semantics into PTLMs using knowledge bases.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_219", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_219.flac", "type": "audio", "duration": 334.251}, {"from": "Assistant", "value": "Identifying emotions from text is crucial for a variety of real world tasks. We consider the two largest now-available corpora for emotion classification: GoEmotions, with 58k messages labelled by readers, and Vent, with 33M writer-labelled messages. We design a benchmark and evaluate several feature spaces and learning algorithms, including two simple yet novel models on top of BERT that outperform previous strong baselines on GoEmotions. Through an experiment with human participants, we also analyze the differences between how writers express emotions and how readers perceive them. Our results suggest that emotions expressed by writers are harder to identify than emotions that readers perceive. We share a public web interface for researchers to explore our models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_220", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_220.flac", "type": "audio", "duration": 327.007}, {"from": "Assistant", "value": "Entity Linking (EL) systems have achieved impressive results on standard benchmarks mainly thanks to the contextualized representations provided by recent pretrained language models. However, such systems still require massive amounts of data – millions of labeled examples – to perform at their best, with training times that often exceed several days, especially when limited computational resources are available. In this paper, we look at how Named Entity Recognition (NER) can be exploited to narrow the gap between EL systems trained on high and low amounts of labeled data. More specifically, we show how and to what extent an EL system can benefit from NER to enhance its entity representations, improve candidate selection, select more effective negative samples and enforce hard and soft constraints on its output entities. We release our software – code and model checkpoints – athttps://github.com/Babelscape/ner4el.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_221", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_221.flac", "type": "audio", "duration": 172.06}, {"from": "Assistant", "value": "Numeracy plays a key role in natural language understanding. However, existing NLP approaches, not only traditional word2vec approach or contextualized transformer-based language models, fail to learn numeracy. As the result, the performance of these models is limited when they are applied to number-intensive applications in clinical and financial domains. In this work, we propose a simple number embedding approach based on knowledge graph. We construct a knowledge graph consisting of number entities and magnitude relations. Knowledge graph embedding method is then applied to obtain number vectors. Our approach is easy to implement, and experiment results on various numeracy-related NLP tasks demonstrate the effectiveness and efficiency of our method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_222", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_222.flac", "type": "audio", "duration": 365.018}, {"from": "Assistant", "value": "Weakly supervised semantic parsing (WSP) aims at training a parser via utterance-denotation pairs. This task is challenging because it requires (1) searching consistent logical forms in a huge space; and (2) dealing with spurious logical forms. In this work, we propose Learning from Mistakes (LFM), a simple yet effective learning framework for WSP. LFM utilizes the mistakes made by a parser during searching, i.e., generating logical forms that do not execute to correct denotations, for tackling the two challenges. In a nutshell, LFM additionally trains a parser using utterance-logical form pairs created from mistakes, which can quickly bootstrap the parser to search consistent logical forms. Also, it can motivate the parser to learn the correct mapping between utterances and logical forms, thus dealing with the spuriousness of logical forms. We evaluate LFM on WikiTableQuestions, WikiSQL, and TabFact in the WSP setting. The parser trained with LFM outperforms the previous state-of-the-art semantic parsing approaches on the three datasets. Also, we find that LFM can substantially reduce the need for labeled data. Using only 10% of utterance-denotation pairs, the parser achieves 84.2 denotation accuracy on WikiSQL, which is competitive with the previous state-of-the-art approaches using 100% labeled data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_223", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_223.flac", "type": "audio", "duration": 281.449}, {"from": "Assistant", "value": "We propose CodeQA, a free-form question answering dataset for the purpose of source code comprehension: given a code snippet and a question, a textual answer is required to be generated. CodeQA contains a Java dataset with 119,778 question-answer pairs and a Python dataset with 70,085 question-answer pairs. To obtain natural and faithful questions and answers, we implement syntactic rules and semantic analysis to transform code comments into question-answer pairs. We present the construction process and conduct systematic analysis of our dataset. Experiment results achieved by several neural baselines on our dataset are shown and discussed. While research on question-answering and machine reading comprehension develops rapidly, few prior work has drawn attention to code question answering. This new dataset can serve as a useful research benchmark for source code comprehension.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_224", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_224.flac", "type": "audio", "duration": 540.003}, {"from": "Assistant", "value": "State-of-the-art multilingual systems rely on shared vocabularies that sufficiently cover all considered languages. To this end, a simple and frequently used approach makes use of subword vocabularies constructed jointly over several languages. We hypothesize that such vocabularies are suboptimal due to false positives (identical subwords with different meanings across languages) and false negatives (different subwords with similar meanings). To address these issues, we propose Subword Mapping and Anchoring across Languages (SMALA), a method to construct bilingual subword vocabularies. SMALA extracts subword alignments using an unsupervised state-of-the-art mapping technique and uses them to create cross-lingual anchors based on subword similarities. We demonstrate the benefits of SMALA for cross-lingual natural language inference (XNLI), where it improves zero-shot transfer to an unseen language without task-specific data, but only by sharing subword embeddings. Moreover, in neural machine translation, we show that joint subword vocabularies obtained with SMALA lead to higher BLEU scores on sentences that contain many false positives and false negatives.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_225", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_225.flac", "type": "audio", "duration": 370.01}, {"from": "Assistant", "value": "We introduce a new pretraining approach geared for multi-document language modeling, incorporating two key ideas into the masked language modeling self-supervised objective. First, instead of considering documents in isolation, we pretrain over sets of multiple related documents, encouraging the model to learn cross-document relationships. Second, we improve over recent long-range transformers by introducing dynamic global attention that has access to the entire input to predict masked tokens. We release CDLM (Cross-Document Language Model), a new general language model for multi-document setting that can be easily applied to downstream tasks. Our extensive analysis shows that both ideas are essential for the success of CDLM, and work in synergy to set new state-of-the-art results for several multi-text tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_226", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_226.flac", "type": "audio", "duration": 352.061}, {"from": "Assistant", "value": "One of the central aspects of contextualised language models is that they should be able to distinguish the meaning of lexically ambiguous words by their contexts. In this paper we investigate the extent to which the contextualised embeddings of word forms that display multiplicity of sense reflect traditional distinctions of polysemy and homonymy. To this end, we introduce an extended, human-annotated dataset of graded word sense similarity and co-predication acceptability, and evaluate how well the similarity of embeddings predicts similarity in meaning. Both types of human judgements indicate that the similarity of polysemic interpretations falls in a continuum between identity of meaning and homonymy. However, we also observe significant differences within the similarity ratings of polysemes, forming consistent patterns for different types of polysemic sense alternation. Our dataset thus appears to capture a substantial part of the complexity of lexical ambiguity, and can provide a realistic test bed for contextualised embeddings. Among the tested models, BERT Large shows the strongest correlation with the collected word sense similarity ratings, but struggles to consistently replicate the observed similarity patterns. When clustering ambiguous word forms based on their embeddings, the model displays high confidence in discerning homonyms and some types of polysemic alternations, but consistently fails for others.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_227", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_227.flac", "type": "audio", "duration": 193.004}, {"from": "Assistant", "value": "Leveled reading (LR) aims to automatically classify texts by the cognitive levels of readers, which is fundamental in providing appropriate reading materials regarding different reading capabilities. However, most state-of-the-art LR methods rely on the availability of copious annotated resources, which prevents their adaptation to low-resource languages like Chinese. In our work, to tackle LR in Chinese, we explore how different language transfer methods perform on English-Chinese LR. Specifically, we focus on adversarial training and cross-lingual pre-training method to transfer the LR knowledge learned from annotated data in the resource-rich English language to Chinese. For evaluation, we first introduce the age-based standard to align datasets with different leveling standards. Then we conduct experiments in both zero-shot and few-shot settings. Comparing these two methods, quantitative and qualitative evaluations show that the cross-lingual pre-training method effectively captures the language-invariant features between English and Chinese. We conduct analysis to propose further improvement in cross-lingual LR.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_228", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_228.flac", "type": "audio", "duration": 356.055}, {"from": "Assistant", "value": "Framing a news article means to portray the reported event from a specific perspective, e.g., from an economic or a health perspective. Reframing means to change this perspective. Depending on the audience or the submessage, reframing can become necessary to achieve the desired effect on the readers. Reframing is related to adapting style and sentiment, which can be tackled with neural text generation techniques. However, it is more challenging since changing a frame requires rewriting entire sentences rather than single phrases. In this paper, we study how to computationally reframe sentences in news articles while maintaining their coherence to the context. We treat reframing as a sentence-level fill-in-the-blank task for which we train neural models on an existing media frame corpus. To guide the training, we propose three strategies: framed-language pretraining, named-entity preservation, and adversarial learning. We evaluate respective models automatically and manually for topic consistency, coherence, and successful reframing. Our results indicate that generating properly-framed text works well but with tradeoffs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_229", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_229.flac", "type": "audio", "duration": 374.123}, {"from": "Assistant", "value": "Emotion dynamics formulates principles explaining the emotional fluctuation during conversations. Recent studies explore the emotion dynamics from the self and inter-personal dependencies, however, ignoring the temporal and spatial dependencies in the situation of multi-modal conversations. To address the issue, we extend the concept of emotion dynamics to multi-modal settings and propose a Dialogue Transformer for simultaneously modeling the intra-modal and inter-modal emotion dynamics. Specifically, the intra-modal emotion dynamics is to not only capture the temporal dependency but also satisfy the context preference in every single modality. The inter-modal emotional dynamics aims at handling multi-grained spatial dependency across all modalities. Our models outperform the state-of-the-art with a margin of 4%-16% for most of the metrics on three benchmark datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_230", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_230.flac", "type": "audio", "duration": 176.147}, {"from": "Assistant", "value": "Standard accuracy metrics have shown that Math Word Problem (MWP) solvers have achieved high performance on benchmark datasets. However, the extent to which existing MWP solvers truly understand language and its relation with numbers is still unclear. In this paper, we generate adversarial attacks to evaluate the robustness of state-of-the-art MWP solvers. We propose two methods, Question Reordering and Sentence Paraphrasing to generate adversarial attacks. We conduct experiments across three neural MWP solvers over two benchmark datasets. On average, our attack method is able to reduce the accuracy of MWP solvers by over 40% on these datasets. Our results demonstrate that existing MWP solvers are sensitive to linguistic variations in the problem text. We verify the validity and quality of generated adversarial examples through human evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_231", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_231.flac", "type": "audio", "duration": 177.052}, {"from": "Assistant", "value": "Numerical reasoning skills are essential for complex question answering (CQA) over text. It requires opertaions including counting, comparison, addition and subtraction. A successful approach to CQA on text, Neural Module Networks (NMNs), follows the programmer-interpreter paradigm and leverages specialised modules to perform compositional reasoning. However, the NMNs framework does not consider the relationship between numbers and entities in both questions and paragraphs. We propose effective techniques to improve NMNs’ numerical reasoning capabilities by making the interpreter question-aware and capturing the relationship between entities and numbers. On the same subset of the DROP dataset for CQA on text, experimental results show that our additions outperform the original NMNs by 3.0 points for the overall F1 score.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_232", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_232.flac", "type": "audio", "duration": 374.059}, {"from": "Assistant", "value": "Software developers write a lot of source code and documentation during software development. Intrinsically, developers often recall parts of source code or code summaries that they had written in the past while implementing software or documenting them. To mimic developers’ code or summary generation behavior, we propose a retrieval augmented framework, REDCODER, that retrieves relevant code or summaries from a retrieval database and provides them as a supplement to code generation or summarization models. REDCODER has a couple of uniqueness. First, it extends the state-of-the-art dense retrieval technique to search for relevant code or summaries. Second, it can work with retrieval databases that include unimodal (only code or natural language description) or bimodal instances (code-description pairs). We conduct experiments and extensive analysis on two benchmark datasets of code generation and summarization in Java and Python, and the promising results endorse the effectiveness of our proposed retrieval augmented framework.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_233", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_233.flac", "type": "audio", "duration": 319.253}, {"from": "Assistant", "value": "Can pre-trained BERT for one language and GPT for another be glued together to translate texts? Self-supervised training using only monolingual data has led to the success of pre-trained (masked) language models in many NLP tasks. However, directly connecting BERT as an encoder and GPT as a decoder can be challenging in machine translation, for GPT-like models lack a cross-attention component that is needed in seq2seq decoders. In this paper, we propose Graformer to graft separately pre-trained (masked) language models for machine translation. With monolingual data for pre-training and parallel data for grafting training, we maximally take advantage of the usage of both types of data. Experiments on 60 directions show that our method achieves average improvements of 5.8 BLEU in x2en and 2.9 BLEU in en2x directions comparing with the multilingual Transformer of the same size.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_234", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_234.flac", "type": "audio", "duration": 164.139}, {"from": "Assistant", "value": "This paper proposes AEDA (An Easier Data Augmentation) technique to help improve the performance on text classification tasks. AEDA includes only random insertion of punctuation marks into the original text. This is an easier technique to implement for data augmentation than EDA method (Wei and Zou, 2019) with which we compare our results. In addition, it keeps the order of the words while changing their positions in the sentence leading to a better generalized performance. Furthermore, the deletion operation in EDA can cause loss of information which, in turn, misleads the network, whereas AEDA preserves all the input information. Following the baseline, we perform experiments on five different datasets for text classification. We show that using the AEDA-augmented data for training, the models show superior performance compared to using the EDA-augmented data in all five datasets. The source code will be made available for further study and reproduction of the results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_235", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_235.flac", "type": "audio", "duration": 351.016}, {"from": "Assistant", "value": "Coreference Resolution is an important NLP task and most state-of-the-art methods rely on word embeddings for word representation. However, one issue that has been largely overlooked in literature is that of comparing the performance of different embeddings across and within families. Therefore, we frame our study in the context of Event and Entity Coreference Resolution (EvCR & EnCR), and address two questions : 1) Is there a trade-off between performance (predictive and run-time) and embedding size? 2) How do the embeddings’ performance compare within and across families? Our experiments reveal several interesting findings. First, we observe diminishing returns in performance with respect to embedding size. E.g. a model using solely a character embedding achieves 86% of the performance of the largest model (Elmo, GloVe, Character) while being 1.2% of its size. Second, the larger models using multiple embeddings learns faster despite being slower per epoch. However, it is still slower at test time. Finally, Elmo performs best on both EvCR and EnCR, while GloVe and FastText perform best in EvCR and EnCR respectively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_236", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_236.flac", "type": "audio", "duration": 324.928}, {"from": "Assistant", "value": "Unifying acoustic and linguistic representation learning has become increasingly crucial to transfer the knowledge learned on the abundance of high-resource language data for low-resource speech recognition. Existing approaches simply cascade pre-trained acoustic and language models to learn the transfer from speech to text. However, how to solve the representation discrepancy of speech and text is unexplored, which hinders the utilization of acoustic and linguistic information. Moreover, previous works simply replace the embedding layer of the pre-trained language model with the acoustic features, which may cause the catastrophic forgetting problem. In this work, we introduce Wav-BERT, a cooperative acoustic and linguistic representation learning method to fuse and utilize the contextual information of speech and text. Specifically, we unify a pre-trained acoustic model (wav2vec 2.0) and a language model (BERT) into an end-to-end trainable framework. A Representation Aggregation Module is designed to aggregate acoustic and linguistic representation, and an Embedding Attention Module is introduced to incorporate acoustic information into BERT, which can effectively facilitate the cooperation of two pre-trained models and thus boost the representation learning. Extensive experiments show that our Wav-BERT significantly outperforms the existing approaches and achieves state-of-the-art performance on low-resource speech recognition.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_238", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_238.flac", "type": "audio", "duration": 360.049}, {"from": "Assistant", "value": "An open-domain knowledge graph (KG) has entities as nodes and natural language relations as edges, and is constructed by extracting (subject, relation, object) triples from text. The task of open-domain link prediction is to infer missing relations in the KG. Previous work has used standard link prediction for the task. Since triples are extracted from text, we can ground them in the larger textual context in which they were originally found. However, standard link prediction methods only rely on the KG structure and ignore the textual context that each triple was extracted from. In this paper, we introduce the new task of open-domain contextual link prediction which has access to both the textual context and the KG structure to perform link prediction. We build a dataset for the task and propose a model for it. Our experiments show that context is crucial in predicting missing relations. We also demonstrate the utility of contextual link prediction in discovering context-independent entailments between relations, in the form of entailment graphs (EG), in which the nodes are the relations. The reverse holds too: context-independent EGs assist in predicting relations in context.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_239", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_239.flac", "type": "audio", "duration": 191.893}, {"from": "Assistant", "value": "We analyze language change over time in a collaborative, goal-oriented instructional task, where utility-maximizing participants form conventions and increase their expertise. Prior work studied such scenarios mostly in the context of reference games, and consistently found that language complexity is reduced along multiple dimensions, such as utterance length, as conventions are formed. In contrast, we find that, given the ability to increase instruction utility, instructors increase language complexity along these previously studied dimensions to better collaborate with increasingly skilled instruction followers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_240", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_240.flac", "type": "audio", "duration": 360.363}, {"from": "Assistant", "value": "Developing a unified multilingual model has been a long pursuing goal for machine translation. However, existing approaches suffer from performance degradation - a single multilingual model is inferior to separately trained bilingual ones on rich-resource languages. We conjecture that such a phenomenon is due to interference brought by joint training with multiple languages. To accommodate the issue, we propose CIAT, an adapted Transformer model with a small parameter overhead for multilingual machine translation. We evaluate CIAT on multiple benchmark datasets, including IWSLT, OPUS-100, and WMT. Experiments show that the CIAT consistently outperforms strong multilingual baselines on 64 of total 66 language directions, 42 of which have above 0.5 BLEU improvement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_241", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_241.flac", "type": "audio", "duration": 212.053}, {"from": "Assistant", "value": "Inspired by Curriculum Learning, we propose a consecutive (i.e., image-to-text-to-text) generation framework where we divide the problem of radiology report generation into two steps. Contrary to generating the full radiology report from the image at once, the model generates global concepts from the image in the first step and then reforms them into finer and coherent texts using transformer-based architecture. We follow the transformer-based sequence-to-sequence paradigm at each step. We improve upon the state-of-the-art on two benchmark datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_242", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_242.flac", "type": "audio", "duration": 369.045}, {"from": "Assistant", "value": "In this paper, we focus on the detection of sexist hate speech against women in tweets studying for the first time the impact of gender stereotype detection on sexism classification. We propose: (1) the first dataset annotated for gender stereotype detection, (2) a new method for data augmentation based on sentence similarity with multilingual external datasets, and (3) a set of deep learning experiments first to detect gender stereotypes and then, to use this auxiliary task for sexism detection. Although the presence of stereotypes does not necessarily entail hateful content, our results show that sexism classification can definitively benefit from gender stereotype detection.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_243", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_243.flac", "type": "audio", "duration": 351.016}, {"from": "Assistant", "value": "In this paper, we address the problem of automatically discriminating between inherited and borrowed Latin words. We introduce a new dataset and investigate the case of Romance languages (Romanian, Italian, French, Spanish, Portuguese and Catalan), where words directly inherited from Latin coexist with words borrowed from Latin, and explore whether automatic discrimination between them is possible. Having entered the language at a later stage, borrowed words are no longer subject to historical sound shift rules, hence they are presumably less eroded, which is why we expect them to have a different intrinsic structure distinguishable by computational means. We employ several machine learning models to automatically discriminate between inherited and borrowed words and compare their performance with various feature sets. We analyze the models’ predictive power on two versions of the datasets, orthographic and phonetic. We also investigate whether prior knowledge of the etymon provides better results, employing n-gram character features extracted from the word-etymon pairs and from their alignment.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_244", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_244.flac", "type": "audio", "duration": 339.058}, {"from": "Assistant", "value": "Large pre-trained language models (LMs) such as GPT-3 have acquired a surprising ability to perform zero-shot learning. For example, to classify sentiment without any training examples, we can “prompt” the LM with the review and the label description “Does the user like this movie?”, and ask whether the next word is “yes” or “no”. However, the next word prediction training objective is still misaligned with the target zero-shot learning objective. To address this weakness, we propose meta-tuning, which directly optimizes the zero-shot learning objective by fine-tuning pre-trained language models on a collection of datasets. We focus on classification tasks, and construct the meta-dataset by aggregating 43 existing datasets and annotating 441 label descriptions in a question-answering (QA) format. When evaluated on unseen tasks, meta-tuned models outperform a same-sized QA model and the previous SOTA zero-shot learning system based on natural language inference. Additionally, increasing parameter count from 220M to 770M improves AUC-ROC scores by 6.3%, and we forecast that even larger models would perform better. Therefore, measuring zero-shot learning performance on language models out-of-the-box might underestimate their true potential, and community-wide efforts on aggregating datasets and unifying their formats can help build models that answer prompts better.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_245", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_245.flac", "type": "audio", "duration": 262.528}, {"from": "Assistant", "value": "Emotion Recognition in Conversation (ERC) has gained much attention from the NLP community recently. Some models concentrate on leveraging commonsense knowledge or multi-task learning to help complicated emotional reasoning. However, these models neglect direct utterance-knowledge interaction. In addition, these models utilize emotion-indirect auxiliary tasks, which provide limited affective information for the ERC task. To address the above issues, we propose a Knowledge-Interactive Network with sentiment polarity intensity-aware multi-task learning, namely KI-Net, which leverages both commonsense knowledge and sentiment lexicon to augment semantic information. Specifically, we use a self-matching module for internal utterance-knowledge interaction. Considering correlations with the ERC task, a phrase-level Sentiment Polarity Intensity Prediction (SPIP) task is devised as an auxiliary task. Experiments show that all knowledge integration, self-matching and SPIP modules improve the model performance respectively on three datasets. Moreover, our KI-Net model shows 1.04% performance improvement over the state-of-the-art model on the IEMOCAP dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_246", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_246.flac", "type": "audio", "duration": 362.022}, {"from": "Assistant", "value": "We address the annotation data bottleneck for sequence classification. Specifically we ask the question: if one has a budget of N annotations, which samples should we select for annotation? The solution we propose looks for diversity in the selected sample, by maximizing the amount of information that is useful for the learning algorithm, or equivalently by minimizing the redundancy of samples in the selection. This is formulated in the context of spectral learning of recurrent functions for sequence classification. Our method represents unlabeled data in the form of a Hankel matrix, and uses the notion of spectral max-volume to find a compact sub-block from which annotation samples are drawn. Experiments on sequence classification confirm that our spectral sampling strategy is in fact efficient and yields good models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_247", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_247.flac", "type": "audio", "duration": 174.057}, {"from": "Assistant", "value": "Pre-training (PT) and back-translation (BT) are two simple and powerful methods to utilize monolingual data for improving the model performance of neural machine translation (NMT). This paper takes the first step to investigate the complementarity between PT and BT. We introduce two probing tasks for PT and BT respectively and find that PT mainly contributes to the encoder module while BT brings more benefits to the decoder. Experimental results show that PT and BT are nicely complementary to each other, establishing state-of-the-art performances on the WMT16 English-Romanian and English-Russian benchmarks. Through extensive analyses on sentence originality and word frequency, we also demonstrate that combining Tagged BT with PT is more helpful to their complementarity, leading to better translation quality. Source code is freely available athttps://github.com/SunbowLiu/PTvsBT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_248", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_248.flac", "type": "audio", "duration": 346.539}, {"from": "Assistant", "value": "Precise information of word boundary can alleviate the problem of lexical ambiguity to improve the performance of natural language processing (NLP) tasks. Thus, Chinese word segmentation (CWS) is a fundamental task in NLP. Due to the development of pre-trained language models (PLM), pre-trained knowledge can help neural methods solve the main problems of the CWS in significant measure. Existing methods have already achieved high performance on several benchmarks (e.g., Bakeoff-2005). However, recent outstanding studies are limited by the small-scale annotated corpus. To further improve the performance of CWS methods based on fine-tuning the PLMs, we propose a novel neural framework, LBGCN, which incorporates a lexicon-based graph convolutional network into the Transformer encoder. Experimental results on five benchmarks and four cross-domain datasets show the lexicon-based graph convolutional network successfully captures the information of candidate words and helps to improve performance on the benchmarks (Bakeoff-2005 and CTB6) and the cross-domain datasets (SIGHAN-2010). Further experiments and analyses demonstrate that our proposed framework effectively models the lexicon to enhance the ability of basic neural frameworks and strengthens the robustness in the cross-domain scenario.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_249", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_249.flac", "type": "audio", "duration": 333.044}, {"from": "Assistant", "value": "Pre-trained language models have led to substantial gains over a broad range of natural language processing (NLP) tasks, but have been shown to have limitations for natural language generation tasks with high-quality requirements on the output, such as commonsense generation and ad keyword generation. In this work, we present a novel Knowledge Filtering and Contrastive learning Network (KFCNet) which references external knowledge and achieves better generation performance. Specifically, we propose a BERT-based filter model to remove low-quality candidates, and apply contrastive learning separately to each of the encoder and decoder, within a general encoder–decoder architecture. The encoder contrastive module helps to capture global target semantics during encoding, and the decoder contrastive module enhances the utility of retrieved prototypes while learning general features. Extensive experiments on the CommonGen benchmark show that our model outperforms the previous state of the art by a large margin: +6.6 points (42.5 vs. 35.9) for BLEU-4, +3.7 points (33.3 vs. 29.6) for SPICE, and +1.3 points (18.3 vs. 17.0) for CIDEr. We further verify the effectiveness of the proposed contrastive module on ad keyword generation, and show that our model has potential commercial value.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_250", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_250.flac", "type": "audio", "duration": 349.056}, {"from": "Assistant", "value": "The development of automated approaches to linguistic acceptability has been greatly fostered by the availability of the English CoLA corpus, which has also been included in the widely used GLUE benchmark. However, this kind of research for languages other than English, as well as the analysis of cross-lingual approaches, has been hindered by the lack of resources with a comparable size in other languages. We have therefore developed the ItaCoLA corpus, containing almost 10,000 sentences with acceptability judgments, which has been created following the same approach and the same steps as the English one. In this paper we describe the corpus creation, we detail its content, and we present the first experiments on this new resource. We compare in-domain and out-of-domain classification, and perform a specific evaluation of nine linguistic phenomena. We also present the first cross-lingual experiments, aimed at assessing whether multilingual transformer-based approaches can benefit from using sentences in two languages during fine-tuning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_251", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_251.flac", "type": "audio", "duration": 178.731}, {"from": "Assistant", "value": "Knowledge graph embedding (KGE) using low-dimensional representations to predict missing information is widely applied in knowledge completion. Existing embedding methods are mostly built on Euclidean space, which are difficult to handle hierarchical structures. Hyperbolic embedding methods have shown the promise of high fidelity and concise representation for hierarchical data. However, the logical patterns in knowledge graphs are not considered well in these methods. To address this problem, we propose a novel KGE model with extended Poincaré Ball and polar coordinate system to capture hierarchical structures. We use the tangent space and exponential transformation to initialize and map the corresponding vectors to the Poincaré Ball in hyperbolic space. To solve the boundary conditions, the boundary is stretched and zoomed by expanding the modulus length in the Poincaré Ball. We optimize our model using polar coordinate and changing operators in the extended Poincaré Ball. Experiments achieve new state-of-the-art results on part of link prediction tasks, which demonstrates the effectiveness of our method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_252", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_252.flac", "type": "audio", "duration": 278.016}, {"from": "Assistant", "value": "Emotion recognition in multi-party conversation (ERMC) is becoming increasingly popular as an emerging research topic in natural language processing. Prior research focuses on exploring sequential information but ignores the discourse structures of conversations. In this paper, we investigate the importance of discourse structures in handling informative contextual cues and speaker-specific features for ERMC. To this end, we propose a discourse-aware graph neural network (ERMC-DisGCN) for ERMC. In particular, we design a relational convolution to lever the self-speaker dependency of interlocutors to propagate contextual information. Furthermore, we exploit a gated convolution to select more informative cues for ERMC from dependent utterances. The experimental results show our method outperforms multiple baselines, illustrating that discourse structures are of great value to ERMC.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_253", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_253.flac", "type": "audio", "duration": 178.197}, {"from": "Assistant", "value": "Much of natural language processing is focused on leveraging large capacity language models, typically trained over single messages with a task of predicting one or more tokens. However, modeling human language at higher-levels of context (i.e., sequences of messages) is under-explored. In stance detection and other social media tasks where the goal is to predict an attribute of a message, we have contextual data that is loosely semantically connected by authorship. Here, we introduce Message-Level Transformer (MeLT) – a hierarchical message-encoder pre-trained over Twitter and applied to the task of stance prediction. We focus on stance prediction as a task benefiting from knowing the context of the message (i.e., the sequence of previous messages). The model is trained using a variant of masked-language modeling; where instead of predicting tokens, it seeks to generate an entire masked (aggregated) message vector via reconstruction loss. We find that applying this pre-trained masked message-level transformer to the downstream task of stance detection achieves F1 performance of 67%.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_254", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_254.flac", "type": "audio", "duration": 229.042}, {"from": "Assistant", "value": "While large-scale pretrained language models have been shown to learn effective linguistic representations for many NLP tasks, there remain many real-world contextual aspects of language that current approaches do not capture. For instance, consider a cloze test “I enjoyed the _____ game this weekend”: the correct answer depends heavily on where the speaker is from, when the utterance occurred, and the speaker’s broader social milieu and preferences. Although language depends heavily on the geographical, temporal, and other social contexts of the speaker, these elements have not been incorporated into modern transformer-based language models. We propose a simple but effective approach to incorporate speaker social context into the learned representations of large-scale language models. Our method first learns dense representations of social contexts using graph representation learning algorithms and then primes language model pretraining with these social context representations. We evaluate our approach on geographically-sensitive language modeling tasks and show a substantial improvement (more than 100% relative lift on MRR) compared to baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_255", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_255.flac", "type": "audio", "duration": 329.05}, {"from": "Assistant", "value": "In this paper, we present a new verification style reading comprehension dataset named VGaokao from Chinese Language tests of Gaokao. Different from existing efforts, the new dataset is originally designed for native speakers’ evaluation, thus requiring more advanced language understanding skills. To address the challenges in VGaokao, we propose a novel Extract-Integrate-Compete approach, which iteratively selects complementary evidence with a novel query updating mechanism and adaptively distills supportive evidence, followed by a pairwise competition to push models to learn the subtle difference among similar text pieces. Experiments show that our methods outperform various baselines on VGaokao with retrieved complementary evidence, while having the merits of efficiency and explainability. Our dataset and code are released for further research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_256", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_256.flac", "type": "audio", "duration": 359.019}, {"from": "Assistant", "value": "This paper contributes to the thread of research on the learnability of different dependency annotation schemes: one (‘semantic’) favouring content words as heads of dependency relations and the other (‘syntactic’) favouring syntactic heads. Several studies have lent support to the idea that choosing syntactic criteria for assigning heads in dependency trees improves the performance of dependency parsers. This may be explained by postulating that syntactic approaches are generally more learnable. In this study, we test this hypothesis by comparing the performance of five parsing systems (both transition- and graph-based) on a selection of 21 treebanks, each in a ‘semantic’ variant, represented by standard UD (Universal Dependencies), and a ‘syntactic’ variant, represented by SUD (Surface-syntactic Universal Dependencies): unlike previously reported experiments, which considered learnability of ‘semantic’ and ‘syntactic’ annotations of particular constructions in vitro, the experiments reported here consider whole annotation schemes in vivo. Additionally, we compare these annotation schemes using a range of quantitative syntactic properties, which may also reflect their learnability. The results of the experiments show that SUD tends to be more learnable than UD, but the advantage of one or the other scheme depends on the parser and the corpus in question.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_257", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_257.flac", "type": "audio", "duration": 173.058}, {"from": "Assistant", "value": "Evidence association in criminal cases is dividing a set of judicial evidence into several non-overlapping subsets, improving the interpretability and legality of conviction. Observably, evidence divided into the same subset usually supports the same claim. Therefore, we propose an argumentation-driven supervised learning method to calculate the distance between evidence pairs for the following evidence association step in this paper. Experimental results on a real-world dataset demonstrate the effectiveness of our method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_258", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_258.flac", "type": "audio", "duration": 349.056}, {"from": "Assistant", "value": "Aspect-level sentiment classification (ALSC) aims at identifying the sentiment polarity of a specified aspect in a sentence. ALSC is a practical setting in aspect-based sentiment analysis due to no opinion term labeling needed, but it fails to interpret why a sentiment polarity is derived for the aspect. To address this problem, recent works fine-tune pre-trained Transformer encoders for ALSC to extract an aspect-centric dependency tree that can locate the opinion words. However, the induced opinion words only provide an intuitive cue far below human-level interpretability. Besides, the pre-trained encoder tends to internalize an aspect’s intrinsic sentiment, causing sentiment bias and thus affecting model performance. In this paper, we propose a span-based anti-bias aspect representation learning framework. It first eliminates the sentiment bias in the aspect embedding by adversarial learning against aspects’ prior sentiment. Then, it aligns the distilled opinion candidates with the aspect by span-based dependency modeling to highlight the interpretable opinion terms. Our method achieves new state-of-the-art performance on five benchmarks, with the capability of unsupervised opinion extraction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_259", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_259.flac", "type": "audio", "duration": 358.655}, {"from": "Assistant", "value": "Masked language modeling (MLM) is one of the key sub-tasks in vision-language pretraining. In the cross-modal setting, tokens in the sentence are masked at random, and the model predicts the masked tokens given the image and the text. In this paper, we observe several key disadvantages of MLM in this setting. First, as captions tend to be short, in a third of the sentences no token is sampled. Second, the majority of masked tokens are stop-words and punctuation, leading to under-utilization of the image. We investigate a range of alternative masking strategies specific to the cross-modal setting that address these shortcomings, aiming for better fusion of text and image in the learned representation. When pre-training the LXMERT model, our alternative masking strategies consistently improve over the original masking strategy on three downstream tasks, especially in low resource settings. Further, our pre-training approach substantially outperforms the baseline model on a prompt-based probing task designed to elicit image objects. These results and our analysis indicate that our method allows for better utilization of the training data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_260", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_260.flac", "type": "audio", "duration": 357.973}, {"from": "Assistant", "value": "Multilingual neural machine translation models typically handle one source language at a time. However, prior work has shown that translating from multiple source languages improves translation quality. Different from existing approaches on multi-source translation that are limited to the test scenario where parallel source sentences from multiple languages are available at inference time, we propose to improve multilingual translation in a more common scenario by exploiting synthetic source sentences from auxiliary languages. We train our model on synthetic multi-source corpora and apply random masking to enable flexible inference with single-source or bi-source inputs. Extensive experiments on Chinese/English-Japanese and a large-scale multilingual translation benchmark show that our model outperforms the multilingual baseline significantly by up to +4.0 BLEU with the largest improvements on low-resource or distant language pairs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_261", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_261.flac", "type": "audio", "duration": 180.331}, {"from": "Assistant", "value": "It is widely accepted that fine-tuning pre-trained language models usually brings about performance improvements in downstream tasks. However, there are limited studies on the reasons behind this effectiveness, particularly from the viewpoint of structural changes in the embedding space. Trying to fill this gap, in this paper, we analyze the extent to which the isotropy of the embedding space changes after fine-tuning. We demonstrate that, even though isotropy is a desirable geometrical property, fine-tuning does not necessarily result in isotropy enhancements. Moreover, local structures in pre-trained contextual word representations (CWRs), such as those encoding token types or frequency, undergo a massive change during fine-tuning. Our experiments show dramatic growth in the number of elongated directions in the embedding space, which, in contrast to pre-trained CWRs, carry the essential linguistic knowledge in the fine-tuned embedding space, making existing isotropy enhancement methods ineffective.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_262", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_262.flac", "type": "audio", "duration": 301.568}, {"from": "Assistant", "value": "Although researches on word embeddings have made great progress in recent years, many tasks in natural language processing are on the sentence level. Thus, it is essential to learn sentence embeddings. Recently, Sentence BERT (SBERT) is proposed to learn embeddings on the sentence level, and it uses the inner product (or, cosine similarity) to compute semantic similarity between sentences. However, this measurement cannot well describe the semantic structures among sentences. The reason is that sentences may lie on a manifold in the ambient space rather than distribute in an Euclidean space. Thus, cosine similarity cannot approximate distances on the manifold. To tackle the severe problem, we propose a novel sentence embedding method called Sentence BERT with Locality Preserving (SBERT-LP), which discovers the sentence submanifold from a high-dimensional space and yields a compact sentence representation subspace by locally preserving geometric structures of sentences. We compare the SBERT-LP with several existing sentence embedding approaches from three perspectives: sentence similarity, sentence classification and sentence clustering. Experimental results and case studies demonstrate that our method encodes sentences better in the sense of semantic structures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_263", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_263.flac", "type": "audio", "duration": 132.203}, {"from": "Assistant", "value": "Knowledge representation learning (KRL) has been used in plenty of knowledge-driven tasks. Despite fruitfully progress, existing methods still suffer from the immaturity on tackling potentially-imperfect knowledge graphs and highly-imbalanced positive-negative instances during training, both of which would hinder the performance of KRL. In this paper, we propose Contrastive Completion Coding (C3), a novel KRL framework that is composed of two functional components:1.Hierarchical Architecture, which integrates both low-level standalone features and high-level topology-aware features to yield robust embedding for each entity/relation.2.Normalized Contrasitive Training, which conducts normalized one-to-many contrasitive learning to emphasize different negatives with different weights, delivering better convergence compared to conventional training losses. Extensive experiments on several benchmarks verify the efficacy of the two proposed techniques and combing them together generally achieves superior performance against state-of-the-art approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_265", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_265.flac", "type": "audio", "duration": 331.0}, {"from": "Assistant", "value": "The transformer-based pre-trained language models have been tremendously successful in most of the conventional NLP tasks. But they often struggle in those tasks where numerical understanding is required. Some possible reasons can be the tokenizers and pre-training objectives which are not specifically designed to learn and preserve numeracy. Here we investigate the ability of text-to-text transfer learning model (T5), which has outperformed its predecessors in the conventional NLP tasks, to learn numeracy. We consider four numeracy tasks: numeration, magnitude order prediction, finding minimum and maximum in a series, and sorting. We find that, although T5 models perform reasonably well in the interpolation setting, they struggle considerably in the extrapolation setting across all four tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_266", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_266.flac", "type": "audio", "duration": 348.16}, {"from": "Assistant", "value": "Natural language models often fall short when understanding and generating mathematical notation. What is not clear is whether these shortcomings are due to fundamental limitations of the models, or the absence of appropriate tasks. In this paper, we explore the extent to which natural language models can learn semantics between mathematical notation and their surrounding text. We propose two notation prediction tasks, and train a model that selectively masks notation tokens and encodes left and/or right sentences as context. Compared to baseline models trained by masked language modeling, our method achieved significantly better performance at the two tasks, showing that this approach is a good first step towards modeling mathematical texts. However, the current models rarely predict unseen symbols correctly, and token-level predictions are more accurate than symbol-level predictions, indicating more work is needed to represent structural patterns. Based on the results, we suggest future works toward modeling mathematical texts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_267", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_267.flac", "type": "audio", "duration": 177.045}, {"from": "Assistant", "value": "Much of the world’s population experiences some form of disability during their lifetime. Caution must be exercised while designing natural language processing (NLP) systems to prevent systems from inadvertently perpetuating ableist bias against people with disabilities, i.e., prejudice that favors those with typical abilities. We report on various analyses based on word predictions of a large-scale BERT language model. Statistically significant results demonstrate that people with disabilities can be disadvantaged. Findings also explore overlapping forms of discrimination related to interconnected gender and race identities.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_268", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_268.flac", "type": "audio", "duration": 352.448}, {"from": "Assistant", "value": "Researches on dialogue empathy aim to endow an agent with the capacity of accurate understanding and proper responding for emotions. Existing models for empathetic dialogue generation focus on the emotion flow in one direction, that is, from the context to response. We argue that conducting an empathetic conversation is a bidirectional process, where empathy occurs when the emotions of two interlocutors could converge on the same point, i.e., reaching an emotional consensus. Besides, we also find that the empathetic dialogue corpus is extremely limited, which further restricts the model performance. To address the above issues, we propose a dual-generative model, Dual-Emp, to simultaneously construct the emotional consensus and utilize some external unpaired data. Specifically, our model integrates a forward dialogue model, a backward dialogue model, and a discrete latent variable representing the emotional consensus into a unified architecture. Then, to alleviate the constraint of paired data, we extract unpaired emotional data from open-domain conversations and employ Dual-Emp to produce pseudo paired empathetic samples, which is more efficient and low-cost than the human annotation. Automatic and human evaluations demonstrate that our method outperforms competitive baselines in producing coherent and empathetic responses.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_269", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_269.flac", "type": "audio", "duration": 352.363}, {"from": "Assistant", "value": "The understanding of time expressions includes two sub-tasks: recognition and normalization. In recent years, significant progress has been made in the recognition of time expressions while research on normalization has lagged behind. Existing SOTA normalization methods highly rely on rules or grammars designed by experts, which limits their performance on emerging corpora, such as social media texts. In this paper, we model time expression normalization as a sequence of operations to construct the normalized temporal value, and we present a novel method called ARTime, which can automatically generate normalization rules from training data without expert interventions. Specifically, ARTime automatically captures possible operation sequences from annotated data and generates normalization rules on time expressions with common surface forms. The experimental results show that ARTime can significantly surpass SOTA methods on the Tweets benchmark, and achieves competitive results with existing expert-engineered rule methods on the TempEval-3 benchmark.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_270", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_270.flac", "type": "audio", "duration": 170.24}, {"from": "Assistant", "value": "Knowledge Distillation (KD) is extensively used in Natural Language Processing to compress the pre-training and task-specific fine-tuning phases of large neural language models. A student model is trained to minimize a convex combination of the prediction loss over the labels and another over the teacher output. However, most existing works either fix the interpolating weight between the two losses apriori or vary the weight using heuristics. In this work, we propose a novel sample-wise loss weighting method, RW-KD. A meta-learner, simultaneously trained with the student, adaptively re-weights the two losses for each sample. We demonstrate, on 7 datasets of the GLUE benchmark, that RW-KD outperforms other loss re-weighting methods for KD.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_271", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_271.flac", "type": "audio", "duration": 357.056}, {"from": "Assistant", "value": "Neural Machine Translation models are sensitive to noise in the input texts, such as misspelled words and ungrammatical constructions. Existing robustness techniques generally fail when faced with unseen types of noise and their performance degrades on clean texts. In this paper, we focus on three types of realistic noise that are commonly generated by humans and introduce the idea of visual context to improve translation robustness for noisy texts. In addition, we describe a novel error correction training regime that can be used as an auxiliary task to further improve translation robustness. Experiments on English-French and English-German translation show that both multimodal and error correction components improve model robustness to noisy texts, while still retaining translation quality on clean texts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_272", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_272.flac", "type": "audio", "duration": 179.051}, {"from": "Assistant", "value": "As large-scale, pre-trained language models achieve human-level and superhuman accuracy on existing language understanding tasks, statistical bias in benchmark data and probing studies have recently called into question their true capabilities. For a more informative evaluation than accuracy on text classification tasks can offer, we propose evaluating systems through a novel measure of prediction coherence. We apply our framework to two existing language understanding benchmarks with different properties to demonstrate its versatility. Our experimental results show that this evaluation framework, although simple in ideas and implementation, is a quick, effective, and versatile measure to provide insight into the coherence of machines’ predictions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_273", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_273.flac", "type": "audio", "duration": 150.208}, {"from": "Assistant", "value": "Pretraining techniques leveraging enormous datasets have driven recent advances in text summarization. While folk explanations suggest that knowledge transfer accounts for pretraining’s benefits, little is known about why it works or what makes a pretraining task or dataset suitable. In this paper, we challenge the knowledge transfer story, showing that pretraining on documents consisting of character n-grams selected at random, we can nearly match the performance of models pretrained on real corpora. This work holds the promise of eliminating upstream corpora, which may alleviate some concerns over offensive language, bias, and copyright issues. To see whether the small residual benefit of using real data could be accounted for by the structure of the pretraining task, we design several tasks motivated by a qualitative study of summarization corpora. However, these tasks confer no appreciable benefit, leaving open the possibility of a small role for knowledge transfer.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_274", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_274.flac", "type": "audio", "duration": 370.773}, {"from": "Assistant", "value": "Training data for machine translation (MT) is often sourced from a multitude of large corpora that are multi-faceted in nature, e.g. containing contents from multiple domains or different levels of quality or complexity. Naturally, these facets do not occur with equal frequency, nor are they equally important for the test scenario at hand. In this work, we propose to optimize this balance jointly with MT model parameters to relieve system developers from manual schedule design. A multi-armed bandit is trained to dynamically choose between facets in a way that is most beneficial for the MT system. We evaluate it on three different multi-facet applications: balancing translationese and natural training data, or data from multiple domains or multiple language pairs. We find that bandit learning leads to competitive MT systems across tasks, and our analysis provides insights into its learned strategies and the underlying data sets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_275", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_275.flac", "type": "audio", "duration": 316.64}, {"from": "Assistant", "value": "Rapid progress in Neural Machine Translation (NMT) systems over the last few years has focused primarily on improving translation quality, and as a secondary focus, improving robustness to perturbations (e.g. spelling). While performance and robustness are important objectives, by over-focusing on these, we risk overlooking other important properties. In this paper, we draw attention to the fact that for some applications, faithfulness to the original (input) text is important to preserve, even if it means introducing unusual language patterns in the (output) translation. We propose a simple, novel way to quantify whether an NMT system exhibits robustness or faithfulness, by focusing on the case of word-order perturbations. We explore a suite of functions to perturb the word order of source sentences without deleting or injecting tokens, and measure their effects on the target side. Across several experimental conditions, we observe a strong tendency towards robustness rather than faithfulness. These results allow us to better understand the trade-off between faithfulness and robustness in NMT, and opens up the possibility of developing systems where users have more autonomy and control in selecting which property is best suited for their use case.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_276", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_276.flac", "type": "audio", "duration": 328.005}, {"from": "Assistant", "value": "Online conversations include more than just text. Increasingly, image-based responses such as memes and animated gifs serve as culturally recognized and often humorous responses in conversation. However, while NLP has broadened to multimodal models, conversational dialog systems have largely focused only on generating text replies. Here, we introduce a new dataset of 1.56M text-gif conversation turns and introduce a new multimodal conversational model Pepe the King Prawn for selecting gif-based replies. We demonstrate that our model produces relevant and high-quality gif responses and, in a large randomized control trial of multiple models replying to real users, we show that our model replies with gifs that are significantly better received by the community.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_277", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_277.flac", "type": "audio", "duration": 179.444}, {"from": "Assistant", "value": "Researchers use figures to communicate rich, complex information in scientific papers. The captions of these figures are critical to conveying effective messages. However, low-quality figure captions commonly occur in scientific articles and may decrease understanding. In this paper, we propose an end-to-end neural framework to automatically generate informative, high-quality captions for scientific figures. To this end, we introduce SCICAP, a large-scale figure-caption dataset based on computer science arXiv papers published between 2010 and 2020. After pre-processing – including figure-type classification, sub-figure identification, text normalization, and caption text selection – SCICAP contained more than two million figures extracted from over 290,000 papers. We then established baseline models that caption graph plots, the dominant (19.2%) figure type. The experimental results showed both opportunities and steep challenges of generating captions for scientific figures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_278", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_278.flac", "type": "audio", "duration": 199.018}, {"from": "Assistant", "value": "In this paper, we propose an annotated sentiment analysis dataset made of informally written Bangla texts. This dataset comprises public comments on news and videos collected from social media covering 13 different domains, including politics, education, and agriculture. These comments are labeled with one of the polarity labels, namely positive, negative, and neutral. One significant characteristic of the dataset is that each of the comments is noisy in terms of the mix of dialects and grammatical incorrectness. Our experiments to develop a benchmark classification system show that hand-crafted lexical features provide superior performance than neural network and pretrained language models. We have made the dataset and accompanying models presented in this paper publicly available athttps://git.io/JuuNB.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_279", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_279.flac", "type": "audio", "duration": 328.043}, {"from": "Assistant", "value": "While multilingual pretrained language models (LMs) fine-tuned on a single language have shown substantial cross-lingual task transfer capabilities, there is still a wide performance gap in semantic parsing tasks when target language supervision is available. In this paper, we propose a novel Translate-and-Fill (TaF) method to produce silver training data for a multilingual semantic parser. This method simplifies the popular Translate-Align-Project (TAP) pipeline and consists of a sequence-to-sequence filler model that constructs a full parse conditioned on an utterance and a view of the same parse. Our filler is trained on English data only but can accurately complete instances in other languages (i.e., translations of the English training utterances), in a zero-shot fashion. Experimental results on three multilingual semantic parsing datasets show that data augmentation with TaF reaches accuracies competitive with similar systems which rely on traditional alignment techniques.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_280", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_280.flac", "type": "audio", "duration": 345.443}, {"from": "Assistant", "value": "Pre-trained language models (PLMs) like BERT have made great progress in NLP. News articles usually contain rich textual information, and PLMs have the potentials to enhance news text modeling for various intelligent news applications like news recommendation and retrieval. However, most existing PLMs are in huge size with hundreds of millions of parameters. Many online news applications need to serve millions of users with low latency tolerance, which poses great challenges to incorporating PLMs in these scenarios. Knowledge distillation techniques can compress a large PLM into a much smaller one and meanwhile keeps good performance. However, existing language models are pre-trained and distilled on general corpus like Wikipedia, which has gaps with the news domain and may be suboptimal for news intelligence. In this paper, we propose NewsBERT, which can distill PLMs for efficient and effective news intelligence. In our approach, we design a teacher-student joint learning and distillation framework to collaboratively learn both teacher and student models, where the student model can learn from the learning experience of the teacher model. In addition, we propose a momentum distillation method by incorporating the gradients of teacher model into the update of student model to better transfer the knowledge learned by the teacher model. Thorough experiments on two real-world datasets with three tasks show that NewsBERT can empower various intelligent news applications with much smaller models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_281", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_281.flac", "type": "audio", "duration": 358.805}, {"from": "Assistant", "value": "Question answering (QA) systems are now available through numerous commercial applications for a wide variety of domains, serving millions of users that interact with them via speech interfaces. However, current benchmarks in QA research do not account for the errors that speech recognition models might introduce, nor do they consider the language variations (dialects) of the users. To address this gap, we augment an existing QA dataset to construct a multi-dialect, spoken QA benchmark on five languages (Arabic, Bengali, English, Kiswahili, Korean) with more than 68k audio prompts in 24 dialects from 255 speakers. We provide baseline results showcasing the real-world performance of QA systems and analyze the effect of language variety and other sensitive speaker attributes on downstream performance. Last, we study the fairness of the ASR and QA models with respect to the underlying user populations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_282", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_282.flac", "type": "audio", "duration": 660.004}, {"from": "Assistant", "value": "A “bigger is better” explosion in the number of parameters in deep neural networks has made it increasingly challenging to make state-of-the-art networks accessible in compute-restricted environments. Compression techniques have taken on renewed importance as a way to bridge the gap. However, evaluation of the trade-offs incurred by popular compression techniques has been centered on high-resource datasets. In this work, we instead consider the impact of compression in a data-limited regime. We introduce the term low-resource double bind to refer to the co-occurrence of data limitations and compute resource constraints. This is a common setting for NLP for low-resource languages, yet the trade-offs in performance are poorly studied. Our work offers surprising insights into the relationship between capacity and generalization in data-limited regimes for the task of machine translation. Our experiments on magnitude pruning for translations from English into Yoruba, Hausa, Igbo and German show that in low-resource regimes, sparsity preserves performance on frequent sentences but has a disparate impact on infrequent ones. However, it improves robustness to out-of-distribution shifts, especially for datasets that are very distinct from the training distribution. Our findings suggest that sparsity can play a beneficial role at curbing memorization of low frequency attributes, and therefore offers a promising solution to the low-resource double bind.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_283", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_283.flac", "type": "audio", "duration": 178.048}, {"from": "Assistant", "value": "This paper proposes a transformer over transformer framework, called Transformerˆ2, to perform neural text segmentation. It consists of two components: bottom-level sentence encoders using pre-trained transformers, and an upper-level transformer-based segmentation model based on the sentence embeddings. The bottom-level component transfers the pre-trained knowledge learnt from large external corpora under both single and pair-wise supervised NLP tasks to model the sentence embeddings for the documents. Given the sentence embeddings, the upper-level transformer is trained to recover the segmentation boundaries as well as the topic labels of each sentence. Equipped with a multi-task loss and the pre-trained knowledge, Transformerˆ2 can better capture the semantic coherence within the same segments. Our experiments show that (1) Transformerˆ2$manages to surpass state-of-the-art text segmentation models in terms of a commonly-used semantic coherence measure; (2) in most cases, both single and pair-wise pre-trained knowledge contribute to the model performance; (3) bottom-level sentence encoders pre-trained on specific languages yield better performance than those pre-trained on specific domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_284", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_284.flac", "type": "audio", "duration": 428.06}, {"from": "Assistant", "value": "Topic models are useful tools for analyzing and interpreting the main underlying themes of large corpora of text. Most topic models rely on word co-occurrence for computing a topic, i.e., a weighted set of words that together represent a high-level semantic concept. In this paper, we propose a new light-weight Self-Supervised Neural Topic Model (SNTM) that learns a rich context by learning a topic representation jointly from three co-occurring words and a document that the triple originates from. Our experimental results indicate that our proposed neural topic model, SNTM, outperforms previously existing topic models in coherence metrics as well as document clustering accuracy. Moreover, apart from the topic coherence and clustering performance, the proposed neural topic model has a number of advantages, namely, being computationally efficient and easy to train.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_285", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_285.flac", "type": "audio", "duration": 199.018}, {"from": "Assistant", "value": "Recent evidence supports a role for coreference processing in guiding human expectations about upcoming words during reading, based on covariation between reading times and word surprisal estimated by a coreference-aware semantic processing model (Jaffe et al. 2020).The present study reproduces and elaborates on this finding by (1) enabling the parser to process subword information that might better approximate human morphological knowledge, and (2) extending evaluation of coreference effects from self-paced reading to human brain imaging data. Results show that an expectation-based processing effect of coreference is still evident even in the presence of the stronger psycholinguistic baseline provided by the subword model, and that the coreference effect is observed in both self-paced reading and fMRI data, providing evidence of the effect’s robustness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_286", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_286.flac", "type": "audio", "duration": 358.329}, {"from": "Assistant", "value": "Despite the remarkable performance of large-scale generative models in open-domain conversation, they are known to be less practical for building real-time conversation systems due to high latency. On the other hand, retrieval models could return responses with much lower latency but show inferior performance to the large-scale generative models since the conversation quality is bounded by the pre-defined response set. To take advantage of both approaches, we propose a new training method called G2R (Generative-to-Retrieval distillation) that preserves the efficiency of a retrieval model while leveraging the conversational ability of a large-scale generative model by infusing the knowledge of the generative model into the retrieval model. G2R consists of two distinct techniques of distillation: the data-level G2R augments the dialogue dataset with additional responses generated by the large-scale generative model, and the model-level G2R transfers the response quality score assessed by the generative model to the score of the retrieval model by the knowledge distillation loss. Through extensive experiments including human evaluation, we demonstrate that our retrieval-based conversation system trained with G2R shows a substantially improved performance compared to the baseline retrieval model while showing significantly lower inference latency than the large-scale generative models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_288", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_288.flac", "type": "audio", "duration": 355.01}, {"from": "Assistant", "value": "Online platforms and communities establish their own norms that govern what behavior is acceptable within the community. Substantial effort in NLP has focused on identifying unacceptable behaviors and, recently, on forecasting them before they occur. However, these efforts have largely focused on toxicity as the sole form of community norm violation. Such focus has overlooked the much larger set of rules that moderators enforce. Here, we introduce a new dataset focusing on a more complete spectrum of community norms and their violations in the local conversational and global community contexts. We introduce a series of models that use this data to develop context- and community-sensitive norm violation detection, showing that these changes give high performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_289", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_289.flac", "type": "audio", "duration": 180.416}, {"from": "Assistant", "value": "While contrastive learning is proven to be an effective training strategy in computer vision, Natural Language Processing (NLP) is only recently adopting it as a self-supervised alternative to Masked Language Modeling (MLM) for improving sequence representations. This paper introduces SupCL-Seq, which extends the supervised contrastive learning from computer vision to the optimization of sequence representations in NLP. By altering the dropout mask probability in standard Transformer architectures (e.g. BERT-base), for every representation (anchor), we generate augmented altered views. A supervised contrastive loss is then utilized to maximize the system’s capability of pulling together similar samples (e.g., anchors and their altered views) and pushing apart the samples belonging to the other classes. Despite its simplicity, SupCL-Seq leads to large gains in many sequence classification tasks on the GLUE benchmark compared to a standard BERT-base, including 6% absolute improvement on CoLA, 5.4% on MRPC, 4.7% on RTE and 2.6% on STS-B. We also show consistent gains over self-supervised contrastively learned representations, especially in non-semantic tasks. Finally we show that these gains are not solely due to augmentation, but rather to a downstream optimized sequence representation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_290", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_290.flac", "type": "audio", "duration": 280.043}, {"from": "Assistant", "value": "Domain adaptive pretraining, i.e. the continued unsupervised pretraining of a language model on domain-specific text, improves the modelling of text for downstream tasks within the domain. Numerous real-world applications are based on domain-specific text, e.g. working with financial or biomedical documents, and these applications often need to support multiple languages. However, large-scale domain-specific multilingual pretraining data for such scenarios can be difficult to obtain, due to regulations, legislation, or simply a lack of language- and domain-specific text. One solution is to train a single multilingual model, taking advantage of the data available in as many languages as possible. In this work, we explore the benefits of domain adaptive pretraining with a focus on adapting to multiple languages within a specific domain. We propose different techniques to compose pretraining corpora that enable a language model to both become domain-specific and multilingual. Evaluation on nine domain-specific datasets—for biomedical named entity recognition and financial sentence classification—covering seven different languages show that a single multilingual domain-specific model can outperform the general multilingual model, and performs close to its monolingual counterpart. This finding holds across two different pretraining methods, adapter-based pretraining and full model pretraining.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_291", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_291.flac", "type": "audio", "duration": 360.043}, {"from": "Assistant", "value": "Developers of text generation models rely on automated evaluation metrics as a stand-in for slow and expensive manual evaluations. However, image captioning metrics have struggled to give accurate learned estimates of the semantic and pragmatic success of output text. We address this weakness by introducing the first discourse-aware learned generation metric for evaluating image descriptions. Our approach is inspired by computational theories of discourse for capturing information goals using coherence. We present a dataset of image–description pairs annotated with coherence relations. We then train a coherence-aware metric on a subset of the Conceptual Captions dataset and measure its effectiveness—its ability to predict human ratings of output captions—on a test set composed of out-of-domain images. We demonstrate a higher Kendall Correlation Coefficient for our proposed metric with the human judgments for the results of a number of state-of-the-art coherence-aware caption generation models when compared to several other metrics including recently proposed learned metrics such as BLEURT and BERTScore.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_292", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_292.flac", "type": "audio", "duration": 348.064}, {"from": "Assistant", "value": "Answering complex open-domain questions requires understanding the latent relations between involving entities. However, we found that the existing QA datasets are extremely imbalanced in some types of relations, which hurts the generalization performance over questions with long-tail relations. To remedy this problem, in this paper, we propose a Relation-Guided Pre-Training (RGPT-QA) framework. We first generate a relational QA dataset covering a wide range of relations from both the Wikidata triplets and Wikipedia hyperlinks. We then pre-train a QA model to infer the latent relations from the question, and then conduct extractive QA to get the target answer entity. We demonstrate that by pre-training with propoed RGPT-QA techique, the popular open-domain QA model, Dense Passage Retriever (DPR), achieves 2.2%, 2.4%, and 6.3% absolute improvement in Exact Match accuracy on Natural Questions, TriviaQA, and WebQuestions. Particularly, we show that RGPT-QA improves significantly on questions with long-tail relations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_293", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_293.flac", "type": "audio", "duration": 466.079}, {"from": "Assistant", "value": "Both image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages. We use both types of pairs in MURAL (MUltimodal, MUltitask Representations Across Languages), a dual encoder that solves two tasks: 1) image-text matching and 2) translation pair matching. By incorporating billions of translation pairs, MURAL extends ALIGN (Jia et al.)–a state-of-the-art dual encoder learned from 1.8 billion noisy image-text pairs. When using the same encoders, MURAL’s performance matches or exceeds ALIGN’s cross-modal retrieval performance on well-resourced languages across several datasets. More importantly, it considerably improves performance on under-resourced languages, showing that text-text learning can overcome a paucity of image-caption examples for these languages. On the Wikipedia Image-Text dataset, for example, MURAL-base improves zero-shot mean recall by 8.1% on average for eight under-resourced languages and by 6.8% on average when fine-tuning. We additionally show that MURAL’s text representations cluster not only with respect to genealogical connections but also based on areal linguistics, such as the Balkan Sprachbund.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_294", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_294.flac", "type": "audio", "duration": 357.972}, {"from": "Assistant", "value": "Despite their success in a variety of NLP tasks, pre-trained language models, due to their heavy reliance on compositionality, fail in effectively capturing the meanings of multiword expressions (MWEs), especially idioms. Therefore, datasets and methods to improve the representation of MWEs are urgently needed. Existing datasets are limited to providing the degree of idiomaticity of expressions along with the literal and, where applicable, (a single) non-literal interpretation of MWEs. This work presents a novel dataset of naturally occurring sentences containing MWEs manually classified into a fine-grained set of meanings, spanning both English and Portuguese. We use this dataset in two tasks designed to test i) a language model’s ability to detect idiom usage, and ii) the effectiveness of a language model in generating representations of sentences containing idioms. Our experiments demonstrate that, on the task of detecting idiomatic usage, these models perform reasonably well in the one-shot and few-shot scenarios, but that there is significant scope for improvement in the zero-shot scenario. On the task of representing idiomaticity, we find that pre-training is not always effective, while fine-tuning could provide a sample efficient method of learning representations of sentences containing MWEs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_295", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_295.flac", "type": "audio", "duration": 352.64}, {"from": "Assistant", "value": "Persuasion dialogue system reflects the machine’s ability to make strategic moves beyond verbal communication, and therefore differentiates itself from task-oriented or open-domain dialogues and has its own unique values. However, the repetition and inconsistency problems still persist in dialogue response generation and could substantially impact user experience and impede the persuasion outcome. Besides, although reinforcement learning (RL) approaches have achieved big success in strategic tasks such as games, it requires a sophisticated user simulator to provide real-time feedback to the dialogue system, which limits the application of RL on persuasion dialogues. To address these issues towards a better persuasion dialogue system, we apply RL to refine a language model baseline without user simulators, and distill sentence-level information about repetition, inconsistency, and task relevance through rewards. Moreover, to better accomplish the persuasion task, the model learns from human demonstration to imitate human persuasion behavior and selects the most persuasive responses. Experiments show that our model outperforms previous state-of-the-art dialogue models on both automatic metrics and human evaluation results on a donation persuasion task, and generates more diverse, consistent and persuasive conversations according to the user feedback. We will make the code and model publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_296", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_296.flac", "type": "audio", "duration": 186.015}, {"from": "Assistant", "value": "In this paper we study pejorative language, an under-explored topic in computational linguistics. Unlike existing models of offensive language and hate speech, pejorative language manifests itself primarily at the lexical level, and describes a word that is used with a negative connotation, making it different from offensive language or other more studied categories. Pejorativity is also context-dependent: the same word can be used with or without pejorative connotations, thus pejorativity detection is essentially a problem similar to word sense disambiguation. We leverage online dictionaries to build a multilingual lexicon of pejorative terms for English, Spanish, Italian, and Romanian. We additionally release a dataset of tweets annotated for pejorative use. Based on these resources, we present an analysis of the usage and occurrence of pejorative words in social media, and present an attempt to automatically disambiguate pejorative usage in our dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_297", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_297.flac", "type": "audio", "duration": 320.363}, {"from": "Assistant", "value": "The task of verifying the truthfulness of claims in textual documents, or fact-checking, has received significant attention in recent years. Many existing evidence-based factchecking datasets contain synthetic claims and the models trained on these data might not be able to verify real-world claims. Particularly few studies addressed evidence-based fact-checking of health-related claims that require medical expertise or evidence from the scientific literature. In this paper, we introduce HEALTHVER, a new dataset for evidence-based fact-checking of health-related claims that allows to study the validity of real-world claims by evaluating their truthfulness against scientific articles. Using a three-step data creation method, we first retrieved real-world claims from snippets returned by a search engine for questions about COVID-19. Then we automatically retrieved and re-ranked relevant scientific papers using a T5 relevance-based model. Finally, the relations between each evidence statement and the associated claim were manually annotated as SUPPORT, REFUTE and NEUTRAL. To validate the created dataset of 14,330 evidence-claim pairs, we developed baseline models based on pretrained language models. Our experiments showed that training deep learning models on real-world medical claims greatly improves performance compared to models trained on synthetic and open-domain claims. Our results and manual analysis suggest that HEALTHVER provides a realistic and challenging dataset for future efforts on evidence-based fact-checking of health-related claims. The dataset, source code, and a leaderboard are available athttps://github.com/sarrouti/healthver.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_298", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_298.flac", "type": "audio", "duration": 357.056}, {"from": "Assistant", "value": "Undirected neural sequence models have achieved performance competitive with the state-of-the-art directed sequence models that generate monotonically from left to right in machine translation tasks. In this work, we train a policy that learns the generation order for a pre-trained, undirected translation model via reinforcement learning. We show that the translations decoded by our learned orders achieve higher BLEU scores than the outputs decoded from left to right or decoded by the learned order from Mansimov et al. (2019) on the WMT’14 German-English translation task. On examples with a maximum source and target length of 30 from De-En and WMT’16 English-Romanian tasks, our learned order outperforms all heuristic generation orders on three out of four language pairs. We next carefully analyze the learned order patterns via qualitative and quantitative analysis. We show that our policy generally follows an outer-to-inner order, predicting the left-most and right-most positions first, and then moving toward the middle while skipping less important words at the beginning. Furthermore, the policy usually predicts positions for a single syntactic constituent structure in consecutive steps. We believe our findings could provide more insights on the mechanism of undirected generation models and encourage further research in this direction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_299", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_299.flac", "type": "audio", "duration": 389.05}, {"from": "Assistant", "value": "We describe the task of bilingual markup transfer, which involves placing markup tags from a source sentence into a fixed target translation. This task arises in practice when a human translator generates the target translation without markup, and then the system infers the placement of markup tags. This task contrasts from previous work in which markup transfer is performed jointly with machine translation. We propose two novel metrics and evaluate several approaches based on unsupervised word alignments as well as a supervised neural sequence-to-sequence model. Our best approach achieves an average accuracy of 94.7% across six language pairs, indicating its potential usefulness for real-world localization tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_300", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_300.flac", "type": "audio", "duration": 358.123}, {"from": "Assistant", "value": "Adverse Events (AE) are harmful events resulting from the use of medical products. Although social media may be crucial for early AE detection, the sheer scale of this data makes it logistically intractable to analyze using human agents, with NLP representing the only low-cost and scalable alternative. In this paper, we frame AE Detection and Extraction as a sequence-to-sequence problem using the T5 model architecture and achieve strong performance improvements over the baselines on several English benchmarks (F1 = 0.71, 12.7% relative improvement for AE Detection; Strict F1 = 0.713, 12.4% relative improvement for AE Extraction). Motivated by the strong commonalities between AE tasks, the class imbalance in AE benchmarks, and the linguistic and structural variety typical of social media texts, we propose a new strategy for multi-task training that accounts, at the same time, for task and dataset characteristics. Our approach increases model robustness, leading to further performance gains. Finally, our framework shows some language transfer capabilities, obtaining higher performance than Multilingual BERT in zero-shot learning on French data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_301", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_301.flac", "type": "audio", "duration": 342.059}, {"from": "Assistant", "value": "The ability of learning disentangled representations represents a major step for interpretable NLP systems as it allows latent linguistic features to be controlled. Most approaches to disentanglement rely on continuous variables, both for images and text. We argue that despite being suitable for image datasets, continuous variables may not be ideal to model features of textual data, due to the fact that most generative factors in text are discrete. We propose a Variational Autoencoder based method which models language features as discrete variables and encourages independence between variables for learning disentangled representations. The proposed model outperforms continuous and discrete baselines on several qualitative and quantitative benchmarks for disentanglement as well as on a text style transfer downstream application.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_302", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_302.flac", "type": "audio", "duration": 237.76}, {"from": "Assistant", "value": "To reduce a model size but retain performance, we often rely on knowledge distillation (KD) which transfers knowledge from a large “teacher” model to a smaller “student” model. However, KD on multimodal datasets such as vision-language tasks is relatively unexplored, and digesting multimodal information is challenging since different modalities present different types of information. In this paper, we perform a large-scale empirical study to investigate the importance and effects of each modality in knowledge distillation. Furthermore, we introduce a multimodal knowledge distillation framework, modality-specific distillation (MSD), to transfer knowledge from a teacher on multimodal tasks by learning the teacher’s behavior within each modality. The idea aims at mimicking a teacher’s modality-specific predictions by introducing auxiliary loss terms for each modality. Furthermore, because each modality has different saliency for predictions, we define saliency scores for each modality and investigate saliency-based weighting schemes for the auxiliary losses. We further study a weight learning approach to learn the optimal weights on these loss terms. In our empirical analysis, we examine the saliency of each modality in KD, demonstrate the effectiveness of the weighting scheme in MSD, and show that it achieves better performance than KD on four multimodal datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_303", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_303.flac", "type": "audio", "duration": 548.885}, {"from": "Assistant", "value": "One can find dozens of data resources for various languages in which coreference - a relation between two or more expressions that refer to the same real-world entity - is manually annotated. One could also assume that such expressions usually constitute syntactically meaningful units; however, mention spans have been annotated simply by delimiting token intervals in most coreference projects, i.e., independently of any syntactic representation. We argue that it could be advantageous to make syntactic and coreference annotations convergent in the long term. We present a pilot empirical study focused on matches and mismatches between hand-annotated linear mention spans and automatically parsed syntactic trees that follow Universal Dependencies conventions. The study covers 9 datasets for 8 different languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_304", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_304.flac", "type": "audio", "duration": 325.056}, {"from": "Assistant", "value": "Sparse Mixture-of-Experts (MoE) has been a successful approach for scaling multilingual translation models to billions of parameters without a proportional increase in training computation. However, MoE models are prohibitively large and practitioners often resort to methods such as distillation for serving. In this work, we investigate routing strategies at different granularity (token, sentence, task) in MoE models to bypass distillation. Experiments on WMT and a web-scale dataset suggest that task-level routing (task-MoE) enables us to extract smaller, ready-to-deploy sub-networks from large sparse models. On WMT, our task-MoE with 32 experts (533M parameters) outperforms the best performing token-level MoE model (token-MoE) by +1.0 BLEU on average across 30 language pairs. The peak inference throughput is also improved by a factor of 1.9x when we route by tasks instead of tokens. While distilling a token-MoE to a smaller dense model preserves only 32% of the BLEU gains, our sub-network task-MoE, by design, preserves all the gains with the same inference cost as the distilled student model. Finally, when scaling up to 200 language pairs, our 128-expert task-MoE (13B parameters) performs competitively with a token-level counterpart, while improving the peak inference throughput by a factor of 2.6x.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_305", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_305.flac", "type": "audio", "duration": 295.145}, {"from": "Assistant", "value": "Although distributed learning has increasingly gained attention in terms of effectively utilizing local devices for data privacy enhancement, recent studies show that publicly shared gradients in the training process can reveal the private training data (gradient leakage) to a third-party. We have, however, no systematic understanding of the gradient leakage mechanism on the Transformer based language models. In this paper, as the first attempt, we formulate the gradient attack problem on the Transformer-based language models and propose a gradient attack algorithm, TAG, to reconstruct the local training data. Experimental results on Transformer, TinyBERT4, TinyBERT6 BERT_BASE, and BERT_LARGE using GLUE benchmark show that compared with DLG, TAG works well on more weight distributions in reconstructing training data and achieves 1.5x recover rate and 2.5x ROUGE-2 over prior methods without the need of ground truth label. TAG can obtain up to 90% data by attacking gradients in CoLA dataset. In addition, TAG is stronger than previous approaches on larger models, smaller dictionary size, and smaller input length. We hope the proposed TAG will shed some light on the privacy leakage problem in Transformer-based NLP models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_306", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_306.flac", "type": "audio", "duration": 313.045}, {"from": "Assistant", "value": "Counterfactuals are a valuable means for understanding decisions made by ML systems. However, the counterfactuals generated by the methods currently available for natural language text are either unrealistic or introduce imperceptible changes. We propose CounterfactualGAN: a method that combines a conditional GAN and the embeddings of a pretrained BERT encoder to model-agnostically generate realistic natural language text counterfactuals for explaining regression and classification tasks. Experimental results show that our method produces perceptibly distinguishable counterfactuals, while outperforming four baseline methods on fidelity and human judgments of naturalness, across multiple datasets and multiple predictive models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_307", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_307.flac", "type": "audio", "duration": 136.085}, {"from": "Assistant", "value": "In this paper, we address unsupervised chunking as a new task of syntactic structure induction, which is helpful for understanding the linguistic structures of human languages as well as processing low-resource languages. We propose a knowledge-transfer approach that heuristically induces chunk labels from state-of-the-art unsupervised parsing models; a hierarchical recurrent neural network (HRNN) learns from such induced chunk labels to smooth out the noise of the heuristics. Experiments show that our approach largely bridges the gap between supervised and unsupervised chunking.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_308", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_308.flac", "type": "audio", "duration": 179.051}, {"from": "Assistant", "value": "A popular approach to decompose the neural bases of language consists in correlating, across individuals, the brain responses to different stimuli (e.g. regular speech versus scrambled words, sentences, or paragraphs). Although successful, this ‘model-free’ approach necessitates the acquisition of a large and costly set of neuroimaging data. Here, we show that a model-based approach can reach equivalent results within subjects exposed to natural stimuli. We capitalize on the recently-discovered similarities between deep language models and the human brain to compute the mapping between i) the brain responses to regular speech and ii) the activations of deep language models elicited by modified stimuli (e.g. scrambled words, sentences, or paragraphs). Our model-based approach successfully replicates the seminal study of Lerner et al. (2011), which revealed the hierarchy of language areas by comparing the functional-magnetic resonance imaging (fMRI) of seven subjects listening to 7min of both regular and scrambled narratives. We further extend and precise these results to the brain signals of 305 individuals listening to 4.1 hours of narrated stories. Overall, this study paves the way for efficient and flexible analyses of the brain bases of language.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_309", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_309.flac", "type": "audio", "duration": 325.056}, {"from": "Assistant", "value": "Robust sequence-to-sequence modelling is an essential task in the real world where the inputs are often noisy. Both user-generated and machine generated inputs contain various kinds of noises in the form of spelling mistakes, grammatical errors, character recognition errors, all of which impact downstream tasks and affect interpretability of texts. In this work, we devise a novel sequence-to-sequence architecture for detecting and correcting different real world and artificial noises (adversarial attacks) from English texts. Towards that we propose a modified Transformer-based encoder-decoder architecture that uses a gating mechanism to detect types of corrections required and accordingly corrects texts. Experimental results show that our gated architecture with pre-trained language models perform significantly better that the non-gated counterparts and other state-of-the-art error correction models in correcting spelling and grammatical errors. Extrinsic evaluation of our model on Machine Translation (MT) and Summarization tasks show the competitive performance of the model against other generative sequence-to-sequence models under noisy inputs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_310", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_310.flac", "type": "audio", "duration": 359.05}, {"from": "Assistant", "value": "Existing curriculum learning approaches to Neural Machine Translation (NMT) require sampling sufficient amounts of “easy” samples from training data at the early training stage. This is not always achievable for low-resource languages where the amount of training data is limited. To address such a limitation, we propose a novel token-wise curriculum learning approach that creates sufficient amounts of easy samples. Specifically, the model learns to predict a short sub-sequence from the beginning part of each target sentence at the early stage of training. Then the sub-sequence is gradually expanded as the training progresses. Such a new curriculum design is inspired by the cumulative effect of translation errors, which makes the latter tokens more challenging to predict than the beginning ones. Extensive experiments show that our approach can consistently outperform baselines on five language pairs, especially for low-resource languages. Combining our approach with sentence-level methods further improves the performance of high-resource languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_311", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_311.flac", "type": "audio", "duration": 372.007}, {"from": "Assistant", "value": "The relationships that exist between entities can be a reliable indicator for classifying sensitive information, such as commercially sensitive information. For example, the relation person-IsDirectorOf-company can indicate whether an individual’s salary should be considered as sensitive personal information. Representations of such relations are often learned using a knowledge graph to produce embeddings for relation types, generalised across different entity-pairs. However, a relation type may or may not correspond to a sensitivity depending on the entities that participate to the relation. Therefore, generalised relation embeddings are typically insufficient for classifying sensitive information. In this work, we propose a novel method for representing entities and relations within a single embedding to better capture the relationship between the entities. Moreover, we show that our proposed entity-relation-entity embedding approach can significantly improve (McNemar’s test, p <0.05) the effectiveness of sensitivity classification, compared to classification approaches that leverage relation embedding approaches from the literature. (0.426 F1 vs 0.413 F1)", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_312", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_312.flac", "type": "audio", "duration": 361.047}, {"from": "Assistant", "value": "Extractive summarization has been the mainstay of automatic summarization for decades. Despite all the progress, extractive summarizers still suffer from shortcomings including coreference issues arising from extracting sentences away from their original context in the source document. This affects the coherence and readability of extractive summaries. In this work, we propose a lightweight post-editing step for extractive summaries that centers around a single linguistic decision: the definiteness of noun phrases. We conduct human evaluation studies that show that human expert judges substantially prefer the output of our proposed system over the original summaries. Moreover, based on an automatic evaluation study, we provide evidence for our system’s ability to generate linguistic decisions that lead to improved extractive summaries. We also draw insights about how the automatic system is exploiting some local cues related to the writing style of the main article texts or summary texts to make the decisions, rather than reasoning about the contexts pragmatically.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_313", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_313.flac", "type": "audio", "duration": 374.72}, {"from": "Assistant", "value": "Fine-tuning pretrained models for automatically summarizing doctor-patient conversation transcripts presents many challenges: limited training data, significant domain shift, long and noisy transcripts, and high target summary variability. In this paper, we explore the feasibility of using pretrained transformer models for automatically summarizing doctor-patient conversations directly from transcripts. We show that fluent and adequate summaries can be generated with limited training data by fine-tuning BART on a specially constructed dataset. The resulting models greatly surpass the performance of an average human annotator and the quality of previous published work for the task. We evaluate multiple methods for handling long conversations, comparing them to the obvious baseline of truncating the conversation to fit the pretrained model length limit. We introduce a multistage approach that tackles the task by learning two fine-tuned models: one for summarizing conversation chunks into partial summaries, followed by one for rewriting the collection of partial summaries into a complete summary. Using a carefully chosen fine-tuning dataset, this method is shown to be effective at handling longer conversations, improving the quality of generated summaries. We conduct both an automatic evaluation (through ROUGE and two concept-based metrics focusing on medical findings) and a human evaluation (through qualitative examples from literature, assessing hallucination, generalization, fluency, and general quality of the generated summaries).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_314", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_314.flac", "type": "audio", "duration": 357.888}, {"from": "Assistant", "value": "Empathy is the link between self and others. Detecting and understanding empathy is a key element for improving human-machine interaction. However, annotating data for detecting empathy at a large scale is a challenging task. This paper employs multi-task training with knowledge distillation to incorporate knowledge from available resources (emotion and sentiment) to detect empathy from the natural language in different domains. This approach yields better results on an existing news-related empathy dataset compared to strong baselines. In addition, we build a new dataset for empathy prediction with fine-grained empathy direction, seeking or providing empathy, from Twitter. We release our dataset for research purposes.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_315", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_315.flac", "type": "audio", "duration": 199.317}, {"from": "Assistant", "value": "How would you explain Bill Gates to a German? He is associated with founding a company in the United States, so perhaps the German founder Carl Benz could stand in for Gates in those contexts. This type of translation is called adaptation in the translation community. Until now, this task has not been done computationally. Automatic adaptation could be used in natural language processing for machine translation and indirectly for generating new question answering datasets and education. We propose two automatic methods and compare them to human results for this novel NLP task. First, a structured knowledge base adapts named entities using their shared properties. Second, vector-arithmetic and orthogonal embedding mappings methods identify better candidates, but at the expense of interpretable features. We evaluate our methods through a new dataset of human adaptations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_316", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_316.flac", "type": "audio", "duration": 181.888}, {"from": "Assistant", "value": "In this work, we address the open-world classification problem with a method called ODIST, open world classification via distributionally shifted instances. This novel and straightforward method can create out-of-domain instances from the in-domain training instances with the help of a pre-trained generative language model. Experimental results show that ODIST performs better than state-of-the-art decision boundary finding method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_317", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_317.flac", "type": "audio", "duration": 180.053}, {"from": "Assistant", "value": "In Arabic Language, diacritics are used to specify meanings as well as pronunciations. However, diacritics are often omitted from written texts, which increases the number of possible meanings and pronunciations. This leads to an ambiguous text and makes the computational process on undiacritized text more difficult. In this paper, we propose a Linguistic Attentional Model for Arabic text Diacritization (LAMAD). In LAMAD, a new linguistic feature representation is presented, which utilizes both word and character contextual features. Then, a linguistic attention mechanism is proposed to capture the important linguistic features. In addition, we explore the impact of the linguistic features extracted from the text on Arabic text diacritization (ATD) by introducing them to the linguistic attention mechanism. The extensive experimental results on three datasets with different sizes illustrate that LAMAD outperforms the existing state-of-the-art models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_318", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_318.flac", "type": "audio", "duration": 168.043}, {"from": "Assistant", "value": "Non-autoregressive machine translation (NAT) approaches enable fast generation by utilizing parallelizable generative processes. The remaining bottleneck in these models is their decoder layers; unfortunately unlike in autoregressive models (Kasai et al., 2020), removing decoder layers from NAT models significantly degrades accuracy. This work proposes a sequence-to-lattice model that replaces the decoder with a search lattice. Our approach first constructs a candidate lattice using efficient lookup operations, generates lattice scores from a deep encoder, and finally finds the best path using dynamic programming. Experiments on three machine translation datasets show that our method is faster than past non-autoregressive generation approaches, and more accurate than naively reducing the number of decoder layers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_319", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_319.flac", "type": "audio", "duration": 188.96}, {"from": "Assistant", "value": "There is an increasing interest in continuous learning (CL), as data privacy is becoming a priority for real-world machine learning applications. Meanwhile, there is still a lack of academic NLP benchmarks that are applicable for realistic CL settings, which is a major challenge for the advancement of the field. In this paper we discuss some of the unrealistic data characteristics of public datasets, study the challenges of realistic single-task continuous learning as well as the effectiveness of data rehearsal as a way to mitigate accuracy loss. We construct a CL NER dataset from an existing publicly available dataset and release it along with the code to the research community.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_320", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_320.flac", "type": "audio", "duration": 375.573}, {"from": "Assistant", "value": "Despite showing increasingly human-like conversational abilities, state-of-the-art dialogue models often suffer from factual incorrectness and hallucination of knowledge (Roller et al., 2020). In this work we explore the use of neural-retrieval-in-the-loop architectures - recently shown to be effective in open-domain QA (Lewis et al., 2020b; Izacard and Grave, 2020) - for knowledge-grounded dialogue, a task that is arguably more challenging as it requires querying based on complex multi-turn dialogue context and generating conversationally coherent responses. We study various types of architectures with multiple components - retrievers, rankers, and encoder-decoders - with the goal of maximizing knowledgeability while retaining conversational ability. We demonstrate that our best models obtain state-of-the-art performance on two knowledge-grounded conversational tasks. The models exhibit open-domain conversational capabilities, generalize effectively to scenarios not within the training data, and, as verified by human evaluations, substantially reduce the well-known problem of knowledge hallucination in state-of-the-art chatbots.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_321", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_321.flac", "type": "audio", "duration": 181.013}, {"from": "Assistant", "value": "With the recent surge in social applications relying on knowledge graphs, the need for techniques to ensure fairness in KG based methods is becoming increasingly evident. Previous works have demonstrated that KGs are prone to various social biases, and have proposed multiple methods for debiasing them. However, in such studies, the focus has been on debiasing techniques, while the relations to be debiased are specified manually by the user. As manual specification is itself susceptible to human cognitive bias, there is a need for a system capable of quantifying and exposing biases, that can support more informed decisions on what to debias. To address this gap in the literature, we describe a framework for identifying biases present in knowledge graph embeddings, based on numerical bias metrics. We illustrate the framework with three different bias measures on the task of profession prediction, and it can be flexibly extended to further bias definitions and applications. The relations flagged as biased can then be handed to decision makers for judgement upon subsequent debiasing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_322", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_322.flac", "type": "audio", "duration": 534.08}, {"from": "Assistant", "value": "Computational models of human language often involve combinatorial problems. For instance, a probabilistic parser may marginalize over exponentially many trees to make predictions. Algorithms for such problems often employ dynamic programming and are not always unique. Finding one with optimal asymptotic runtime can be unintuitive, time-consuming, and error-prone. Our work aims to automate this laborious process. Given an initial correct declarative program, we search for a sequence of semantics-preserving transformations to improve its running time as much as possible. To this end, we describe a set of program transformations, a simple metric for assessing the efficiency of a transformed program, and a heuristic search procedure to improve this metric. We show that in practice, automated search—like the mental search performed by human programmers—can find substantial improvements to the initial program. Empirically, we show that many speed-ups described in the NLP literature could have been discovered automatically by our system.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_323", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_323.flac", "type": "audio", "duration": 361.047}, {"from": "Assistant", "value": "Transformers have brought a remarkable improvement in the performance of neural machine translation (NMT) systems but they could be surprisingly vulnerable to noise. In this work, we try to investigate how noise breaks Transformers and if there exist solutions to deal with such issues. There is a large body of work in the NMT literature on analyzing the behavior of conventional models for the problem of noise but Transformers are relatively understudied in this context. Motivated by this, we introduce a novel data-driven technique called Target Augmented Fine-tuning (TAFT) to incorporate noise during training. This idea is comparable to the well-known fine-tuning strategy. Moreover, we propose two other novel extensions to the original Transformer: Controlled Denoising (CD) and Dual-Channel Decoding (DCD), that modify the neural architecture as well as the training process to handle noise. One important characteristic of our techniques is that they only impact the training phase and do not impose any overhead at inference time. We evaluated our techniques to translate the English–German pair in both directions and observed that our models have a higher tolerance to noise. More specifically, they perform with no deterioration where up to 10% of entire test words are infected by noise.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_324", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_324.flac", "type": "audio", "duration": 349.056}, {"from": "Assistant", "value": "To build robust question answering systems, we need the ability to verify whether answers to questions are truly correct, not just “good enough” in the context of imperfect QA datasets. We explore the use of natural language inference (NLI) as a way to achieve this goal, as NLI inherently requires the premise (document context) to contain all necessary information to support the hypothesis (proposed answer to the question). We leverage large pre-trained models and recent prior datasets to construct powerful question conversion and decontextualization modules, which can reformulate QA instances as premise-hypothesis pairs with very high reliability. Then, by combining standard NLI datasets with NLI examples automatically derived from QA training data, we can train NLI models to evaluate QA models’ proposed answers. We show that our approach improves the confidence estimation of a QA model across different domains, evaluated in a selective QA setting. Careful manual analysis over the predictions of our NLI model shows that it can further identify cases where the QA model produces the right answer for the wrong reason, i.e., when the answer sentence cannot address all aspects of the question.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_325", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_325.flac", "type": "audio", "duration": 328.725}, {"from": "Assistant", "value": "Domain-specific pre-trained language models (PLMs) have achieved great success over various downstream tasks in different domains. However, existing domain-specific PLMs mostly rely on self-supervised learning over large amounts of domain text, without explicitly integrating domain-specific knowledge, which can be essential in many domains. Moreover, in knowledge-sensitive areas such as the biomedical domain, knowledge is stored in multiple sources and formats, and existing biomedical PLMs either neglect them or utilize them in a limited manner. In this work, we introduce an architecture to integrate domain knowledge from diverse sources into PLMs in a parameter-efficient way. More specifically, we propose to encode domain knowledge viaadapters, which are small bottleneck feed-forward networks inserted between intermediate transformer layers in PLMs. These knowledge adapters are pre-trained for individual domain knowledge sources and integrated via an attention-based knowledge controller to enrich PLMs. Taking the biomedical domain as a case study, we explore three knowledge-specific adapters for PLMs based on the UMLS Metathesaurus graph, the Wikipedia articles for diseases, and the semantic grouping information for biomedical concepts. Extensive experiments on different biomedical NLP tasks and datasets demonstrate the benefits of the proposed architecture and the knowledge-specific adapters across multiple PLMs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_326", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_326.flac", "type": "audio", "duration": 179.925}, {"from": "Assistant", "value": "Pre-trained language models learn socially harmful biases from their training corpora, and may repeat these biases when used for generation. We study gender biases associated with the protagonist in model-generated stories. Such biases may be expressed either explicitly (“women can’t park”) or implicitly (e.g. an unsolicited male character guides her into a parking space). We focus on implicit biases, and use a commonsense reasoning engine to uncover them. Specifically, we infer and analyze the protagonist’s motivations, attributes, mental states, and implications on others. Our findings regarding implicit biases are in line with prior work that studied explicit biases, for example showing that female characters’ portrayal is centered around appearance, while male figures’ focus on intellect.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_327", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_327.flac", "type": "audio", "duration": 345.899}, {"from": "Assistant", "value": "Recent progress in pretrained Transformer-based language models has shown great success in learning contextual representation of text. However, due to the quadratic self-attention complexity, most of the pretrained Transformers models can only handle relatively short text. It is still a challenge when it comes to modeling very long documents. In this work, we propose to use a graph attention network on top of the available pretrained Transformers model to learn document embeddings. This graph attention network allows us to leverage the high-level semantic structure of the document. In addition, based on our graph document model, we design a simple contrastive learning strategy to pretrain our models on a large amount of unlabeled corpus. Empirically, we demonstrate the effectiveness of our approaches in document classification and document retrieval tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_328", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_328.flac", "type": "audio", "duration": 311.04}, {"from": "Assistant", "value": "Recent advances in text autoencoders have significantly improved the quality of the latent space, which enables models to generate grammatical and consistent text from aggregated latent vectors. As a successful application of this property, unsupervised opinion summarization models generate a summary by decoding the aggregated latent vectors of inputs. More specifically, they perform the aggregation via simple average. However, little is known about how the vector aggregation step affects the generation quality. In this study, we revisit the commonly used simple average approach by examining the latent space and generated summaries. We found that text autoencoders tend to generate overly generic summaries from simply averaged latent vectors due to an unexpectedL2-norm shrinkage in the aggregated latent vectors, which we refer to as summary vector degeneration. To overcome this issue, we develop a framework Coop, which searches input combinations for the latent vector aggregation using input-output word overlap. Experimental results show that Coop successfully alleviates the summary vector degeneration issue and establishes new state-of-the-art performance on two opinion summarization benchmarks. Code is available athttps://github.com/megagonlabs/coop.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_329", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_329.flac", "type": "audio", "duration": 360.002}, {"from": "Assistant", "value": "Recent studies have proposed different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target embedding spaces. For contextualized embeddings, alignment becomes more complex as we additionally take context into consideration. In this work, we propose using Optimal Transport (OT) as an alignment objective during fine-tuning to further improve multilingual contextualized representations for downstream cross-lingual transfer. This approach does not require word-alignment pairs prior to fine-tuning that may lead to sub-optimal matching and instead learns the word alignments within context in an unsupervised manner. It also allows different types of mappings due to soft matching between source and target sentences. We benchmark our proposed method on two tasks (XNLI and XQuAD) and achieve improvements over baselines as well as competitive results compared to similar recent works.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_330", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_330.flac", "type": "audio", "duration": 625.011}, {"from": "Assistant", "value": "Several neural-based metrics have been recently proposed to evaluate machine translation quality. However, all of them resort to point estimates, which provide limited information at segment level. This is made worse as they are trained on noisy, biased and scarce human judgements, often resulting in unreliable quality predictions. In this paper, we introduce uncertainty-aware MT evaluation and analyze the trustworthiness of the predicted quality. We combine the COMET framework with two uncertainty estimation methods, Monte Carlo dropout and deep ensembles, to obtain quality scores along with confidence intervals. We compare the performance of our uncertainty-aware MT evaluation methods across multiple language pairs from the QT21 dataset and the WMT20 metrics task, augmented with MQM annotations. We experiment with varying numbers of references and further discuss the usefulness of uncertainty-aware quality estimation (without references) to flag possibly critical translation mistakes.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_331", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_331.flac", "type": "audio", "duration": 411.063}, {"from": "Assistant", "value": "Automated Theorem Proving (ATP) deals with the development of computer programs being able to show that some conjectures (queries) are a logical consequence of a set of axioms (facts and rules). There exists several successful ATPs where conjectures and axioms are formally provided (e.g. formalised as First Order Logic formulas). Recent approaches, such as Clark et al., have proposed transformer-based architectures for deriving conjectures given axioms expressed in natural language (English). The conjecture is verified through a binary text classifier, where the transformers model is trained to predict the truth value of a conjecture given the axioms. The RuleTaker approach of Clark et al. achieves appealing results both in terms of accuracy and in the ability to generalize, showing that when the model is trained with deep enough queries (at least 3 inference steps), the transformers are able to correctly answer the majority of queries (97.6%) that require up to 5 inference steps. In this work we propose a new architecture, namely the Neural Unifier, and a relative training procedure, which achieves state-of-the-art results in term of generalisation, showing that mimicking a well-known inference procedure, the backward chaining, it is possible to answer deep queries even when the model is trained only on shallow ones. The approach is demonstrated in experiments using a diverse set of benchmark data and the source code is released to the research community for reproducibility.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_332", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_332.flac", "type": "audio", "duration": 159.616}, {"from": "Assistant", "value": "In this paper, we introduce the task of predicting severity of age-restricted aspects of movie content based solely on the dialogue script. We first investigate categorizing the ordinal severity of movies on 5 aspects: Sex, Violence, Profanity, Substance consumption, and Frightening scenes. The problem is handled using a siamese network-based multitask framework which concurrently improves the interpretability of the predictions. The experimental results show that our method outperforms the previous state-of-the-art model and provides useful information to interpret model predictions. The proposed dataset and source code are publicly available at our GitHub repository.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_333", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_333.flac", "type": "audio", "duration": 302.976}, {"from": "Assistant", "value": "In the last few years, several methods have been proposed to build meta-embeddings. The general aim was to obtain new representations integrating complementary knowledge from different source pre-trained embeddings thereby improving their overall quality. However, previous meta-embeddings have been evaluated using a variety of methods and datasets, which makes it difficult to draw meaningful conclusions regarding the merits of each approach. In this paper we propose a unified common framework, including both intrinsic and extrinsic tasks, for a fair and objective meta-embeddings evaluation. Furthermore, we present a new method to generate meta-embeddings, outperforming previous work on a large number of intrinsic evaluation benchmarks. Our evaluation framework also allows us to conclude that previous extrinsic evaluations of meta-embeddings have been overestimated.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_334", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_334.flac", "type": "audio", "duration": 366.059}, {"from": "Assistant", "value": "Large pre-trained language models have repeatedly shown their ability to produce fluent text. Yet even when starting from a prompt, generation can continue in many plausible directions. Current decoding methods with the goal of controlling generation, e.g., to ensure specific words are included, either require additional models or fine-tuning, or work poorly when the task at hand is semantically unconstrained, e.g., story generation. In this work, we present a plug-and-play decoding method for controlled language generation that is so simple and intuitive, it can be described in a single sentence: given a topic or keyword, we add a shift to the probability distribution over our vocabulary towards semantically similar words. We show how annealing this distribution can be used to impose hard constraints on language generation, something no other plug-and-play method is currently able to do with SOTA language generators. Despite the simplicity of this approach, we see it works incredibly well in practice: decoding from GPT-2 leads to diverse and fluent sentences while guaranteeing the appearance of given guide words. We perform two user studies, revealing that (1) our method outperforms competing methods in human evaluations; and (2) forcing the guide words to appear in the generated text has no impact on the fluency of the generated text.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_335", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_335.flac", "type": "audio", "duration": 354.048}, {"from": "Assistant", "value": "Coordination is a phenomenon of language that conjoins two or more terms or phrases using a coordinating conjunction. Although coordination has been explored extensively in the linguistics literature, the rules and constraints that govern its structure are still largely elusive and widely debated amongst linguists. This paper presents a study of two-termed unlike coordinations in particular, where the two conjuncts of the coordination phrase form valid constituents but have distinct categories. We conducted a syntactic analysis of the phrasal categories that can be conjoined in such unlike coordinations through a computational corpus-based approach, utilizing the Corpus of Contemporary American English (COCA) as the main data source, as well as the Penn Treebank (PTB). The results show that the two conjuncts within unlike coordinations display different properties based on their position, supporting an antisymmetric view of the structure of coordination. This research provides new data and perspectives through the use of statistical techniques that can help shape future theories and models of coordination.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_336", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_336.flac", "type": "audio", "duration": 178.048}, {"from": "Assistant", "value": "Radiology report generation aims at generating descriptive text from radiology images automatically, which may present an opportunity to improve radiology reporting and interpretation. A typical setting consists of training encoder-decoder models on image-report pairs with a cross entropy loss, which struggles to generate informative sentences for clinical diagnoses since normal findings dominate the datasets. To tackle this challenge and encourage more clinically-accurate text outputs, we propose a novel weakly supervised contrastive loss for medical report generation. Experimental results demonstrate that our method benefits from contrasting target reports with incorrect but semantically-close ones. It outperforms previous work on both clinical correctness and text generation metrics for two public benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_337", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_337.flac", "type": "audio", "duration": 182.059}, {"from": "Assistant", "value": "Existing conversational systems are mostly agent-centric, which assumes the user utterances will closely follow the system ontology. However, in real-world scenarios, it is highly desirable that users can speak freely and naturally. In this work, we attempt to build a user-centric dialogue system for conversational recommendation. As there is no clean mapping for a user’s free form utterance to an ontology, we first model the user preferences as estimated distributions over the system ontology and map the user’s utterances to such distributions. Learning such a mapping poses new challenges on reasoning over various types of knowledge, ranging from factoid knowledge, commonsense knowledge to the users’ own situations. To this end, we build a new dataset named NUANCED that focuses on such realistic settings, with 5.1k dialogues, 26k turns of high-quality user responses. We conduct experiments, showing both the usefulness and challenges of our problem setting. We believe NUANCED can serve as a valuable resource to push existing research from the agent-centric system to the user-centric system. The code and data are publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_338", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_338.flac", "type": "audio", "duration": 340.459}, {"from": "Assistant", "value": "Tables provide valuable knowledge that can be used to verify textual statements. While a number of works have considered table-based fact verification, direct alignments of tabular data with tokens in textual statements are rarely available. Moreover, training a generalized fact verification model requires abundant labeled training data. In this paper, we propose a novel system to address these problems. Inspired by counterfactual causality, our system identifies token-level salience in the statement with probing-based salience estimation. Salience estimation allows enhanced learning of fact verification from two perspectives. From one perspective, our system conducts masked salient token prediction to enhance the model for alignment and reasoning between the table and the statement. From the other perspective, our system applies salience-aware data augmentation to generate a more diverse set of training instances by replacing non-salient terms. Experimental results on TabFact show the effective improvement by the proposed salience-aware learning techniques, leading to the new SOTA performance on the benchmark.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_339", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_339.flac", "type": "audio", "duration": 399.808}, {"from": "Assistant", "value": "News media structure their reporting of events or issues using certain perspectives. When describing an incident involving gun violence, for example, some journalists may focus on mental health or gun regulation, while others may emphasize the discussion of gun rights. Such perspectives are called “frames” in communication research. We study, for the first time, the value of combining lead images and their contextual information with text to identify the frame of a given news article. We observe that using multiple modes of information(article- and image-derived features) improves prediction of news frames over any single mode of information when the images are relevant to the frames of the headlines. We also observe that frame image relevance is related to the ease of conveying frames via images, which we call frame concreteness. Additionally, we release the first multimodal news framing dataset related to gun violence in the U.S., curated and annotated by communication researchers. The dataset will allow researchers to further examine the use of multiple information modalities for studying media framing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_340", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_340.flac", "type": "audio", "duration": 181.056}, {"from": "Assistant", "value": "Training a robust and reliable deep learning model requires a large amount of data. In the crisis domain, building deep learning models to identify actionable information from the huge influx of data posted by eyewitnesses of crisis events on social media, in a time-critical manner, is central for fast response and relief operations. However, building a large, annotated dataset to train deep learning models is not always feasible in a crisis situation. In this paper, we investigate a multi-task learning approach to concurrently leverage available annotated data for several related tasks from the crisis domain to improve the performance on a main task with limited annotated data. Specifically, we focus on using multi-task learning to improve the performance on the task of identifying location mentions in crisis tweets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_341", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_341.flac", "type": "audio", "duration": 177.045}, {"from": "Assistant", "value": "The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders. In this work, we explore an alternative paradigm. We formulate semantic parsing as a dependency parsing task, applying graph-based decoding techniques developed for syntactic parsing. We compare various decoding techniques given the same pre-trained Transformer encoder on the TOP dataset, including settings where training data is limited or contains only partially-annotated examples. We find that our graph-based approach is competitive with sequence decoders on the standard setting, and offers significant improvements in data efficiency and settings where partially-annotated data is available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_342", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_342.flac", "type": "audio", "duration": 177.006}, {"from": "Assistant", "value": "Research in NLP is often supported by experimental results, and improved reporting of such results can lead to better understanding and more reproducible science. In this paper we analyze three statistical estimators for expected validation performance, a tool used for reporting performance (e.g., accuracy) as a function of computational budget (e.g., number of hyperparameter tuning experiments). Where previous work analyzing such estimators focused on the bias, we also examine the variance and mean squared error (MSE). In both synthetic and realistic scenarios, we evaluate three estimators and find the unbiased estimator has the highest variance, and the estimator with the smallest variance has the largest bias; the estimator with the smallest MSE strikes a balance between bias and variance, displaying a classic bias-variance tradeoff. We use expected validation performance to compare between different models, and analyze how frequently each estimator leads to drawing incorrect conclusions about which of two models performs best. We find that the two biased estimators lead to the fewest incorrect conclusions, which hints at the importance of minimizing variance and MSE.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_344", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_344.flac", "type": "audio", "duration": 159.01}, {"from": "Assistant", "value": "Transformers have shown improved performance when compared to previous architectures for sequence processing such as RNNs. Despite their sizeable performance gains, as recently suggested, the model is computationally expensive to train and with a high parameter budget. In light of this, we explore parameter-sharing methods in Transformers with a specific focus on generative models. We perform an analysis of different parameter sharing/reduction methods and develop the Subformer. Our model combines sandwich-style parameter sharing, which overcomes naive cross-layer parameter sharing in generative models, and self-attentive embedding factorization (SAFE). Experiments on machine translation, abstractive summarization and language modeling show that the Subformer can outperform the Transformer even when using significantly fewer parameters.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_345", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_345.flac", "type": "audio", "duration": 232.085}, {"from": "Assistant", "value": "This paper presents an unsupervised extractive approach to summarize scientific long documents based on the Information Bottleneck principle. Inspired by previous work which uses the Information Bottleneck principle for sentence compression, we extend it to document level summarization with two separate steps. In the first step, we use signal(s) as queries to retrieve the key content from the source document. Then, a pre-trained language model conducts further sentence search and edit to return the final extracted summaries. Importantly, our work can be flexibly extended to a multi-view framework by different signals. Automatic evaluation on three scientific document datasets verifies the effectiveness of the proposed framework. The further human evaluation suggests that the extracted summaries cover more content aspects than previous systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_346", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_346.flac", "type": "audio", "duration": 159.211}, {"from": "Assistant", "value": "We present Hidden-State Optimization (HSO), a gradient-based method for improving the performance of transformer language models at inference time. Similar to dynamic evaluation (Krause et al., 2018), HSO computes the gradient of the log-probability the language model assigns to an evaluation text, but uses it to update the cached hidden states rather than the model parameters. We test HSO with pretrained Transformer-XL and GPT-2 language models, finding improvement on the WikiText-103 and PG-19 datasets in terms of perplexity, especially when evaluating a model outside of its training distribution. We also demonstrate downstream applicability by showing gains in the recently developed prompt-based few-shot evaluation setting, again with no extra parameters or training data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_347", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_347.flac", "type": "audio", "duration": 358.059}, {"from": "Assistant", "value": "Few-shot table-to-text generation is a task of composing fluent and faithful sentences to convey table content using limited data. Despite many efforts having been made towards generating impressive fluent sentences by fine-tuning powerful pre-trained language models, the faithfulness of generated content still needs to be improved. To this end, this paper proposes a novel approach Attend, Memorize and Generate (called AMG), inspired by the text generation process of humans. In particular, AMG (1) attends over the multi-granularity of context using a novel strategy based on table slot level and traditional token-by-token level attention to exploit both the table structure and natural linguistic information; (2) dynamically memorizes the table slot allocation states; and (3) generates faithful sentences according to both the context and memory allocation states. Comprehensive experiments with human evaluation on three domains (i.e., humans, songs, and books) of the Wiki dataset show that our model can generate higher qualified texts when compared with several state-of-the-art baselines, in both fluency and faithfulness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_348", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_348.flac", "type": "audio", "duration": 339.179}, {"from": "Assistant", "value": "Adversarial regularization can improve model generalization in many natural language processing tasks. However, conventional approaches are computationally expensive since they need to generate a perturbation for each sample in each epoch. We propose a new adversarial regularization method ARCH (adversarial regularization with caching), where perturbations are generated and cached once every several epochs. As caching all the perturbations imposes memory usage concerns, we adopt a K-nearest neighbors-based strategy to tackle this issue. The strategy only requires caching a small amount of perturbations, without introducing additional training time. We evaluate our proposed method on a set of neural machine translation and natural language understanding tasks. We observe that ARCH significantly eases the computational burden (saves up to 70% of computational time in comparison with conventional approaches). More surprisingly, by reducing the variance of stochastic gradients, ARCH produces a notably better (in most of the tasks) or comparable model generalization. Our code is publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_349", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_349.flac", "type": "audio", "duration": 370.923}, {"from": "Assistant", "value": "Humans use commonsense reasoning (CSR) implicitly to produce natural and coherent responses in conversations. Aiming to close the gap between current response generation (RG) models and human communication abilities, we want to understand why RG models respond as they do by probing RG model’s understanding of commonsense reasoning that elicits proper responses. We formalize the problem by framing commonsense as a latent variable in the RG task and using explanations for responses as textual form of commonsense. We collect 6k annotated explanations justifying responses from four dialogue datasets and ask humans to verify them and propose two probing settings to evaluate RG models’ CSR capabilities. Probing results show that models fail to capture the logical relations between commonsense explanations and responses and fine-tuning on in-domain data and increasing model sizes do not lead to understanding of CSR for RG. We hope our study motivates more research in making RG models emulate the human reasoning process in pursuit of smooth human-AI communication.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_350", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_350.flac", "type": "audio", "duration": 342.059}, {"from": "Assistant", "value": "While diverse question answering (QA) datasets have been proposed and contributed significantly to the development of deep learning models for QA tasks, the existing datasets fall short in two aspects. First, we lack QA datasets covering complex questions that involve answers as well as the reasoning processes to get them. As a result, the state-of-the-art QA research on numerical reasoning still focuses on simple calculations and does not provide the mathematical expressions or evidence justifying the answers. Second, the QA community has contributed a lot of effort to improve the interpretability of QA models. However, they fail to explicitly show the reasoning process, such as the evidence order for reasoning and the interactions between different pieces of evidence. To address the above shortcoming, we introduce NOAHQA, a conversational and bilingual QA dataset with questions requiring numerical reasoning with compound mathematical expressions. With NOAHQA, we develop an interpretable reasoning graph as well as the appropriate evaluation metric to measure the answer quality. We evaluate the state-of-the-art QA models trained using existing QA datasets on NOAHQA and show that the best among them can only achieve 55.5 exact match scores, while the human performance is 89.7. We also present a new QA model for generating a reasoning graph where the reasoning graph metric still has a large gap compared with that of humans, eg, 28 scores.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_351", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_351.flac", "type": "audio", "duration": 362.592}, {"from": "Assistant", "value": "Natural language processing systems such as dialogue agents should be able to reason about other people’s beliefs, intentions and desires. This capability, called theory of mind (ToM), is crucial, as it allows a model to predict and interpret the needs of users based on their mental states. A recent line of research evaluates the ToM capability of existing memory-augmented neural models through question-answering. These models perform poorly on false belief tasks where beliefs differ from reality, especially when the dataset contains distracting sentences. In this paper, we propose a new temporally informed approach for improving the ToM capability of memory-augmented neural models. Our model incorporates priors about the entities’ minds and tracks their mental states as they evolve over time through an extended passage. It then responds to queries through textual time travel–i.e., by accessing the stored memory of an earlier time step. We evaluate our model on ToM datasets and find that this approach improves performance, particularly by correcting the predicted mental states to match the false belief.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_352", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_352.flac", "type": "audio", "duration": 177.006}, {"from": "Assistant", "value": "Written language carries explicit and implicit biases that can distract from meaningful signals. For example, letters of reference may describe male and female candidates differently, or their writing style may indirectly reveal demographic characteristics. At best, such biases distract from the meaningful content of the text; at worst they can lead to unfair outcomes. We investigate the challenge of re-generating input sentences to ‘neutralize’ sensitive attributes while maintaining the semantic meaning of the original text (e.g. is the candidate qualified?). We propose a gradient-based rewriting framework, Detect and Perturb to Neutralize (DEPEN), that first detects sensitive components and masks them for regeneration, then perturbs the generation model at decoding time under a neutralizing constraint that pushes the (predicted) distribution of sensitive attributes towards a uniform distribution. Our experiments in two different scenarios show that DEPEN can regenerate fluent alternatives that are neutral in the sensitive attribute while maintaining the semantics of other attributes.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_353", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_353.flac", "type": "audio", "duration": 360.049}, {"from": "Assistant", "value": "Taxonomies are valuable resources for many applications, but the limited coverage due to the expensive manual curation process hinders their general applicability. Prior works attempt to automatically expand existing taxonomies to improve their coverage by learning concept embeddings in Euclidean space, while taxonomies, inherently hierarchical, more naturally align with the geometric properties of a hyperbolic space. In this paper, we present HyperExpan, a taxonomy expansion algorithm that seeks to preserve the structure of a taxonomy in a more expressive hyperbolic embedding space and learn to represent concepts and their relations with a Hyperbolic Graph Neural Network (HGNN). Specifically, HyperExpan leverages position embeddings to exploit the structure of the existing taxonomies, and characterizes the concept profile information to support the inference on new concepts that are unseen during training. Experiments show that our proposed HyperExpan outperforms baseline models with representation learning in a Euclidean feature space and achieves state-of-the-art performance on the taxonomy expansion benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_354", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_354.flac", "type": "audio", "duration": 339.051}, {"from": "Assistant", "value": "Data annotation is a time-consuming and labor-intensive process for many NLP tasks. Although there exist various methods to produce pseudo data labels, they are often task-specific and require a decent amount of labeled data to start with. Recently, the immense language model GPT-3 with 170 billion parameters has achieved tremendous improvement across many few-shot learning tasks. In this paper, we explore ways to leverage GPT-3 as a low-cost data labeler to train other models. We find that to make the downstream model achieve the same performance on a variety of NLU and NLG tasks, it costs 50% to 96% less to use labels from GPT-3 than using labels from humans. Furthermore, we propose a novel framework of combining pseudo labels from GPT-3 with human labels, which leads to even better performance. These results present a cost-effective data labeling methodology that is generalizable to many practical applications.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_355", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_355.flac", "type": "audio", "duration": 362.046}, {"from": "Assistant", "value": "This paper demonstrates that aggregating crowdsourced forecasts benefits from modeling the written justifications provided by forecasters. Our experiments show that the majority and weighted vote baselines are competitive, and that the written justifications are beneficial to call a question throughout its life except in the last quarter. We also conduct an error analysis shedding light into the characteristics that make a justification unreliable.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_356", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_356.flac", "type": "audio", "duration": 325.845}, {"from": "Assistant", "value": "Substantial amounts of work are required to clean large collections of digitized books for NLP analysis, both because of the presence of errors in the scanned text and the presence of duplicate volumes in the corpora. In this paper, we consider the issue of deduplication in the presence of optical character recognition (OCR) errors. We present methods to handle these errors, evaluated on a collection of 19,347 texts from the Project Gutenberg dataset and 96,635 texts from the HathiTrust Library. We demonstrate that improvements in language models now enable the detection and correction of OCR errors without consideration of the scanning image itself. The inconsistencies found by aligning pairs of scans of the same underlying work provides training data to build models for detecting and correcting errors. We identify the canonical version for each of 17,136 repeatedly-scanned books from 58,808 scans. Finally, we investigate methods to detect and correct errors in single-copy texts. We show that on average, our method corrects over six times as many errors as it introduces. We also provide interesting analysis on the relation between scanning quality and other factors such as location and publication year.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_357", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_357.flac", "type": "audio", "duration": 174.335}, {"from": "Assistant", "value": "Improving Transformer efficiency has become increasingly attractive recently. A wide range of methods has been proposed, e.g., pruning, quantization, new architectures and etc. But these methods are either sophisticated in implementation or dependent on hardware. In this paper, we show that the efficiency of Transformer can be improved by combining some simple and hardware-agnostic methods, including tuning hyper-parameters, better design choices and training strategies. On the WMT news translation tasks, we improve the inference efficiency of a strong Transformer system by 3.80x on CPU and 2.52x on GPU.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_358", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_358.flac", "type": "audio", "duration": 174.059}, {"from": "Assistant", "value": "Recently, kNN-MT (Khandelwal et al., 2020) has shown the promising capability of directly incorporating the pre-trained neural machine translation (NMT) model with domain-specific token-level k-nearest-neighbor (kNN) retrieval to achieve domain adaptation without retraining. Despite being conceptually attractive, it heavily relies on high-quality in-domain parallel corpora, limiting its capability on unsupervised domain adaptation, where in-domain parallel corpora are scarce or nonexistent. In this paper, we propose a novel framework that directly uses in-domain monolingual sentences in the target language to construct an effective datastore for k-nearest-neighbor retrieval. To this end, we first introduce an autoencoder task based on the target language, and then insert lightweight adapters into the original NMT model to map the token-level representation of this task to the ideal representation of the translation task. Experiments on multi-domain datasets demonstrate that our proposed approach significantly improves the translation accuracy with target-side monolingual data, while achieving comparable performance with back-translation. Our implementation is open-sourced athttps://github.com/zhengxxn/UDA-KNN.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_359", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_359.flac", "type": "audio", "duration": 360.049}, {"from": "Assistant", "value": "Authorship attribution is the problem of identifying the most plausible author of an anonymous text from a set of candidate authors. Researchers have investigated same-topic and cross-topic scenarios of authorship attribution, which differ according to whether new, unseen topics are used in the testing phase. However, neither scenario allows us to explain whether errors are caused by failure to capture authorship writing style or by the topic shift. Motivated by this, we propose thetopic confusiontask where we switch the author-topic configuration between the training and testing sets. This setup allows us to investigate two types of errors: one caused by the topic shift and one caused by the features’ inability to capture the writing styles. We show that stylometric features with part-of-speech tags are the least susceptible to topic variations. We further show that combining them with other features leads to significantly lower topic confusion and higher attribution accuracy. Finally, we show that pretrained language models such as BERT and RoBERTa perform poorly on this task and are surpassed by simple features such as word-leveln-gram.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_360", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_360.flac", "type": "audio", "duration": 354.454}, {"from": "Assistant", "value": "Many statistical models have high accuracy on test benchmarks, but are not explainable, struggle in low-resource scenarios, cannot be reused for multiple tasks, and cannot easily integrate domain expertise. These factors limit their use, particularly in settings such as mental health, where it is difficult to annotate datasets and model outputs have significant impact. We introduce a micromodel architecture to address these challenges. Our approach allows researchers to build interpretable representations that embed domain knowledge and provide explanations throughout the model’s decision process. We demonstrate the idea on multiple mental health tasks: depression classification, PTSD classification, and suicidal risk assessment. Our systems consistently produce strong results, even in low-resource scenarios, and are more interpretable than alternative methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_361", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_361.flac", "type": "audio", "duration": 361.045}, {"from": "Assistant", "value": "Legal texts routinely use concepts that are difficult to understand. Lawyers elaborate on the meaning of such concepts by, among other things, carefully investigating how they have been used in the past. Finding text snippets that mention a particular concept in a useful way is tedious, time-consuming, and hence expensive. We assembled a data set of 26,959 sentences, coming from legal case decisions, and labeled them in terms of their usefulness for explaining selected legal concepts. Using the dataset we study the effectiveness of transformer models pre-trained on large language corpora to detect which of the sentences are useful. In light of models’ predictions, we analyze various linguistic properties of the explanatory sentences as well as their relationship to the legal concept that needs to be explained. We show that the transformer-based models are capable of learning surprisingly sophisticated features and outperform the prior approaches to the task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_362", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_362.flac", "type": "audio", "duration": 302.251}, {"from": "Assistant", "value": "Despite the success of neural dialogue systems in achieving high performance on the leader-board, they cannot meet users’ requirements in practice, due to their poor reasoning skills. The underlying reason is that most neural dialogue models only capture the syntactic and semantic information, but fail to model the logical consistency between the dialogue history and the generated response. Recently, a new multi-turn dialogue reasoning task has been proposed, to facilitate dialogue reasoning research. However, this task is challenging, because there are only slight differences between the illogical response and the dialogue history. How to effectively solve this challenge is still worth exploring. This paper proposes a Fine-grained Comparison Model (FCM) to tackle this problem. Inspired by human’s behavior in reading comprehension, a comparison mechanism is proposed to focus on the fine-grained differences in the representation of each response candidate. Specifically, each candidate representation is compared with the whole history to obtain a history consistency representation. Furthermore, the consistency signals between each candidate and the speaker’s own history are considered to drive a model prefer a candidate that is logically consistent with the speaker’s history logic. Finally, the above consistency representations are employed to output a ranking list of the candidate responses for multi-turn dialogue reasoning. Experimental results on two public dialogue datasets show that our method obtains higher ranking scores than the baseline models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_363", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_363.flac", "type": "audio", "duration": 193.92}, {"from": "Assistant", "value": "Answer Sentence Selection (AS2) models are core components of efficient retrieval-based Question Answering (QA) systems. We present the Reference-based Weak Supervision (RWS), a fully automatic large-scale data pipeline that harvests high-quality weakly- supervised answer sentences from Web data, only requiring a question-reference pair as input. We evaluated the quality of the RWS-derived data by training TANDA models, which are the state of the art for AS2. Our results show that the data consistently bolsters TANDA on three different datasets. In particular, we set the new state of the art for AS2 to P@1=90.1%, and MAP=92.9%, on WikiQA. We record similar performance gains of RWS on a much larger dataset named Web-based Question Answering (WQA).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_364", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_364.flac", "type": "audio", "duration": 437.023}, {"from": "Assistant", "value": "Recently, disentanglement based on a generative adversarial network or a variational autoencoder has significantly advanced the performance of diverse applications in CV and NLP domains. Nevertheless, those models still work on coarse levels in the disentanglement of closely related properties, such as syntax and semantics in human languages. This paper introduces a deep decomposable model based on VAE to disentangle syntax and semantics by using total correlation penalties on KL divergences. Notably, we decompose the KL divergence term of the original VAE so that the generated latent variables can be separated in a more clear-cut and interpretable way. Experiments on benchmark datasets show that our proposed model can significantly improve the disentanglement quality between syntactic and semantic representations for semantic similarity tasks and syntactic similarity tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_365", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_365.flac", "type": "audio", "duration": 356.053}, {"from": "Assistant", "value": "Current state-of-the-art supervised word sense disambiguation (WSD) systems (such as GlossBERT and bi-encoder model) yield surprisingly good results by purely leveraging pre-trained language models and short dictionary definitions (or glosses) of the different word senses. While concise and intuitive, the sense gloss is just one of many ways to provide information about word senses. In this paper, we focus on enhancing the sense representations via incorporating synonyms, example phrases or sentences showing usage of word senses, and sense gloss of hypernyms. We show that incorporating such additional information boosts the performance on WSD. With the proposed enhancements, our system achieves an F1 score of 82.0% on the standard benchmark test dataset of the English all-words WSD task, surpassing all previous published scores on this benchmark dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_366", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_366.flac", "type": "audio", "duration": 496.001}, {"from": "Assistant", "value": "Zero-shot translation, directly translating between language pairs unseen in training, is a promising capability of multilingual neural machine translation (NMT). However, it usually suffers from capturing spurious correlations between the output language and language invariant semantics due to the maximum likelihood training objective, leading to poor transfer performance on zero-shot translation. In this paper, we introduce a denoising autoencoder objective based on pivot language into traditional training objective to improve the translation accuracy on zero-shot directions. The theoretical analysis from the perspective of latent variables shows that our approach actually implicitly maximizes the probability distributions for zero-shot directions. On two benchmark machine translation datasets, we demonstrate that the proposed method is able to effectively eliminate the spurious correlations and significantly outperforms state-of-the-art methods with a remarkable performance. Our code is available athttps://github.com/Victorwz/zs-nmt-dae.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_367", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_367.flac", "type": "audio", "duration": 356.053}, {"from": "Assistant", "value": "Error correction is widely used in automatic speech recognition (ASR) to post-process the generated sentence, and can further reduce the word error rate (WER). Although multiple candidates are generated by an ASR system through beam search, current error correction approaches can only correct one sentence at a time, failing to leverage the voting effect from multiple candidates to better detect and correct error tokens. In this work, we propose FastCorrect 2, an error correction model that takes multiple ASR candidates as input for better correction accuracy. FastCorrect 2 adopts non-autoregressive generation for fast inference, which consists of an encoder that processes multiple source sentences and a decoder that generates the target sentence in parallel from the adjusted source sentence, where the adjustment is based on the predicted duration of each source token. However, there are some issues when handling multiple source sentences. First, it is non-trivial to leverage the voting effect from multiple source sentences since they usually vary in length. Thus, we propose a novel alignment algorithm to maximize the degree of token alignment among multiple sentences in terms of token and pronunciation similarity. Second, the decoder can only take one adjusted source sentence as input, while there are multiple source sentences. Thus, we develop a candidate predictor to detect the most suitable candidate for the decoder. Experiments on our inhouse dataset and AISHELL-1 show that FastCorrect 2 can further reduce the WER over the previous correction model with single candidate by 3.2% and 2.6%, demonstrating the effectiveness of leveraging multiple candidates in ASR error correction. FastCorrect 2 achieves better performance than the cascaded re-scoring and correction pipeline and can serve as a unified post-processing module for ASR.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_368", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_368.flac", "type": "audio", "duration": 325.376}, {"from": "Assistant", "value": "A reliable clustering algorithm for task-oriented dialogues can help developer analysis and define dialogue tasks efficiently. It is challenging to directly apply prior normal text clustering algorithms for task-oriented dialogues, due to the inherent differences between them, such as coreference, omission and diversity expression. In this paper, we propose a Dialogue Task Clustering Network model for task-oriented clustering. The proposed model combines context-aware utterance representations and cross-dialogue utterance cluster representations for task-oriented dialogues clustering. An iterative end-to-end training strategy is utilized for dialogue clustering and representation learning jointly. Experiments on three public datasets show that our model significantly outperform strong baselines in all metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_369", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_369.flac", "type": "audio", "duration": 163.2}, {"from": "Assistant", "value": "NLP models are vulnerable to data poisoning attacks. One type of attack can plant a backdoor in a model by injecting poisoned examples in training, causing the victim model to misclassify test instances which include a specific pattern. Although defences exist to counter these attacks, they are specific to an attack type or pattern. In this paper, we propose a generic defence mechanism by making the training process robust to poisoning attacks through gradient shaping methods, based on differentially private training. We show that our method is highly effective in mitigating, or even eliminating, poisoning attacks on text classification, with only a small cost in predictive accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_370", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_370.flac", "type": "audio", "duration": 287.436}, {"from": "Assistant", "value": "Linguistic representations derived from text alone have been criticized for their lack of grounding, i.e., connecting words to their meanings in the physical world. Vision-and- Language (VL) models, trained jointly on text and image or video data, have been offered as a response to such criticisms. However, while VL pretraining has shown success on multimodal tasks such as visual question answering, it is not yet known how the internal linguistic representations themselves compare to their text-only counterparts. This paper compares the semantic representations learned via VL vs. text-only pretraining for two recent VL models using a suite of analyses (clustering, probing, and performance on a commonsense question answering task) in a language-only setting. We find that the multimodal models fail to significantly outperform the text-only variants, suggesting that future work is required if multimodal pretraining is to be pursued as a means of improving NLP in general.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_371", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_371.flac", "type": "audio", "duration": 369.33}, {"from": "Assistant", "value": "Unsupervised PCFG induction models, which build syntactic structures from raw text, can be used to evaluate the extent to which syntactic knowledge can be acquired from distributional information alone. However, many state-of-the-art PCFG induction models are word-based, meaning that they cannot directly inspect functional affixes, which may provide crucial information for syntactic acquisition in child learners. This work first introduces a neural PCFG induction model that allows a clean ablation of the influence of subword information in grammar induction. Experiments on child-directed speech demonstrate first that the incorporation of subword information results in more accurate grammars with categories that word-based induction models have difficulty finding, and second that this effect is amplified in morphologically richer languages that rely on functional affixes to express grammatical relations. A subsequent evaluation on multilingual treebanks shows that the model with subword information achieves state-of-the-art results on many languages, further supporting a distributional model of syntactic acquisition.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_372", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_372.flac", "type": "audio", "duration": 351.488}, {"from": "Assistant", "value": "Word embedding is essential for neural network models for various natural language processing tasks. Since the word embedding usually has a considerable size, in order to deploy a neural network model having it on edge devices, it should be effectively compressed. There was a study for proposing a block-wise low-rank approximation method for word embedding, called GroupReduce. Even if their structure is effective, the properties behind the concept of the block-wise word embedding compression were not sufficiently explored. Motivated by this, we improve GroupReduce in terms of word weighting and structuring. For word weighting, we propose a simple yet effective method inspired by the term frequency-inverse document frequency method and a novel differentiable method. Based on them, we construct a discriminative word embedding compression algorithm. In the experiments, we demonstrate that the proposed algorithm more effectively finds word weights than competitors in most cases. In addition, we show that the proposed algorithm can act like a framework through successful cooperation with quantization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_373", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_373.flac", "type": "audio", "duration": 188.053}, {"from": "Assistant", "value": "Code-switching (CS), a ubiquitous phenomenon due to the ease of communication it offers in multilingual communities still remains an understudied problem in language processing. The primary reasons behind this are: (1) minimal efforts in leveraging large pretrained multilingual models, and (2) the lack of annotated data. The distinguishing case of low performance of multilingual models in CS is the intra-sentence mixing of languages leading to switch points. We first benchmark two sequence labeling tasks – POS and NER on 4 different language pairs with a suite of pretrained models to identify the problems and select the best performing char-BERT model among them (addressing (1)). We then propose a self training method to repurpose the existing pretrained models using a switch-point bias by leveraging unannotated data (addressing (2)). We finally demonstrate that our approach performs well on both tasks by reducing the gap between the switch point performance while retaining the overall performance on two distinct language pairs in both the tasks. We plan to release our models and the code for all our experiments.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_374", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_374.flac", "type": "audio", "duration": 360.002}, {"from": "Assistant", "value": "Among the most critical limitations of deep learning NLP models are their lack of interpretability, and their reliance on spurious correlations. Prior work proposed various approaches to interpreting the black-box models to unveil the spurious correlations, but the research was primarily used in human-computer interaction scenarios. It still remains underexplored whether or how such model interpretations can be used to automatically “unlearn” confounding features. In this work, we propose influence tuning—a procedure that leverages model interpretations to update the model parameters towards a plausible interpretation (rather than an interpretation that relies on spurious patterns in the data) in addition to learning to predict the task labels. We show that in a controlled setup, influence tuning can help deconfounding the model from spurious patterns in data, significantly outperforming baseline methods that use adversarial training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_375", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_375.flac", "type": "audio", "duration": 229.042}, {"from": "Assistant", "value": "It has been shown that training multi-task models with auxiliary tasks can improve the target task quality through cross-task transfer. However, the importance of each auxiliary task to the primary task is likely not known a priori. While the importance weights of auxiliary tasks can be manually tuned, it becomes practically infeasible with the number of tasks scaling up. To address this, we propose a search method that automatically assigns importance weights. We formulate it as a reinforcement learning problem and learn a task sampling schedule based on the evaluation accuracy of the multi-task model. Our empirical evaluation on XNLI and GLUE shows that our method outperforms uniform sampling and the corresponding single-task baseline.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_376", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_376.flac", "type": "audio", "duration": 357.888}, {"from": "Assistant", "value": "This paper presents a neural framework of untied independent modules, used here for integrating off the shelf knowledge sources such as language models, lexica, POS information, and dependency relations. Each knowledge source is implemented as an independent component that can interact and share information with other knowledge sources. We report proof of concept experiments for several standard sentiment analysis tasks and show that the knowledge sources interoperate effectively without interference. As a second use-case, we show that the proposed framework is suitable for optimizing BERT-like language models even without the help of external knowledge sources. We cast each Transformer layer as a separate module and demonstrate performance improvements from this explicit integration of the different information encoded at the different Transformer layers .", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_377", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_377.flac", "type": "audio", "duration": 165.047}, {"from": "Assistant", "value": "Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series. However, real-world dialogues pose a great challenge to current summarization models, as the dialogue length typically exceeds the input limits imposed by recent transformer-based pre-trained models, and the interactive nature of dialogues makes relevant information more context-dependent and sparsely distributed than news articles. In this work, we perform a comprehensive study on long dialogue summarization by investigating three strategies to deal with the lengthy input problem and locate relevant information: (1) extended transformer models such as Longformer, (2) retrieve-then-summarize pipeline models with several dialogue utterance retrieval methods, and (3) hierarchical dialogue encoding models such as HMNet. Our experimental results on three long dialogue datasets (QMSum, MediaSum, SummScreen) show that the retrieve-then-summarize pipeline models yield the best performance. We also demonstrate that the summary quality can be further improved with a stronger retrieval model and pretraining on proper external summarization datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_378", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_378.flac", "type": "audio", "duration": 181.046}, {"from": "Assistant", "value": "Language models such as GPT-2 have performed well on constructing syntactically sound sentences for text auto-completion tasks. However, such models often require considerable training effort to adapt to specific writing domains (e.g., medical). In this paper, we propose an intermediate training strategy to enhance pre-trained language models’ performance in the text auto-completion task and fastly adapt them to specific domains. Our strategy includes a novel self-supervised training objective called Next Phrase Prediction (NPP), which encourages a language model to complete the partial query with enriched phrases and eventually improve the model’s text auto-completion performance. Preliminary experiments have shown that our approach is able to outperform the baselines in auto-completion for email and academic-writing domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_379", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_379.flac", "type": "audio", "duration": 414.012}, {"from": "Assistant", "value": "Internet memes have become powerful means to transmit political, psychological, and socio-cultural ideas. Although memes are typically humorous, recent days have witnessed an escalation of harmful memes used for trolling, cyberbullying, and abuse. Detecting such memes is challenging as they can be highly satirical and cryptic. Moreover, while previous work has focused on specific aspects of memes such as hate speech and propaganda, there has been little work on harm in general. Here, we aim to bridge this gap. In particular, we focus on two tasks: (i)detecting harmful memes, and (ii) identifying the social entities they target. We further extend the recently released HarMeme dataset, which covered COVID-19, with additional memes and a new topic: US politics. To solve these tasks, we propose MOMENTA (MultimOdal framework for detecting harmful MemEs aNd Their tArgets), a novel multimodal deep neural network that uses global and local perspectives to detect harmful memes. MOMENTA systematically analyzes the local and the global perspective of the input meme (in both modalities) and relates it to the background context. MOMENTA is interpretable and generalizable, and our experiments show that it outperforms several strong rivaling approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_380", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_380.flac", "type": "audio", "duration": 355.051}, {"from": "Assistant", "value": "Emotion and empathy are examples of human qualities lacking in many human-machine interactions. The goal of our work is to generate engaging dialogue grounded in a user-shared image with increased emotion and empathy while minimizing socially inappropriate or offensive outputs. We release the Neural Image Commenting with Empathy (NICE) dataset consisting of almost two million images and the corresponding human-generated comments, a set of human annotations, and baseline performance on a range of models. In-stead of relying on manually labeled emotions, we also use automatically generated linguistic representations as a source of weakly supervised labels. Based on these annotations, we define two different tasks for the NICE dataset. Then, we propose a novel pre-training model - Modeling Affect Generation for Image Comments (MAGIC) - which aims to generate comments for images, conditioned on linguistic representations that capture style and affect, and to help generate more empathetic, emotional, engaging and socially appropriate comments. Using this model we achieve state-of-the-art performance on one of our NICE tasks. The experiments show that the approach can generate more human-like and engaging image comments.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_381", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_381.flac", "type": "audio", "duration": 350.018}, {"from": "Assistant", "value": "Jupyter notebook allows data scientists to write machine learning code together with its documentation in cells. In this paper, we propose a new task of code documentation generation (CDG) for computational notebooks. In contrast to the previous CDG tasks which focus on generating documentation for single code snippets, in a computational notebook, one documentation in a markdown cell often corresponds to multiple code cells, and these code cells have an inherent structure. We proposed a new model (HAConvGNN) that uses a hierarchical attention mechanism to consider the relevant code cells and the relevant code tokens information when generating the documentation. Tested on a new corpus constructed from well-documented Kaggle notebooks, we show that our model outperforms other baseline models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_382", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_382.flac", "type": "audio", "duration": 357.099}, {"from": "Assistant", "value": "We propose using a multilabel probing task to assess the morphosyntactic representations of multilingual word embeddings. This tweak on canonical probing makes it easy to explore morphosyntactic representations, both holistically and at the level of individual features (e.g., gender, number, case), and leads more naturally to the study of how language models handle co-occurring features (e.g., agreement phenomena). We demonstrate this task with multilingual BERT (Devlin et al., 2018), training probes for seven typologically diverse languages: Afrikaans, Croatian, Finnish, Hebrew, Korean, Spanish, and Turkish. Through this simple but robust paradigm, we verify that multilingual BERT renders many morphosyntactic features simultaneously extractable. We further evaluate the probes on six held-out languages: Arabic, Chinese, Marathi, Slovenian, Tagalog, and Yoruba. This zero-shot style of probing has the added benefit of revealing which cross-linguistic properties a language model recognizes as being shared by multiple languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_383", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_383.flac", "type": "audio", "duration": 354.859}, {"from": "Assistant", "value": "Shared tasks have a long history and have become the mainstream of NLP research. Most of the shared tasks require participants to submit only system outputs and descriptions. It is uncommon for the shared task to request submission of the system itself because of the license issues and implementation differences. Therefore, many systems are abandoned without being used in real applications or contributing to better systems. In this research, we propose a scheme to utilize all those systems which participated in the shared tasks. We use all participated system outputs as task teachers in this scheme and develop a new model as a student aiming to learn the characteristics of each system. We call this scheme “Co-Teaching.” This scheme creates a unified system that performs better than the task’s single best system. It only requires the system outputs, and slightly extra effort is needed for the participants and organizers. We apply this scheme to the “SHINRA2019-JP” shared task, which has nine participants with various output accuracies, confirming that the unified system outperforms the best system. Moreover, the code used in our experiments has been released.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_385", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_385.flac", "type": "audio", "duration": 178.004}, {"from": "Assistant", "value": "Understanding when a text snippet does not provide a sought after information is an essential part of natural language utnderstanding. Recent work (SQuAD 2.0; Rajpurkar et al., 2018) has attempted to make some progress in this direction by enriching the SQuAD dataset for the Extractive QA task with unanswerable questions. However, as we show, the performance of a top system trained on SQuAD 2.0 drops considerably in out-of-domain scenarios, limiting its use in practical situations. In order to study this we build an out-of-domain corpus, focusing on simple event-based questions and distinguish between two types of IDK questions: competitive questions, where the context includes an entity of the same type as the expected answer, and simpler, non-competitive questions where there is no entity of the same type in the context. We find that SQuAD 2.0-based models fail even in the case of the simpler questions. We then analyze the similarities and differences between the IDK phenomenon in Extractive QA and the Recognizing Textual Entailments task (RTE; Dagan et al., 2013) and investigate the extent to which the latter can be used to improve the performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_386", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_386.flac", "type": "audio", "duration": 166.059}, {"from": "Assistant", "value": "Sememes are defined as the atomic units to describe the semantic meaning of concepts. Due to the difficulty of manually annotating sememes and the inconsistency of annotations between experts, the lexical sememe prediction task has been proposed. However, previous methods heavily rely on word or character embeddings, and ignore the fine-grained information. In this paper, we propose a novel pre-training method which is designed to better incorporate the internal information of Chinese character. The Glyph enhanced Chinese Character representation (GCC) is used to assist sememe prediction. We experiment and evaluate our model on HowNet, which is a famous sememe knowledge base. The experimental results show that our method outperforms existing non-external information models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_387", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_387.flac", "type": "audio", "duration": 348.053}, {"from": "Assistant", "value": "Social media has emerged as a key channel for seeking information. Online users spend several hours reading, posting, and searching for news on microblogging platforms daily. However, this could act as a double-edged sword especially when not all information online is reliable. Moreover, the inherently unmoderated nature of social media renders identifying unverified information ever more challenging. Most of the existing approaches for rumor tracking are not scalable because of their dependency on a significant amount of labeled data. In this work, we investigate this problem from different angles. We design an Active-Transfer Learning (ATL) strategy to identify rumors with a limited amount of annotated data. We go beyond that and investigate the impact of leveraging various machine learning approaches in addition to different contextual representations. We discuss the impact of multiple classifiers on a limited amount of annotated data followed by an interactive approach to gradually update the models by adding the least certain samples (LCS) from the pool of unlabeled data. Our proposed Active Learning (AL) strategy achieves faster convergence in terms of the F-score while requiring fewer annotated samples (42% of the whole dataset for the best model).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_388", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_388.flac", "type": "audio", "duration": 183.296}, {"from": "Assistant", "value": "Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare entities. Existing approaches are limited by the presence of coarse-grained structural resources in biomedical knowledge bases as well as the use of training datasets that provide low coverage over uncommon resources. In this work, we address these issues by proposing a cross-domain data integration method that transfers structural knowledge from a general text knowledge base to the medical domain. We utilize our integration scheme to augment structural resources and generate a large biomedical NED dataset for pretraining. Our pretrained model with injected structural knowledge achieves state-of-the-art performance on two benchmark medical NED datasets: MedMentions and BC5CDR. Furthermore, we improve disambiguation of rare entities by up to 57 accuracy points.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_389", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_389.flac", "type": "audio", "duration": 143.019}, {"from": "Assistant", "value": "We tackle the problem of self-training networks for NLU in low-resource environment—few labeled data and lots of unlabeled data. The effectiveness of self-training is a result of increasing the amount of training data while training. Yet it becomes less effective in low-resource settings due to unreliable labels predicted by the teacher model on unlabeled data. Rules of grammar, which describe the grammatical structure of data, have been used in NLU for better explainability. We propose to use rules of grammar in self-training as a more reliable pseudo-labeling mechanism, especially when there are few labeled data. We design an effective algorithm that constructs and expands rules of grammar without human involvement. Then we integrate the constructed rules as a pseudo-labeling mechanism into self-training. There are two possible scenarios regarding data distribution: it is unknown or known in prior to training. We empirically demonstrate that our approach substantially outperforms the state-of-the-art methods in three benchmark datasets for both scenarios.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_390", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_390.flac", "type": "audio", "duration": 338.923}, {"from": "Assistant", "value": "Aspect-based sentiment analysis (ABSA) typically focuses on extracting aspects and predicting their sentiments on individual sentences such as customer reviews. Recently, another kind of opinion sharing platform, namely question answering (QA) forum, has received increasing popularity, which accumulates a large number of user opinions towards various aspects. This motivates us to investigate the task of ABSA on QA forums (ABSA-QA), aiming to jointly detect the discussed aspects and their sentiment polarities for a given QA pair. Unlike review sentences, a QA pair is composed of two parallel sentences, which requires interaction modeling to align the aspect mentioned in the question and the associated opinion clues in the answer. To this end, we propose a model with a specific design of cross-sentence aspect-opinion interaction modeling to address this task. The proposed method is evaluated on three real-world datasets and the results show that our model outperforms several strong baselines adopted from related state-of-the-art models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_392", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_392.flac", "type": "audio", "duration": 361.047}, {"from": "Assistant", "value": "Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering. This can be primarily attributed to the absence of standard benchmark datasets. In this paper, we meticulously create a large amount of data connected with E-manuals and develop a suitable algorithm to exploit it. We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals, and pretrain RoBERTa on this large corpus. We create various benchmark QA datasets which include question answer pairs curated by experts based upon two E-manuals, real user questions from Community Question Answering Forum pertaining to E-manuals etc. We introduce EMQAP (E-Manual Question Answering Pipeline) that answers questions pertaining to electronics devices. Built upon the pretrained RoBERTa, it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section. For E-Manual annotated question-answer pairs, we show an improvement of about 40% in ROUGE-L F1 scores over most competitive baseline. We perform a detailed ablation study and establish the versatility of EMQAP across different circumstances. The code and datasets are shared athttps://github.com/abhi1nandy2/EMNLP-2021-Findings, and the corresponding project website ishttps://sites.google.com/view/emanualqa/home.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_393", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_393.flac", "type": "audio", "duration": 374.059}, {"from": "Assistant", "value": "Punctuation restoration is a fundamental requirement for the readability of text derived from Automatic Speech Recognition (ASR) systems. Most contemporary solutions are limited to predicting only a few of the most frequently occurring marks, such as periods, commas, and question marks - and only one per word. However, in written language, we deal with a much larger number of punctuation characters (such as parentheses, hyphens, etc.), and their combinations (like parenthesis followed by a dot). Such comprehensive punctuation cannot always be unambiguously reduced to a basic set of the most frequently occurring marks. In this work, we evaluate several methods in the comprehensive punctuation reconstruction task. We conduct experiments on parallel corpora of two different languages, English and Polish - languages with a relatively simple and complex morphology, respectively. We also investigate the influence of building a model on comprehensive punctuation on the quality of the basic punctuation restoration task", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_394", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_394.flac", "type": "audio", "duration": 288.299}, {"from": "Assistant", "value": "Generative conversation systems tend to produce meaningless and generic responses, which significantly reduce the user experience. In order to generate informative and diverse responses, recent studies proposed to fuse knowledge to improve informativeness and adopt latent variables to enhance the diversity. However, utilizing latent variables will lead to the inaccuracy of knowledge in the responses, and the dissemination of wrong knowledge will mislead the communicators. To address this problem, we propose a Syntactically Diverse Adversarial Network (SDAN) for knowledge-grounded conversation model. SDAN contains an adversarial hierarchical semantic network to keep the semantic coherence, a knowledge-aware network to attend more related knowledge for improving the informativeness and a syntactic latent variable network to generate syntactically diverse responses. Additionally, in order to increase the controllability of syntax, we adopt adversarial learning to decouple semantic and syntactic representations. Experimental results show that our model can not only generate syntactically diverse and knowledge-accurate responses but also significantly achieve the balance between improving the syntactic diversity and maintaining the knowledge accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_395", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_395.flac", "type": "audio", "duration": 179.051}, {"from": "Assistant", "value": "In this paper we propose QACE, a new metric based on Question Answering for Caption Evaluation to evaluate image captioning based on Question Generation(QG) and Question Answering(QA) systems. QACE generates questions on the evaluated caption and check its content by asking the questions on either the reference caption or the source image. We first develop QACE_Ref that compares the answers of the evaluated caption to its reference, and report competitive results with the state-of-the-art metrics. To go further, we propose QACE_Img, that asks the questions directly on the image, instead of reference. A Visual-QA system is necessary for QACE_Img. Unfortunately, the standard VQA models are actually framed a classification among only few thousands categories. Instead, we propose Visual-T5, an abstractive VQA system. The resulting metric, QACE_Img is multi-modal, reference-less and explainable. Our experiments show that QACE_Img compares favorably w.r.t. other reference-less metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_396", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_396.flac", "type": "audio", "duration": 179.371}, {"from": "Assistant", "value": "This paper presents Self-correcting Encoding (Secoco), a framework that effectively deals with noisy input for robust neural machine translation by introducing self-correcting predictors. Different from previous robust approaches, Secoco enables NMT to explicitly correct noisy inputs and delete specific errors simultaneously with the translation decoding process. Secoco is able to achieve significant improvements over strong baselines on two real-world test sets and a benchmark WMT dataset with good interpretability. We will make our code and dataset publicly available soon.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_397", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_397.flac", "type": "audio", "duration": 356.053}, {"from": "Assistant", "value": "The ability to generate natural-language questions with controlled complexity levels is highly desirable as it further expands the applicability of question generation. In this paper, we propose an end-to-end neural complexity-controllable question generation model, which incorporates a mixture of experts (MoE) as the selector of soft templates to improve the accuracy of complexity control and the quality of generated questions. The soft templates capture question similarity while avoiding the expensive construction of actual templates. Our method introduces a novel, cross-domain complexity estimator to assess the complexity of a question, taking into account the passage, the question, the answer and their interactions. The experimental results on two benchmark QA datasets demonstrate that our QG model is superior to state-of-the-art methods in both automatic and manual evaluation. Moreover, our complexity estimator is significantly more accurate than the baselines in both in-domain and out-domain settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_398", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_398.flac", "type": "audio", "duration": 362.046}, {"from": "Assistant", "value": "We investigate predictors of anti-Asian hate among Twitter users throughout COVID-19. With the rise of xenophobia and polarization that has accompanied widespread social media usage in many nations, online hate has become a major social issue, attracting many researchers. Here, we apply natural language processing techniques to characterize social media users who began to post anti-Asian hate messages during COVID-19. We compare two user groups—those who posted anti-Asian slurs and those who did not—with respect to a rich set of features measured with data prior to COVID-19 and show that it is possible to predict who later publicly posted anti-Asian slurs. Our analysis of predictive features underlines the potential impact of news media and information sources that report on online hate and calls for further investigation into the role of polarized communication networks and news media.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_399", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_399.flac", "type": "audio", "duration": 340.053}, {"from": "Assistant", "value": "Analyzing microblogs where we post what we experience enables us to perform various applications such as social-trend analysis and entity recommendation. To track emerging trends in a variety of areas, we want to categorize information on emerging entities (e.g., Avatar 2) in microblog posts according to their types (e.g., Film). We thus introduce a new entity typing task that assigns a fine-grained type to each emerging entity when a burst of posts containing that entity is first observed in a microblog. The challenge is to perform typing from noisy microblog posts without relying on prior knowledge of the target entity. To tackle this task, we build large-scale Twitter datasets for English and Japanese using time-sensitive distant supervision. We then propose a modular neural typing model that encodes not only the entity and its contexts but also meta information in multiple posts. To type ‘homographic’ emerging entities (e.g., ‘Go’ means an emerging programming language and a classic board game), which contexts are noisy, we devise a context selector that finds related contexts of the target entity. Experiments on the Twitter datasets confirm the effectiveness of our typing model and the context selector.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_400", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_400.flac", "type": "audio", "duration": 179.499}, {"from": "Assistant", "value": "Many existing works have demonstrated that language is a helpful guider for image understanding by neural networks. We focus on a language-shaped learning problem in a few-shot setting, i.e., using language to improve few-shot image classification when language descriptions are only available during training. We propose a data-efficient method that can make the best usage of the few-shot images and the language available only in training. Experimental results on datasetShapeWorldandBirdsshow that our method outperforms other state-of-the-art baselines in language-shaped few-shot learning area, especially when training data is more severely limited. Therefore, we call our approach data-efficient language-shaped learning (DF-LSL).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_401", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_401.flac", "type": "audio", "duration": 353.059}, {"from": "Assistant", "value": "Quality Estimation (QE) plays an essential role in applications of Machine Translation (MT). Traditionally, a QE system accepts the original source text and translation from a black-box MT system as input. Recently, a few studies indicate that as a by-product of translation, QE benefits from the model and training data’s information of the MT system where the translations come from, and it is called the “glass-box QE”. In this paper, we extend the definition of “glass-box QE” generally to uncertainty quantification with both “black-box” and “glass-box” approaches and design several features deduced from them to blaze a new trial in improving QE’s performance. We propose a framework to fuse the feature engineering of uncertainty quantification into a pre-trained cross-lingual language model to predict the translation quality. Experiment results show that our method achieves state-of-the-art performances on the datasets of WMT 2020 QE shared task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_402", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_402.flac", "type": "audio", "duration": 172.013}, {"from": "Assistant", "value": "Automatic hate speech detection is hampered by the scarcity of labeled datasetd, leading to poor generalization. We employ pretrained language models (LMs) to alleviate this data bottleneck. We utilize the GPT LM for generating large amounts of synthetic hate speech sequences from available labeled examples, and leverage the generated data in fine-tuning large pretrained LMs on hate detection. An empirical study using the models of BERT, RoBERTa and ALBERT, shows that this approach improves generalization significantly and consistently within and across data distributions. In fact, we find that generating relevant labeled hate speech sequences is preferable to using out-of-domain, and sometimes also within-domain, human-labeled examples.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_403", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_403.flac", "type": "audio", "duration": 174.059}, {"from": "Assistant", "value": "There has been a significant progress in the field of Extractive Question Answering (EQA) in the recent years. However, most of them are reliant on annotations of answer-spans in the corresponding passages. In this work, we address the problem of EQA when no annotations are present for the answer span, i.e., when the dataset contains only questions and corresponding passages. Our method is based on auto-encoding of the question that performs a question answering task during encoding and a question generation task during decoding. We show that our method performs well in a zero-shot setting and can provide an additional loss to boost performance for EQA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_405", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_405.flac", "type": "audio", "duration": 187.478}, {"from": "Assistant", "value": "Existing techniques for mitigating dataset bias often leverage a biased model to identify biased instances. The role of these biased instances is then reduced during the training of the main model to enhance its robustness to out-of-distribution data. A common core assumption of these techniques is that the main model handles biased instances similarly to the biased model, in that it will resort to biases whenever available. In this paper, we show that this assumption does not hold in general. We carry out a critical investigation on two well-known datasets in the domain, MNLI and FEVER, along with two biased instance detection methods, partial-input and limited-capacity models. Our experiments show that in around a third to a half of instances, the biased model is unable to predict the main model’s behavior, highlighted by the significantly different parts of the input on which they base their decisions. Based on a manual validation, we also show that this estimate is highly in line with human interpretation. Our findings suggest that down-weighting of instances detected by bias detection methods, which is a widely-practiced procedure, is an unnecessary waste of training data. We release our code to facilitate reproducibility and future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_406", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_406.flac", "type": "audio", "duration": 350.041}, {"from": "Assistant", "value": "Lacking sufficient human-annotated data is one main challenge for abstract meaning representation (AMR) parsing. To alleviate this problem, previous works usually make use of silver data or pre-trained language models. In particular, one recent seq-to-seq work directly fine-tunes AMR graph sequences on the encoder-decoder pre-trained language model and achieves new state-of-the-art results, outperforming previous works by a large margin. However, it makes the decoding relatively slower. In this work, we investigate alternative approaches to achieve competitive performance at faster speeds. We propose a simplified AMR parser and a pre-training technique for the effective usage of silver data. We conduct extensive experiments on the widely used AMR2.0 dataset and the results demonstrate that our Transformer-based AMR parser achieves the best performance among the seq2graph-based models. Furthermore, with silver data, our model achieves competitive results with the SOTA model, and the speed is an order of magnitude faster. Detailed analyses are conducted to gain more insights into our proposed model and the effectiveness of the pre-training technique.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_407", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_407.flac", "type": "audio", "duration": 173.056}, {"from": "Assistant", "value": "Variational autoencoders have been studied as a promising approach to model one-to-many mappings from context to response in chat response generation. However, they often fail to learn proper mappings. One of the reasons for this failure is the discrepancy between a response and a latent variable sampled from an approximated distribution in training. Inappropriately sampled latent variables hinder models from constructing a modulated latent space. As a result, the models stop handling uncertainty in conversations. To resolve that, we propose speculative sampling of latent variables. Our method chooses the most probable one from redundantly sampled latent variables for tying up the variable with a given response. We confirm the efficacy of our method in response generation with massive dialogue data constructed from Twitter posts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_409", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_409.flac", "type": "audio", "duration": 176.054}, {"from": "Assistant", "value": "Exemplar-Guided Paraphrase Generation (EGPG) aims to generate a target sentence which conforms to the style of the given exemplar while encapsulating the content information of the source sentence. In this paper, we propose a new method with the goal of learning a better representation of the style and the content. This method is mainly motivated by the recent success of contrastive learning which has demonstrated its power in unsupervised feature extraction tasks. The idea is to design two contrastive losses with respect to the content and the style by considering two problem characteristics during training. One characteristic is that the target sentence shares the same content with the source sentence, and the second characteristic is that the target sentence shares the same style with the exemplar. These two contrastive losses are incorporated into the general encoder-decoder paradigm. Experiments on two datasets, namely QQP-Pos and ParaNMT, demonstrate the effectiveness of our proposed constrastive losses.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_411", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_411.flac", "type": "audio", "duration": 344.811}, {"from": "Assistant", "value": "Unfair stereotypical biases (e.g., gender, racial, or religious biases) encoded in modern pretrained language models (PLMs) have negative ethical implications for widespread adoption of state-of-the-art language technology. To remedy for this, a wide range of debiasing techniques have recently been introduced to remove such stereotypical biases from PLMs. Existing debiasing methods, however, directly modify all of the PLMs parameters, which – besides being computationally expensive – comes with the inherent risk of (catastrophic) forgetting of useful language knowledge acquired in pretraining. In this work, we propose a more sustainable modular debiasing approach based on dedicated debiasing adapters, dubbed ADELE. Concretely, we (1) inject adapter modules into the original PLM layers and (2) update only the adapters (i.e., we keep the original PLM parameters frozen) via language modeling training on a counterfactually augmented corpus. We showcase ADELE, in gender debiasing of BERT: our extensive evaluation, encompassing three intrinsic and two extrinsic bias measures, renders ADELE, very effective in bias mitigation. We further show that – due to its modular nature – ADELE, coupled with task adapters, retains fairness even after large-scale downstream training. Finally, by means of multilingual BERT, we successfully transfer ADELE, to six target languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_412", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_412.flac", "type": "audio", "duration": 346.133}, {"from": "Assistant", "value": "Relation detection in knowledge base question answering, aims to identify the path(s) of relations starting from the topic entity node that is linked to the answer node in knowledge graph. Such path might consist of multiple relations, which we call multi-hop. Moreover, for a single question, there may exist multiple relation paths to the correct answer, which we call multi-label. However, most of existing approaches only detect one single path to obtain the answer without considering other correct paths, which might affect the final performance. Therefore, in this paper, we propose a novel divide-and-conquer approach for multi-label multi-hop relation detection (DC-MLMH) by decomposing it into head relation detection and conditional relation path generation. In specific, a novel path sampling mechanism is proposed to generate diverse relation paths for the inference stage. A majority-vote policy is employed to detect final KB answer. Comprehensive experiments were conducted on the FreebaseQA benchmark dataset. Experimental results show that the proposed approach not only outperforms other competitive multi-label baselines, but also has superiority over some state-of-art KBQA methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_413", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_413.flac", "type": "audio", "duration": 347.733}, {"from": "Assistant", "value": "Deep learning models exhibit a preference for statistical fitting over logical reasoning. Spurious correlations might be memorized when there exists statistical bias in training data, which severely limits the model performance especially in small data scenarios. In this work, we introduce Counterfactual Adversarial Training framework (CAT) to tackle the problem from a causality perspective. Particularly, for a specific sample, CAT first generates a counterfactual representation through latent space interpolation in an adversarial manner, and then performs Counterfactual Risk Minimization (CRM) on each original-counterfactual pair to adjust sample-wise loss weight dynamically, which encourages the model to explore the true causal effect. Extensive experiments demonstrate that CAT achieves substantial performance improvement over SOTA across different downstream tasks, including sentence classification, natural language inference and question answering.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_414", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_414.flac", "type": "audio", "duration": 385.003}, {"from": "Assistant", "value": "A key part of the NLP ethics movement is responsible use of data, but exactly what that means or how it can be best achieved remain unclear. This position paper discusses the core legal and ethical principles for collection and sharing of textual data, and the tensions between them. We propose a potential checklist for responsible data (re-)use that could both standardise the peer review of conference submissions, as well as enable a more in-depth view of published research across the community. Our proposal aims to contribute to the development of a consistent standard for data (re-)use, embraced across NLP conferences.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_415", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_415.flac", "type": "audio", "duration": 178.051}, {"from": "Assistant", "value": "Generative Adversarial Networks (GANs) have achieved great success in image synthesis, but have proven to be difficult to generate natural language. Challenges arise from the uninformative learning signals passed from the discriminator. In other words, the poor learning signals limit the learning capacity for generating languages with rich structures and semantics. In this paper, we propose to adopt the counter-contrastive learning (CCL) method to support the generator’s training in language GANs. In contrast to standard GANs that adopt a simple binary classifier to discriminate whether a sample is real or fake, we employ a counter-contrastive learning signal that advances the training of language synthesizers by (1) pulling the language representations of generated and real samples together and (2) pushing apart representations of real samples to compete with the discriminator and thus prevent the discriminator from being overtrained. We evaluate our method on both synthetic and real benchmarks and yield competitive performance compared to previous GANs for adversarial sequence generation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_416", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_416.flac", "type": "audio", "duration": 304.043}, {"from": "Assistant", "value": "The narrative event prediction aims to predict what happens after a sequence of events, which is essential to modeling sophisticated real-world events. Existing studies focus on mining the inter-events relationships while ignoring how the events happened, which we called circumstances. With our observation, the event circumstances indicate what will happen next. To incorporate event circumstances into the narrative event prediction, we propose the CircEvent, which adopts the two multi-head attention to retrieve circumstances at the local and global levels. We also introduce a regularization of attention weights to leverage the alignment between events and local circumstances. The experimental results demonstrate our CircEvent outperforms existing baselines by 12.2%. The further analysis demonstrates the effectiveness of our multi-head attention modules and regularization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_417", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_417.flac", "type": "audio", "duration": 166.059}, {"from": "Assistant", "value": "We consider the problem of learning to repair erroneous C programs by learning optimal alignments with correct programs. Since the previous approaches fix a single error in a line, it is inevitable to iterate the fixing process until no errors remain. In this work, we propose a novel sequence-to-sequence learning framework for fixing multiple program errors at a time. We introduce the edit-distance-based data labeling approach for program error correction. Instead of labeling a program repair example by pairing an erroneous program with a line fix, we label the example by paring an erroneous program with an optimal alignment to the corresponding correct program produced by the edit-distance computation. We evaluate our proposed approach on a publicly available dataset (DeepFix dataset) that consists of erroneous C programs submitted by novice programming students. On a set of 6,975 erroneous C programs from the DeepFix dataset, our approach achieves the state-of-the-art result in terms of full repair rate on the DeepFix dataset (without extra data such as compiler error message or additional source codes for pre-training).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_418", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_418.flac", "type": "audio", "duration": 360.683}, {"from": "Assistant", "value": "Natural language processing (NLP) is often the backbone of today’s systems for user interactions, information retrieval and others. Many of such NLP applications rely on specialized learned representations (e.g. neural word embeddings, topic models) that improve the ability to reason about the relationships between documents of a corpus. Paired with the progress in learned representations, the similarity metrics used to compare representations of documents are also evolving, with numerous proposals differing in computation time or interpretability. In this paper we propose an extension to a specific emerging hybrid document distance metric which combines topic models and word embeddings: the Hierarchical Optimal Topic Transport (HOTT). In specific, we extend HOTT by using context-enhanced word representations. We provide a validation of our approach on public datasets, using the language model BERT for a document categorization task. Results indicate competitive performance of the extended HOTT metric. We furthermore apply the HOTT metric and its extension to support educational media research, with a retrieval task of matching topics in German curricula to educational textbooks passages, along with offering an auxiliary explanatory document representing the dominant topic of the retrieved document. In a user study, our explanation method is preferred over regular topic keywords.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_419", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_419.flac", "type": "audio", "duration": 159.057}, {"from": "Assistant", "value": "Although grammatical error correction (GEC) has achieved good performance on texts written by learners of English as a second language, performance on low error density domains where texts are written by English speakers of varying levels of proficiency can still be improved. In this paper, we propose a contrastive learning approach to encourage the GEC model to assign a higher probability to a correct sentence while reducing the probability of incorrect sentences that the model tends to generate, so as to improve the accuracy of the model. Experimental results show that our approach significantly improves the performance of GEC models in low error density domains, when evaluated on the benchmark CWEB dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_420", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_420.flac", "type": "audio", "duration": 360.683}, {"from": "Assistant", "value": "Recent methods based on pre-trained language models have shown strong supervised performance on commonsense reasoning. However, they rely on expensive data annotation and time-consuming training. Thus, we focus on unsupervised commonsense reasoning. We show the effectiveness of using a common framework, Natural Language Inference (NLI), to solve diverse commonsense reasoning tasks. By leveraging transfer learning from large NLI datasets, and injecting crucial knowledge from commonsense sources such as ATOMIC 2020 and ConceptNet, our method achieved state-of-the-art unsupervised performance on two commonsense reasoning tasks: WinoWhy and CommonsenseQA. Further analysis demonstrated the benefits of multiple categories of knowledge, but problems about quantities and antonyms are still challenging.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_421", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_421.flac", "type": "audio", "duration": 363.563}, {"from": "Assistant", "value": "Many crowdsourced NLP datasets contain systematic artifacts that are identified only after data collection is complete. Earlier identification of these issues should make it easier to create high-quality training and evaluation data. We attempt this by evaluating protocols in which expert linguists work ‘in the loop’ during data collection to identify and address these issues by adjusting task instructions and incentives. Using natural language inference as a test case, we compare three data collection protocols: (i) a baseline protocol with no linguist involvement, (ii) a linguist-in-the-loop intervention with iteratively-updated constraints on the writing task, and (iii) an extension that adds direct interaction between linguists and crowdworkers via a chatroom. We find that linguist involvement does not lead to increased accuracy on out-of-domain test sets compared to baseline, and adding a chatroom has no effect on the data. Linguist involvement does, however, lead to more challenging evaluation data and higher accuracy on some challenge sets, demonstrating the benefits of integrating expert analysis during data collection.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_findings-emnlp_423", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_findings-emnlp_423.flac", "type": "audio", "duration": 354.048}, {"from": "Assistant", "value": "For interpreting the behavior of a probabilistic model, it is useful to measure a model’s calibration—the extent to which it produces reliable confidence scores. We address the open problem of calibration for tagging models with sparse tagsets, and recommend strategies to measure and reduce calibration error (CE) in such models. We show that several post-hoc recalibration techniques all reduce calibration error across the marginal distribution for two existing sequence taggers. Moreover, we propose tag frequency grouping (TFG) as a way to measure calibration error in different frequency bands. Further, recalibrating each group separately promotes a more equitable reduction of calibration error across the tag frequency spectrum.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_blackboxnlp-1_1", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_blackboxnlp-1_1.flac", "type": "audio", "duration": 713.897}, {"from": "Assistant", "value": "Given the increasingly prominent role NLP models (will) play in our lives, it is important for human expectations of model behavior to align with actual model behavior. Using Natural Language Inference (NLI) as a case study, we investigate the extent to which human-generated explanations of models’ inference decisions align with how models actually make these decisions. More specifically, we define three alignment metrics that quantify how well natural language explanations align with model sensitivity to input words, as measured by integrated gradients. Then, we evaluate eight different models (the base and large versions of BERT,RoBERTa and ELECTRA, as well as anRNN and bag-of-words model), and find that the BERT-base model has the highest alignment with human-generated explanations, for all alignment metrics. Focusing in on transformers, we find that the base versions tend to have higher alignment with human-generated explanations than their larger counterparts, suggesting that increasing the number of model parameters leads, in some cases, to worse alignment with human explanations. Finally, we find that a model’s alignment with human explanations is not predicted by the model’s accuracy, suggesting that accuracy and alignment are complementary ways to evaluate models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_blackboxnlp-1_2", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_blackboxnlp-1_2.flac", "type": "audio", "duration": 691.003}, {"from": "Assistant", "value": "Previous work on probing word representations for linguistic knowledge has focused on interpolation tasks. In this paper, we instead analyse probes in an extrapolation setting, where the inputs at test time are deliberately chosen to be ‘harder’ than the training examples. We argue that such an analysis can shed further light on the open question whether probes actually decode linguistic knowledge, or merely learn the diagnostic task from shallow features. To quantify the hardness of an example, we consider scoring functions based on linguistic, statistical, and learning-related criteria, all of which are applicable to a broad range of NLP tasks. We discuss the relative merits of these criteria in the context of two syntactic probing tasks, part-of-speech tagging and syntactic dependency labelling. From our theoretical and experimental analysis, we conclude that distance-based and hard statistical criteria show the clearest differences between interpolation and extrapolation settings, while at the same time being transparent, intuitive, and easy to control.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_blackboxnlp-1_3", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_blackboxnlp-1_3.flac", "type": "audio", "duration": 623.015}, {"from": "Assistant", "value": "Natural language inference (NLI) requires models to learn and apply commonsense knowledge. These reasoning abilities are particularly important for explainable NLI systems that generate a natural language explanation in addition to their label prediction. The integration of external knowledge has been shown to improve NLI systems, here we investigate whether it can also improve their explanation capabilities. For this, we investigate different sources of external knowledge and evaluate the performance of our models on in-domain data as well as on special transfer datasets that are designed to assess fine-grained reasoning capabilities. We find that different sources of knowledge have a different effect on reasoning abilities, for example, implicit knowledge stored in language models can hinder reasoning on numbers and negations. Finally, we conduct the largest and most fine-grained explainable NLI crowdsourcing study to date. It reveals that even large differences in automatic performance scores do neither reflect in human ratings of label, explanation, commonsense nor grammar correctness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_blackboxnlp-1_4", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_blackboxnlp-1_4.flac", "type": "audio", "duration": 716.011}, {"from": "Assistant", "value": "Temporary syntactic ambiguities arise when the beginning of a sentence is compatible with multiple syntactic analyses. We inspect to which extent neural language models (LMs) exhibit uncertainty over such analyses when processing temporarily ambiguous inputs, and how that uncertainty is modulated by disambiguating cues. We probe the LM’s expectations by generating from it: we use stochastic decoding to derive a set of sentence completions, and estimate the probability that the LM assigns to each interpretation based on the distribution of parses across completions. Unlike scoring-based methods for targeted syntactic evaluation, this technique makes it possible to explore completions that are not hypothesized in advance by the researcher. We apply this method to study the behavior of two LMs (GPT2 and an LSTM) on three types of temporary ambiguity, using materials from human sentence processing experiments. We find that LMs can track multiple analyses simultaneously; the degree of uncertainty varies across constructions and contexts. As a response to disambiguating cues, the LMs often select the correct interpretation, but occasional errors point to potential areas of improvement", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_blackboxnlp-1_5", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_blackboxnlp-1_5.flac", "type": "audio", "duration": 616.683}, {"from": "Assistant", "value": "Minimal sentence pairs are frequently used to analyze the behavior of language models. It is often assumed that model behavior on contrastive pairs is predictive of model behavior at large. We argue that two conditions are necessary for this assumption to hold: First, a tested hypothesis should be well-motivated, since experiments show that contrastive evaluation can lead to false positives. Secondly, test data should be chosen such as to minimize distributional discrepancy between evaluation time and deployment time. For a good approximation of deployment-time decoding, we recommend that minimal pairs are created based on machine-generated text, as opposed to human-written references. We present a contrastive evaluation suite for English–German MT that implements this recommendation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_blackboxnlp-1_6", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_blackboxnlp-1_6.flac", "type": "audio", "duration": 672.009}, {"from": "Assistant", "value": "Adversarial attacks curated against NLP models are increasingly becoming practical threats. Although various methods have been developed to detect adversarial attacks, securing learning-based NLP systems in practice would require more than identifying and evading perturbed instances. To address these issues, we propose a new set of adversary identification tasks, Attacker Attribute Classification via Textual Analysis (AACTA), that attempts to obtain more detailed information about the attackers from adversarial texts. Specifically, given a piece of adversarial text, we hope to accomplish tasks such as localizing perturbed tokens, identifying the attacker’s access level to the target model, determining the evasion mechanism imposed, and specifying the perturbation type employed by the attacking algorithm. Our contributions are as follows: we formalize the task of classifying attacker attributes, and create a benchmark on various target models from sentiment classification and abuse detection domains. We show that signals from BERT models and target models can be used to train classifiers that reveal the properties of the attacking algorithms. We demonstrate that adversarial attacks leave interpretable traces in both feature spaces of pre-trained language models and target models, making AACTA a promising direction towards more trustworthy NLP systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_blackboxnlp-1_15", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_blackboxnlp-1_15.flac", "type": "audio", "duration": 179.003}, {"from": "Assistant", "value": "Recent work has shown evidence that the knowledge acquired by multilingual BERT (mBERT) has two components: a language-specific and a language-neutral one. This paper analyses the relationship between them, in the context of fine-tuning on two tasks – POS tagging and natural language inference – which require the model to bring to bear different degrees of language-specific knowledge. Visualisations reveal that mBERT loses the ability to cluster representations by language after fine-tuning, a result that is supported by evidence from language identification experiments. However, further experiments on ‘unlearning’ language-specific representations using gradient reversal and iterative adversarial learning are shown not to add further improvement to the language-independent component over and above the effect of fine-tuning. The results presented here suggest that the process of fine-tuning causes a reorganisation of the model’s limited representational capacity, enhancing language-independent representations at the expense of language-specific ones.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_blackboxnlp-1_20", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_blackboxnlp-1_20.flac", "type": "audio", "duration": 809.006}, {"from": "Assistant", "value": "The high performance of large pretrained language models (LLMs) such as BERT on NLP tasks has prompted questions about BERT’s linguistic capabilities, and how they differ from humans’. In this paper, we approach this question by examining BERT’s knowledge of lexical semantic relations. We focus on hypernymy, the “is-a” relation that relates a word to a superordinate category. We use a prompting methodology to simply ask BERT what the hypernym of a given word is. We find that, in a setting where all hypernyms are guessable via prompting, BERT knows hypernyms with up to 57% accuracy. Moreover, BERT with prompting outperforms other unsupervised models for hypernym discovery even in an unconstrained scenario. However, BERT’s predictions and performance on a dataset containing uncommon hyponyms and hypernyms indicate that its knowledge of hypernymy is still limited.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_codi-main_1", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_codi-main_1.flac", "type": "audio", "duration": 681.088}, {"from": "Assistant", "value": "Indirect answers are replies to polar questions without the direct use of word cues such as ‘yes’ and ‘no’. Humans are very good at understanding indirect answers, such as ‘I gotta go home sometime’, when asked ‘You wanna crash on the couch?’. Understanding indirect answers is a challenging problem for dialogue systems. In this paper, we introduce a new English corpus to study the problem of understanding indirect answers. Instead of crowd-sourcing both polar questions and answers, we collect questions and indirect answers from transcripts of a prominent TV series and manually annotate them for answer type. The resulting dataset contains 5,930 question-answer pairs. We release both aggregated and raw human annotations. We present a set of experiments in which we evaluate Convolutional Neural Networks (CNNs) for this task, including a cross-dataset evaluation and experiments with learning from disagreements in annotation. Our results show that the task of interpreting indirect answers remains challenging, yet we obtain encouraging improvements when explicitly modeling human disagreement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_codi-main_2", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_codi-main_2.flac", "type": "audio", "duration": 654.013}, {"from": "Assistant", "value": "In the field of humor research, there has been a recent surge of interest in the sub-domain of Conversational Humor (CH). This study has two main objectives. (a) develop a conversational (humorous and non-humorous) dataset in Telugu. (b) detect CH in the compiled dataset. In this paper, the challenges faced while collecting the data and experiments carried out are elucidated. Transfer learning and non-transfer learning techniques are implemented by utilizing pre-trained models such as FastText word embeddings, BERT language models and Text GCN, which learns the word and document embeddings simultaneously of the corpus given. State-of-the-art results are observed with a 99.3% accuracy and a 98.5% f1 score achieved by BERT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_codi-main_3", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_codi-main_3.flac", "type": "audio", "duration": 411.016}, {"from": "Assistant", "value": "We investigate linguistic markers associated with schizophrenia in clinical conversations by detecting predictive features among French-speaking patients. Dealing with human-human dialogues makes for a realistic situation, but it calls for strategies to represent the context and face data sparsity. We compare different approaches for data representation – from individual speech turns to entire conversations –, and data modeling, using lexical, morphological, syntactic, and discourse features, dimensions presumed to be tightly connected to the language of schizophrenia. Previous English models were mostly lexical and reached high performance, here replicated (93.7% acc.). However, our analysis reveals that these models are heavily biased, which probably concerns most datasets on this task. Our new delexicalized models are more general and robust, with the best accuracy score at 77.9%.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_codi-main_4", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_codi-main_4.flac", "type": "audio", "duration": 428.269}, {"from": "Assistant", "value": "Development environments for spoken dialogue systems are popular today because they enable rapid creation of the dialogue systems in times when usage of the voice AI Assistants is constantly growing. We describe a graphical Discourse-Driven Integrated Dialogue Development Environment (DD-IDDE) for spoken open-domain dialogue systems. The DD-IDDE allows dialogue architects to interactively define dialogue flows of their skills/chatbots with the aid of the discourse-driven recommendation system, enhance these flows in the Python-based DSL, deploy, and then further improve based on the skills/chatbots usage statistics. We show how these skills/chatbots can be specified through a graphical user interface within the VS Code Extension, and then run on top of the Dialog Flow Framework (DFF). An earlier version of this framework has been adopted in one of the Alexa Prize 4 socialbots while the updated version was specifically designed to power the described DD-IDDE solution.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_codi-main_6", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_codi-main_6.flac", "type": "audio", "duration": 712.361}, {"from": "Assistant", "value": "The usage of (co-)referring expressions in discourse contributes to the coherence of a text. However, text comprehension can be difficult when referring expressions are non-verbalized and have to be resolved in the discourse context. In this paper, we propose a novel dataset of such implicit references, which we automatically derive from insertions of references in collaboratively edited how-to guides. Our dataset consists of 6,014 instances, making it one of the largest datasets of implicit references and a useful starting point to investigate misunderstandings caused by underspecified language. We test different methods for resolving implicit references in our dataset based on the Generative Pre-trained Transformer model (GPT) and compare them to heuristic baselines. Our experiments indicate that GPT can accurately resolve the majority of implicit references in our data. Finally, we investigate remaining errors and examine human preferences regarding different resolutions of an implicit reference given the discourse context.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_codi-main_7", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_codi-main_7.flac", "type": "audio", "duration": 778.008}, {"from": "Assistant", "value": "In data-driven natural language generation, we typically know what relation should be expressed and need to select a connective to lexicalize it. In the current contribution, we analyse whether a sophisticated connective generation module is necessary to select a connective, or whether this can be solved with simple methods (such as random choice between connectives that are known to express a given relation, or usage of a generic language model). Comparing these methods to the distributions of connective choices from a human connective insertion task, we find mixed results: for some relations, it is acceptable to lexicalize them using any of the connectives that mark this relation. However, for other relations (temporals, concessives) either a more detailed relation distinction needs to be introduced, or a more sophisticated connective choice module would be necessary.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_codi-main_8", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_codi-main_8.flac", "type": "audio", "duration": 759.014}, {"from": "Assistant", "value": "Cross-linguistic research on discourse structure and coherence marking requires discourse-annotated corpora and connective lexicons in a large number of languages. However, the availability of such resources is limited, especially for languages for which linguistic resources are scarce in general, such as Nigerian Pidgin. In this study, we demonstrate how a semi-automatic approach can be used to source connectives and their relation senses and develop a discourse-annotated corpus in a low-resource language. Connectives and their relation senses were extracted from a parallel corpus combining automatic (PDTB end-to-end parser) and manual annotations. This resulted in Naija-Lex, a lexicon of discourse connectives in Nigerian Pidgin with English translations. The lexicon shows that the majority of Nigerian Pidgin connectives are borrowed from its English lexifier, but that there are also some connectives that are unique to Nigerian Pidgin.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_codi-main_9", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_codi-main_9.flac", "type": "audio", "duration": 742.017}, {"from": "Assistant", "value": "Existing parse methods use varying approaches to identify explicit discourse connectives, but their performance has not been consistently evaluated in comparison to each other, nor have they been evaluated consistently on text other than newspaper articles. We here assess the performance on explicit connective identification of three parse methods (PDTB e2e, Lin et al., 2014; the winner of CONLL2015, Wang et al., 2015; and DisSent, Nie et al., 2019), along with a simple heuristic. We also examine how well these systems generalize to different datasets, namely written newspaper text (PDTB), written scientific text (BioDRB), prepared spoken text (TED-MDB) and spontaneous spoken text (Disco-SPICE). The results show that the e2e parser outperforms the other parse methods in all datasets. However, performance drops significantly from the PDTB to all other datasets. We provide a more fine-grained analysis of domain differences and connectives that prove difficult to parse, in order to highlight the areas where gains can be made.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_codi-main_11", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_codi-main_11.flac", "type": "audio", "duration": 404.096}, {"from": "Assistant", "value": "While multi-party conversations are often less structured than monologues and documents, they are implicitly organized by semantic level correlations across the interactive turns, and dialogue discourse analysis can be applied to predict the dependency structure and relations between the elementary discourse units, and provide feature-rich structural information for downstream tasks. However, the existing corpora with dialogue discourse annotation are collected from specific domains with limited sample sizes, rendering the performance of data-driven approaches poor on incoming dialogues without any domain adaptation. In this paper, we first introduce a Transformer-based parser, and assess its cross-domain performance. We next adopt three methods to gain domain integration from both data and language modeling perspectives to improve the generalization capability. Empirical results show that the neural parser can benefit from our proposed methods, and performs better on cross-domain dialogue samples.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_codi-main_12", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_codi-main_12.flac", "type": "audio", "duration": 417.007}, {"from": "Assistant", "value": "This paper demonstrates discopy, a novel framework that makes it easy to design components for end-to-end shallow discourse parsing. For the purpose of demonstration, we implement recent neural approaches and integrate contextualized word embeddings to predict explicit and non-explicit discourse relations. Our proposed neural feature-free system performs competitively to systems presented at the latest Shared Task on Shallow Discourse Parsing. Finally, a web front end is shown that simplifies the inspection of annotated documents. The source code, documentation, and pretrained models are publicly accessible.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_codi-main_13", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_codi-main_13.flac", "type": "audio", "duration": 600.021}, {"from": "Assistant", "value": "In the present paper, we explore lexical contexts of discourse markers in translation and interpreting on the basis of word embeddings. Our special interest is on contextual variation of the same discourse markers in (written) translation vs. (simultaneous) interpreting. To explore this variation at the lexical level, we use a data-driven approach: we compare bilingual neural word embeddings trained on source-to-translation and source-to-interpreting aligned corpora. Our results show more variation of semantically related items in translation spaces vs. interpreting ones and a more consistent use of fewer connectives in interpreting. We also observe different trends with regard to the discourse relation types.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_codi-main_15", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_codi-main_15.flac", "type": "audio", "duration": 652.139}, {"from": "Assistant", "value": "Text discourse parsing weighs importantly in understanding information flow and argumentative structure in natural language, making it beneficial for downstream tasks. While previous work significantly improves the performance of RST discourse parsing, they are not readily applicable to practical use cases: (1) EDU segmentation is not integrated into most existing tree parsing frameworks, thus it is not straightforward to apply such models on newly-coming data. (2) Most parsers cannot be used in multilingual scenarios, because they are developed only in English. (3) Parsers trained from single-domain treebanks do not generalize well on out-of-domain inputs. In this work, we propose a document-level multilingual RST discourse parsing framework, which conducts EDU segmentation and discourse tree parsing jointly. Moreover, we propose a cross-translation augmentation strategy to enable the framework to support multilingual parsing and improve its domain generality. Experimental results show that our model achieves state-of-the-art performance on document-level multilingual RST parsing in all sub-tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_codi-main_16", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_codi-main_16.flac", "type": "audio", "duration": 432.007}, {"from": "Assistant", "value": "This paper presents an interactive data dashboard that provides users with an overview of the preservation of discourse relations among 28 language pairs. We display a graph network depicting the cross-lingual discourse relations between a pair of languages for multilingual TED talks and provide a search function to look for sentences with specific keywords or relation types, facilitating ease of analysis on the cross-lingual discourse relations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_2", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_2.flac", "type": "audio", "duration": 592.734}, {"from": "Assistant", "value": "Most previous work on grammar induction focuses on learning phrasal or dependency structure purely from text. However, because the signal provided by text alone is limited, recently introduced visually grounded syntax models make use of multimodal information leading to improved performance in constituency grammar induction. However, as compared to dependency grammars, constituency grammars do not provide a straightforward way to incorporate visual information without enforcing language-specific heuristics. In this paper, we propose an unsupervised grammar induction model that leverages word concreteness and a structural vision-based heuristic to jointly learn constituency-structure and dependency-structure grammars. Our experiments find that concreteness is a strong indicator for learning dependency grammars, improving the direct attachment score (DAS) by over 50% as compared to state-of-the-art models trained on pure text. Next, we propose an extension of our model that leverages both word concreteness and visual semantic role labels in constituency and dependency parsing. Our experiments show that the proposed extension outperforms the current state-of-the-art visually grounded models in constituency parsing even with a smaller grammar size.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_3", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_3.flac", "type": "audio", "duration": 600.704}, {"from": "Assistant", "value": "We present VQA-MHUG – a novel 49-participant dataset of multimodal human gaze on both images and questions during visual question answering (VQA) collected using a high-speed eye tracker. We use our dataset to analyze the similarity between human and neural attentive strategies learned by five state-of-the-art VQA models: Modular Co-Attention Network (MCAN) with either grid or region features, Pythia, Bilinear Attention Network (BAN), and the Multimodal Factorized Bilinear Pooling Network (MFB). While prior work has focused on studying the image modality, our analyses show – for the first time – that for all models, higher correlation with human attention on text is a significant predictor of VQA performance. This finding points at a potential for improving VQA performance and, at the same time, calls for further research on neural text attention mechanisms and their integration into architectures for vision and language tasks, including but potentially also beyond VQA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_4", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_4.flac", "type": "audio", "duration": 673.702}, {"from": "Assistant", "value": "Previous research has found that task-oriented conversational agents are perceived more positively by users when they provide information in an empathetic manner compared to a plain, emotionless information exchange. However, users’ perception and ethical considerations related to a dialog systems’ response language style have received comparatively little attention in the field of human-computer interaction. To bridge this gap, we explored these ethical implications through a scenario-based user study. 127 participants interacted with one of three variants of an affective, task-oriented conversational agent, each variant providing responses in a different language style. After the interaction, participants filled out a survey about their feelings during the experiment and their perception of various aspects of the chatbot. Based on statistical and qualitative analysis of the responses, we found language style played an important role in how human-like participants perceived a dialog agent as well as how likable. Language style also had a direct effect on how users perceived the use of personal pronouns ‘I’ and ‘You’ and how they projected gender onto the chatbot. Finally, we identify and discuss ethical implications. In particular we focus on what factors/stereotypes influenced participants’ impressions of gender, and what trade-offs a more human-like chatbot brings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_5", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_5.flac", "type": "audio", "duration": 723.023}, {"from": "Assistant", "value": "Creole languages such as Nigerian Pidgin English and Haitian Creole are under-resourced and largely ignored in the NLP literature. Creoles typically result from the fusion of a foreign language with multiple local languages, and what grammatical and lexical features are transferred to the creole is a complex process. While creoles are generally stable, the prominence of some features may be much stronger with certain demographics or in some linguistic situations. This paper makes several contributions: We collect existing corpora and release models for Haitian Creole, Nigerian Pidgin English, and Singaporean Colloquial English. We evaluate these models on intrinsic and extrinsic tasks. Motivated by the above literature, we compare standard language models with distributionally robust ones and find that, somewhat surprisingly, the standard language models are superior to the distributionally robust ones. We investigate whether this is an effect of over-parameterization or relative distributional stability, and find that the difference persists in the absence of over-parameterization, and that drift is limited, confirming the relative stability of creole languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_8", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_8.flac", "type": "audio", "duration": 721.003}, {"from": "Assistant", "value": "Inflectional morphology has since long been a useful testing ground for broader questions about generalisation in language and the viability of neural network models as cognitive models of language. Here, in line with that tradition, we explore how recurrent neural networks acquire the complex German plural system and reflect upon how their strategy compares to human generalisation and rule-based models of this system. We perform analyses including behavioural experiments, diagnostic classification, representation analysis and causal interventions, suggesting that the models rely on features that are also key predictors in rule-based models of German plurals. However, the models also display shortcut learning, which is crucial to overcome in search of more cognitively plausible generalisation behaviour.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_9", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_9.flac", "type": "audio", "duration": 716.011}, {"from": "Assistant", "value": "Pretrained language models have been shown to encode relational information, such as the relations between entities or concepts in knowledge-bases — (Paris, Capital, France). However, simple relations of this type can often be recovered heuristically and the extent to which models implicitly reflect topological structure that is grounded in world, such as perceptual structure, is unknown. To explore this question, we conduct a thorough case study on color. Namely, we employ a dataset of monolexemic color terms and color chips represented in CIELAB, a color space with a perceptually meaningful distance metric. Using two methods of evaluating the structural alignment of colors in this space with text-derived color term representations, we find significant correspondence. Analyzing the differences in alignment across the color spectrum, we find that warmer colors are, on average, better aligned to the perceptual color space than cooler ones, suggesting an intriguing connection to findings from recent work on efficient communication in color naming. Further analysis suggests that differences in alignment are, in part, mediated by collocationality and differences in syntactic usage, posing questions as to the relationship between color perception and usage and context.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_10", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_10.flac", "type": "audio", "duration": 478.006}, {"from": "Assistant", "value": "Empathetic dialog generation aims at generating coherent responses following previous dialog turns and, more importantly, showing a sense of caring and a desire to help. Existing models either rely on pre-defined emotion labels to guide the response generation, or use deterministic rules to decide the emotion of the response. With the advent of advanced language models, it is possible to learn subtle interactions directly from the dataset, providing that the emotion categories offer sufficient nuances and other non-emotional but emotional regulating intents are included. In this paper, we describe how to incorporate a taxonomy of 32 emotion categories and 8 additional emotion regulating intents to succeed the task of empathetic response generation. To facilitate the training, we also curated a large-scale emotional dialog dataset from movie subtitles. Through a carefully designed crowdsourcing experiment, we evaluated and demonstrated how our model produces more empathetic dialogs compared with its baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_11", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_11.flac", "type": "audio", "duration": 681.568}, {"from": "Assistant", "value": "Language models are trained only on text despite the fact that humans learn their first language in a highly interactive and multimodal environment where the first set of learned words are largely concrete, denoting physical entities and embodied states. To enrich language models with some of this missing experience, we leverage two sources of information: (1) the Lancaster Sensorimotor norms, which provide ratings (means and standard deviations) for over 40,000 English words along several dimensions of embodiment, and which capture the extent to which something is experienced across 11 different sensory modalities, and (2) vectors from coefficients of binary classifiers trained on images for the BERT vocabulary. We pre-trained the ELECTRA model and fine-tuned the RoBERTa model with these two sources of information then evaluate using the established GLUE benchmark and the Visual Dialog benchmark. We find that enriching language models with the Lancaster norms and image vectors improves results in both tasks, with some implications for robust language models that capture holistic linguistic meaning in a language learning context.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_12", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_12.flac", "type": "audio", "duration": 480.003}, {"from": "Assistant", "value": "Language grounding aims at linking the symbolic representation of language (e.g., words) into the rich perceptual knowledge of the outside world. The general approach is to embed both textual and visual information into a common space -the grounded space- confined by an explicit relationship. We argue that since concrete and abstract words are processed differently in the brain, such approaches sacrifice the abstract knowledge obtained from textual statistics in the process of acquiring perceptual information. The focus of this paper is to solve this issue by implicitly grounding the word embeddings. Rather than learning two mappings into a joint space, our approach integrates modalities by implicit alignment. This is achieved by learning a reversible mapping between the textual and the grounded space by means of multi-task training. Intrinsic and extrinsic evaluations show that our way of visual grounding is highly beneficial for both abstract and concrete words. Our embeddings are correlated with human judgments and outperform previous works using pretrained word embeddings on a wide range of benchmarks. Our grounded embeddings are publicly available here.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_13", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_13.flac", "type": "audio", "duration": 466.816}, {"from": "Assistant", "value": "Vision models trained on multimodal datasets can benefit from the wide availability of large image-caption datasets. A recent model (CLIP) was found to generalize well in zero-shot and transfer learning settings. This could imply that linguistic or “semantic grounding” confers additional generalization abilities to the visual feature space. Here, we systematically evaluate various multimodal architectures and vision-only models in terms of unsupervised clustering, few-shot learning, transfer learning and adversarial robustness. In each setting, multimodal training produced no additional generalization capability compared to standard supervised visual training. We conclude that work is still required for semantic grounding to help improve vision models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_14", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_14.flac", "type": "audio", "duration": 477.331}, {"from": "Assistant", "value": "Image captioning models generally lack the capability to take into account user interest, and usually default to global descriptions that try to balance readability, informativeness, and information overload. We present a Transformer-based model with the ability to produce captions focused on specific objects, concepts or actions in an image by providing them as guiding text to the model. Further, we evaluate the quality of these guided captions when trained on Conceptual Captions which contain 3.3M image-level captions compared to Visual Genome which contain 3.6M object-level captions. Counter-intuitively, we find that guided captions produced by the model trained on Conceptual Captions generalize better on out-of-domain data. Our human-evaluation results indicate that attempting in-the-wild guided image captioning requires access to large, unrestricted-domain training datasets, and that increased style diversity (even without increasing the number of unique tokens) is a key factor for improved performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_15", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_15.flac", "type": "audio", "duration": 481.952}, {"from": "Assistant", "value": "When language models process syntactically complex sentences, do they use their representations of syntax in a manner that is consistent with the grammar of the language? We propose AlterRep, an intervention-based method to address this question. For any linguistic feature of a given sentence, AlterRep generates counterfactual representations by altering how the feature is encoded, while leaving in- tact all other aspects of the original representation. By measuring the change in a model’s word prediction behavior when these counterfactual representations are substituted for the original ones, we can draw conclusions about the causal effect of the linguistic feature in question on the model’s behavior. We apply this method to study how BERT models of different sizes process relative clauses (RCs). We find that BERT variants use RC boundary information during word prediction in a manner that is consistent with the rules of English grammar; this RC boundary information generalizes to a considerable extent across different RC types, suggesting that BERT represents RCs as an abstract linguistic category.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_16", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_16.flac", "type": "audio", "duration": 483.625}, {"from": "Assistant", "value": "The capabilities of today’s natural language processing systems are typically evaluated using large datasets of curated questions and answers. While these are critical benchmarks of progress, they also suffer from weakness due to artificial distributions and incomplete knowledge. Artifacts arising from artificial distributions can overstate language model performance, while incomplete knowledge limits fine-grained analysis. In this work, we introduce a complementary benchmarking approach based on SimPlified Language Activity Traces (SPLAT). SPLATs are corpora of language encodings of activity in some closed domain (we study traces from chess and baseball games in this work). SPLAT datasets use naturally-arising distributions, allow the generation of question-answer pairs at scale, and afford complete knowledge in their closed domains. We show that language models of three different architectures can answer questions about world states using only verb-like encodings of activity. Our approach is extensible to new language models and additional question-answering tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_17", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_17.flac", "type": "audio", "duration": 480.0}, {"from": "Assistant", "value": "Data augmentation aims at expanding training data with clean text using noising schemes to improve the performance of grammatical error correction (GEC). In practice, there are a great number of real error patterns in the manually annotated training data. We argue that these real error patterns can be introduced into clean text to effectively generate more real and high quality synthetic data, which is not fully explored by previous studies. Moreover, we also find that linguistic knowledge can be incorporated into data augmentation for generating more representative and more diverse synthetic data. In this paper, we propose a novel data augmentation method that fully considers the real error patterns and the linguistic knowledge for the GEC task. We conduct extensive experiments on public data sets and the experimental results show that our method outperforms several strong baselines with far less external unlabeled clean text data, highlighting its extraordinary effectiveness in the GEC task that lacks large-scale labeled training data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_19", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_19.flac", "type": "audio", "duration": 464.004}, {"from": "Assistant", "value": "Negation is one of the most fundamental concepts in human cognition and language, and several natural language inference (NLI) probes have been designed to investigate pretrained language models’ ability to detect and reason with negation. However, the existing probing datasets are limited to English only, and do not enable controlled probing of performance in the absence or presence of negation. In response, we present a multilingual (English, Bulgarian, German, French and Chinese) benchmark collection of NLI examples that are grammatical and correctly labeled, as a result of manual inspection and reformulation. We use the benchmark to probe the negation-awareness of multilingual language models and find that models that correctly predict examples with negation cues, often fail to correctly predict their counter-examples without negation cues, even when the cues are irrelevant for semantic inference.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_20", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_20.flac", "type": "audio", "duration": 378.283}, {"from": "Assistant", "value": "Natural language processing for program synthesis has been widely researched. In this work, we focus on generating Bash commands from natural language invocations with explanations. We propose a novel transformer based solution by utilizing Bash Abstract Syntax Trees and manual pages. Our method incorporates tree structure information in the transformer architecture and provides explanations for its predictions via alignment matrices between user invocation and manual page text. Our method performs on par with the state of the art performance on Natural Language Context to Command task and performs better than fine-tuned T5 and Seq2Seq models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_21", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_21.flac", "type": "audio", "duration": 477.565}, {"from": "Assistant", "value": "This paper measures the impact of increased exposure on whether learned construction grammars converge onto shared representations when trained on data from different registers. Register influences the frequency of constructions, with some structures common in formal but not informal usage. We expect that a grammar induction algorithm exposed to different registers will acquire different constructions. To what degree does increased exposure lead to the convergence of register-specific grammars? The experiments in this paper simulate language learning in 12 languages (half Germanic and half Romance) with corpora representing three registers (Twitter, Wikipedia, Web). These simulations are repeated with increasing amounts of exposure, from 100k to 2 million words, to measure the impact of exposure on the convergence of grammars. The results show that increased exposure does lead to converging grammars across all languages. In addition, a shared core of register-universal constructions remains constant across increasing amounts of exposure.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_22", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_22.flac", "type": "audio", "duration": 367.061}, {"from": "Assistant", "value": "We consider the following tokenization repair problem: Given a natural language text with any combination of missing or spurious spaces, correct these. Spelling errors can be present, but it’s not part of the problem to correct them. For example, given: “Tispa per isabout token izaionrep air”, compute “Tis paper is about tokenizaion repair”. We identify three key ingredients of high-quality tokenization repair, all missing from previous work: deep language models with a bidirectional component, training the models on text with spelling errors, and making use of the space information already present. Our methods also improve existing spell checkers by fixing not only more tokenization errors but also more spelling errors: once it is clear which characters form a word, it is much easier for them to figure out the correct word. We provide six benchmarks that cover three use cases (OCR errors, text extraction from PDF, human errors) and the cases of partially correct space information and all spaces missing. We evaluate our methods against the best existing methods and a non-trivial baseline. We provide full reproducibility underhttps://ad.informatik.uni-freiburg.de/publications.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_23", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_23.flac", "type": "audio", "duration": 452.023}, {"from": "Assistant", "value": "The most straightforward approach to joint word segmentation (WS), part-of-speech (POS) tagging, and constituent parsing is converting a word-level tree into a char-level tree, which, however, leads to two severe challenges. First, a larger label set (e.g., ≥ 600) and longer inputs both increase computational costs. Second, it is difficult to rule out illegal trees containing conflicting production rules, which is important for reliable model evaluation. If a POS tag (like VV) is above a phrase tag (like VP) in the output tree, it becomes quite complex to decide word boundaries. To deal with both challenges, this work proposes a two-stage coarse-to-fine labeling framework for joint WS-POS-PAR. In the coarse labeling stage, the joint model outputs a bracketed tree, in which each node corresponds to one of four labels (i.e., phrase, subphrase, word, subword). The tree is guaranteed to be legal via constrained CKY decoding. In the fine labeling stage, the model expands each coarse label into a final label (such as VP, VP*, VV, VV*). Experiments on Chinese Penn Treebank 5.1 and 7.0 show that our joint model consistently outperforms the pipeline approach on both settings of w/o and w/ BERT, and achieves new state-of-the-art performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_24", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_24.flac", "type": "audio", "duration": 446.016}, {"from": "Assistant", "value": "Reference-based metrics such as ROUGE or BERTScore evaluate the content quality of a summary by comparing the summary to a reference. Ideally, this comparison should measure the summary’s information quality by calculating how much information the summaries have in common. In this work, we analyze the token alignments used by ROUGE and BERTScore to compare summaries and argue that their scores largely cannot be interpreted as measuring information overlap. Rather, they are better estimates of the extent to which the summaries discuss the same topics. Further, we provide evidence that this result holds true for many other summarization evaluation metrics. The consequence of this result is that the most frequently used summarization evaluation metrics do not align with the community’s research goal, to generate summaries with high-quality information. However, we conclude by demonstrating that a recently proposed metric, QAEval, which scores summaries using question-answering, appears to better capture information quality than current evaluations, highlighting a direction for future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_25", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_25.flac", "type": "audio", "duration": 696.018}, {"from": "Assistant", "value": "Aligning sentences in a reference summary with their counterparts in source documents was shown as a useful auxiliary summarization task, notably for generating training data for salience detection. Despite its assessed utility, the alignment step was mostly approached with heuristic unsupervised methods, typically ROUGE-based, and was never independently optimized or evaluated. In this paper, we propose establishing summary-source alignment as an explicit task, while introducing two major novelties: (1) applying it at the more accurate proposition span level, and (2) approaching it as a supervised classification task. To that end, we created a novel training dataset for proposition-level alignment, derived automatically from available summarization evaluation data. In addition, we crowdsourced dev and test datasets, enabling model development and proper evaluation. Utilizing these data, we present a supervised proposition alignment baseline model, showing improved alignment-quality over the unsupervised approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_26", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_26.flac", "type": "audio", "duration": 741.013}, {"from": "Assistant", "value": "Metaphor generation is a difficult task, and has seen tremendous improvement with the advent of deep pretrained models. We focus here on the specific task of metaphoric paraphrase generation, in which we provide a literal sentence and generate a metaphoric sentence which paraphrases that input. We compare naive, “free” generation models with those that exploit forms of control over the generation process, adding additional information based on conceptual metaphor theory. We evaluate two methods for generating paired training data, which is then used to train T5 models for free and controlled generation. We use crowdsourcing to evaluate the results, showing that free models tend to generate more fluent paraphrases, while controlled models are better at generating novel metaphors. We then analyze evaluation metrics, showing that different metrics are necessary to capture different aspects of metaphoric paraphrasing. We release our data and models, as well as our annotated results in order to facilitate development of better evaluation metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_27", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_27.flac", "type": "audio", "duration": 740.02}, {"from": "Assistant", "value": "Though language model text embeddings have revolutionized NLP research, their ability to capture high-level semantic information, such as relations between entities in text, is limited. In this paper, we propose a novel contrastive learning framework that trains sentence embeddings to encode the relations in a graph structure. Given a sentence (unstructured text) and its graph, we use contrastive learning to impose relation-related structure on the token level representations of the sentence obtained with a CharacterBERT (El Boukkouri et al., 2020) model. The resulting relation-aware sentence embeddings achieve state-of-the-art results on the relation extraction task using only a simple KNN classifier, thereby demonstrating the success of the proposed method. Additional visualization by a tSNE analysis shows the effectiveness of the learned representation space compared to baselines. Furthermore, we show that we can learn a different space for named entity recognition, again using a contrastive learning objective, and demonstrate how to successfully combine both representation spaces in an entity-relation task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_28", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_28.flac", "type": "audio", "duration": 736.514}, {"from": "Assistant", "value": "Understanding language requires grasping not only the overtly stated content, but also making inferences about things that were left unsaid. These inferences include presuppositions, a phenomenon by which a listener learns about new information through reasoning about what a speaker takes as given. Presuppositions require complex understanding of the lexical and syntactic properties that trigger them as well as the broader conversational context. In this work, we introduce the Naturally-Occurring Presuppositions in English (NOPE) Corpus to investigate the context-sensitivity of 10 different types of presupposition triggers and to evaluate machine learning models’ ability to predict human inferences. We find that most of the triggers we investigate exhibit moderate variability. We further find that transformer-based models draw correct inferences in simple cases involving presuppositions, but they fail to capture the minority of exceptional cases in which human judgments reveal complex interactions between context and triggers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_29", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_29.flac", "type": "audio", "duration": 683.015}, {"from": "Assistant", "value": "As pre-trained language models (LMs) continue to dominate NLP, it is increasingly important that we understand the depth of language capabilities in these models. In this paper, we target pre-trained LMs’ competence in pragmatics, with a focus on pragmatics relating to discourse connectives. We formulate cloze-style tests using a combination of naturally-occurring data and controlled inputs drawn from psycholinguistics. We focus on testing models’ ability to use pragmatic cues to predict discourse connectives, models’ ability to understand implicatures relating to connectives, and the extent to which models show humanlike preferences regarding temporal dynamics of connectives. We find that although models predict connectives reasonably well in the context of naturally-occurring data, when we control contexts to isolate high-level pragmatic cues, model sensitivity is much lower. Models also do not show substantial humanlike temporal preferences. Overall, the findings suggest that at present, dominant pre-training paradigms do not result in substantial pragmatic competence in our models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_30", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_30.flac", "type": "audio", "duration": 482.859}, {"from": "Assistant", "value": "Judging the readability of text has many important applications, for instance when performing text simplification or when sourcing reading material for language learners. In this paper, we present a 518 participant study which investigates how scrolling behaviour relates to the readability of English texts. We make our dataset publicly available and show that (1) there are statistically significant differences in the way readers interact with text depending on the text level, (2) such measures can be used to predict the readability of text, and (3) the background of a reader impacts their reading interactions and the factors contributing to text difficulty.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_31", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_31.flac", "type": "audio", "duration": 536.085}, {"from": "Assistant", "value": "Children learn the meaning of words and sentences in their native language at an impressive speed and from highly ambiguous input. To account for this learning, previous computational modeling has focused mainly on the study of perception-based mechanisms like cross-situational learning. However, children do not learn only by exposure to the input. As soon as they start to talk, they practice their knowledge in social interactions and they receive feedback from their caregivers. In this work, we propose a model integrating both perception- and production-based learning using artificial neural networks which we train on a large corpus of crowd-sourced images with corresponding descriptions. We found that production-based learning improves performance above and beyond perception-based learning across a wide range of semantic tasks including both word- and sentence-level semantics. In addition, we documented a synergy between these two mechanisms, where their alternation allows the model to converge on more balanced semantic knowledge. The broader impact of this work is to highlight the importance of modeling language learning in the context of social interactions where children are not only understood as passively absorbing the input, but also as actively participating in the construction of their linguistic knowledge.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_33", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_33.flac", "type": "audio", "duration": 493.44}, {"from": "Assistant", "value": "Semantics, morphology and syntax are strongly interdependent. However, the majority of computational methods for semantic change detection use distributional word representations which encode mostly semantics. We investigate an alternative method, grammatical profiling, based entirely on changes in the morphosyntactic behaviour of words. We demonstrate that it can be used for semantic change detection and even outperforms some distributional semantic methods. We present an in-depth qualitative and quantitative analysis of the predictions made by our grammatical profiling system, showing that they are plausible and interpretable.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_34", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_34.flac", "type": "audio", "duration": 511.002}, {"from": "Assistant", "value": "Within the currently dominant Minimalist framework for syntax (Chomsky, 1995, 2000), it is not uncommon to encounter multiple proposals for the same natural language pattern in the literature. We investigate the possibility of evaluating and comparing analyses of syntax phenomena, implemented as minimalist grammars (Stabler, 1997), from a quantitative point of view. This paper introduces a principled way of making linguistic generalizations by detecting and eliminating syntactic and phonological redundancies in the data. As proof of concept, we first provide a small step-by-step example transforming a naive grammar over unsegmented words into a linguistically motivated grammar over morphemes, and then discuss a description of the English auxiliary system, passives, and raising verbs produced by a prototype implementation of a procedure for automated grammar optimization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_35", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_35.flac", "type": "audio", "duration": 413.013}, {"from": "Assistant", "value": "Commonsense Question Answering is an important natural language processing (NLP) task that aims to predict the correct answer to a question through commonsense reasoning. Previous studies utilize pre-trained models on large-scale corpora such as BERT, or perform reasoning on knowledge graphs. However, these methods do not explicitly model therelationsthat connect entities, which are informational and can be used to enhance reasoning. To address this issue, we propose a relation-aware reasoning method. Our method uses a relation-aware graph neural network to capture the rich contextual information from both entities and relations. Compared with methods that use fixed relation embeddings from pre-trained models, our model dynamically updates relations with contextual information from a multi-source subgraph, built from multiple external knowledge sources. The enhanced representations of relations are then fed to a bidirectional reasoning module. A bidirectional attention mechanism is applied between the question sequence and the paths that connect entities, which provides us with transparent interpretability. Experimental results on the CommonsenseQA dataset illustrate that our method results in significant improvements over the baselines while also providing clear reasoning paths.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_36", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_36.flac", "type": "audio", "duration": 480.767}, {"from": "Assistant", "value": "It is often posited that more predictable parts of a speaker’s meaning tend to be made less explicit, for instance using shorter, less informative words. Studying these dynamics in the domain of referring expressions has proven difficult, with existing studies, both psycholinguistic and corpus-based, providing contradictory results. We test the hypothesis that speakers produce less informative referring expressions (e.g., pronouns vs. full noun phrases) when the context is more informative about the referent, using novel computational estimates of referent predictability. We obtain these estimates training an existing coreference resolution system for English on a new task, masked coreference resolution, giving us a probability distribution over referents that is conditioned on the context but not the referring expression. The resulting system retains standard coreference resolution performance while yielding a better estimate of human-derived referent predictability than previous attempts. A statistical analysis of the relationship between model output and mention form supports the hypothesis that predictability affects the form of a mention, both its morphosyntactic type and its length.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_37", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_37.flac", "type": "audio", "duration": 496.864}, {"from": "Assistant", "value": "Hierarchical relationships are invaluable information for many natural language processing (NLP) tasks. Distributional representation has become a fundamental approach for encoding word relationships, particularly embeddings in hyperbolic space showed great performance in representing hierarchies by taking advantage of their spatial properties. However, most machine learning systems do not suppose to use in such complex non-Euclidean geometries. To achieve hierarchy representations in commonly used Euclidean space, we propose Polar Embedding that learns word embeddings with the polar coordinate system. Utilizing characteristics of polar coordinates, the hierarchy of words is expressed with two independent variables: radius (generality) and angles (similarity), and their variables are optimized separately. Polar embedding shows word hierarchies explicitly and allows us to use beneficial resources such as word frequencies or word generality annotations for computing radiuses. We introduce an optimization method for learning angles in limited ranges of polar coordinates, which combining a loss function controlling gradient and distribution uniformization. Experimental results on hypernymy datasets indicate that our approach outperforms other embeddings in low-dimensional Euclidean space and competitively performs even with hyperbolic embeddings, which possess a geometric advantage.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_38", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_38.flac", "type": "audio", "duration": 493.006}, {"from": "Assistant", "value": "Humans use countless basic, shared facts about the world to efficiently navigate in their environment. This commonsense knowledge is rarely communicated explicitly, however, understanding how commonsense knowledge is represented in different paradigms is important for (a) a deeper understanding of human cognition and (b) augmenting automatic reasoning systems. This paper presents an in-depth comparison of two large-scale resources of general knowledge: ConceptNet, an engineered relational database, and SWOW, a knowledge graph derived from crowd-sourced word associations. We examine the structure, overlap and differences between the two graphs, as well as the extent of situational commonsense knowledge present in the two resources. We finally show empirically that both resources improve downstream task performance on commonsense reasoning benchmarks over text-only baselines, suggesting that large-scale word association data, which have been obtained for several languages through crowd-sourcing, can be a valuable complement to curated knowledge graphs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_39", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_39.flac", "type": "audio", "duration": 463.006}, {"from": "Assistant", "value": "In this paper, we study the identity of textual events from different documents. While the complex nature of event identity is previously studied (Hovy et al., 2013), the case of events across documents is unclear. Prior work on cross-document event coreference has two main drawbacks. First, they restrict the annotations to a limited set of event types. Second, they insufficiently tackle the concept of event identity. Such annotation setup reduces the pool of event mentions and prevents one from considering the possibility of quasi-identity relations. We propose a dense annotation approach for cross-document event coreference, comprising a rich source of event mentions and a dense annotation effort between related document pairs. To this end, we design a new annotation workflow with careful quality control and an easy-to-use annotation interface. In addition to the links, we further collect overlapping event contexts, including time, location, and participants, to shed some light on the relation between identity decisions and context. We present an open-access dataset for cross-document event coreference, CDEC-WN, collected from English Wikinews and open-source our annotation toolkit to encourage further research on cross-document tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_40", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_40.flac", "type": "audio", "duration": 440.018}, {"from": "Assistant", "value": "Zero pronoun resolution aims at recognizing dropped pronouns and pointing out their anaphoric mentions, while non-zero coreference resolution targets at clustering mentions referring to the same entity. Existing efforts often deal with the two problems separately regardless of their close essential correlations. In this paper, we investigate the possibility of jointly solving zero pronoun resolution and coreference resolution via a novel end-to-end neural model. Specifically, we design a gap-masked self-attention model that encodes gaps and tokens in the same space, where gaps could capture valuable contextual information according to their surrounding tokens while tokens could maintain original sequential information without disturbance. Additionally, we also propose a two-stage interaction mechanism to make full use of the exclusive relationship between zero pronouns and mentions. Our empirical study conducted on the OntoNotes 5.0 Chinese dataset shows that our model could outperform corresponding state-of-the-art approaches on both tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_41", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_41.flac", "type": "audio", "duration": 485.204}, {"from": "Assistant", "value": "In this paper, we revisit the task of negation resolution, which includes the subtasks of cue detection (e.g. “not”, “never”) and scope resolution. In the context of previous shared tasks, a variety of evaluation metrics have been proposed. Subsequent works usually use different subsets of these, including variations and custom implementations, rendering meaningful comparisons between systems difficult. Examining the problem both from a linguistic perspective and from a downstream viewpoint, we here argue for a negation-instance based approach to evaluating negation resolution. Our proposed metrics correspond to expectations over per-instance scores and hence are intuitively interpretable. To render research comparable and to foster future work, we provide results for a set of current state-of-the-art systems for negation resolution on three English corpora, and make our implementation of the evaluation scripts publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_42", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_42.flac", "type": "audio", "duration": 390.639}, {"from": "Assistant", "value": "While End-2-End Text-to-Speech (TTS) has made significant progresses over the past few years, these systems still lack intuitive user controls over prosody. For instance, generating speech with fine-grained prosody control (prosodic prominence, contextually appropriate emotions) is still an open challenge. In this paper, we investigate whether we can control prosody directly from the input text, in order to code information related to contrastive focus which emphasizes a specific word that is contrary to the presuppositions of the interlocutor. We build and share a specific dataset for this purpose and show that it allows to train a TTS system were this fine-grained prosodic feature can be correctly conveyed using control tokens. Our evaluation compares synthetic and natural utterances and shows that prosodic patterns of contrastive focus (variations of Fo, Intensity and Duration) can be learnt accurately. Such a milestone is important to allow, for example, smart speakers to be programmatically controlled in terms of output prosody.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_43", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_43.flac", "type": "audio", "duration": 471.84}, {"from": "Assistant", "value": "As users in online communities suffer from severe side effects of abusive language, many researchers attempted to detect abusive texts from social media, presenting several datasets for such detection. However, none of them contain both comprehensive labels and contextual information, which are essential for thoroughly detecting all kinds of abusiveness from texts, since datasets with such fine-grained features demand a significant amount of annotations, leading to much increased complexity. In this paper, we propose a Comprehensive Abusiveness Detection Dataset (CADD), collected from the English Reddit posts, with multifaceted labels and contexts. Our dataset is annotated hierarchically for an efficient annotation through crowdsourcing on a large-scale. We also empirically explore the characteristics of our dataset and provide a detailed analysis for novel insights. The results of our experiments with strong pre-trained natural language understanding models on our dataset show that our dataset gives rise to meaningful performance, assuring its practicality for abusive language detection.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_44", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_44.flac", "type": "audio", "duration": 361.641}, {"from": "Assistant", "value": "Recent work indicated that pretrained language models (PLMs) such as BERT and RoBERTa can be transformed into effective sentence and word encoders even via simple self-supervised techniques. Inspired by this line of work, in this paper we propose a fully unsupervised approach to improving word-in-context (WiC) representations in PLMs, achieved via a simple and efficient WiC-targeted fine-tuning procedure: MirrorWiC. The proposed method leverages only raw texts sampled from Wikipedia, assuming no sense-annotated data, and learns context-aware word representations within a standard contrastive learning setup. We experiment with a series of standard and comprehensive WiC benchmarks across multiple languages. Our proposed fully unsupervised MirrorWiC models obtain substantial gains over off-the-shelf PLMs across all monolingual, multilingual and cross-lingual setups. Moreover, on some standard WiC benchmarks, MirrorWiC is even on-par with supervised models fine-tuned with in-task data and sense labels.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_45", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_45.flac", "type": "audio", "duration": 494.005}, {"from": "Assistant", "value": "Relation classification (sometimes called ‘extraction’) requires trustworthy datasets for fine-tuning large language models, as well as for evaluation. Data collection is challenging for Indian languages, because they are syntactically and morphologically diverse, as well as different from resource-rich languages like English. Despite recent interest in deep generative models for Indian languages, relation classification is still not well-served by public data sets. In response, we present IndoRE, a dataset with 39K entity- and relation-tagged gold sentences in three Indian languages, plus English. We start with a multilingual BERT (mBERT) based system that captures entity span positions and type information and provides competitive monolingual relation classification. Using this system, we explore and compare transfer mechanisms between languages. In particular, we study the accuracy-efficiency tradeoff between expensive gold instances vs. translated and aligned ‘silver’ instances.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_46", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_46.flac", "type": "audio", "duration": 479.746}, {"from": "Assistant", "value": "What is the first word that comes to your mind when you hear giraffe, or damsel, or freedom? Such free associations contain a huge amount of information on the mental representations of the corresponding concepts, and are thus an extremely valuable testbed for the evaluation of semantic representations extracted from corpora. In this paper, we present FAST (Free ASsociation Tasks), a free association dataset for English rigorously sampled from two standard free association norms collections (the Edinburgh Associative Thesaurus and the University of South Florida Free Association Norms), discuss two evaluation tasks, and provide baseline results. In parallel, we discuss methodological considerations concerning the desiderata for a proper evaluation of semantic representations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_47", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_47.flac", "type": "audio", "duration": 456.928}, {"from": "Assistant", "value": "We present ARETA, an automatic error type annotation system for Modern Standard Arabic. We design ARETA to address Arabic’s morphological richness and orthographic ambiguity. We base our error taxonomy on the Arabic Learner Corpus (ALC) Error Tagset with some modifications. ARETA achieves a performance of 85.8% (micro average F1 score) on a manually annotated blind test portion of ALC. We also demonstrate ARETA’s usability by applying it to a number of submissions from the QALB 2014 shared task for Arabic grammatical error correction. The resulting analyses give helpful insights on the strengths and weaknesses of different submissions, which is more useful than the opaque M2 scoring metrics used in the shared task. ARETA employs a large Arabic morphological analyzer, but is completely unsupervised otherwise. We make ARETA publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_48", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_48.flac", "type": "audio", "duration": 792.009}, {"from": "Assistant", "value": "By the age of two, children tend to assume that new word categories are based on objects’ shape, rather than their color or texture; this assumption is called the shape bias. They are thought to learn this bias by observing that their caregiver’s language is biased towards shape based categories. This presents a chicken and egg problem: if the shape bias must be present in the language in order for children to learn it, how did it arise in language in the first place? In this paper, we propose that communicative efficiency explains both how the shape bias emerged and why it persists across generations. We model this process with neural emergent language agents that learn to communicate about raw pixelated images. First, we show that the shape bias emerges as a result of efficient communication strategies employed by agents. Second, we show that pressure brought on by communicative need is also necessary for it to persist across generations; simply having a shape bias in an agent’s input language is insufficient. These results suggest that, over and above the operation of other learning strategies, the shape bias in human learners may emerge and be sustained by communicative pressures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_49", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_49.flac", "type": "audio", "duration": 672.064}, {"from": "Assistant", "value": "Transformer-based language models have taken the NLP world by storm. However, their potential for addressing important questions in language acquisition research has been largely ignored. In this work, we examined the grammatical knowledge of RoBERTa (Liu et al., 2019) when trained on a 5M word corpus of language acquisition data to simulate the input available to children between the ages 1 and 6. Using the behavioral probing paradigm, we found that a smaller version of RoBERTa-base that never predicts unmasked tokens, which we term BabyBERTa, acquires grammatical knowledge comparable to that of pre-trained RoBERTa-base - and does so with approximately 15X fewer parameters and 6,000X fewer words. We discuss implications for building more efficient models and the learnability of grammar from input available to children. Lastly, to support research on this front, we release our novel grammar test suite that is compatible with the small vocabulary of child-directed input.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_50", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_50.flac", "type": "audio", "duration": 706.848}, {"from": "Assistant", "value": "Speakers are thought to use rational information transmission strategies for efficient communication (Genzel and Charniak, 2002; Aylett and Turk, 2004; Jaeger and Levy, 2007). Previous work analysing these strategies in sentence production has failed to take into account how the information content of sentences varies as a function of the available discourse context. In this study, we estimate sentence information content within discourse context. We find that speakers transmit information at a stable rate—i.e., rationally—in English newspaper articles but that this rate decreases in spoken open domain and written task-oriented dialogues. We also observe that speakers’ choices are not oriented towards local uniformity of information, which is another hypothesised rational strategy. We suggest that a more faithful model of communication should explicitly include production costs and goal-oriented rewards.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_51", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_51.flac", "type": "audio", "duration": 777.195}, {"from": "Assistant", "value": "Our native language influences the way we perceive speech sounds, affecting our ability to discriminate non-native sounds. We compare two ideas about the influence of the native language on speech perception: the Perceptual Assimilation Model, which appeals to a mental classification of sounds into native phoneme categories, versus the idea that rich, fine-grained phonetic representations tuned to the statistics of the native language, are sufficient. We operationalise this idea using representations from two state-of-the-art speech models, a Dirichlet process Gaussian mixture model and the more recent wav2vec 2.0 model. We present a new, open dataset of French- and English-speaking participants’ speech perception behaviour for 61 vowel sounds from six languages. We show that phoneme assimilation is a better predictor than fine-grained phonetic modelling, both for the discrimination behaviour as a whole, and for predicting differences in discriminability associated with differences in native language background. We also show that wav2vec 2.0, while not good at capturing the effects of native language on speech perception, is complementary to information about native phoneme assimilation, and provides a good model of low-level phonetic representations, supporting the idea that both categorical and fine-grained perception are used during speech perception.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_conll-1_52", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_conll-1_52.flac", "type": "audio", "duration": 647.001}, {"from": "Assistant", "value": "A child who is unfamiliar with the correct spelling of a word often employs a “sound it out” approach: breaking the word down into its constituent sounds and then choosing letters to represent the identified sounds. This often results in a misspelling that is orthographically very different to the intended target. Recently, efforts have been made to develop phonetic based spellcheckers to tackle the more deviant nature of children’s misspellings. However, little work has been done to investigate the potential of spelling correction tools that incorporate regional pronunciation variation. If a child must first identify the sounds that make up a word, it stands to reason their pronunciation would influence this process. We investigate this hypothesis along with the feasibility and potential benefits of adapting spelling correction tools to more specific language variants - particularly Irish Accented English. We use misspelling data from schoolchildren across Ireland to adapt an existing English phonetic-based spellchecker and demonstrate improvements in performance. These results not only prompt consideration of language varieties in the development of spellcheckers but also contribute to existing literature on the role of regional accent in the acquisition of writing proficiency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_crac-1_1", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_crac-1_1.flac", "type": "audio", "duration": 587.0}, {"from": "Assistant", "value": "Pronoun Coreference Resolution (PCR) is the task of resolving pronominal expressions to all mentions they refer to. Compared with the general coreference resolution task, the main challenge of PCR is the coreference relation prediction rather than the mention detection. As one important natural language understanding (NLU) component, pronoun resolution is crucial for many downstream tasks and still challenging for existing models, which motivates us to survey existing approaches and think about how to do better. In this survey, we first introduce representative datasets and models for the ordinary pronoun coreference resolution task. Then we focus on recent progress on hard pronoun coreference resolution problems (e.g., Winograd Schema Challenge) to analyze how well current models can understand commonsense. We conduct extensive experiments to show that even though current models are achieving good performance on the standard evaluation set, they are still not ready to be used in real applications (e.g., all SOTA models struggle on correctly resolving pronouns to infrequent objects). All experiment codes will be available upon acceptance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_crac-1_2", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_crac-1_2.flac", "type": "audio", "duration": 602.001}, {"from": "Assistant", "value": "Issues with coreference resolution are one of the most frequently mentioned challenges for information extraction from the biomedical literature. Thus, the biomedical genre has long been the second most researched genre for coreference resolution after the news domain, and the subject of a great deal of research for NLP in general. In recent years this interest has grown enormously leading to the development of a number of substantial datasets, of domain-specific contextual language models, and of several architectures. In this paper we review the state of-the-art of coreference in the biomedical domain with a particular attention on these most recent developments.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_crac-1_3", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_crac-1_3.flac", "type": "audio", "duration": 693.333}, {"from": "Assistant", "value": "This paper presents a new corpus and annotation guideline for a novel coreference resolution task on fictional texts, and analyzes its unique characteristics. FantasyCoref contains 211 stories of Grimms’ Fairy Tales and 3 other fantasy literature annotated in the omniscient writer’s point of view (OWV) to handle distinctive aspects in this genre. This task is more challenging than general coreference resolution in two ways. First, documents in our corpus are 2.5 times longer than the ones in OntoNotes, raising a new layer of difficulty in resolving long-distant referents. Second, annotation of literary styles and concepts raise several issues which are not sufficiently addressed in the existing annotation guidelines. Hence, considerations on such issues and the concept of OWV are necessary to achieve high inter-annotator agreement (IAA) in coreference resolution of fictional texts. We carefully conduct annotation tasks in four stages to ensure the quality of our annotation. As a result, a high IAA score of 87% is achieved using the standard coreference evaluation metric. Finally, state-of-the-art coreference resolution approaches are evaluated on our corpus. After training with our annotated dataset, there was a 2.59% and 3.06% improvement over the model trained on the OntoNotes dataset. Also, we observe that the portion of errors specific to fictional texts declines after the training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_crac-1_4", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_crac-1_4.flac", "type": "audio", "duration": 581.376}, {"from": "Assistant", "value": "We present a system for resolving coreference on theater plays, DramaCoref. The system uses neural network techniques to provide a list of potential mentions. These mentions are assigned to common entities using generic and domain-specific rules. We find that DramaCoref works well on the theater plays when compared to corpora from other domains and profits from the inclusion of information specific to theater plays. On the best-performing setup, it achieves a CoNLL score of 32% when using automatically detected mentions and 55% when using gold mentions. Single rules achieve high precision scores; however, rules designed on other domains are often not applicable or yield unsatisfactory results. Error analysis shows that the mention detection is the main weakness of the system, providing directions for future improvements.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_crac-1_5", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_crac-1_5.flac", "type": "audio", "duration": 1003.845}, {"from": "Assistant", "value": "We introduce a modular, hybrid coreference resolution system that extends a rule-based baseline with three neural classifiers for the subtasks mention detection, mention attributes (gender, animacy, number), and pronoun resolution. The classifiers substantially increase coreference performance in our experiments with Dutch literature across all metrics on the development set: mention detection, LEA, CoNLL, and especially pronoun accuracy. However, on the test set, the best results are obtained with rule-based pronoun resolution. This inconsistent result highlights that the rule-based system is still a strong baseline, and more work is needed to improve pronoun resolution robustly for this dataset. While end-to-end neural systems require no feature engineering and achieve excellent performance in standard benchmarks with large training sets, our simple hybrid system scales well to long document coreference (>10k words) and attains superior results in our experiments on literature.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_crac-1_6", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_crac-1_6.flac", "type": "audio", "duration": 573.653}, {"from": "Assistant", "value": "Large annotated corpora for coreference resolution are available for few languages. For machine translation, however, strong black-box systems exist for many languages. We empirically explore the appealing idea of leveraging such translation tools for bootstrapping coreference resolution in languages with limited resources. Two scenarios are analyzed, in which a large coreference corpus in a high-resource language is used for coreference predictions in a smaller language, i.e., by machine translating either the training corpus or the test data. In our empirical evaluation of coreference resolution using the two scenarios on several medium-resource languages, we find no improvement over monolingual baseline models. Our analysis of the various sources of error inherent to the studied scenarios, reveals that in fact the quality of contemporary machine translation tools is the main limiting factor.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_crac-1_7", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_crac-1_7.flac", "type": "audio", "duration": 329.899}, {"from": "Assistant", "value": "Automatic coreference resolution is understudied in Danish even though most of the Danish Dependency Treebank (Buch-Kromann, 2003) is annotated with coreference relations. This paper describes a conversion of its partial, yet well-documented, coreference relations into coreference clusters and the training and evaluation of coreference models on this data. To the best of our knowledge, these are the first publicly available, neural coreference models for Danish. We also present a new entity linking annotation on the dataset using WikiData identifiers, a named entity disambiguation (NED) dataset, and a larger automatically created NED dataset enabling wikily supervised NED models. The entity linking annotation is benchmarked using a state-of-the-art neural entity disambiguation model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_crac-1_8", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_crac-1_8.flac", "type": "audio", "duration": 503.014}, {"from": "Assistant", "value": "Language Models are the underpin of all modern Natural Language Processing (NLP) tasks. The introduction of the Transformers architecture has contributed significantly into making Language Modeling very effective across many NLP task, leading to significant advancements in the field. However, Transformers come with a big computational cost, which grows quadratically with respect to the input length. This presents a challenge as to understand long texts requires a lot of context. In this paper, we propose a Fine-Tuning framework, named CoreLM, that extends the architecture of current Pretrained Language Models so that they incorporate explicit entity information. By introducing entity representations, we make available information outside the contextual space of the model, which results in a better Language Model for a fraction of the computational cost. We implement our approach using GPT2 and compare the fine-tuned model to the original. Our proposed model achieves a lower Perplexity in GUMBY and LAMBDADA datasets when compared to GPT2 and a fine-tuned version of GPT2 without any changes. We also compare the models’ performance in terms of Accuracy in LAMBADA and Children’s Book Test, with and without the use of model-created coreference annotations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_crac-1_9", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_crac-1_9.flac", "type": "audio", "duration": 636.018}, {"from": "Assistant", "value": "In pro-drop language like Arabic, Chinese, Italian, Japanese, Spanish, and many others, unrealized (null) arguments in certain syntactic positions can refer to a previously introduced entity, and are thus called anaphoric zero pronouns. The existing resources for studying anaphoric zero pronoun interpretation are however still limited. In this paper, we use five data augmentation methods to generate and detect anaphoric zero pronouns automatically. We use the augmented data as additional training materials for two anaphoric zero pronoun systems for Arabic. Our experimental results show that data augmentation improves the performance of the two systems, surpassing the state-of-the-art results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_crac-1_10", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_crac-1_10.flac", "type": "audio", "duration": 864.768}, {"from": "Assistant", "value": "In this paper, we develop bilingual transfer learning approaches to improve Arabic coreference resolution by leveraging additional English annotation via bilingual or multilingual pre-trained transformers. We show that bilingual transfer learning improves the strong transformer-based neural coreference models by 2-4 F1. We also systemically investigate the effectiveness of several pre-trained transformer models that differ in training corpora, languages covered, and model capacity. Our best model achieves a new state-of-the-art performance of 64.55 F1 on the Arabic OntoNotes dataset. Our code is publicly available athttps://github.com/bnmin/arabic_coref.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_crac-1_11", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_crac-1_11.flac", "type": "audio", "duration": 720.683}, {"from": "Assistant", "value": "Coreference decisions among event mentions and among co-occurring entity mentions are highly interdependent, thus motivating joint inference. Capturing the uncertainty over each variable can be crucial for inference among multiple dependent variables. Previous work on joint coreference employs heuristic approaches, lacking well-defined objectives, and lacking modeling of uncertainty on each side of the joint problem. We present a new approach of joint coreference, including (1) a formal cost function inspired by Dasgupta’s cost for hierarchical clustering, and (2) a representation for uncertainty of clustering of event and entity mentions, again based on a hierarchical structure. We describe an alternating optimization method for inference that when clustering event mentions, considers the uncertainty of the clustering of entity mentions and vice-versa. We show that our proposed joint model provides empirical advantages over state-of-the-art independent and joint models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_crac-1_12", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_crac-1_12.flac", "type": "audio", "duration": 472.015}, {"from": "Assistant", "value": "While coreference resolution is defined independently of dataset domain, most models for performing coreference resolution do not transfer well to unseen domains. We consolidate a set of 8 coreference resolution datasets targeting different domains to evaluate the off-the-shelf performance of models. We then mix three datasets for training; even though their domain, annotation guidelines, and metadata differ, we propose a method for jointly training a single model on this heterogeneous data mixture by using data augmentation to account for annotation differences and sampling to balance the data quantities. We find that in a zero-shot setting, models trained on a single dataset transfer poorly while joint training yields improved overall performance, leading to better generalization in coreference resolution models. This work contributes a new benchmark for robust coreference resolution and multiple new state-of-the-art results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_crac-1_13", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_crac-1_13.flac", "type": "audio", "duration": 589.021}, {"from": "Assistant", "value": "Recent work has shown fine-tuning neural coreference models can produce strong performance when adapting to different domains. However, at the same time, this can require a large amount of annotated target examples. In this work, we focus on supervised domain adaptation for clinical notes, proposing the use of concept knowledge to more efficiently adapt coreference models to a new domain. We develop methods to improve the span representations via (1) a retrofitting loss to incentivize span representations to satisfy a knowledge-based distance function and (2) a scaffolding loss to guide the recovery of knowledge from the span representation. By integrating these losses, our model is able to improve our baseline precision and F-1 score. In particular, we show that incorporating knowledge with end-to-end coreference models results in better performance on the most challenging, domain-specific spans.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_crac-1_14", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_crac-1_14.flac", "type": "audio", "duration": 584.005}, {"from": "Assistant", "value": "Event coreference resolution is critical to understand events in the growing number of online news with multiple modalities including text, video, speech, etc. However, the events and entities depicting in different modalities may not be perfectly aligned and can be difficult to annotate, which makes the task especially challenging with little supervision available. To address the above issues, we propose a supervised model based on attention mechanism and an unsupervised model based on statistical machine translation, capable of learning the relative importance of modalities for event coreference resolution. Experiments on a video multimedia event dataset show that our multimodal models outperform text-only systems in event coreference resolution tasks. A careful analysis reveals that the performance gain of the multimodal model especially under unsupervised settings comes from better learning of visually salient events.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_crac-1_15", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_crac-1_15.flac", "type": "audio", "duration": 541.002}, {"from": "Assistant", "value": "SOTA coreference resolution produces increasingly impressive scores on the OntoNotes benchmark. However lack of comparable data following the same scheme for more genres makes it difficult to evaluate generalizability to open domain data. Zhu et al. (2021) introduced the creation of the OntoGUM corpus for evaluating geralizability of the latest neural LM-based end-to-end systems. This paper covers details of the mapping process which is a set of deterministic rules applied to the rich syntactic and discourse annotations manually annotated in the GUM corpus. Out-of-domain evaluation across 12 genres shows nearly 15-20% degradation for both deterministic and deep learning systems, indicating a lack of generalizability or covert overfitting in existing coreference resolution models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_crac-1_16", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_crac-1_16.flac", "type": "audio", "duration": 357.481}, {"from": "Assistant", "value": "Despite significant recent progress in coreference resolution, the quality of current state-of-the-art systems still considerably trails behind human-level performance. Using the CoNLL-2012 and PreCo datasets, we dissect the best instantiation of the mainstream end-to-end coreference resolution model that underlies most current best-performing coreference systems, and empirically analyze the behavior of its two components: mention detector and mention linker. While the detector traditionally focuses heavily on recall as a design decision, we demonstrate the importance of precision, calling for their balance. However, we point out the difficulty in building a precise detector due to its inability to make important anaphoricity decisions. We also highlight the enormous room for improving the linker and show that the rest of its errors mainly involve pronoun resolution. We propose promising next steps and hope our findings will help future research in coreference resolution.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_econlp-1_2", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_econlp-1_2.flac", "type": "audio", "duration": 553.006}, {"from": "Assistant", "value": "We release EDGAR-CORPUS, a novel corpus comprising annual reports from all the publicly traded companies in the US spanning a period of more than 25 years. To the best of our knowledge, EDGAR-CORPUS is the largest financial NLP corpus available to date. All the reports are downloaded, split into their corresponding items (sections), and provided in a clean, easy-to-use JSON format. We use EDGAR-CORPUS to train and release EDGAR-W2V, which are WORD2VEC embeddings for the financial domain. We employ these embeddings in a battery of financial NLP tasks and showcase their superiority over generic GloVe embeddings and other existing financial word embeddings. We also open-source EDGAR-CRAWLER, a toolkit that facilitates downloading and extracting future annual reports.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_econlp-1_3", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_econlp-1_3.flac", "type": "audio", "duration": 749.006}, {"from": "Assistant", "value": "A domain specific question answering (QA) dataset dramatically improves the machine comprehension performance. This paper presents a new Global Banking Standards QA dataset (GBS-QA) in the banking regulation domain. The GBS-QA has three values. First, it contains actual questions from market players and answers from global rule setter, the Basel Committee on Banking Supervision (BCBS) in the middle of creating and revising banking regulations. Second, financial regulation experts analyze and verify pairs of questions and answers in the annotation process. Lastly, the GBS-QA is a totally different dataset with existing datasets in finance and is applicable to stimulate transfer learning research in the banking regulation domain.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_econlp-1_5", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_econlp-1_5.flac", "type": "audio", "duration": 895.013}, {"from": "Assistant", "value": "With the recent rise in popularity of Transformer models in Natural Language Processing, research efforts have been dedicated to the development of domain-adapted versions of BERT-like architectures. In this study, we focus on FinBERT, a Transformer model trained on text from the financial domain. By comparing its performances with the original BERT on a wide variety of financial text processing tasks, we found continual pretraining from the original model to be the more beneficial option. Domain-specific pretraining from scratch, conversely, seems to be less effective.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_econlp-1_6", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_econlp-1_6.flac", "type": "audio", "duration": 702.009}, {"from": "Assistant", "value": "We present a method to automatically identify financially relevant news using stock price movements and news headlines as input. The method repurposes the attention weights of a neural network initially trained to predict stock prices to assign a relevance score to each headline, eliminating the need for manually labeled training data. Our experiments on the four most relevant US stock indices and 1.5M news headlines show that the method ranks relevant news highly, positively correlated with the accuracy of the initial stock price prediction task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_econlp-1_7", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_econlp-1_7.flac", "type": "audio", "duration": 650.019}, {"from": "Assistant", "value": "Privacy is of primary importance when it comes to the Financial Domain as the data is highly confidential and no third party can be having access to it. Natural Language Processing (NLP) techniques can be applied for text classification and entity detection purposes in financial domains like customer feedback sentiment analysis, invoice entity detection, categorisation of financial documents by type etc. Due to the sensitive nature of such data, privacy measures need to be taken for handling and training large models with such data. In this work, we propose a contextualized transformer (BERT and RoBERTa) based text classification model integrated with privacy features like Differential Privacy (DP) and Federated Learning (FL). We present how to privately train NLP models and desirable privacy utility trade-offs and evaluate it on the Financial Phrase Bank dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_econlp-1_8", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_econlp-1_8.flac", "type": "audio", "duration": 900.073}, {"from": "Assistant", "value": "Documents have been an essential tool of communication for governments to announce their policy operations. Most policy announcements have taken the form of text to inform their new policies or changes to the public. To understand such policymakers’ communication, many researchers exploit published policy documents. However, the methods well-used in other research domains such as sentiment analysis or topic modeling are not suitable for studying policy communications. Their training corpora and methods are not for policy documents where technical terminologies are used, and sentiment expressions are refrained. We leverage word embedding techniques to extract semantic changes in the monetary policy documents. Our empirical study shows that the policymaker uses different semantics according to the type of documents when they change their policy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_econlp-1_9", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_econlp-1_9.flac", "type": "audio", "duration": 886.004}, {"from": "Assistant", "value": "In decision making in the economic field, an especially important requirement is to rapidly understand news to absorb ever-changing economic situations. Given that most economic news is written in English, the ability to read such information without waiting for a translation is particularly valuable in economics in contrast to other fields. In consideration of this issue, this research investigated the extent to which non-native English speakers are able to read economic news to make decisions accordingly – an issue that has been rarely addressed in previous studies. Using an existing standard dataset as training data, we created a classifier that automatically evaluates the readability of text with high accuracy for English learners. Our assessment of the readability of an economic news corpus revealed that most news texts can be read by intermediate English learners. We also found that in some cases, readability varies considerably depending on the knowledge of certain words specific to the economic field.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_econlp-1_10", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_econlp-1_10.flac", "type": "audio", "duration": 1340.0}, {"from": "Assistant", "value": "Event extraction in commodity news is a less researched area as compared to generic event extraction. However, accurate event extraction from commodity news is useful in abroad range of applications such as under-standing event chains and learning event-event relations, which can then be used for commodity price prediction. The events found in commodity news exhibit characteristics different from generic events, hence posing a unique challenge in event extraction using existing methods. This paper proposes an effective use of Graph Convolutional Networks(GCN) with a pruned dependency parse tree, termed contextual sub-tree, for better event ex-traction in commodity news. The event ex-traction model is trained using feature embed-dings from ComBERT, a BERT-based masked language model that was produced through domain-adaptive pre-training on a commodity news corpus. Experimental results show the efficiency of the proposed solution, which out-performs existing methods with F1 scores as high as 0.90. Furthermore, our pre-trained language model outperforms GloVe by 23%, and BERT and RoBERTa by 7% in terms of argument roles classification. For the goal of re-producibility, the code and trained models are made publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_econlp-1_11", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_econlp-1_11.flac", "type": "audio", "duration": 1082.027}, {"from": "Assistant", "value": "With 56 million people actively trading and investing in cryptocurrency online and globally in 2020, there is an increasing need for automatic social media analysis tools to help understand trading discourse and behavior. In this work, we present a dual natural language modeling pipeline which leverages language and social network behaviors for the prediction of cryptocurrency day trading actions and their associated framing patterns. This pipeline first predicts if tweets can be used to guide day trading behavior, specifically if a cryptocurrency investor should buy, sell, or hold their cryptocurrencies in order to make a profit. Next, tweets are input to an unsupervised deep clustering approach to automatically detect trading framing patterns. Our contributions include the modeling pipeline for this novel task, a new Cryptocurrency Tweets Dataset compiled from influential accounts, and a Historical Price Dataset. Our experiments show that our approach achieves an 88.78% accuracy for day trading behavior prediction and reveals framing fluctuations prior to and during the COVID-19 pandemic that could be used to guide investment actions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_econlp-1_12", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_econlp-1_12.flac", "type": "audio", "duration": 1569.019}, {"from": "Assistant", "value": "Estimating the effects of monetary policy is one of the fundamental research questions in monetary economics. Many economies are facing ultra-low interest rate environments ever since the global financial crisis of 2007-9. The Covid pandemic recently reinforced this situation. In the US and Europe, interest rates are close to (or even below) zero, which limits the scope of traditional monetary policy measures for central banks. Dedicated central bank communication has hence become an increasingly important tool to steer and control market expectations these days. However, incorporating central bank language directly as features into economic models is still a very nascent research area. In particular, the content and effect of central bank speeches has been mostly neglected from monetary policy modelling so far. With our paper, we aim to provide to the research community a novel, monetary policy shock series based on central bank speeches. We use a supervised topic modeling approach that can deal with text as well as numeric covariates to estimate a monetary policy signal dispersion index along three key economic dimensions: GDP, CPI and unemployment. This “dispersion shock” series is not only more frequent than series that classically focus on policy announcement dates, it also opens up the possibility of answering new questions that have up until now been difficult to analyse. For example, do markets form different expectations when facing a “cacophony of policy voices”? Our initial findings for the US point towards the fact that more dispersed or incongruent monetary policy stance communication in the build up to Federal Open Market Committee (FOMC) meetings might be associated with stronger subsequent market surprises at FOMC policy announcement time.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_eval4nlp-1_3", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_eval4nlp-1_3.flac", "type": "audio", "duration": 614.301}, {"from": "Assistant", "value": "Despite their success, modern language models are fragile. Even small changes in their training pipeline can lead to unexpected results. We study this phenomenon by examining the robustness of ALBERT (Lan et al., 2020) in combination with Stochastic Weight Averaging (SWA)—a cheap way of ensembling—on a sentiment analysis task (SST-2). In particular, we analyze SWA’s stability via CheckList criteria (Ribeiro et al., 2020), examining the agreement on errors made by models differing only in their random seed. We hypothesize that SWA is more stable because it ensembles model snapshots taken along the gradient descent trajectory. We quantify stability by comparing the models’ mistakes with Fleiss’ Kappa (Fleiss, 1971) and overlap ratio scores. We find that SWA reduces error rates in general; yet the models still suffer from their own distinct biases (according to CheckList).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_eval4nlp-1_6", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_eval4nlp-1_6.flac", "type": "audio", "duration": 585.221}, {"from": "Assistant", "value": "In this paper, we introduce a new embedding-based metric relying on trainable ranking models to evaluate the semantic accuracy of neural data-to-text generators. This metric is especially well suited to semantically and factually assess the performance of a text generator when tables can be associated with multiple references and table values contain textual utterances. We first present how one can implement and further specialize the metric by training the underlying ranking models on a legal Data-to-Text dataset. We show how it may provide a more robust evaluation than other evaluation schemes in challenging settings using a dataset comprising paraphrases between the table values and their respective references. Finally, we evaluate its generalization capabilities on a well-known dataset, WebNLG, by comparing it with human evaluation and a recently introduced metric based on natural language inference. We then illustrate how it naturally characterizes, both quantitatively and qualitatively, omissions and hallucinations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_eval4nlp-1_7", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_eval4nlp-1_7.flac", "type": "audio", "duration": 599.005}, {"from": "Assistant", "value": "Automatic readability assessment (ARA) is the task of automatically assessing readability with little or no human supervision. ARA is essential for many second language acquisition applications to reduce the workload of annotators, who are usually language teachers. Previous unsupervised approaches manually searched textual features that correlated well with readability labels, such as perplexity scores of large language models. This paper argues that, to evaluate an assessors’ performance, rank-correlation coefficients should be used instead of Pearson’s correlation coefficient (𝜌). In the experiments, we show that its performance can be easily underestimated using Pearson’s𝜌, which is significantly affected by the linearity of the output readability scores. We also propose a lightweight unsupervised readability assessor that achieved the best performance in both the rank correlations and Pearson’s𝜌among all unsupervised assessors compared.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_eval4nlp-1_10", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_eval4nlp-1_10.flac", "type": "audio", "duration": 592.016}, {"from": "Assistant", "value": "We propose a new reference-free summary quality evaluation measure, with emphasis on the faithfulness. The measure is based on finding and counting all probable potential inconsistencies of the summary with respect to the source document. The proposed ESTIME, Estimator of Summary-to-Text Inconsistency by Mismatched Embeddings, correlates with expert scores in summary-level SummEval dataset stronger than other common evaluation measures not only in Consistency but also in Fluency. We also introduce a method of generating subtle factual errors in human summaries. We show that ESTIME is more sensitive to subtle errors than other common evaluation measures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_eval4nlp-1_11", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_eval4nlp-1_11.flac", "type": "audio", "duration": 597.077}, {"from": "Assistant", "value": "Detecting lexical semantic change in smaller data sets, e.g. in historical linguistics and digital humanities, is challenging due to a lack of statistical power. This issue is exacerbated by non-contextual embedding models that produce one embedding per word and, therefore, mask the variability present in the data. In this article, we propose an approach to estimate semantic shift by combining contextual word embeddings with permutation-based statistical tests. We use the false discovery rate procedure to address the large number of hypothesis tests being conducted simultaneously. We demonstrate the performance of this approach in simulation where it achieves consistently high precision by suppressing false positives. We additionally analyze real-world data from SemEval-2020 Task 1 and the Liverpool FC subreddit corpus. We show that by taking sample variation into account, we can improve the robustness of individual semantic shift estimates without degrading overall performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_eval4nlp-1_12", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_eval4nlp-1_12.flac", "type": "audio", "duration": 767.488}, {"from": "Assistant", "value": "Reference-based automatic evaluation metrics are notoriously limited for NLG due to their inability to fully capture the range of possible outputs. We examine a referenceless alternative: evaluating the adequacy of English sentences generated from Abstract Meaning Representation (AMR) graphs by parsing into AMR and comparing the parse directly to the input. We find that the errors introduced by automatic AMR parsing substantially limit the effectiveness of this approach, but a manual editing study indicates that as parsing improves, parsing-based evaluation has the potential to outperform most reference-based metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_eval4nlp-1_13", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_eval4nlp-1_13.flac", "type": "audio", "duration": 483.022}, {"from": "Assistant", "value": "Code-mixing is a phenomenon of mixing words and phrases from two or more languages in a single utterance of speech and text. Due to the high linguistic diversity, code-mixing presents several challenges in evaluating standard natural language generation (NLG) tasks. Various widely popular metrics perform poorly with the code-mixed NLG tasks. To address this challenge, we present a metric in- dependent evaluation pipeline MIPE that significantly improves the correlation between evaluation metrics and human judgments on the generated code-mixed text. As a use case, we demonstrate the performance of MIPE on the machine-generated Hinglish (code-mixing of Hindi and English languages) sentences from the HinGE corpus. We can extend the proposed evaluation strategy to other code-mixed language pairs, NLG tasks, and evaluation metrics with minimal to no effort.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_eval4nlp-1_14", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_eval4nlp-1_14.flac", "type": "audio", "duration": 549.013}, {"from": "Assistant", "value": "We present the joint contribution of Instituto Superior Técnico (IST) and Unbabel to the Explainable Quality Estimation (QE) shared task, where systems were submitted to two tracks: constrained (without word-level supervision) and unconstrained (with word-level supervision). For the constrained track, we experimented with several explainability methods to extract the relevance of input tokens from sentence-level QE models built on top of multilingual pre-trained transformers. Among the different tested methods, composing explanations in the form of attention weights scaled by the norm of value vectors yielded the best results. When word-level labels are used during training, our best results were obtained by using word-level predicted probabilities. We further improve the performance of our methods on the two tracks by ensembling explanation scores extracted from models trained with different pre-trained transformers, achieving strong results for in-domain and zero-shot language pairs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_eval4nlp-1_15", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_eval4nlp-1_15.flac", "type": "audio", "duration": 376.674}, {"from": "Assistant", "value": "Quality Estimation (QE) for Machine Translation has been shown to reach relatively high accuracy in predicting sentence-level scores, relying on pretrained contextual embeddings and human-produced quality scores. However, the lack of explanations along with decisions made by end-to-end neural models makes the results difficult to interpret. Furthermore, word-level annotated datasets are rare due to the prohibitive effort required to perform this task, while they could provide interpretable signals in addition to sentence-level QE outputs. In this paper, we propose a novel QE architecture which tackles both the word-level data scarcity and the interpretability limitations of recent approaches. Sentence-level and word-level components are jointly pretrained through an attention mechanism based on synthetic data and a set of MT metrics embedded in a common space. Our approach is evaluated on the Eval4NLP 2021 shared task and our submissions reach the first position in all language pairs. The extraction of metric-to-input attention weights show that different metrics focus on different parts of the source and target text, providing strong rationales in the decision-making process of the QE model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_eval4nlp-1_16", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_eval4nlp-1_16.flac", "type": "audio", "duration": 588.022}, {"from": "Assistant", "value": "Many modern machine translation evaluation metrics like BERTScore, BLEURT, COMET, MonoTransquest or XMoverScore are based on black-box language models. Hence, it is difficult to explain why these metrics return certain scores. This year’s Eval4NLP shared task tackles this challenge by searching for methods that can extract feature importance scores that correlate well with human word-level error annotations. In this paper we show that unsupervised metrics that are based on tokenmatching can intrinsically provide such scores. The submitted system interprets the similarities of the contextualized word-embeddings that are used to compute (X)BERTScore as word-level importance scores.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_eval4nlp-1_18", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_eval4nlp-1_18.flac", "type": "audio", "duration": 541.568}, {"from": "Assistant", "value": "Authorship attribution is the task of assigning an unknown document to an author from a set of candidates. In the past, studies in this field use various evaluation datasets to demonstrate the effectiveness of preprocessing steps, features, and models. However, only a small fraction of works use more than one dataset to prove claims. In this paper, we present a collection of highly diverse authorship attribution datasets, which better generalizes evaluation results from authorship attribution research. Furthermore, we implement a wide variety of previously used machine learning models and show that many approaches show vastly different performances when applied to different datasets. We include pre-trained language models, for the first time testing them in this field in a systematic way. Finally, we propose a set of aggregated scores to evaluate different aspects of the dataset collection.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_eval4nlp-1_19", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_eval4nlp-1_19.flac", "type": "audio", "duration": 600.004}, {"from": "Assistant", "value": "Product reviews and satisfaction surveys seek customer feedback in the form of ranked scales. In these settings, widely used evaluation metrics including F1 and accuracy ignore the rank in the responses (e.g., ‘very likely’ is closer to ‘likely’ than ‘not at all’). In this paper, we hypothesize that the order of class values is important for evaluating classifiers on ordinal target variables and should not be disregarded. To test this hypothesis, we compared Multi-class Classification (MC) and Ordinal Regression (OR) by applying OR and MC to benchmark tasks involving ordinal target variables using the same underlying model architecture. Experimental results show that while MC outperformed OR for some datasets in accuracy and F1, OR is significantly better than MC for minimizing the error between prediction and target for all benchmarks, as revealed by error-sensitive metrics, e.g. mean-squared error (MSE) and Spearman correlation. Our findings motivate the need to establish consistent, error-sensitive metrics for evaluating benchmarks with ordinal target variables, and we hope that it stimulates interest in exploring alternative losses for ordinal problems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_eval4nlp-1_20", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_eval4nlp-1_20.flac", "type": "audio", "duration": 514.036}, {"from": "Assistant", "value": "Text generation is a highly active area of research in the computational linguistic community. The evaluation of the generated text is a challenging task and multiple theories and metrics have been proposed over the years. Unfortunately, text generation and evaluation are relatively understudied due to the scarcity of high-quality resources in code-mixed languages where the words and phrases from multiple languages are mixed in a single utterance of text and speech. To address this challenge, we present a corpus (HinGE) for a widely popular code-mixed language Hinglish (code-mixing of Hindi and English languages). HinGE has Hinglish sentences generated by humans as well as two rule-based algorithms corresponding to the parallel Hindi-English sentences. In addition, we demonstrate the in- efficacy of widely-used evaluation metrics on the code-mixed data. The HinGE dataset will facilitate the progress of natural language generation research in code-mixed languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_eval4nlp-1_21", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_eval4nlp-1_21.flac", "type": "audio", "duration": 444.181}, {"from": "Assistant", "value": "SemEval is the primary venue in the NLP community for the proposal of new challenges and for the systematic empirical evaluation of NLP systems. This paper provides a systematic quantitative analysis of SemEval aiming to evidence the patterns of the contributions behind SemEval. By understanding the distribution of task types, metrics, architectures, participation and citations over time we aim to answer the question on what is being evaluated by SemEval.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_eval4nlp-1_22", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_eval4nlp-1_22.flac", "type": "audio", "duration": 513.024}, {"from": "Assistant", "value": "This paper describes the UMD submission to the Explainable Quality Estimation Shared Task at the EMNLP 2021 Workshop on “Evaluation & Comparison of NLP Systems”. We participated in the word-level and sentence-level MT Quality Estimation (QE) constrained tasks for all language pairs: Estonian-English, Romanian-English, German-Chinese, and Russian-German. Our approach combines the predictions of a word-level explainer model on top of a sentence-level QE model and a sequence labeler trained on synthetic data. These models are based on pre-trained multilingual language models and do not require any word-level annotations for training, making them well suited to zero-shot settings. Our best-performing system improves over the best baseline across all metrics and language pairs, with an average gain of 0.1 in AUC, Average Precision, and Recall at Top-K score.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_eval4nlp-1_23", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_eval4nlp-1_23.flac", "type": "audio", "duration": 504.012}, {"from": "Assistant", "value": "Current research on quality estimation of machine translation focuses on the sentence-level quality of the translations. By using explainability methods, we can use these quality estimations for word-level error identification. In this work, we compare different explainability techniques and investigate gradient-based and perturbation-based methods by measuring their performance and required computational efforts. Throughout our experiments, we observed that using absolute word scores boosts the performance of gradient-based explainers significantly. Further, we combine explainability methods to ensembles to exploit the strengths of individual explainers to get better explanations. We propose the usage of absolute gradient-based methods. These work comparably well to popular perturbation-based ones while being more time-efficient.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_fever-1_1", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_fever-1_1.flac", "type": "audio", "duration": 573.035}, {"from": "Assistant", "value": "The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) shared task, asks participating systems to determine whether human-authored claims are Supported or Refuted based on evidence retrieved from Wikipedia (or NotEnoughInfo if the claim cannot be verified). Compared to the FEVER 2018 shared task, the main challenge is the addition of structured data (tables and lists) as a source of evidence. The claims in the FEVEROUS dataset can be verified using only structured evidence, only unstructured evidence, or a mixture of both. Submissions are evaluated using the FEVEROUS score that combines label accuracy and evidence retrieval. Unlike FEVER 2018, FEVEROUS requires partial evidence to be returned for NotEnoughInfo claims, and the claims are longer and thus more complex. The shared task received 13 entries, six of which were able to beat the baseline system. The winning team was “Bust a move!”, achieving a FEVEROUS score of 27% (+9% compared to the baseline). In this paper we describe the shared task, present the full results and highlight commonalities and innovations among the participating systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_fever-1_2", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_fever-1_2.flac", "type": "audio", "duration": 755.2}, {"from": "Assistant", "value": "In Automated Claim Verification, we retrieve evidence from a knowledge base to determine the veracity of a claim. Intuitively, the retrieval of the correct evidence plays a crucial role in this process. Often, evidence selection is tackled as a pairwise sentence classification task, i.e., we train a model to predict for each sentence individually whether it is evidence for a claim. In this work, we fine-tune document level transformers to extract all evidence from a Wikipedia document at once. We show that this approach performs better than a comparable model classifying sentences individually on all relevant evidence selection metrics in FEVER. Our complete pipeline building on this evidence selection procedure produces a new state-of-the-art result on FEVER, a popular claim verification benchmark.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_fever-1_4", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_fever-1_4.flac", "type": "audio", "duration": 607.04}, {"from": "Assistant", "value": "As part of the FEVEROUS shared task, we developed a robust and finely tuned architecture to handle the joint retrieval and entailment on text data as well as structured data like tables. We proposed two training schemes to tackle the hurdles inherent to multi-hop multi-modal datasets. The first one allows having a robust retrieval of full evidence sets, while the second one enables entailment to take full advantage of noisy evidence inputs. In addition, our work has revealed important insights and potential avenue of research for future improvement on this kind of dataset. In preliminary evaluation on the FEVEROUS shared task test set, our system achieves 0.271 FEVEROUS score, with 0.4258 evidence recall and 0.5607 entailment accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_fever-1_5", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_fever-1_5.flac", "type": "audio", "duration": 551.712}, {"from": "Assistant", "value": "We develop a system for the FEVEROUS fact extraction and verification task that ranks an initial set of potential evidence and then pursues missing evidence in subsequent hops by trying to generate it, with a “next hop prediction module” whose output is matched against page elements in a predicted article. Seeking evidence with the next hop prediction module continues to improve FEVEROUS score for up to seven hops. Label classification is trained on possibly incomplete extracted evidence chains, utilizing hints that facilitate numerical comparison. The system achieves .281 FEVEROUS score and .658 label accuracy on the development set, and finishes in second place with .259 FEVEROUS score and .576 label accuracy on the test set.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_fever-1_10", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_fever-1_10.flac", "type": "audio", "duration": 559.02}, {"from": "Assistant", "value": "This paper describes a method for retrieving evidence and predicting the veracity of factual claims, on the FEVEROUS dataset. The evidence consists of both sentences and table cells. The proposed method is part of the FEVER shared task. It uses similarity scores between TF-IDF vectors to retrieve the textual evidence and similarity scores between dense vectors created by fine-tuned TaPaS models for tabular evidence retrieval. The evidence is passed through a dense neural network to produce a veracity label. The FEVEROUS score for the proposed system is 0.126.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_fever-1_11", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_fever-1_11.flac", "type": "audio", "duration": 380.672}, {"from": "Assistant", "value": "Automatic fact-checking is crucial for recognizing misinformation spreading on the internet. Most existing fact-checkers break down the process into several subtasks, one of which determines candidate evidence sentences that can potentially support or refute the claim to be verified; typically, evidence sentences with gold-standard labels are needed for this. In a more realistic setting, however, such sentence-level annotations are not available. In this paper, we tackle the natural language inference (NLI) subtask—given a document and a (sentence) claim, determine whether the document supports or refutes the claim—only using document-level annotations. Using fine-tuned BERT and multiple instance learning, we achieve 81.9% accuracy, significantly outperforming the existing results on the WikiFactCheck-English dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_1", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_1.flac", "type": "audio", "duration": 236.031}, {"from": "Assistant", "value": "Mikolov et al. (2013a) observed that continuous bag-of-words (CBOW) word embeddings tend to underperform Skip-gram (SG) embeddings, and this finding has been reported in subsequent works. We find that these observations are driven not by fundamental differences in their training objectives, but more likely on faulty negative sampling CBOW implementations in popular libraries such as the official implementation, word2vec.c, and Gensim. We show that after correcting a bug in the CBOW gradient update, one can learn CBOW word embeddings that are fully competitive with SG on various intrinsic and extrinsic tasks, while being many times faster to train.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_2", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_2.flac", "type": "audio", "duration": 425.018}, {"from": "Assistant", "value": "Sarcasm detection is important for several NLP tasks such as sentiment identification in product reviews, user feedback, and online forums. It is a challenging task requiring a deep understanding of language, context, and world knowledge. In this paper, we investigate whether incorporating commonsense knowledge helps in sarcasm detection. For this, we incorporate commonsense knowledge into the prediction process using a graph convolution network with pre-trained language model embeddings as input. Our experiments with three sarcasm detection datasets indicate that the approach does not outperform the baseline model. We perform an exhaustive set of experiments to analyze where commonsense support adds value and where it hurts classification. Our implementation is publicly available at:https://github.com/brcsomnath/commonsense-sarcasm.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_3", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_3.flac", "type": "audio", "duration": 280.915}, {"from": "Assistant", "value": "In previous work, it has been shown that BERT can adequately align cross-lingual sentences on the word level. Here we investigate whether BERT can also operate as a char-level aligner. The languages examined are English, Fake English, German and Greek. We show that the closer two languages are, the better BERT can align them on the character level. BERT indeed works well in English to Fake English alignment, but this does not generalize to natural languages to the same extent. Nevertheless, the proximity of two languages does seem to be a factor. English is more related to German than to Greek and this is reflected in how well BERT aligns them; English to German is better than English to Greek. We examine multiple setups and show that the similarity matrices for natural languages show weaker relations the further apart two languages are.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_4", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_4.flac", "type": "audio", "duration": 415.01}, {"from": "Assistant", "value": "In the field of natural language processing, ensembles are broadly known to be effective in improving performance. This paper analyzes how ensemble of neural machine translation (NMT) models affect performance improvement by designing various experimental setups (i.e., intra-, inter-ensemble, and non-convergence ensemble). To an in-depth examination, we analyze each ensemble method with respect to several aspects such as different attention models and vocab strategies. Experimental results show that ensembling is not always resulting in performance increases and give noteworthy negative findings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_5", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_5.flac", "type": "audio", "duration": 359.374}, {"from": "Assistant", "value": "Text variational autoencoders (VAEs) are notorious for posterior collapse, a phenomenon where the model’s decoder learns to ignore signals from the encoder. Because posterior collapse is known to be exacerbated by expressive decoders, Transformers have seen limited adoption as components of text VAEs. Existing studies that incorporate Transformers into text VAEs (Li et al., 2020; Fang et al., 2021) mitigate posterior collapse using massive pretraining, a technique unavailable to most of the research community without extensive computing resources. We present a simple two-phase training scheme to convert a sequence-to-sequence Transformer into a VAE with just finetuning. The resulting language model is competitive with massively pretrained Transformer-based VAEs in some internal metrics while falling short on others. To facilitate training we comprehensively explore the impact of common posterior collapse alleviation techniques in the literature. We release our code for reproducability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_6", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_6.flac", "type": "audio", "duration": 360.002}, {"from": "Assistant", "value": "With the essays part from The International Corpus Network of Asian Learners of English (ICNALE) and the TOEFL11 corpus, we fine-tuned neural language models based on BERT to predict English learners’ native languages. Results showed neural models can learn to represent and detect such native language impacts, but multilingually trained models have no advantage in doing so.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_7", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_7.flac", "type": "audio", "duration": 303.02}, {"from": "Assistant", "value": "The training of NLP models often requires large amounts of labelled training data, which makes it difficult to expand existing models to new languages. While zero-shot cross-lingual transfer relies on multilingual word embeddings to apply a model trained on one language to another, Yarowski and Ngai (2001) propose the method of annotation projection to generate training data without manual annotation. This method was successfully used for the tasks of named entity recognition and coarse-grained entity typing, but we show that it is outperformed by zero-shot cross-lingual transfer when applied to the similar task of fine-grained entity typing. In our study of fine-grained entity typing with the FIGER type ontology for German, we show that annotation projection amplifies the English model’s tendency to underpredict level 2 labels and is beaten by zero-shot cross-lingual transfer on three novel test sets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_8", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_8.flac", "type": "audio", "duration": 321.527}, {"from": "Assistant", "value": "Nickel and Kiela (2017) present a new method for embedding tree nodes in the Poincare ball, and suggest that these hyperbolic embeddings are far more effective than Euclidean embeddings at embedding nodes in large, hierarchically structured graphs like the WordNet nouns hypernymy tree. This is especially true in low dimensions (Nickel and Kiela, 2017, Table 1). In this work, we seek to reproduce their experiments on embedding and reconstructing the WordNet nouns hypernymy graph. Counter to what they report, we find that Euclidean embeddings are able to represent this tree at least as well as Poincare embeddings, when allowed at least 50 dimensions. We note that this does not diminish the significance of their work given the impressive performance of hyperbolic embeddings in very low-dimensional settings. However, given the wide influence of their work, our aim here is to present an updated and more accurate comparison between the Euclidean and hyperbolic embeddings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_9", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_9.flac", "type": "audio", "duration": 359.004}, {"from": "Assistant", "value": "Further pre-training language models on in-domain data (domain-adaptive pre-training, DAPT) or task-relevant data (task-adaptive pre-training, TAPT) before fine-tuning has been shown to improve downstream tasks’ performances. However, in task-oriented dialog modeling, we observe that further pre-training MLM does not always boost the performance on a downstream task. We find that DAPT is beneficial in the low-resource setting, but as the fine-tuning data size grows, DAPT becomes less beneficial or even useless, and scaling the size of DAPT data does not help. Through Representational Similarity Analysis, we conclude that more data for fine-tuning yields greater change of the model’s representations and thus reduces the influence of initialization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_10", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_10.flac", "type": "audio", "duration": 571.002}, {"from": "Assistant", "value": "In this work, we conduct a comprehensive investigation on one of the centerpieces of modern machine translation systems: the encoder-decoder attention mechanism. Motivated by the concept of first-order alignments, we extend the (cross-)attention mechanism by a recurrent connection, allowing direct access to previous attention/alignment decisions. We propose several ways to include such a recurrency into the attention mechanism. Verifying their performance across different translation tasks we conclude that these extensions and dependencies are not beneficial for the translation performance of the Transformer architecture.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_11", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_11.flac", "type": "audio", "duration": 391.696}, {"from": "Assistant", "value": "Word segmentation, the problem of finding word boundaries in speech, is of interest for a range of tasks. Previous papers have suggested that for sequence-to-sequence models trained on tasks such as speech translation or speech recognition, attention can be used to locate and segment the words. We show, however, that even on monolingual data this approach is brittle. In our experiments with different input types, data sizes, and segmentation algorithms, only models trained to predict phones from words succeed in the task. Models trained to predict words from either phones or speech (i.e., the opposite direction needed to generalize to new data), yield much worse results, suggesting that attention-based segmentation is only useful in limited scenarios.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_12", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_12.flac", "type": "audio", "duration": 360.811}, {"from": "Assistant", "value": "Machine translation systems are vulnerable to domain mismatch, especially in a low-resource scenario. Out-of-domain translations are often of poor quality and prone to hallucinations, due to exposure bias and the decoder acting as a language model. We adopt two approaches to alleviate this problem: lexical shortlisting restricted by IBM statistical alignments, and hypothesis reranking based on similarity. The methods are computationally cheap and show success on low-resource out-of-domain test sets. However, the methods lose advantage when there is sufficient data or too great domain mismatch. This is due to both the IBM model losing its advantage over the implicitly learned neural alignment, and issues with subword segmentation of unseen words.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_13", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_13.flac", "type": "audio", "duration": 360.002}, {"from": "Assistant", "value": "Backtranslation is a common technique for leveraging unlabeled data in low-resource scenarios in machine translation. The method is directly applicable to morphological inflection generation if unlabeled word forms are available. This paper evaluates the potential of backtranslation for morphological inflection using data from six languages with labeled data drawn from the SIGMORPHON shared task resource and unlabeled data from different sources. Our core finding is that backtranslation can offer modest improvements in low-resource scenarios, but only if the unlabeled data is very clean and has been filtered by the same annotation standards as the labeled data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_14", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_14.flac", "type": "audio", "duration": 250.009}, {"from": "Assistant", "value": "Despite its proven efficiency in other fields, data augmentation is less popular in the context of natural language processing (NLP) due to its complexity and limited results. A recent study (Longpre et al., 2020) showed for example that task-agnostic data augmentations fail to consistently boost the performance of pretrained transformers even in low data regimes. In this paper, we investigate whether data-driven augmentation scheduling and the integration of a wider set of transformations can lead to improved performance where fixed and limited policies were unsuccessful. Our results suggest that, while this approach can help the training process in some settings, the improvements are unsubstantial. This negative result is meant to help researchers better understand the limitations of data augmentation for NLP.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_15", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_15.flac", "type": "audio", "duration": 360.148}, {"from": "Assistant", "value": "In natural language understanding, topics that touch upon figurative language and pragmatics are notably difficult. We probe a novel use of locally aggregated descriptors – specifically, an architecture called NeXtVLAD – motivated by its accomplishments in computer vision, achieve tremendous success in the FigLang2020 sarcasm detection task. The reported F1 score of 93.1% is 14% higher than the next best result. We specifically investigate the extent to which the novel architecture is responsible for this boost, and find that it does not provide statistically significant benefits. Deep learning approaches are expensive, and we hope our insights highlighting the lack of benefits from introducing a resource-intensive component will aid future research to distill the effective elements from long and complex pipelines, thereby providing a boost to the wider research community.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_16", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_16.flac", "type": "audio", "duration": 338.007}, {"from": "Assistant", "value": "Understanding linguistic modality is widely seen as important for downstream tasks such as Question Answering and Knowledge Graph Population. Entailment Graph learning might also be expected to benefit from attention to modality. We build Entailment Graphs using a news corpus filtered with a modality parser, and show that stripping modal modifiers from predicates in fact increases performance. This suggests that for some tasks, the pragmatics of modal modification of predicates allows them to contribute as evidence of entailment.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_17", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_17.flac", "type": "audio", "duration": 316.0}, {"from": "Assistant", "value": "Although neural models have shown strong performance in datasets such as SNLI, they lack the ability to generalize out-of-distribution (OOD). In this work, we formulate a few-shot learning setup and examine the effects of natural language explanations on OOD generalization. We leverage the templates in the HANS dataset and construct templated natural language explanations for each template. Although generated explanations show competitive BLEU scores against ground truth explanations, they fail to improve prediction performance. We further show that generated explanations often hallucinate information and miss key elements that indicate the label.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_18", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_18.flac", "type": "audio", "duration": 353.515}, {"from": "Assistant", "value": "Much of recent progress in NLU was shown to be due to models’ learning dataset-specific heuristics. We conduct a case study of generalization in NLI (from MNLI to the adversarially constructed HANS dataset) in a range of BERT-based architectures (adapters, Siamese Transformers, HEX debiasing), as well as with subsampling the data and increasing the model size. We report 2 successful and 3 unsuccessful strategies, all providing insights into how Transformer-based models learn to generalize.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_19", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_19.flac", "type": "audio", "duration": 358.005}, {"from": "Assistant", "value": "Semi-Supervised Variational Autoencoders (SSVAEs) are widely used models for data efficient learning. In this paper, we question the adequacy of the standard design of sequence SSVAEs for the task of text classification as we exhibit two sources of overcomplexity for which we provide simplifications. These simplifications to SSVAEs preserve their theoretical soundness while providing a number of practical advantages in the semi-supervised setup where the result of training is a text classifier. These simplifications are the removal of (i) the Kullback-Liebler divergence from its objective and (ii) the fully unobserved latent variable from its probabilistic model. These changes relieve users from choosing a prior for their latent variables, make the model smaller and faster, and allow for a better flow of information into the latent variables. We compare the simplified versions to standard SSVAEs on 4 text classification tasks. On top of the above-mentioned simplification, experiments show a speed-up of 26%, while keeping equivalent classification scores. The code to reproduce our experiments is public.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_insights-1_20", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_insights-1_20.flac", "type": "audio", "duration": 289.236}, {"from": "Assistant", "value": "High-quality arguments are an essential part of decision-making. Automatically predicting the quality of an argument is a complex task that recently got much attention in argument mining. However, the annotation effort for this task is exceptionally high. Therefore, we test uncertainty-based active learning (AL) methods on two popular argument-strength data sets to estimate whether sample-efficient learning can be enabled. Our extensive empirical evaluation shows that uncertainty-based acquisition functions can not surpass the accuracy reached with the random acquisition on these data sets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_latechclfl-1_1", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_latechclfl-1_1.flac", "type": "audio", "duration": 1325.0}, {"from": "Assistant", "value": "While the production of information in the European early modern period is a well-researched topic, the question how people were engaging with the information explosion that occurred in early modern Europe, is still underexposed. This paper presents the annotations and experiments aimed at exploring whether we can automatically extract media related information (source, perception, and receiver) from a corpus of early modern Dutch chronicles in order to get insight in the mediascape of early modern middle class people from a historic perspective. In a number of classification experiments with Conditional Random Fields, three categories of features are tested: (i) raw and binary word embedding features, (ii) lexicon features, and (iii) character features. Overall, the classifier that uses raw embeddings performs slightly better. However, given that the best F-scores are around 0.60, we conclude that the machine learning approach needs to be combined with a close reading approach for the results to be useful to answer history research questions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_latechclfl-1_2", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_latechclfl-1_2.flac", "type": "audio", "duration": 1159.348}, {"from": "Assistant", "value": "Although olfactory references play a crucial role in our cultural memory, only few works in NLP have tried to capture them from a computational perspective. Currently, the main challenge is not much the development of technological components for olfactory information extraction, given recent advances in semantic processing and natural language understanding, but rather the lack of a theoretical framework to capture this information from a linguistic point of view, as a preliminary step towards the development of automated systems. Therefore, in this work we present the annotation guidelines, developed with the help of history scholars and domain experts, aimed at capturing all the relevant elements involved in olfactory situations or events described in texts. These guidelines have been inspired by FrameNet annotation, but underwent some adaptations, which are detailed in this paper. Furthermore, we present a case study concerning the annotation of olfactory situations in English historical travel writings describing trips to Italy. An analysis of the most frequent role fillers show that olfactory descriptions pertain to some typical domains such as religion, food, nature, ancient past, poor sanitation, all supporting the creation of a stereotypical imagery related to Italy. On the other hand, positive feelings triggered by smells are prevalent, and contribute to framing travels to Italy as an exciting experience involving all senses.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_latechclfl-1_3", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_latechclfl-1_3.flac", "type": "audio", "duration": 1069.0}, {"from": "Assistant", "value": "Pretrained language models like BERT have advanced the state of the art for many NLP tasks. For resource-rich languages, one has the choice between a number of language-specific models, while multilingual models are also worth considering. These models are well known for their crosslingual performance, but have also shown competitive in-language performance on some tasks. We consider monolingual and multilingual models from the perspective of historical texts, and in particular for texts enriched with editorial notes: how do language models deal with the historical and editorial content in these texts? We present a new Named Entity Recognition dataset for Dutch based on 17th and 18th century United East India Company (VOC) reports extended with modern editorial notes. Our experiments with multilingual and Dutch pretrained language models confirm the crosslingual abilities of multilingual models while showing that all language models can leverage mixed-variant data. In particular, language models successfully incorporate notes for the prediction of entities in historical texts. We also find that multilingual models outperform monolingual models on our data, but that this superiority is linked to the task at hand: multilingual models lose their advantage when confronted with more semantical tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_latechclfl-1_4", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_latechclfl-1_4.flac", "type": "audio", "duration": 998.549}, {"from": "Assistant", "value": "We report on an inter-annotator agreement experiment involving instances of text reuse focusing on the well-known case of biblical intertextuality in medieval literature. We target the application use case of literary scholars whose aim is to document instances of biblical references in the ‘apparatus fontium’ of a prospective digital edition. We develop a Bayesian implementation of Cohen’s kappa for multiple annotators that allows us to assess the influence of various contextual effects on the inter-annotator agreement, producing both more robust estimates of the agreement indices as well as insights into the annotation process that leads to the estimated indices. As a result, we are able to produce a novel and nuanced estimation of inter-annotator agreement in the context of intertextuality, exploring the challenges that arise from manually annotating a dataset of biblical references in the writings of Bernard of Clairvaux. Among others, our method was able to unveil the fact that the obtained agreement depends heavily on the biblical source book of the proposed reference, as well as the underlying algorithm used to retrieve the candidate match.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_latechclfl-1_7", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_latechclfl-1_7.flac", "type": "audio", "duration": 1011.995}, {"from": "Assistant", "value": "In this work, we design an end-to-end model for poetry generation based on conditioned recurrent neural network (RNN) language models whose goal is to learn stylistic features (poem length, sentiment, alliteration, and rhyming) from examples alone. We show this model successfully learns the ‘meaning’ of length and sentiment, as we can control it to generate longer or shorter as well as more positive or more negative poems. However, the model does not grasp sound phenomena like alliteration and rhyming, but instead exploits low-level statistical cues. Possible reasons include the size of the training data, the relatively low frequency and difficulty of these sublexical phenomena as well as model biases. We show that more recent GPT-2 models also have problems learning sublexical phenomena such as rhyming from examples alone.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_latechclfl-1_9", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_latechclfl-1_9.flac", "type": "audio", "duration": 1368.004}, {"from": "Assistant", "value": "In this paper we take up the problem of “limerick detection” and describe a system to identify five-line poems as limericks or not. This turns out to be a surprisingly difficult challenge with many subtleties. More precisely, we produce an algorithm which focuses on the structural aspects of the limerick – rhyme scheme and rhythm (i.e., stress patterns) – and when tested on a a culled data set of 98,454 publicly available limericks, our “limerick filter” accepts 67% as limericks. The primary failure of our filter is on the detection of “non-standard” rhymes, which we highlight as an outstanding challenge in computational poetics. Our accent detection algorithm proves to be very robust. Our main contributions are (1) a novel rhyme detection algorithm that works on English words including rare proper nouns and made-up words (and thus, words not in the widely used CMUDict database); (2) a novel rhythm-identifying heuristic that is robust to language noise at moderate levels and comparable in accuracy to state-of-the-art scansion algorithms. As a third significant contribution (3) we make publicly available a large corpus of limericks that includes tags of “limerick” or “not-limerick” as determined by our identification software, thereby providing a benchmark for the community. The poetic tasks that we have identified as challenges for machines suggest that the limerick is a useful “model organism” for the study of machine capabilities in poetry and more broadly literature and language. We include a list of open challenges as well. Generally, we anticipate that this work will provide useful material and benchmarks for future explorations in the field.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_latechclfl-1_12", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_latechclfl-1_12.flac", "type": "audio", "duration": 1902.037}, {"from": "Assistant", "value": "The paper reports the results of a translationese study of literary texts based on translated and non-translated Russian. We aim to find out if translations deviate from non-translated literary texts, and if the established differences can be attributed to typological relations between source and target languages. We expect that literary translations from typologically distant languages should exhibit more translationese, and the fingerprints of individual source languages (and their families) are traceable in translations. We explore linguistic properties that distinguish non-translated Russian literature from translations into Russian. Our results show that non-translated fiction is different from translations to the degree that these two language varieties can be automatically classified. As expected, language typology is reflected in translations of literary texts. We identified features that point to linguistic specificity of Russian non-translated literature and to shining-through effects. Some of translationese features cut across all language pairs, while others are characteristic of literary translations from languages belonging to specific language families.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_latechclfl-1_18", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_latechclfl-1_18.flac", "type": "audio", "duration": 1423.808}, {"from": "Assistant", "value": "Despite the increasing popularity of NLP in the humanities and social sciences, advances in model performance and complexity have been accompanied by concerns about interpretability and explanatory power for sociocultural analysis. One popular model that takes a middle road is Word Mover’s Distance (WMD). Ostensibly adapted for its interpretability, WMD has nonetheless been used and further developed in ways which frequently discard its most interpretable aspect: namely, the word-level distances required for translating a set of words into another set of words. To address this apparent gap, we introduce WMDecompose: a model and Python library that 1) decomposes document-level distances into their constituent word-level distances, and 2) subsequently clusters words to induce thematic elements, such that useful lexical information is retained and summarized for analysis. To illustrate its potential in a social scientific context, we apply it to a longitudinal social media corpus to explore the interrelationship between conspiracy theories and conservative American discourses. Finally, because of the full WMD model’s high time-complexity, we additionally suggest a method of sampling document pairs from large datasets in a reproducible way, with tight bounds that prevent extrapolation of unreliable results due to poor sampling practices.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_latechclfl-1_20", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_latechclfl-1_20.flac", "type": "audio", "duration": 1325.824}, {"from": "Assistant", "value": "We apply statistical techniques from natural language processing to Western and Hong Kong–based English language newspaper articles that discuss the 2019–2020 Hong Kong protests of the Anti-Extradition Law Amendment Bill Movement. Topic modeling detects central themes of the reporting and shows the differing agendas towardone country, two systems. Embedding-based usage shift (at the word level) and sentiment analysis (at the document level) both support that Hong Kong–based reporting is more negative and more emotionally charged. A two-way test shows that while July 1, 2019 is a turning point for media portrayal, the differences between western- and Hong Kong–based reporting did not magnify when the protests began; rather, they already existed. Taken together, these findings clarify how the portrayal of activism in Hong Kong evolved throughout the Movement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_latechclfl-1_21", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_latechclfl-1_21.flac", "type": "audio", "duration": 1176.324}, {"from": "Assistant", "value": "This paper applies stylometry to quantify the literariness of 73 novels and novellas by American author Stephen King, chosen as an extraordinary case of a writer who has been dubbed both “high” and “low” in literariness in critical reception. We operationalize literariness using a measure of stylistic distance (Cosine Delta) based on the 1000 most frequent words in two bespoke comparison corpora used as proxies for literariness: one of popular genre fiction, another of National Book Award-winning authors. We report that a supervised model is highly effective in distinguishing the two categories, with 94.6% macro average in a binary classification. We define two subsets of texts by King—“high” and “low” literariness works as suggested by critics and ourselves—and find that a predictive model does identify King’s Dark Tower series and novels such as Dolores Claiborne as among his most “literary” texts, consistent with critical reception, which has also ascribed postmodern qualities to the Dark Tower novels. Our results demonstrate the efficacy of Cosine Delta-based stylometry in quantifying the literariness of texts, while also highlighting the methodological challenges of literariness, especially in the case of Stephen King. The code and data to reproduce our results are available athttps://github.com/andreasvc/kinglit", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrl-1_1", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrl-1_1.flac", "type": "audio", "duration": 1179.016}, {"from": "Assistant", "value": "General-purpose language models have demonstrated impressive capabilities, performing on par with state-of-the-art approaches on a range of downstream natural language processing (NLP) tasks and benchmarks when inferring instructions from very few examples. Here, we evaluate the multilingual skills of the GPT and T5 models in conducting multi-class classification on non-English languages without any parameter updates. We show that, given a few English examples as context, pre-trained language models can predict not only English test samples but also non-English ones. Finally, we find the in-context few-shot cross-lingual prediction results of language models are significantly better than random prediction, and they are competitive compared to the existing state-of-the-art cross-lingual models and translation models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrl-1_2", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrl-1_2.flac", "type": "audio", "duration": 1131.021}, {"from": "Assistant", "value": "We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus (e.g. a few hundred sentence pairs). Our method obtains word embeddings via an LSTM encoder-decoder model that simultaneously translates and reconstructs an input sentence. Through sharing model parameters among different languages, our model jointly trains the word embeddings in a common cross-lingual space. We also propose to combine word and subword embeddings to make use of orthographic similarities across different languages. We base our experiments on real-world data from endangered languages, namely Yongning Na, Shipibo-Konibo, and Griko. Our experiments on bilingual lexicon induction and word alignment tasks show that our model outperforms existing methods by a large margin for most language pairs. These results demonstrate that, contrary to common belief, an encoder-decoder translation model is beneficial for learning cross-lingual representations even in extremely low-resource conditions. Furthermore, our model also works well on high-resource conditions, achieving state-of-the-art performance on a German-English word-alignment task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrl-1_3", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrl-1_3.flac", "type": "audio", "duration": 437.023}, {"from": "Assistant", "value": "Multilingual language models exhibit better performance for some languages than for others (Singh et al., 2019), and many languages do not seem to benefit from multilingual sharing at all, presumably as a result of poor multilingual segmentation (Pyysal o et al., 2020). This work explores the idea of learning multilingual language models based on clustering of monolingual segments. We show significant improvements over standard multilingual segmentation and training across nine languages on a question answering task, both in a small model regime and for a model of the size of BERT-base.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrl-1_4", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrl-1_4.flac", "type": "audio", "duration": 464.004}, {"from": "Assistant", "value": "Cross-lingual word embeddings (CLWEs) have proven indispensable for various natural language processing tasks, e.g., bilingual lexicon induction (BLI). However, the lack of data often impairs the quality of representations. Various approaches requiring only weak cross-lingual supervision were proposed, but current methods still fail to learn good CLWEs for languages with only a small monolingual corpus. We therefore claim that it is necessary to explore further datasets to improve CLWEs in low-resource setups. In this paper we propose to incorporate data of related high-resource languages. In contrast to previous approaches which leverage independently pre-trained embeddings of languages, we (i) train CLWEs for the low-resource and a related language jointly and (ii) map them to the target language to build the final multilingual space. In our experiments we focus on Occitan, a low-resource Romance language which is often neglected due to lack of resources. We leverage data from French, Spanish and Catalan for training and evaluate on the Occitan-English BLI task. By incorporating supporting languages our method outperforms previous approaches by a large margin. Furthermore, our analysis shows that the degree of relatedness between an incorporated language and the low-resource language is critically important.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrl-1_5", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrl-1_5.flac", "type": "audio", "duration": 622.016}, {"from": "Assistant", "value": "Pretrained multilingual language models have become a common tool in transferring NLP capabilities to low-resource languages, often with adaptations. In this work, we study the performance, extensibility, and interaction of two such adaptations: vocabulary augmentation and script transliteration. Our evaluations on part-of-speech tagging, universal dependency parsing, and named entity recognition in nine diverse low-resource languages uphold the viability of these approaches while raising new questions around how to optimally adapt multilingual models to low-resource settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrl-1_6", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrl-1_6.flac", "type": "audio", "duration": 558.951}, {"from": "Assistant", "value": "We study a new problem of cross-lingual transfer learning for event coreference resolution (ECR) where models trained on data from a source language are adapted for evaluations in different target languages. We introduce the first baseline model for this task based on XLM-RoBERTa, a state-of-the-art multilingual pre-trained language model. We also explore language adversarial neural networks (LANN) that present language discriminators to distinguish texts from the source and target languages to improve the language generalization for ECR. In addition, we introduce two novel mechanisms to further enhance the general representation learning of LANN, featuring: (i) multi-view alignment to penalize cross coreference-label alignment of examples in the source and target languages, and (ii) optimal transport to select close examples in the source and target languages to provide better training signals for the language discriminators. Finally, we perform extensive experiments for cross-lingual ECR from English to Spanish and Chinese to demonstrate the effectiveness of the proposed methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrl-1_7", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrl-1_7.flac", "type": "audio", "duration": 552.008}, {"from": "Assistant", "value": "Virtual Adversarial Training (VAT) has been effective in learning robust models under supervised and semi-supervised settings for both computer vision and NLP tasks. However, the efficacy of VAT for multilingual and multilabel emotion recognition has not been explored before. In this work, we explore VAT for multilabel emotion recognition with a focus on leveraging unlabelled data from different languages to improve the model performance. We perform extensive semi-supervised experiments on SemEval2018 multilabel and multilingual emotion recognition dataset and show performance gains of 6.2% (Arabic), 3.8% (Spanish) and 1.8% (English) over supervised learning with same amount of labelled data (10% of training data). We also improve the existing state-of-the-art by 7%, 4.5% and 1% (Jaccard Index) for Spanish, Arabic and English respectively and perform probing experiments for understanding the impact of different layers of the contextual models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrl-1_9", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrl-1_9.flac", "type": "audio", "duration": 461.45}, {"from": "Assistant", "value": "In this work, we analyze the performance and properties of cross-lingual word embedding models created by mapping-based alignment methods. We use several measures of corpus and embedding similarity to predict BLI scores of cross-lingual embedding mappings over three types of corpora, three embedding methods and 55 language pairs. Our experimental results corroborate that instead of mere size, the amount of common content in the training corpora is essential. This phenomenon manifests in that i) despite of the smaller corpus sizes, using only the comparable parts of Wikipedia for training the monolingual embedding spaces to be mapped is often more efficient than relying on all the contents of Wikipedia, ii) the smaller, in return less diversified Spanish Wikipedia works almost always much better as a training corpus for bilingual mappings than the ubiquitously used English Wikipedia.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrl-1_10", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrl-1_10.flac", "type": "audio", "duration": 251.426}, {"from": "Assistant", "value": "In this paper we analyze the extent to which contextualized sense embeddings, i.e., sense embeddings that are computed based on contextualized word embeddings, are transferable across languages. To this end, we compiled a unified cross-lingual benchmark for Word Sense Disambiguation. We then propose two simple strategies to transfer sense-specific knowledge across languages and test them on the benchmark. Experimental results show that this contextualized knowledge can be effectively transferred to similar languages through pre-trained multilingual language models, to the extent that they can out-perform monolingual representations learnednfrom existing language-specific data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrl-1_12", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrl-1_12.flac", "type": "audio", "duration": 1109.161}, {"from": "Assistant", "value": "We present Mr. TyDi, a multi-lingual benchmark dataset for mono-lingual retrieval in eleven typologically diverse languages, designed to evaluate ranking with learned dense representations. The goal of this resource is to spur research in dense retrieval techniques in non-English languages, motivated by recent observations that existing techniques for representation learning perform poorly when applied to out-of-distribution data. As a starting point, we provide zero-shot baselines for this new dataset based on a multi-lingual adaptation of DPR that we call “mDPR”. Experiments show that although the effectiveness of mDPR is much lower than BM25, dense representations nevertheless appear to provide valuable relevance signals, improving BM25 results in sparse–dense hybrids. In addition to analyses of our results, we also discuss future challenges and present a research agenda in multi-lingual dense retrieval. Mr. TyDi can be downloaded athttps://github.com/castorini/mr.tydi.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrl-1_13", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrl-1_13.flac", "type": "audio", "duration": 1217.004}, {"from": "Assistant", "value": "An exciting frontier in natural language understanding (NLU) and generation (NLG) calls for (vision-and-) language models that can efficiently access external structured knowledge repositories. However, many existing knowledge bases only cover limited domains, or suffer from noisy data, and most of all are typically hard to integrate into neural language pipelines. To fill this gap, we release VisualSem: a high-quality knowledge graph (KG) which includes nodes with multilingual glosses, multiple illustrative images, and visually relevant relations. We also release a neural multi-modal retrieval model that can use images or sentences as inputs and retrieves entities in the KG. This multi-modal retrieval model can be integrated into any (neural network) model pipeline. We encourage the research community to use VisualSem for data augmentation and/or as a source of grounding, among other possible uses. VisualSem as well as the multi-modal retrieval models are publicly available and can be downloaded in this URL:https://github.com/iacercalixto/visualsem.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrl-1_14", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrl-1_14.flac", "type": "audio", "duration": 597.008}, {"from": "Assistant", "value": "While there has been significant progress towards developing NLU resources for Indic languages, syntactic evaluation has been relatively less explored. Unlike English, Indic languages have rich morphosyntax, grammatical genders, free linear word-order, and highly inflectional morphology. In this paper, we introduce Vyākarana: a benchmark of Colorless Green sentences in Indic languages for syntactic evaluation of multilingual language models. The benchmark comprises four syntax-related tasks: PoS Tagging, Syntax Tree-depth Prediction, Grammatical Case Marking, and Subject-Verb Agreement. We use the datasets from the evaluation tasks to probe five multilingual language models of varying architectures for syntax in Indic languages. Due to its prevalence, we also include a code-switching setting in our experiments. Our results show that the token-level and sentence-level representations from the Indic language models (IndicBERT and MuRIL) do not capture the syntax in Indic languages as efficiently as the other highly multilingual language models. Further, our layer-wise probing experiments reveal that while mBERT, DistilmBERT, and XLM-R localize the syntax in middle layers, the Indic language models do not show such syntactic localization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrl-1_16", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrl-1_16.flac", "type": "audio", "duration": 452.023}, {"from": "Assistant", "value": "While recent benchmarks have spurred a lot of new work on improving the generalization of pretrained multilingual language models on multilingual tasks, techniques to improve code-switched natural language understanding tasks have been far less explored. In this work, we propose the use ofbilingual intermediate pretrainingas a reliable technique to derive large and consistent performance gains using code-switched text on three different NLP tasks: Natural Language Inference (NLI), Question Answering (QA) and Sentiment Analysis (SA). We show consistent performance gains on four different code-switched language-pairs (Hindi-English, Spanish-English, Tamil-English and Malayalam-English) for SA and on Hindi-English for NLI and QA. We also present a code-switched masked language modeling (MLM) pretraining technique that consistently benefits SA compared to standard MLM pretraining using real code-switched text.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrl-1_17", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrl-1_17.flac", "type": "audio", "duration": 478.42}, {"from": "Assistant", "value": "Recent research has adopted a new experimental field centered around the concept of text perturbations which has revealed that shuffled word order has little to no impact on the downstream performance of Transformer-based language models across many NLP tasks. These findings contradict the common understanding of how the models encode hierarchical and structural information and even question if the word order is modeled with position embeddings. To this end, this paper proposes nine probing datasets organized by the type of controllable text perturbation for three Indo-European languages with a varying degree of word order flexibility: English, Swedish and Russian. Based on the probing analysis of the M-BERT and M-BART models, we report that the syntactic sensitivity depends on the language and model pre-training objectives. We also find that the sensitivity grows across layers together with the increase of the perturbation granularity. Last but not least, we show that the models barely use the positional information to induce syntactic trees from their intermediate self-attention and contextualized representations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrl-1_20", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrl-1_20.flac", "type": "audio", "duration": 292.014}, {"from": "Assistant", "value": "Many recent works use ‘consistency regularisation’ to improve the generalisation of fine-tuned pre-trained models, both multilingual and English-only. These works encourage model outputs to be similar between a perturbed and normal version of the input, usually via penalising the Kullback–Leibler (KL) divergence between the probability distribution of the perturbed and normal model. We believe that consistency losses may be implicitly regularizing the loss landscape. In particular, we build on work hypothesising that implicitly or explicitly regularizing trace of the Fisher Information Matrix (FIM), amplifies the implicit bias of SGD to avoid memorization. Our initial results show both empirically and theoretically that consistency losses are related to the FIM, and show that the flat minima implied by a small trace of the FIM improves performance when fine-tuning a multilingual model on additional languages. We aim to confirm these initial results on more datasets, and use our insights to develop better multilingual fine-tuning techniques.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrl-1_21", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrl-1_21.flac", "type": "audio", "duration": 134.002}, {"from": "Assistant", "value": "Interpolation-based regularisation methods have proven to be effective for various tasks and modalities. Mixup is a data augmentation method that generates virtual training samples from convex combinations of individual inputs and labels. We extend Mixup and propose DMix, distance-constrained interpolative Mixup for sentence classification leveraging the hyperbolic space. DMix achieves state-of-the-art results on sentence classification over existing data augmentation methods across datasets in four languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrl-1_22", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrl-1_22.flac", "type": "audio", "duration": 182.021}, {"from": "Assistant", "value": "There have been efforts in cross-lingual transfer learning for various tasks. We present an approach utilizing an interpolative data augmentation method, Mixup, to improve the generalizability of models for part-of-speech tagging trained on a source language, improving its performance on unseen target languages. Through experiments on ten languages with diverse structures and language roots, we put forward its applicability for downstream zero-shot cross-lingual tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrl-1_23", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrl-1_23.flac", "type": "audio", "duration": 337.014}, {"from": "Assistant", "value": "Morphological tasks have gained decent popularity within the NLP community in the recent years, with large multi-lingual datasets providing morphological analysis of words, either in or out of context. However, the lack of a clear linguistic definition for words destines the annotative work to be incomplete and mired in inconsistencies, especially cross-linguistically. In this work we expand morphological inflection of words to inflection of sentences to provide true universality disconnected from orthographic traditions of white-space usage. To allow annotation for sentence-inflection we define a morphological annotation scheme by a fixed set of inflectional features. We present a small cross-linguistic dataset including semi-manually generated simple sentences in 4 typologically diverse languages annotated according to our suggested scheme, and show that the task of reinflection gets substantially more difficult but that the change of scope from words to well-defined sentences allows interface with contextualized language models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_mrqa-1_16", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_mrqa-1_16.flac", "type": "audio", "duration": 612.008}, {"from": "Assistant", "value": "Dense retrieval has been shown to be effective for Open Domain Question Answering, surpassing sparse retrieval methods like BM25. One such model, REALM, (Guu et al., 2020) is an end-to-end dense retrieval system that uses MLM based pretraining for improved downstream QA performance. However, the current REALM setup uses limited resources and is not comparable in scale to more recent systems, contributing to its lower performance. Additionally, it relies on noisy supervision for retrieval during fine-tuning. We propose REALM++, where we improve upon the training and inference setups and introduce better supervision signal for improving performance, without any architectural changes. REALM++ achieves ~5.5% absolute accuracy gains over the baseline while being faster to train. It also matches the performance of large models which have 3x more parameters demonstrating the efficiency of our setup.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_newsum-1_1", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_newsum-1_1.flac", "type": "audio", "duration": 512.0}, {"from": "Assistant", "value": "Abstractive summarization models heavily rely on copy mechanisms, such as the pointer network or attention, to achieve good performance, measured by textual overlap with reference summaries. As a result, the generated summaries stay close to the formulations in the source document. We propose the *sentence planner* model to generate more abstractive summaries. It includes a hierarchical decoder that first generates a representation for the next summary sentence, and then conditions the word generator on this representation. Our generated summaries are more abstractive and at the same time achieve high ROUGE scores when compared to human reference summaries. We verify the effectiveness of our design decisions with extensive evaluations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_newsum-1_2", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_newsum-1_2.flac", "type": "audio", "duration": 598.656}, {"from": "Assistant", "value": "Earning calls are among important resources for investors and analysts for updating their price targets. Firms usually publish corresponding transcripts soon after earnings events. However, raw transcripts are often too long and miss the coherent structure. To enhance the clarity, analysts write well-structured reports for some important earnings call events by analyzing them, requiring time and effort. In this paper, we propose TATSum (Template-Aware aTtention model for Summarization), a generalized neural summarization approach for structured report generation, and evaluate its performance in the earnings call domain. We build a large corpus with thousands of transcripts and reports using historical earnings events. We first generate a candidate set of reports from the corpus as potential soft templates which do not impose actual rules on the output. Then, we employ an encoder model with margin-ranking loss to rank the candidate set and select the best quality template. Finally, the transcript and the selected soft template are used as input in a seq2seq framework for report generation. Empirical results on the earnings call dataset show that our model significantly outperforms state-of-the-art models in terms of informativeness and structure.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_newsum-1_3", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_newsum-1_3.flac", "type": "audio", "duration": 288.02}, {"from": "Assistant", "value": "In this paper, we study the abstractive sentence summarization. There are two essential information features that can influence the quality of news summarization, which are topic keywords and the knowledge structure of the news text. Besides, the existing knowledge encoder has poor performance on sparse sentence knowledge structure. Considering these, we propose KAS, a novel Knowledge and Keywords Augmented Abstractive Sentence Summarization framework. Tri-encoders are utilized to integrate contexts of original text, knowledge structure and keywords topic simultaneously, with a special linearized knowledge structure. Automatic and human evaluations demonstrate that KAS achieves the best performances.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_newsum-1_4", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_newsum-1_4.flac", "type": "audio", "duration": 311.168}, {"from": "Assistant", "value": "We consider the problem of topic-focused abstractive summarization, where the goal is to generate an abstractive summary focused on a particular topic, a phrase of one or multiple words. We hypothesize that the task of generating topic-focused summaries can be improved by showing the model what it must not focus on. We introduce a deep reinforcement learning approach to topic-focused abstractive summarization, trained on rewards with a novel negative example baseline. We define the input in this problem as the source text preceded by the topic. We adapt the CNN-Daily Mail and New York Times summarization datasets for this task. We then show through experiments on existing rewards that the use of a negative example baseline can outperform the use of a self-critical baseline, in Rouge, BERTScore, and human evaluation metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_newsum-1_5", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_newsum-1_5.flac", "type": "audio", "duration": 588.264}, {"from": "Assistant", "value": "Cross-lingual summarization is a challenging task for which there are no cross-lingual scientific resources currently available. To overcome the lack of a high-quality resource, we present a new dataset for monolingual and cross-lingual summarization considering the English-German pair. We collect high-quality, real-world cross-lingual data from Spektrum der Wissenschaft, which publishes human-written German scientific summaries of English science articles on various subjects. The generated Spektrum dataset is small; therefore, we harvest a similar dataset from the Wikipedia Science Portal to complement it. The Wikipedia dataset consists of English and German articles, which can be used for monolingual and cross-lingual summarization. Furthermore, we present a quantitative analysis of the datasets and results of empirical experiments with several existing extractive and abstractive summarization models. The results suggest the viability and usefulness of the proposed dataset for monolingual and cross-lingual summarization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_newsum-1_6", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_newsum-1_6.flac", "type": "audio", "duration": 410.539}, {"from": "Assistant", "value": "Summarization systems are ultimately evaluated by human annotators and raters. Usually, annotators and raters do not reflect the demographics of end users, but are recruited through student populations or crowdsourcing platforms with skewed demographics. For two different evaluation scenarios – evaluation against gold summaries and system output ratings – we show that summary evaluation is sensitive to protected attributes. This can severely bias system development and evaluation, leading us to build models that cater for some groups rather than others.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_newsum-1_7", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_newsum-1_7.flac", "type": "audio", "duration": 305.017}, {"from": "Assistant", "value": "We present work on summarising deliberative processes for non-English languages. Unlike commonly studied datasets, such as news articles, this deliberation dataset reflects difficulties of combining multiple narratives, mostly of poor grammatical quality, in a single text. We report an extensive evaluation of a wide range of abstractive summarisation models in combination with an off-the-shelf machine translation model. Texts are translated into English, summarised, and translated back to the original language. We obtain promising results regarding the fluency, consistency and relevance of the summaries produced. Our approach is easy to implement for many languages for production purposes by simply changing the translation model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_newsum-1_8", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_newsum-1_8.flac", "type": "audio", "duration": 292.672}, {"from": "Assistant", "value": "In this paper, we focus on improving the quality of the summary generated by neural abstractive dialogue summarization systems. Even though pre-trained language models generate well-constructed and promising results, it is still challenging to summarize the conversation of multiple participants since the summary should include a description of the overall situation and the actions of each speaker. This paper proposes self-supervised strategies for speaker-focused post-correction in abstractive dialogue summarization. Specifically, our model first discriminates which type of speaker correction is required in a draft summary and then generates a revised summary according to the required type. Experimental results show that our proposed method adequately corrects the draft summaries, and the revised summaries are significantly improved in both quantitative and qualitative evaluations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_newsum-1_9", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_newsum-1_9.flac", "type": "audio", "duration": 597.008}, {"from": "Assistant", "value": "For many NLP applications of online reviews, comparison of two opinion-bearing sentences is key. We argue that, while general purpose text similarity metrics have been applied for this purpose, there has been limited exploration of their applicability to opinion texts. We address this gap in the literature, studying: (1) how humans judge the similarity of pairs of opinion-bearing sentences; and, (2) the degree to which existing text similarity metrics, particularly embedding-based ones, correspond to human judgments. We crowdsourced annotations for opinion sentence pairs and our main findings are: (1) annotators tend to agree on whether or not opinion sentences are similar or different; and (2) embedding-based metrics capture human judgments of “opinion similarity” but not “opinion difference”. Based on our analysis, we identify areas where the current metrics should be improved. We further propose to learn a similarity metric for opinion similarity via fine-tuning the Sentence-BERT sentence-embedding network based on review text and weak supervision by review ratings. Experiments show that our learned metric outperforms existing text similarity metrics and especially show significantly higher correlations with human annotations for differing opinions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_newsum-1_10", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_newsum-1_10.flac", "type": "audio", "duration": 617.001}, {"from": "Assistant", "value": "Current abstractive summarization systems outperform their extractive counterparts, but their widespread adoption is inhibited by the inherent lack of interpretability. Extractive summarization systems, though interpretable, suffer from redundancy and possible lack of coherence. To achieve the best of both worlds, we propose EASE, an extractive-abstractive framework that generates concise abstractive summaries that can be traced back to an extractive summary. Our framework can be applied to any evidence-based text generation problem and can accommodate various pretrained models in its simple architecture. We use the Information Bottleneck principle to jointly train the extraction and abstraction in an end-to-end fashion. Inspired by previous research that humans use a two-stage framework to summarize long documents (Jing and McKeown, 2000), our framework first extracts a pre-defined amount of evidence spans and then generates a summary using only the evidence. Using automatic and human evaluations, we show that the generated summaries are better than strong extractive and extractive-abstractive baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_newsum-1_11", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_newsum-1_11.flac", "type": "audio", "duration": 645.467}, {"from": "Assistant", "value": "Incremental meeting temporal summarization, summarizing relevant information of partial multi-party meeting dialogue, is emerging as the next challenge in summarization research. Here we examine the extent to which human abstractive summaries of the preceding increments (context) can be combined with extractive meeting dialogue to generate abstractive summaries. We find that previous context improves ROUGE scores. Our findings further suggest that contexts begin to outweigh the dialogue. Using keyphrase extraction and semantic role labeling (SRL), we find that SRL captures relevant information without overwhelming the the model architecture. By compressing the previous contexts by ~70%, we achieve better ROUGE scores over our baseline models. Collectively, these results suggest that context matters, as does the way in which context is presented to the model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_newsum-1_12", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_newsum-1_12.flac", "type": "audio", "duration": 594.048}, {"from": "Assistant", "value": "Dialogue summarization is a long-standing task in the field of NLP, and several data sets with dialogues and associated human-written summaries of different styles exist. However, it is unclear for which type of dialogue which type of summary is most appropriate. For this reason, we apply a linguistic model of dialogue types to derive matching summary items and NLP tasks. This allows us to map existing dialogue summarization data sets into this model and identify gaps and potential directions for future work. As part of this process, we also provide an extensive overview of existing dialogue summarization data sets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_newsum-1_13", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_newsum-1_13.flac", "type": "audio", "duration": 637.016}, {"from": "Assistant", "value": "A crucial difference between single- and multi-document summarization is how salient content manifests itself in the document(s). While such content may appear at the beginning of a single document, essential information is frequently reiterated in a set of documents related to a particular topic, resulting in an endorsement effect that increases information salience. In this paper, we model the cross-document endorsement effect and its utilization in multiple document summarization. Our method generates a synopsis from each document, which serves as an endorser to identify salient content from other documents. Strongly endorsed text segments are used to enrich a neural encoder-decoder model to consolidate them into an abstractive summary. The method has a great potential to learn from fewer examples to identify salient content, which alleviates the need for costly retraining when the set of documents is dynamically adjusted. Through extensive experiments on benchmark multi-document summarization datasets, we demonstrate the effectiveness of our proposed method over strong published baselines. Finally, we shed light on future research directions and discuss broader challenges of this task using a case study.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_newsum-1_14", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_newsum-1_14.flac", "type": "audio", "duration": 313.144}, {"from": "Assistant", "value": "Many applications require generation of summaries tailored to the user’s information needs, i.e., their intent. Methods that express intent via explicit user queries fall short when query interpretation is subjective. Several datasets exist for summarization with objective intents where, for each document and intent (e.g., “weather”), a single summary suffices for all users. No datasets exist, however, for subjective intents (e.g., “interesting places”) where different users will provide different summaries. We present SUBSUME, the first dataset for evaluation of SUBjective SUMmary Extraction systems. SUBSUME contains 2,200 (document, intent, summary) triplets over 48 Wikipedia pages, with ten intents of varying subjectivity, provided by 103 individuals over Mechanical Turk. We demonstrate statistically that the intents in SUBSUME vary systematically in subjectivity. To indicate SUBSUME’s usefulness, we explore a collection of baseline algorithms for subjective extractive summarization and show that (i) as expected, example-based approaches better capture subjective intents than query-based ones, and (ii) there is ample scope for improving upon the baseline algorithms, thereby motivating further research on this challenging problem.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_newsum-1_16", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_newsum-1_16.flac", "type": "audio", "duration": 266.88}, {"from": "Assistant", "value": "The task of document-level text simplification is very similar to summarization with the additional difficulty of reducing complexity. We introduce a newly collected data set of German texts, collected from the Swiss news magazine 20 Minuten (‘20 Minutes’) that consists of full articles paired with simplified summaries. Furthermore, we present experiments on automatic text simplification with the pretrained multilingual mBART and a modified version thereof that is more memory-friendly, using both our new data set and existing simplification corpora. Our modifications of mBART let us train at a lower memory cost without much loss in performance, in fact, the smaller mBART even improves over the standard model in a setting with multiple simplification levels.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_nlp4convai-1_7", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_nlp4convai-1_7.flac", "type": "audio", "duration": 608.014}, {"from": "Assistant", "value": "Intent Classification (IC) and Slot Labeling (SL) models, which form the basis of dialogue systems, often encounter noisy data in real-word environments. In this work, we investigate how robust IC/SL models are to noisy data. We collect and publicly release a test-suite for seven common noise types found in production human-to-bot conversations (abbreviations, casing, misspellings, morphological variants, paraphrases, punctuation and synonyms). On this test-suite, we show that common noise types substantially degrade the IC accuracy and SL F1 performance of state-of-the-art BERT-based IC/SL models. By leveraging cross-noise robustness transfer, i.e. training on one noise type to improve robustness on another noise type, we design aggregate data-augmentation approaches that increase the model performance across all seven noise types by+10.8%for IC accuracy and+15points for SL F1 on average. To the best of our knowledge, this is the first work to present a single IC/SL model that is robust to a wide range of noise phenomena.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_nlp4convai-1_10", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_nlp4convai-1_10.flac", "type": "audio", "duration": 471.395}, {"from": "Assistant", "value": "Personalized dialogue systems are an essential step toward better human-machine interaction. Existing personalized dialogue agents rely on properly designed conversational datasets, which are mostly monolingual (e.g., English), which greatly limits the usage of conversational agents in other languages. In this paper, we propose a multi-lingual extension of Persona-Chat, namely XPersona. Our dataset includes persona conversations in six different languages other than English for evaluating multilingual personalized agents. We experiment with both multilingual and cross-lingual trained baselines and evaluate them against monolingual and translation-pipeline models using both automatic and human evaluation. Experimental results show that the multilingual trained models outperform the translation pipeline and that they are on par with the monolingual models, with the advantage of having a single model across multiple languages. On the other hand, the state-of-the-art cross-lingual trained models achieve inferior performance to the other models, showing that cross-lingual conversation modeling is a challenging task. We hope that our dataset and baselines will accelerate research in multilingual dialogue systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_nlp4convai-1_11", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_nlp4convai-1_11.flac", "type": "audio", "duration": 358.631}, {"from": "Assistant", "value": "Intelligent personal assistants (IPAs) such as Amazon Alexa, Google Assistant and Apple Siri extend their built-in capabilities by supporting voice apps developed by third-party developers. Sometimes the smart assistant is not able to successfully respond to user voice commands (aka utterances). There are many reasons including automatic speech recognition (ASR) error, natural language understanding (NLU) error, routing utterances to an irrelevant voice app or simply that the user is asking for a capability that is not supported yet. The failure to handle a voice command leads to customer frustration. In this paper, we introduce a fallback skill recommendation system to suggest a voice app to a customer for an unhandled voice command. One of the prominent challenges of developing a skill recommender system for IPAs is partial observation. To solve the partial observation problem, we propose collaborative data relabeling (CDR) method. In addition, CDR also improves the diversity of the recommended skills. We evaluate the proposed method both offline and online. The offline evaluation results show that the proposed system outperforms the baselines. The online A/B testing results show significant gain of customer experience metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_nlp4convai-1_14", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_nlp4convai-1_14.flac", "type": "audio", "duration": 359.104}, {"from": "Assistant", "value": "Large-scale conversation models are turning to leveraging external knowledge to improve the factual accuracy in response generation. Considering the infeasibility to annotate the external knowledge for large-scale dialogue corpora, it is desirable to learn the knowledge selection and response generation in an unsupervised manner. In this paper, we propose PLATO-KAG (Knowledge-Augmented Generation), an unsupervised learning approach for end-to-end knowledge-grounded conversation modeling. For each dialogue context, the top-k relevant knowledge elements are selected and then employed in knowledge-grounded response generation. The two components of knowledge selection and response generation are optimized jointly and effectively under a balanced objective. Experimental results on two publicly available datasets validate the superiority of PLATO-KAG.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_nlp4convai-1_15", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_nlp4convai-1_15.flac", "type": "audio", "duration": 354.011}, {"from": "Assistant", "value": "Dialogue state tracking models play an important role in a task-oriented dialogue system. However, most of them model the slot types conditionally independently given the input. We discover that it may cause the model to be confused by slot types that share the same data type. To mitigate this issue, we propose TripPy-MRF and TripPy-LSTM that models the slots jointly. Our results show that they are able to alleviate the confusion mentioned above, and they push the state-of-the-art on dataset MultiWoz 2.1 from 58.7 to 61.3.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_nlp4convai-1_17", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_nlp4convai-1_17.flac", "type": "audio", "duration": 357.007}, {"from": "Assistant", "value": "Query rewrite (QR) is an emerging component in conversational AI systems, reducing user defect. User defect is caused by various reasons, such as errors in the spoken dialogue system, users’ slips of the tongue or their abridged language. Many of the user defects stem from personalized factors, such as user’s speech pattern, dialect, or preferences. In this work, we propose a personalized search-based QR framework, which focuses on automatic reduction of user defect. We build a personalized index for each user, which encompasses diverse affinity layers to reflect personal preferences for each user in the conversational AI. Our personalized QR system contains retrieval and ranking layers. Supported by user feedback based learning, training our models does not require hand-annotated data. Experiments on personalized test set showed that our personalized QR system is able to correct systematic and user errors by utilizing phonetic and semantic inputs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_nlp4convai-1_20", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_nlp4convai-1_20.flac", "type": "audio", "duration": 360.002}, {"from": "Assistant", "value": "Graph-to-text generation aims to generate fluent texts from graph-based data. In this paper, we investigate two recent pretrained language models (PLMs) and analyze the impact of different task-adaptive pretraining strategies for PLMs in graph-to-text generation. We present a study across three graph domains: meaning representations, Wikipedia knowledge graphs (KGs) and scientific KGs. We show that approaches based on PLMs BART and T5 achieve new state-of-the-art results and that task-adaptive pretraining strategies improve their performance even further. We report new state-of-the-art BLEU scores of 49.72 on AMR-LDC2017T10, 59.70 on WebNLG, and 25.66 on AGENDA datasets - a relative improvement of 31.8%, 4.5%, and 42.4%, respectively, with our models generating significantly more fluent texts than human references. In an extensive analysis, we identify possible reasons for the PLMs’ success on graph-to-text tasks. Our findings suggest that the PLMs benefit from similar facts seen during pretraining or fine-tuning, such that they perform well even when the input graph is reduced to a simple bag of node and edge labels.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_nlp4convai-1_22", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_nlp4convai-1_22.flac", "type": "audio", "duration": 359.252}, {"from": "Assistant", "value": "Entity tags in human-machine dialog are integral to natural language understanding (NLU) tasks in conversational assistants. However, current systems struggle to accurately parse spoken queries with the typical use of text input alone, and often fail to understand the user intent. Previous work in linguistics has identified a cross-language tendency for longer speech pauses surrounding nouns as compared to verbs. We demonstrate that the linguistic observation on pauses can be used to improve accuracy in machine-learnt language understanding tasks. Analysis of pauses in French and English utterances from a commercial voice assistant shows the statistically significant difference in pause duration around multi-token entity span boundaries compared to within entity spans. Additionally, in contrast to text-based NLU, we apply pause duration to enrich contextual embeddings to improve shallow parsing of entities. Results show that our proposed novel embeddings improve the relative error rate by up to 8% consistently across three domains for French, without any added annotation or alignment costs to the parser.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_nlp4convai-1_24", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_nlp4convai-1_24.flac", "type": "audio", "duration": 389.004}, {"from": "Assistant", "value": "Knowledge Grounded Conversation Models are usually based on a selection/retrieval module and a generation module, trained separately or simultaneously, with or without having access to a ‘gold’ knowledge option. With the introduction of large pre-trained generative models, the selection and generation part have become more and more entangled, shifting the focus towards enhancing knowledge incorporation (from multiple sources) instead of trying to pick the best knowledge option. These approaches however depend on knowledge labels and/or a separate dense retriever for their best performance. In this work we study the unsupervised selection abilities of pre-trained generative models (e.g. BART) and show that by adding a score-and-aggregate module between encoder and decoder, they are capable of learning to pick the proper knowledge through minimising the language modelling loss (i.e. without having access to knowledge labels). Trained as such, our model - K-Mine - shows competitive selection and generation performance against models that benefit from knowledge labels and/or separate dense retriever.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_nlp4convai-1_25", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_nlp4convai-1_25.flac", "type": "audio", "duration": 359.629}, {"from": "Assistant", "value": "Endowing a task-oriented dialogue system with adaptiveness to user personality can greatly help improve the performance of a dialogue task. However, such a dialogue system can be practically challenging to implement, because it is unclear how user personality influences dialogue task performance. To explore the relationship between user personality and dialogue task performance, we enrolled participants via crowdsourcing to first answer specified personality questionnaires and then chat with a dialogue system to accomplish assigned tasks. A rule-based dialogue system on the prevalent Multi-Domain Wizard-of-Oz (MultiWOZ) task was used. A total of 211 participants’ personalities and their 633 dialogues were collected and analyzed. The results revealed that sociable and extroverted people tended to fail the task, whereas neurotic people were more likely to succeed. We extracted features related to user dialogue behaviors and performed further analysis to determine which kind of behavior influences task performance. As a result, we identified that average utterance length and slots per utterance are the key features of dialogue behavior that are highly correlated with both task performance and user personality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sustainlp-1_1", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sustainlp-1_1.flac", "type": "audio", "duration": 681.992}, {"from": "Assistant", "value": "We address the problem oflink predictionbetween entities and relations of knowledge graphs. State of the art techniques that address this problem, while increasingly accurate, are computationally intensive. In this paper we cast link prediction as a sparse convex program whose solution defines a quadratic form that is used as a ranking function. The structure of our convex program is such that standard support vector machine software packages, which are numerically robust and efficient, can solve it. We show that on benchmark data sets, our model’s performance is competitive with state of the art models, but training times can be reduced by a factor of 40 using only CPU-based (and not GPU-accelerated) computing resources. This approach may be suitable for applications where balancing the demands of graph completion performance against computational efficiency is a desirable trade-off.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sustainlp-1_3", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sustainlp-1_3.flac", "type": "audio", "duration": 422.023}, {"from": "Assistant", "value": "Pretrained transformer-based encoders such as BERT have been demonstrated to achieve state-of-the-art performance on numerous NLP tasks. Despite their success, BERT style encoders are large in size and have high latency during inference (especially on CPU machines) which make them unappealing for many online applications. Recently introduced compression and distillation methods have provided effective ways to alleviate this shortcoming. However, the focus of these works has been mainly on monolingual encoders. Motivated by recent successes in zero-shot cross-lingual transfer learning using multilingual pretrained encoders such as mBERT, we evaluate the effectiveness of Knowledge Distillation (KD) both during pretraining stage and during fine-tuning stage on multilingual BERT models. We demonstrate that in contradiction to the previous observation in the case of monolingual distillation, in multilingual settings, distillation during pretraining is more effective than distillation during fine-tuning for zero-shot transfer learning. Moreover, we observe that distillation during fine-tuning may hurt zero-shot cross-lingual performance. Finally, we demonstrate that distilling a larger model (BERT Large) results in the strongest distilled model that performs best both on the source language as well as target languages in zero-shot settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sustainlp-1_4", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sustainlp-1_4.flac", "type": "audio", "duration": 520.011}, {"from": "Assistant", "value": "Previous work has shown that automated essay scoring systems, in particular machine learning-based systems, are not capable of assessing the quality of essays, but are relying on essay length, a factor irrelevant to writing proficiency. In this work, we first show that state-of-the-art systems, recent neural essay scoring systems, might be also influenced by the correlation between essay length and scores in a standard dataset. In our evaluation, a very simple neural model shows the state-of-the-art performance on the standard dataset. To consider essay content without taking essay length into account, we introduce a simple neural model assessing the similarity of content between an input essay and essays assigned different scores. This neural model achieves performance comparable to the state of the art on a standard dataset as well as on a second dataset. Our findings suggest that neural essay scoring systems should consider the characteristics of datasets to focus on text quality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sustainlp-1_5", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sustainlp-1_5.flac", "type": "audio", "duration": 448.006}, {"from": "Assistant", "value": "Following the success of dot-product attention in Transformers, numerous approximations have been recently proposed to address its quadratic complexity with respect to the input length. While these variants are memory and compute efficient, it is not possible to directly use them with popular pre-trained language models trained using vanilla attention, without an expensive corrective pre-training stage. In this work, we propose a simple yet highly accurate approximation for vanilla attention. We process the queries in chunks, and for each query, compute the top-*k* scores with respect to the keys. Our approach offers several advantages: (a) its memory usage is linear in the input size, similar to linear attention variants, such as Performer and RFA (b) it is a drop-in replacement for vanilla attention that does not require any corrective pre-training, and (c) it can also lead to significant memory savings in the feed-forward layers after casting them into the familiar query-key-value framework. We evaluate the quality of top-*k* approximation for multi-head attention layers on the Long Range Arena Benchmark, and for feed-forward layers of T5 and UnifiedQA on multiple QA datasets. We show our approach leads to accuracy that is nearly-identical to vanilla attention in multiple setups including training from scratch, fine-tuning, and zero-shot inference.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sustainlp-1_6", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sustainlp-1_6.flac", "type": "audio", "duration": 574.299}, {"from": "Assistant", "value": "Copy mechanisms explicitly obtain unchanged tokens from the source (input) sequence to generate the target (output) sequence under the neural seq2seq framework. However, most of the existing copy mechanisms only consider single word copying from the source sentences, which results in losing essential tokens while copying long spans. In this work, we propose a plug-and-play architecture, namely BioCopy, to alleviate the problem aforementioned. Specifically, in the training stage, we construct a BIO tag for each token and train the original model with BIO tags jointly. In the inference stage, the model will firstly predict the BIO tag at each time step, then conduct different mask strategies based on the predicted BIO label to diminish the scope of the probability distributions over the vocabulary list. Experimental results on two separate generative tasks show that they all outperform the baseline models by adding our BioCopy to the original model structure.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sustainlp-1_7", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sustainlp-1_7.flac", "type": "audio", "duration": 254.677}, {"from": "Assistant", "value": "In simple open-domain question answering (QA), dense retrieval has become one of the standard approaches for retrieving the relevant passages to infer an answer. Recently, dense retrieval also achieved state-of-the-art results in multi-hop QA, where aggregating information from multiple pieces of information and reasoning over them is required. Despite their success, dense retrieval methods are computationally intensive, requiring multiple GPUs to train. In this work, we introduce a hybrid (lexical and dense) retrieval approach that is highly competitive with the state-of-the-art dense retrieval models, while requiring substantially less computational resources. Additionally, we provide an in-depth evaluation of dense retrieval methods on limited computational resource settings, something that is missing from the current literature.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sustainlp-1_8", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sustainlp-1_8.flac", "type": "audio", "duration": 712.851}, {"from": "Assistant", "value": "It is well known that rerankers built on pretrained transformer models such as BERT have dramatically improved retrieval effectiveness in many tasks. However, these gains have come at substantial costs in terms of efficiency, as noted by many researchers. In this work, we show that it is possible to retain the benefits of transformer-based rerankers in a multi-stage reranking pipeline by first using feature-based learning-to-rank techniques to reduce the number of candidate documents under consideration without adversely affecting their quality in terms of recall. Applied to the MS MARCO passage and document ranking tasks, we are able to achieve the same level of effectiveness, but with up to 18× increase in efficiency. Furthermore, our techniques are orthogonal to other methods focused on accelerating transformer inference, and thus can be combined for even greater efficiency gains. A higher-level message from our work is that, even though pretrained transformers dominate the modern IR landscape, there are still important roles for “traditional” LTR techniques, and that we should not forget history.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sustainlp-1_9", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sustainlp-1_9.flac", "type": "audio", "duration": 663.441}, {"from": "Assistant", "value": "Traditional synonym recommendations often include ill-suited suggestions for writer’s specific contexts. We propose a simple approach for contextual synonym recommendation by combining existing human-curated thesauri, e.g. WordNet, with pre-trained language models. We evaluate our technique by curating a set of word-sentence pairs balanced across corpora and parts of speech, then annotating each word-sentence pair with the contextually appropriate set of synonyms. We found that basic language model approaches have higher precision. Approaches leveraging sentence context have higher recall. Overall, the latter contextual approach had the highest F-score.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sustainlp-1_10", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sustainlp-1_10.flac", "type": "audio", "duration": 599.005}, {"from": "Assistant", "value": "Large pre-trained language models (PLMs) have led to great success on various commonsense question answering (QA) tasks in an end-to-end fashion. However, little attention has been paid to what commonsense knowledge is needed to deeply characterize these QA tasks. In this work, we proposed to categorize the semantics needed for these tasks using the SocialIQA as an example. Building upon our labeled social knowledge categories dataset on top of SocialIQA, we further train neural QA models to incorporate such social knowledge categories and relation information from a knowledge base. Unlike previous work, we observe our models with semantic categorizations of social knowledge can achieve comparable performance with a relatively simple model and smaller size compared to other complex approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sustainlp-1_11", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sustainlp-1_11.flac", "type": "audio", "duration": 573.013}, {"from": "Assistant", "value": "Transformer-based models have greatly advanced the progress in the field of the natural language processing and while they achieve state-of-the-art results on a wide range of tasks, they are cumbersome in parameter size. Subsequently, even when pre-trained transformer models are used for fine-tuning on a given task, if the dataset is large, it may still not be feasible to fine-tune the model within a reasonable time. For this reason, we empirically test 8 subsampling methods for reducing the dataset size on text classification task and report the trade-off between metric score and training time. 7 out of 8 methods are simple methods, while the last one is CRAIG, a method for coreset construction for data-efficient model training. We obtain the best result with the CRAIG method, offering an average decrease of 0.03 points in f-score on test set while speeding up the training time on average by 63.93%, relative to the score and time obtained by using the full dataset. Lastly, we show the trade-off between speed and performance for all sampling methods on three different datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sustainlp-1_12", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sustainlp-1_12.flac", "type": "audio", "duration": 502.016}, {"from": "Assistant", "value": "Training large language models can consume a large amount of energy. We hypothesize that the language model’s configuration impacts its energy consumption, and that there is room for power consumption optimisation in modern large language models. To investigate these claims, we introduce a power consumption factor to the objective function, and explore the range of models and hyperparameter configurations that affect power. We identify multiple configuration factors that can reduce power consumption during language model training while retaining model quality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sustainlp-1_13", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sustainlp-1_13.flac", "type": "audio", "duration": 707.001}, {"from": "Assistant", "value": "Knowledge Distillation (KD) offers a natural way to reduce the latency and memory/energy usage of massive pretrained models that have come to dominate Natural Language Processing (NLP) in recent years. While numerous sophisticated variants of KD algorithms have been proposed for NLP applications, the key factors underpinning the optimal distillation performance are often confounded and remain unclear. We aim to identify how different components in the KD pipeline affect the resulting performance and how much the optimal KD pipeline varies across different datasets/tasks, such as the data augmentation policy, the loss function, and the intermediate representation for transferring the knowledge between teacher and student. To tease apart their effects, we propose Distiller, a meta KD framework that systematically combines a broad range of techniques across different stages of the KD pipeline, which enables us to quantify each component’s contribution. Within Distiller, we unify commonly used objectives for distillation of intermediate representations under a universal mutual information (MI) objective and propose a class of MI-objective functions with better bias/variance trade-off for estimating the MI between the teacher and the student. On a diverse set of NLP datasets, the best Distiller configurations are identified via large-scale hyper-parameter optimization. Our experiments reveal the following: 1) the approach used to distill the intermediate representations is the most important factor in KD performance, 2) among different objectives for intermediate distillation, MI-performs the best, and 3) data augmentation provides a large boost for small training datasets or small student networks. Moreover, we find that different datasets/tasks prefer different KD algorithms, and thus propose a simple AutoDistiller algorithm that can recommend a good KD pipeline for a new dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sustainlp-1_14", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sustainlp-1_14.flac", "type": "audio", "duration": 540.003}, {"from": "Assistant", "value": "Wav2vec 2.0 is a state-of-the-art speech recognition model which maps speech audio waveforms into latent representations. The largest version of wav2vec 2.0 contains 317 million parameters. Hence, the inference latency of wav2vec 2.0 will be a bottleneck in production, leading to high costs and a significant environmental footprint. To improve wav2vec’s applicability to a production setting, we explore multiple model compression methods borrowed from the domain of large language models. Using a teacher-student approach, we distilled the knowledge from the original wav2vec 2.0 model into a student model, which is 2 times faster, 4.8 times smaller than the original model. More importantly, the student model is 2 times more energy efficient than the original model in terms of CO2 emission. This increase in performance is accomplished with only a 7% degradation in word error rate (WER). Our quantized model is 3.6 times smaller than the original model, with only a 0.1% degradation in WER. To the best of our knowledge, this is the first work that compresses wav2vec 2.0.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sustainlp-1_15", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sustainlp-1_15.flac", "type": "audio", "duration": 718.057}, {"from": "Assistant", "value": "Language models pretrained on vast corpora of unstructured text using self-supervised learning framework are used in numerous natural language understanding and generation tasks. Many studies show that language acquisition in humans follows a rather structured simple-to-complex pattern and guided by this intuition, curriculum learning, which enables training of computational models in a meaningful order, such as processing easy samples before hard ones, has been shown to potentially reduce training time. The question remains whether curriculum learning can benefit pretraining of language models. In this work, we perform comprehensive experiments involving multiple curricula strategies varying the criteria for complexity and the training schedules. Empirical results of training transformer language models on English corpus and evaluating it intrinsically as well as after fine-tuning across eight tasks from the GLUE benchmark, show consistent improvement gains over conventional vanilla training. Interestingly, in our experiments, when evaluated on one epoch, the best model following a document-level hard-to-easy curriculum, outperforms the vanilla model by 1.7 points (average GLUE score) and it takes the vanilla model twice as many training steps to reach comparable performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sustainlp-1_16", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sustainlp-1_16.flac", "type": "audio", "duration": 502.015}, {"from": "Assistant", "value": "Contextual embedding-based language models trained on large data sets, such as BERT and RoBERTa, provide strong performance across a wide range of tasks and are ubiquitous in modern NLP. It has been observed that fine-tuning these models on tasks involving data from domains different from that on which they were pretrained can lead to suboptimal performance. Recent work has explored approaches to adapt pretrained language models to new domains by incorporating additional pretraining on domain-specific corpora and task data. We propose an alternative approach for transferring pretrained language models to new domains by adapting their tokenizers. We show that domain-specific subword sequences can be determined efficiently directly from divergences in the conditional token distributions of the base and domain-specific corpora. In datasets from four disparate domains, we find adaptive tokenization on a pretrained RoBERTa model provides greater than 85% of the performance benefits of domain specific pretraining. Our approach produces smaller models and less training and inference time than other approaches using tokenizer augmentation. Although using adaptive tokenization incurs a 6% increase in model parameters (due to the introduction of 10k new domain-specific tokens), our approach, using 64 CPUs, is >72x faster than further pretraining the language model on domain-specific corpora on 8 TPUs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_sustainlp-1_17", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_sustainlp-1_17.flac", "type": "audio", "duration": 844.022}, {"from": "Assistant", "value": "Several NLP tasks need the effective repre-sentation of text documents.Arora et al.,2017 demonstrate that simple weighted aver-aging of word vectors frequently outperformsneural models. SCDV (Mekala et al., 2017)further extends this from sentences to docu-ments by employing soft and sparse cluster-ing over pre-computed word vectors. How-ever, both techniques ignore the polysemyand contextual character of words. In thispaper, we address this issue by proposingSCDV+BERT(ctxd), a simple and effective un-supervised representation that combines con-textualized BERT (Devlin et al., 2019) basedword embedding for word sense disambigua-tion with SCDV soft clustering approach. Weshow that our embeddings outperform origi-nal SCDV, pre-train BERT, and several otherbaselines on many classification datasets. Wealso demonstrate our embeddings effective-ness on other tasks, such as concept match-ing and sentence similarity. In addition,we show that SCDV+BERT(ctxd) outperformsfine-tune BERT and different embedding ap-proaches in scenarios with limited data andonly few shots examples.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_32", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_32.flac", "type": "audio", "duration": 305.505}, {"from": "Assistant", "value": "In this work, two Neural Machine Translation (NMT) systems have been developed and evaluated as part of the bidirectional Tamil-Telugu similar languages translation subtask in WMT21. The OpenNMT-py toolkit has been used to create quick prototypes of the systems, following which models have been trained on the training datasets containing the parallel corpus and finally the models have been evaluated on the dev datasets provided as part of the task. Both the systems have been trained on a DGX station with 4 -V100 GPUs. The first NMT system in this work is a Transformer based 6 layer encoder-decoder model, trained for 100000 training steps, whose configuration is similar to the one provided by OpenNMT-py and this is used to create a model for bidirectional translation. The second NMT system contains two unidirectional translation models with the same configuration as the first system, with the addition of utilizing Byte Pair Encoding (BPE) for subword tokenization through the pre-trained MultiBPEmb model. Based on the dev dataset evaluation metrics for both the systems, the first system i.e. the vanilla Transformer model has been submitted as the Primary system. Since there were no improvements in the metrics during training of the second system with BPE, it has been submitted as a contrastive system.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_56", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_56.flac", "type": "audio", "duration": 722.234}, {"from": "Assistant", "value": "The current approach to collecting human judgments of machine translation quality for the news translation task at WMT – segment rating with document context – is the most recent in a sequence of changes to WMT human annotation protocol. As these annotation protocols have changed over time, they have drifted away from some of the initial statistical assumptions underpinning them, with consequences that call the validity of WMT news task system rankings into question. In simulations based on real data, we show that the rankings can be influenced by the presence of outliers (high- or low-quality systems), resulting in different system rankings and clusterings. We also examine questions of annotation task composition and how ease or difficulty of translating different documents may influence system rankings. We provide discussion of ways to analyze these issues when considering future changes to annotation protocols.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_57", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_57.flac", "type": "audio", "duration": 717.009}, {"from": "Assistant", "value": "Automatic metrics are commonly used as the exclusive tool for declaring the superiority of one machine translation system’s quality over another. The community choice of automatic metric guides research directions and industrial developments by deciding which models are deemed better. Evaluating metrics correlations with sets of human judgements has been limited by the size of these sets. In this paper, we corroborate how reliable metrics are in contrast to human judgements on – to the best of our knowledge – the largest collection of judgements reported in the literature. Arguably, pairwise rankings of two systems are the most common evaluation tasks in research or deployment scenarios. Taking human judgement as a gold standard, we investigate which metrics have the highest accuracy in predicting translation quality rankings for such system pairs. Furthermore, we evaluate the performance of various metrics across different language pairs and domains. Lastly, we show that the sole use of BLEU impeded the development of improved models leading to bad deployment decisions. We release the collection of 2.3M sentence-level human judgements for 4380 systems for further analysis and replication of our work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_58", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_58.flac", "type": "audio", "duration": 615.004}, {"from": "Assistant", "value": "In this paper, we show that automatically-generated questions and answers can be used to evaluate the quality of Machine Translation (MT) systems. Building on recent work on the evaluation of abstractive text summarization, we propose a new metric for system-level MT evaluation, compare it with other state-of-the-art solutions, and show its robustness by conducting experiments for various MT directions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_59", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_59.flac", "type": "audio", "duration": 1227.012}, {"from": "Assistant", "value": "BERTScore, a recently proposed automatic metric for machine translation quality, uses BERT, a large pre-trained language model to evaluate candidate translations with respect to a gold translation. Taking advantage of BERT’s semantic and syntactic abilities, BERTScore seeks to avoid the flaws of earlier approaches like BLEU, instead scoring candidate translations based on their semantic similarity to the gold sentence. However, BERT is not infallible; while its performance on NLP tasks set a new state of the art in general, studies of specific syntactic and semantic phenomena have shown where BERT’s performance deviates from that of humans more generally. This naturally raises the questions we address in this paper: what are the strengths and weaknesses of BERTScore? Do they relate to known weaknesses on the part of BERT? We find that while BERTScore can detect when a candidate differs from a reference in important content words, it is less sensitive to smaller errors, especially if the candidate is lexically or stylistically similar to the reference.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_60", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_60.flac", "type": "audio", "duration": 752.48}, {"from": "Assistant", "value": "Despite the increasing number of large and comprehensive machine translation (MT) systems, evaluation of these methods in various languages has been restrained by the lack of high-quality parallel corpora as well as engagement with the people that speak these languages. In this study, we present an evaluation of state-of-the-art approaches to training and evaluating MT systems in 22 languages from the Turkic language family, most of which being extremely under-explored. First, we adopt the TIL Corpus with a few key improvements to the training and the evaluation sets. Then, we train 26 bilingual baselines as well as a multi-way neural MT (MNMT) model using the corpus and perform an extensive analysis using automatic metrics as well as human evaluations. We find that the MNMT model outperforms almost all bilingual baselines in the out-of-domain test sets and finetuning the model on a downstream task of a single pair also results in a huge performance boost in both low- and high-resource scenarios. Our attentive analysis of evaluation criteria for MT models in Turkic languages also points to the necessity for further research in this direction. We release the corpus splits, test sets as well as models to the public.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_61", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_61.flac", "type": "audio", "duration": 891.228}, {"from": "Assistant", "value": "Human gender bias is reflected in language and text production. Because state-of-the-art machine translation (MT) systems are trained on large corpora of text, mostly generated by humans, gender bias can also be found in MT. For instance when occupations are translated from a language like English, which mostly uses gender neutral words, to a language like German, which mostly uses a feminine and a masculine version for an occupation, a decision must be made by the MT System. Recent research showed that MT systems are biased towards stereotypical translation of occupations. In 2019 the first, and so far only, challenge set, explicitly designed to measure the extent of gender bias in MT systems has been published. In this set measurement of gender bias is solely based on the translation of occupations. With our paper we present an extension of this challenge set, called WiBeMT, which adds gender-biased adjectives and sentences with gender-biased verbs. The resulting challenge set consists of over 70, 000 sentences and has been translated with three commercial MT systems: DeepL Translator, Microsoft Translator, and Google Translate. Results show a gender bias for all three MT systems. This gender bias is to a great extent significantly influenced by adjectives and to a lesser extent by verbs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_62", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_62.flac", "type": "audio", "duration": 569.005}, {"from": "Assistant", "value": "This paper proposes a technique for adding a new source or target language to an existing multilingual NMT model without re-training it on the initial set of languages. It consists in replacing the shared vocabulary with a small language-specific vocabulary and fine-tuning the new embeddings on the new language’s parallel data. Some additional language-specific components may be trained to improve performance (e.g., Transformer layers or adapter modules). Because the parameters of the original model are not modified, its performance on the initial languages does not degrade. We show on two sets of experiments (small-scale on TED Talks, and large-scale on ParaCrawl) that this approach performs as well or better as the more costly alternatives; and that it has excellent zero-shot performance: training on English-centric data is enough to translate between the new language and any of the initial languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_63", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_63.flac", "type": "audio", "duration": 650.019}, {"from": "Assistant", "value": "Recently, the Machine Translation (MT) community has become more interested in document-level evaluation especially in light of reactions to claims of “human parity”, since examining the quality at the level of the document rather than at the sentence level allows for the assessment of suprasentential context, providing a more reliable evaluation. This paper presents a document-level corpus annotated in English with context-aware issues that arise when translating from English into Brazilian Portuguese, namely ellipsis, gender, lexical ambiguity, number, reference, and terminology, with six different domains. The corpus can be used as a challenge test set for evaluation and as a training/testing corpus for MT as well as for deep linguistic analysis of context issues. To the best of our knowledge, this is the first corpus of its kind.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_64", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_64.flac", "type": "audio", "duration": 707.001}, {"from": "Assistant", "value": "Adapter layers are lightweight, learnable units inserted between transformer layers. Recent work explores using such layers for neural machine translation (NMT), to adapt pre-trained models to new domains or language pairs, training only a small set of parameters for each new setting (language pair or domain). In this work we study the compositionality of language and domain adapters in the context of Machine Translation. We aim to study, 1) parameter-efficient adaptation to multiple domains and languages simultaneously (full-resource scenario) and 2) cross-lingual transfer in domains where parallel data is unavailable for certain language pairs (partial-resource scenario). We find that in the partial resource scenario a naive combination of domain-specific and language-specific adapters often results in ‘catastrophic forgetting’ of the missing languages. We study other ways to combine the adapters to alleviate this issue and maximize cross-lingual transfer. With our best adapter combinations, we obtain improvements of 3-4 BLEU on average for source languages that do not have in-domain data. For target languages without in-domain data, we achieve a similar improvement by combining adapters with back-translation. Supplementary material is available athttps://tinyurl.com/r66stbxj.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_65", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_65.flac", "type": "audio", "duration": 500.019}, {"from": "Assistant", "value": "Many works proposed methods to improve the performance of Neural Machine Translation (NMT) models in a domain/multi-domain adaptation scenario. However, an understanding of how NMT baselines represent text domain information internally is still lacking. Here we analyze the sentence representations learned by NMT Transformers and show that these explicitly include the information on text domains, even after only seeing the input sentences without domains labels. Furthermore, we show that this internal information is enough to cluster sentences by their underlying domains without supervision. We show that NMT models produce clusters better aligned to the actual domains compared to pre-trained language models (LMs). Notably, when computed on document-level, NMT cluster-to-domain correspondence nears 100%. We use these findings together with an approach to NMT domain adaptation using automatically extracted domains. Whereas previous work relied on external LMs for text clustering, we propose re-using the NMT model as a source of unsupervised clusters. We perform an extensive experimental study comparing two approaches across two data scenarios, three language pairs, and both sentence-level and document-level clustering, showing equal or significantly superior performance compared to LMs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_66", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_66.flac", "type": "audio", "duration": 792.009}, {"from": "Assistant", "value": "The performance of NMT systems has improved drastically in the past few years but the translation of multi-sense words still poses a challenge. Since word senses are not represented uniformly in the parallel corpora used for training, there is an excessive use of the most frequent sense in MT output. In this work, we propose CmBT (Contextually-mined Back-Translation), an approach for improving multi-sense word translation leveraging pre-trained cross-lingual contextual word representations (CCWRs). Because of their contextual sensitivity and their large pre-training data, CCWRs can easily capture word senses that are missing or very rare in parallel corpora used to train MT. Specifically, CmBT applies bilingual lexicon induction on CCWRs to mine sense-specific target sentences from a monolingual dataset, and then back-translates these sentences to generate a pseudo parallel corpus as additional training data for an MT system. We test the translation quality of ambiguous words on the MuCoW test suite, which was built to test the word sense disambiguation effectiveness of MT systems. We show that our system improves on the translation of difficult unseen and low frequency word senses.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_67", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_67.flac", "type": "audio", "duration": 780.446}, {"from": "Assistant", "value": "Current Machine Translation (MT) systems achieve very good results on a growing variety of language pairs and datasets. However, they are known to produce fluent translation outputs that can contain important meaning errors, thus undermining their reliability in practice. Quality Estimation (QE) is the task of automatically assessing the performance of MT systems at test time. Thus, in order to be useful, QE systems should be able to detect such errors. However, this ability is yet to be tested in the current evaluation practices, where QE systems are assessed only in terms of their correlation with human judgements. In this work, we bridge this gap by proposing a general methodology for adversarial testing of QE for MT. First, we show that despite a high correlation with human judgements achieved by the recent SOTA, certain types of meaning errors are still problematic for QE to detect. Second, we show that on average, the ability of a given model to discriminate between meaning-preserving and meaning-altering perturbations is predictive of its overall performance, thus potentially allowing for comparing QE systems without relying on manual quality annotation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_69", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_69.flac", "type": "audio", "duration": 736.427}, {"from": "Assistant", "value": "Language domains that require very careful use of terminology are abundant and reflect a significant part of the translation industry. In this work we introduce a benchmark for evaluating the quality and consistency of terminology translation, focusing on the medical (and COVID-19 specifically) domain for five language pairs: English to French, Chinese, Russian, and Korean, as well as Czech to German. We report the descriptions and results of the participating systems, commenting on the need for further research efforts towards both more adequate handling of terminologies as well as towards a proper formulation and evaluation of the task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_71", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_71.flac", "type": "audio", "duration": 708.023}, {"from": "Assistant", "value": "We report the results of the WMT 2021 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word and sentence levels. This edition focused on two main novel additions: (i) prediction for unseen languages, i.e. zero-shot settings, and (ii) prediction of sentences with catastrophic errors. In addition, new data was released for a number of languages, especially post-edited data. Participating teams from 19 institutions submitted altogether 1263 systems to different task variants and language pairs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_116", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_116.flac", "type": "audio", "duration": 762.009}, {"from": "Assistant", "value": "Unlike most work on pruning neural networks, we make inference faster. Group lasso regularisation enables pruning entire rows, columns or blocks of parameters that result in a smaller dense network. Because the network is still dense, efficient matrix multiply routines are still used and only minimal software changes are required to support variable layer sizes. Moreover, pruning is applied during training so there is no separate pruning step. Experiments on top of English->German models, which already have state-of-the-art speed and size, show that two-thirds of feedforward connections can be removed with 0.2 BLEU loss. With 6 decoder layers, the pruned model is 34% faster; with 2 tied decoder layers, the pruned model is 14% faster. Pruning entire heads and feedforward connections in a 12–1 encoder-decoder architecture gains an additional 51% speed-up. These push the Pareto frontier with respect to the trade-off between time and quality compared to strong baselines. In the WMT 2021 Efficiency Task, our pruned and quantised models are 1.9–2.7x faster at the cost 0.9–1.7 BLEU in comparison to the unoptimised baselines. Across language pairs, we see similar sparsity patterns: an ascending or U-shaped distribution in encoder feedforward and attention layers and an ascending distribution in the decoder.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_117", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_117.flac", "type": "audio", "duration": 615.004}, {"from": "Assistant", "value": "Neural machine translation (NMT) is sensitive to domain shift. In this paper, we address this problem in an active learning setting where we can spend a given budget on translating in-domain data, and gradually fine-tune a pre-trained out-of-domain NMT model on the newly translated data. Existing active learning methods for NMT usually select sentences based on uncertainty scores, but these methods require costly translation of full sentences even when only one or two key phrases within the sentence are informative. To address this limitation, we re-examine previous work from the phrase-based machine translation (PBMT) era that selected not full sentences, but rather individual phrases. However, while incorporating these phrases into PBMT systems was relatively simple, it is less trivial for NMT systems, which need to be trained on full sequences to capture larger structural properties of sentences unique to the new domain. To overcome these hurdles, we propose to select both full sentences and individual phrases from unlabelled data in the new domain for routing to human translators. In a German-English translation task, our active learning approach achieves consistent improvements over uncertainty-based sentence selection methods, improving up to 1.2 BLEU score over strong active learning baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_118", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_118.flac", "type": "audio", "duration": 880.013}, {"from": "Assistant", "value": "Large web-crawled corpora represent an excellent resource for improving the performance of Neural Machine Translation (NMT) systems across several language pairs. However, since these corpora are typically extremely noisy, their use is fairly limited. Current approaches to deal with this problem mainly focus on filtering using heuristics or single features such as language model scores or bi-lingual similarity. This work presents an alternative approach which learns weights for multiple sentence-level features. These feature weights which are optimized directly for the task of improving translation performance, are used to score and filter sentences in the noisy corpora more effectively. We provide results of applying this technique to building NMT systems using the Paracrawl corpus for Estonian-English and show that it beats strong single feature baselines and hand designed combinations. Additionally, we analyze the sensitivity of this method to different types of noise and explore if the learned weights generalize to other language pairs using the Maltese-English Paracrawl corpus.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_119", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_119.flac", "type": "audio", "duration": 940.013}, {"from": "Assistant", "value": "Recent work in simultaneous machine translation is often trained with conventional full sentence translation corpora, leading to either excessive latency or necessity to anticipate as-yet-unarrived words, when dealing with a language pair whose word orders significantly differ. This is unlike human simultaneous interpreters who produce largely monotonic translations at the expense of the grammaticality of a sentence being translated. In this paper, we thus propose an algorithm to reorder and refine the target side of a full sentence translation corpus, so that the words/phrases between the source and target sentences are aligned largely monotonically, using word alignment and non-autoregressive neural machine translation. We then train a widely used wait-k simultaneous translation model on this reordered-and-refined corpus. The proposed approach improves BLEU scores and resulting translations exhibit enhanced monotonicity with source sentences.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_120", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_120.flac", "type": "audio", "duration": 601.002}, {"from": "Assistant", "value": "Simultaneous translation is a task in which translation begins before the speaker has finished speaking, so it is important to decide when to start the translation process. However, deciding whether to read more input words or start to translate is difficult for language pairs with different word orders such as English and Japanese. Motivated by the concept of pre-reordering, we propose a couple of simple decision rules using the label of the next constituent predicted by incremental constituent label prediction. In experiments on English-to-Japanese simultaneous translation, the proposed method outperformed baselines in the quality-latency trade-off.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wmt-1_121", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wmt-1_121.flac", "type": "audio", "duration": 400.01}, {"from": "Assistant", "value": "Context-aware neural machine translation (NMT) incorporates contextual information of surrounding texts, that can improve the translation quality of document-level machine translation. Many existing works on context-aware NMT have focused on developing new model architectures for incorporating additional contexts and have shown some promising results. However, most of existing works rely on cross-entropy loss, resulting in limited use of contextual information. In this paper, we propose CorefCL, a novel data augmentation and contrastive learning scheme based on coreference between the source and contextual sentences. By corrupting automatically detected coreference mentions in the contextual sentence, CorefCL can train the model to be sensitive to coreference inconsistency. We experimented with our method on common context-aware NMT models and two document-level translation tasks. In the experiments, our method consistently improved BLEU of compared models on English-German and English-Korean tasks. We also show that our method significantly improves coreference resolution in the English-German contrastive test suite.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wnut-1_5", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wnut-1_5.flac", "type": "audio", "duration": 660.283}, {"from": "Assistant", "value": "Fine-grained temporal relation extraction (FineTempRel) aims to recognize the durations and timeline of event mentions in text. A missing part in the current deep learning models for FineTempRel is their failure to exploit the syntactic structures of the input sentences to enrich the representation vectors. In this work, we propose to fill this gap by introducing novel methods to integrate the syntactic structures into the deep learning models for FineTempRel. The proposed model focuses on two types of syntactic information from the dependency trees, i.e., the syntax-based importance scores for representation learning of the words and the syntactic connections to identify important context words for the event mentions. We also present two novel techniques to facilitate the knowledge transfer between the subtasks of FineTempRel, leading to a novel model with the state-of-the-art performance for this task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wnut-1_12", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wnut-1_12.flac", "type": "audio", "duration": 175.009}, {"from": "Assistant", "value": "Existing sarcasm detection systems focus on exploiting linguistic markers, context, or user-level priors. However, social studies suggest that the relationship between the author and the audience can be equally relevant for the sarcasm usage and interpretation. In this work, we propose a framework jointly leveraging (1) a user context from their historical tweets together with (2) the social information from a user’s conversational neighborhood in an interaction graph, to contextualize the interpretation of the post. We use graph attention networks (GAT) over users and tweets in a conversation thread, combined with dense user history representations. Apart from achieving state-of-the-art results on the recently published dataset of 19k Twitter users with 30K labeled tweets, adding 10M unlabeled tweets as context, our results indicate that the model contributes to interpreting the sarcastic intentions of an author more than to predicting the sarcasm perception by others.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_wnut-1_42", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_wnut-1_42.flac", "type": "audio", "duration": 346.001}, {"from": "Assistant", "value": "We evaluate a simple approach to improving zero-shot multilingual transfer of mBERT on social media corpus by adding a pretraining task called translation pair prediction (TPP), which predicts whether a pair of cross-lingual texts are a valid translation. Our approach assumes access to translations (exact or approximate) between source-target language pairs, where we fine-tune a model on source language task data and evaluate the model in the target language. In particular, we focus on language pairs where transfer learning is difficult for mBERT: those where source and target languages are different in script, vocabulary, and linguistic typology. We show improvements from TPP pretraining over mBERT alone in zero-shot transfer from English to Hindi, Arabic, and Japanese on two social media tasks: NER (a 37% average relative improvement in F1 across target languages) and sentiment classification (12% relative improvement in F1) on social media text, while also benchmarking on a non-social media task of Universal Dependency POS tagging (6.7% relative improvement in accuracy). Our results are promising given the lack of social media bitext corpus. Our code can be found at:https://github.com/twitter-research/multilingual-alignment-tpp.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_157", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_157.flac", "type": "audio", "duration": 719.54}, {"from": "Assistant", "value": "Human expertise and the participation of speech communities are essential factors in the success of technologies for low-resource languages. Accordingly, we propose a new computational task which is tuned to the available knowledge and interests in an Indigenous community, and which supports the construction of high quality texts and lexicons. The task is illustrated for Kunwinjku, a morphologically-complex Australian language. We combine a finite state implementation of a published grammar with a partial lexicon, and apply this to a noisy phone representation of the signal. We locate known lexemes in the signal and use the morphological transducer to build these out into hypothetical, morphologically-complex words for human validation. We show that applying a single iteration of this method results in a relative transcription density gain of 17%. Further, we find that 75% of breath groups in the test set receive at least one correct partial or full-word suggestion.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_158", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_158.flac", "type": "audio", "duration": 879.019}, {"from": "Assistant", "value": "Recent state-of-the-art (SOTA) effective neural network methods and fine-tuning methods based on pre-trained models (PTM) have been used in Chinese word segmentation (CWS), and they achieve great results. However, previous works focus on training the models with the fixed corpus at every iteration. The intermediate generated information is also valuable. Besides, the robustness of the previous neural methods is limited by the large-scale annotated data. There are a few noises in the annotated corpus. Limited efforts have been made by previous studies to deal with such problems. In this work, we propose a self-supervised CWS approach with a straightforward and effective architecture. First, we train a word segmentation model and use it to generate the segmentation results. Then, we use a revised masked language model (MLM) to evaluate the quality of the segmentation results based on the predictions of the MLM. Finally, we leverage the evaluations to aid the training of the segmenter by improved minimum risk training. Experimental results show that our approach outperforms previous methods on 9 different CWS datasets with single criterion training and multiple criteria training and achieves better robustness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_159", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_159.flac", "type": "audio", "duration": 624.043}, {"from": "Assistant", "value": "Neural models for the various flavours of morphological reinflection tasks have proven to be extremely accurate given ample labeled data, yet labeled data may be slow and costly to obtain. In this work we aim to overcome this annotation bottleneck by bootstrapping labeled data from a seed as small asfivelabeled inflection tables, accompanied by a large bulk of unlabeled text. Our bootstrapping method exploits the orthographic and semantic regularities in morphological systems in a two-phased setup, where word tagging based onanalogiesis followed by word pairing based ondistances. Our experiments with the Paradigm Cell Filling Problem over eight typologically different languages show that in languages with relatively simple morphology, orthographic regularities on their own allow inflection models to achieve respectable accuracy. Combined orthographic and semantic regularities alleviate difficulties with particularly complex morpho-phonological systems. We further show that our bootstrapping methods substantially outperform hallucination-based methods commonly used for overcoming the annotation bottleneck in morphological reinflection tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_160", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_160.flac", "type": "audio", "duration": 714.06}, {"from": "Assistant", "value": "Tokenization is a fundamental preprocessing step for almost all NLP tasks. In this paper, we propose efficient algorithms for the WordPiece tokenization used in BERT, from single-word tokenization to general text (e.g., sentence) tokenization. When tokenizing a single word, WordPiece uses a longest-match-first strategy, known as maximum matching. The best known algorithms so far are O(nˆ2) (where n is the input length) or O(nm) (where m is the maximum vocabulary token length). We propose a novel algorithm whose tokenization complexity is strictly O(n). Our method is inspired by the Aho-Corasick algorithm. We introduce additional linkages on top of the trie built from the vocabulary, allowing smart transitions when the trie matching cannot continue. For general text, we further propose an algorithm that combines pre-tokenization (splitting the text into words) and our linear-time WordPiece method into a single pass. Experimental results show that our method is 8.2x faster than HuggingFace Tokenizers and 5.1x faster than TensorFlow Text on average for general text tokenization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_161", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_161.flac", "type": "audio", "duration": 763.217}, {"from": "Assistant", "value": "Neural language models typically tokenise input text into sub-word units to achieve an open vocabulary. The standard approach is to use a single canonical tokenisation at both train and test time. We suggest that this approach is unsatisfactory and may bottleneck our evaluation of language model performance. Using only the one-best tokenisation ignores tokeniser uncertainty over alternative tokenisations, which may hurt model out-of-domain performance. In this paper, we argue that instead, language models should be evaluated on their marginal likelihood over tokenisations. We compare different estimators for the marginal likelihood based on sampling, and show that it is feasible to estimate the marginal likelihood with a manageable number of samples. We then evaluate a pretrained language model on both the one-best-tokenisation and marginal perplexities, and show that the marginal perplexity can be significantly better than the one best, especially on out-of-domain data. We link this difference in perplexity to the tokeniser uncertainty as measured by tokeniser entropy. We discuss some implications of our results for language model training and evaluation, particularly with regard to tokenisation robustness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_162", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_162.flac", "type": "audio", "duration": 736.939}, {"from": "Assistant", "value": "Commonsense is defined as the knowledge on which everyone agrees. However, certain types of commonsense knowledge are correlated with culture and geographic locations and they are only shared locally. For example, the scenes of wedding ceremonies vary across regions due to different customs influenced by historical and religious factors. Such regional characteristics, however, are generally omitted in prior work. In this paper, we construct a Geo-Diverse Visual Commonsense Reasoning dataset (GD-VCR) to test vision-and-language models’ ability to understand cultural and geo-location-specific commonsense. In particular, we study two state-of-the-art Vision-and-Language models, VisualBERT and ViLBERT trained on VCR, a standard benchmark with images primarily from Western regions. We then evaluate how well the trained models can generalize to answering the questions in GD-VCR. We find that the performance of both models for non-Western regions including East Asia, South Asia, and Africa is significantly lower than that for Western region. We analyze the reasons behind the performance disparity and find that the performance gap is larger on QA pairs that: 1) are concerned with culture-related scenarios, e.g., weddings, religious activities, and festivals; 2) require high-level geo-diverse commonsense reasoning rather than low-order perception and recognition. Dataset and code are released athttps://github.com/WadeYin9712/GD-VCR.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_163", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_163.flac", "type": "audio", "duration": 720.683}, {"from": "Assistant", "value": "We present a grounded neural dialogue model that successfully collaborates with people in a partially-observable reference game. We focus on a setting where two agents each observe an overlapping part of a world context and need to identify and agree on some object they share. Therefore, the agents should pool their information and communicate pragmatically to solve the task. Our dialogue agent accurately grounds referents from the partner’s utterances using a structured reference resolver, conditions on these referents using a recurrent memory, and uses a pragmatic generation procedure to ensure the partner can resolve the references the agent produces. We evaluate on the OneCommon spatial grounding dialogue task (Udagawa and Aizawa 2019), involving a number of dots arranged on a board with continuously varying positions, sizes, and shades. Our agent substantially outperforms the previous state of the art for the task, obtaining a 20% relative improvement in successful task completion in self-play evaluations and a 50% relative improvement in success in human evaluations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_164", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_164.flac", "type": "audio", "duration": 752.257}, {"from": "Assistant", "value": "One challenge in evaluating visual question answering (VQA) models in the cross-dataset adaptation setting is that the distribution shifts are multi-modal, making it difficult to identify if it is the shifts in visual or language features that play a key role. In this paper, we propose a semi-automatic framework for generating disentangled shifts by introducing a controllable visual question-answer generation (VQAG) module that is capable of generating highly-relevant and diverse question-answer pairs with the desired dataset style. We use it to create CrossVQA, a collection of test splits for assessing VQA generalization based on the VQA2, VizWiz, and Open Images datasets. We provide an analysis of our generated datasets and demonstrate its utility by using them to evaluate several state-of-the-art VQA systems. One important finding is that the visual shifts in cross-dataset VQA matter more than the language shifts. More broadly, we present a scalable framework for systematically evaluating the machine with little human intervention.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_165", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_165.flac", "type": "audio", "duration": 479.979}, {"from": "Assistant", "value": "Understanding what sequence of steps are needed to complete a goal can help artificial intelligence systems reason about human activities. Past work in NLP has examined the task of goal-step inference for text. We introduce the visual analogue. We propose the Visual Goal-Step Inference (VGSI) task, where a model is given a textual goal and must choose which of four images represents a plausible step towards that goal. With a new dataset harvested from wikiHow consisting of 772,277 images representing human actions, we show that our task is challenging for state-of-the-art multimodal models. Moreover, the multimodal representation learned from our data can be effectively transferred to other datasets like HowTo100m, increasing the VGSI accuracy by 15 - 20%. Our task will facilitate multimodal reasoning about procedural events.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_166", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_166.flac", "type": "audio", "duration": 480.043}, {"from": "Assistant", "value": "We analyze thegroundedSCAN (gSCAN) benchmark, which was recently proposed to study systematic generalization for grounded language understanding. First, we study which aspects of the original benchmark can be solved by commonly used methods in multi-modal research. We find that a general-purpose Transformer-based model with cross-modal attention achieves strong performance on a majority of the gSCAN splits, surprisingly outperforming more specialized approaches from prior work. Furthermore, our analysis suggests that many of the remaining errors reveal the same fundamental challenge in systematic generalization of linguistic constructs regardless of visual context. Second, inspired by this finding, we propose challenging new tasks for gSCAN by generating data to incorporate relations between objects in the visual environment. Finally, we find that current models are surprisingly data inefficient given the narrow scope of commands in gSCAN, suggesting another challenge for future work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_167", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_167.flac", "type": "audio", "duration": 455.04}, {"from": "Assistant", "value": "A method for creating a vision-and-language (V&L) model is to extend a language model through structural modifications and V&L pre-training. Such an extension aims to make a V&L model inherit the capability of natural language understanding (NLU) from the original language model. To see how well this is achieved, we propose to evaluate V&L models using an NLU benchmark (GLUE). We compare five V&L models, including single-stream and dual-stream models, trained with the same pre-training. Dual-stream models, with their higher modality independence achieved by approximately doubling the number of parameters, are expected to preserve the NLU capability better. Our main finding is that the dual-stream scores are not much different than the single-stream scores, contrary to expectation. Further analysis shows that pre-training causes the performance drop in NLU tasks with few exceptions. These results suggest that adopting a single-stream structure and devising the pre-training could be an effective method for improving the maintenance of language knowledge in V&L extensions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_168", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_168.flac", "type": "audio", "duration": 582.055}, {"from": "Assistant", "value": "Dialogue systems powered by large pre-trained language models exhibit an innate ability to deliver fluent and natural-sounding responses. Despite their impressive performance, these models are fitful and can often generate factually incorrect statements impeding their widespread adoption. In this paper, we focus on the task of improving faithfulness and reducing hallucination of neural dialogue systems to known facts supplied by a Knowledge Graph (KG). We propose Neural Path Hunter which follows a generate-then-refine strategy whereby a generated response is amended using the KG. Neural Path Hunter leverages a separate token-level fact critic to identify plausible sources of hallucination followed by a refinement stage that retrieves correct entities by crafting a query signal that is propagated over a k-hop subgraph. We empirically validate our proposed approach on the OpenDialKG dataset (Moon et al., 2019) against a suite of metrics and report a relative improvement of faithfulness over dialogue responses by 20.35% based on FeQA (Durmus et al., 2020). The code is available athttps://github.com/nouhadziri/Neural-Path-Hunter.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_169", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_169.flac", "type": "audio", "duration": 692.048}, {"from": "Assistant", "value": "Human dialogue contains evolving concepts, and speakers naturally associate multiple concepts to compose a response. However, current dialogue models with the seq2seq framework lack the ability to effectively manage concept transitions and can hardly introduce multiple concepts to responses in a sequential decoding manner. To facilitate a controllable and coherent dialogue, in this work, we devise a concept-guided non-autoregressive model (CG-nAR) for open-domain dialogue generation. The proposed model comprises a multi-concept planning module that learns to identify multiple associated concepts from a concept graph and a customized Insertion Transformer that performs concept-guided non-autoregressive generation to complete a response. The experimental results on two public datasets show that CG-nAR can produce diverse and coherent responses, outperforming state-of-the-art baselines in both automatic and human evaluations with substantially faster inference speed.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_170", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_170.flac", "type": "audio", "duration": 714.014}, {"from": "Assistant", "value": "Empathy is a complex cognitive ability based on the reasoning of others’ affective states. In order to better understand others and express stronger empathy in dialogues, we argue that two issues must be tackled at the same time: (i) identifying which word is the cause for the other’s emotion from his or her utterance and (ii) reflecting those specific words in the response generation. However, previous approaches for recognizing emotion cause words in text require sub-utterance level annotations, which can be demanding. Taking inspiration from social cognition, we leverage a generative estimator to infer emotion cause words from utterances with no word-level label. Also, we introduce a novel method based on pragmatics to make dialogue models focus on targeted words in the input during generation. Our method is applicable to any dialogue models with no additional training on the fly. We show our approach improves multiple best-performing dialogue agents on generating more focused empathetic responses in terms of both automatic and human evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_171", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_171.flac", "type": "audio", "duration": 399.778}, {"from": "Assistant", "value": "Recently, the focus of dialogue state tracking has expanded from single domain to multiple domains. The task is characterized by the shared slots between domains. As the scenario gets more complex, the out-of-vocabulary problem also becomes severer. Current models are not satisfactory for solving the challenges of ontology integration between domains and out-of-vocabulary problems. To address the problem, we explore the hierarchical semantic of ontology and enhance the interrelation between slots with masked hierarchical attention. In state value decoding stage, we solve the out-of-vocabulary problem by combining generation method and extraction method together. We evaluate the performance of our model on two representative datasets, MultiWOZ in English and CrossWOZ in Chinese. The results show that our model yields a significant performance gain over current state-of-the-art state tracking model and it is more robust to out-of-vocabulary problem compared with other methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_172", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_172.flac", "type": "audio", "duration": 1028.365}, {"from": "Assistant", "value": "Knowledge-grounded dialogue generation has achieved promising performance with the engagement of external knowledge sources. Typical approaches towards this task usually perform relatively independent two sub-tasks, i.e., knowledge selection and knowledge-aware response generation. In this paper, in order to improve the diversity of both knowledge selection and knowledge-aware response generation, we propose a collaborative latent variable (CoLV) model to integrate these two aspects simultaneously in separate yet collaborative latent spaces, so as to capture the inherent correlation between knowledge selection and response generation. During generation, our proposed model firstly draws knowledge candidate from the latent space conditioned on the dialogue context, and then samples a response from another collaborative latent space conditioned on both the context and the selected knowledge. Experimental results on two widely-used knowledge-grounded dialogue datasets show that our model outperforms previous methods on both knowledge selection and response generation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_173", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_173.flac", "type": "audio", "duration": 375.978}, {"from": "Assistant", "value": "Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge. Nevertheless, it is laborious to construct such knowledge-grounded dialogues, and existing models usually perform poorly when transfer to new domains with limited training samples. Therefore, building a knowledge-grounded dialogue system under the low-resource setting is a still crucial issue. In this paper, we propose a novel three-stage learning framework based on weakly supervised learning which benefits from large scale ungrounded dialogues and unstructured knowledge base. To better cooperate with this framework, we devise a variant of Transformer with decoupled decoder which facilitates the disentangled learning of response generation and knowledge incorporation. Evaluation results on two benchmarks indicate that our approach can outperform other state-of-the-art methods with less training data, and even in zero-resource scenario, our approach still performs well.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_174", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_174.flac", "type": "audio", "duration": 633.974}, {"from": "Assistant", "value": "Recent years has witnessed the remarkable success in end-to-end task-oriented dialog system, especially when incorporating external knowledge information. However, the quality of most existing models’ generated response is still limited, mainly due to their lack of fine-grained reasoning on deterministic knowledge (w.r.t. conceptual tokens), which makes them difficult to capture the concept shifts and identify user’s real intention in cross-task scenarios. To address these issues, we propose a novel intention mechanism to better model deterministic entity knowledge. Based on such a mechanism, we further propose an intention reasoning network (IR-Net), which consists of joint and multi-hop reasoning, to obtain intention-aware representations of conceptual tokens that can be used to capture the concept shifts involved in task-oriented conversations, so as to effectively identify user’s intention and generate more accurate responses. Experimental results verify the effectiveness of IR-Net, showing that it achieves the state-of-the-art performance on two representative multi-domain dialog datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_175", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_175.flac", "type": "audio", "duration": 710.577}, {"from": "Assistant", "value": "Despite achieving remarkable performance, previous knowledge-enhanced works usually only use a single-source homogeneous knowledge base of limited knowledge coverage. Thus, they often degenerate into traditional methods because not all dialogues can be linked with knowledge entries. This paper proposes a novel dialogue generation model, MSKE-Dialog, to solve this issue with three unique advantages: (1) Rather than only one, MSKE-Dialog can simultaneously leverage multiple heterogeneous knowledge sources (it includes but is not limited to commonsense knowledge facts, text knowledge, infobox knowledge) to improve the knowledge coverage; (2) To avoid the topic conflict among the context and different knowledge sources, we propose a Multi-Reference Selection to better select context/knowledge; (3) We propose a Multi-Reference Generation to generate informative responses by referring to multiple generation references at the same time. Extensive evaluations on a Chinese dataset show the superior performance of this work against various state-of-the-art approaches. To our best knowledge, this work is the first to use the multi-source heterogeneous knowledge in the open-domain knowledge-enhanced dialogue generation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_176", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_176.flac", "type": "audio", "duration": 542.059}, {"from": "Assistant", "value": "Dialogue state tracking (DST), which estimates user goals given a dialogue context, is an essential component of task-oriented dialogue systems. Conventional DST models are usually trained offline, which requires a fixed dataset prepared in advance. This paradigm is often impractical in real-world applications since online dialogue systems usually involve continually emerging new data and domains. Therefore, this paper explores Domain-Lifelong Learning for Dialogue State Tracking (DLL-DST), which aims to continually train a DST model on new data to learn incessantly emerging new domains while avoiding catastrophically forgetting old learned domains. To this end, we propose a novel domain-lifelong learning method, called Knowledge Preservation Networks (KPN), which consists of multi-prototype enhanced retrospection and multi-strategy knowledge distillation, to solve the problems of expression diversity and combinatorial explosion in the DLL-DST task. Experimental results show that KPN effectively alleviates catastrophic forgetting and outperforms previous state-of-the-art lifelong learning methods by 4.25% and 8.27% of whole joint goal accuracy on the MultiWOZ benchmark and the SGD benchmark, respectively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_177", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_177.flac", "type": "audio", "duration": 437.568}, {"from": "Assistant", "value": "Conversational semantic role labeling (CSRL) is believed to be a crucial step towards dialogue understanding. However, it remains a major challenge for existing CSRL parser to handle conversational structural information. In this paper, we present a simple and effective architecture for CSRL which aims to address this problem. Our model is based on a conversational structure aware graph network which explicitly encodes the speaker dependent information. We also propose a multi-task learning method to further improve the model. Experimental results on benchmark datasets show that our model with our proposed training objectives significantly outperforms previous baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_178", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_178.flac", "type": "audio", "duration": 571.2}, {"from": "Assistant", "value": "Loading models pre-trained on the large-scale corpus in the general domain and fine-tuning them on specific downstream tasks is gradually becoming a paradigm in Natural Language Processing. Previous investigations prove that introducing a further pre-training phase between pre-training and fine-tuning phases to adapt the model on the domain-specific unlabeled data can bring positive effects. However, most of these further pre-training works just keep running the conventional pre-training task, e.g., masked language model, which can be regarded as the domain adaptation to bridge the data distribution gap. After observing diverse downstream tasks, we suggest that different tasks may also need a further pre-training phase with appropriate training tasks to bridge the task formulation gap. To investigate this, we carry out a study for improving multiple task-oriented dialogue downstream tasks through designing various tasks at the further pre-training phase. The experiment shows that different downstream tasks prefer different further pre-training tasks, which have intrinsic correlation and most further pre-training tasks significantly improve certain target tasks rather than all. Our investigation indicates that it is of great importance and effectiveness to design appropriate further pre-training tasks modeling specific information that benefit downstream tasks. Besides, we present multiple constructive empirical conclusions for enhancing task-oriented dialogues.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_179", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_179.flac", "type": "audio", "duration": 718.699}, {"from": "Assistant", "value": "Although pre-training models have achieved great success in dialogue generation, their performance drops dramatically when the input contains an entity that does not appear in pre-training and fine-tuning datasets (unseen entity). To address this issue, existing methods leverage an external knowledge base to generate appropriate responses. In real-world practical, the entity may not be included by the knowledge base or suffer from the precision of knowledge retrieval. To deal with this problem, instead of introducing knowledge base as the input, we force the model to learn a better semantic representation by predicting the information in the knowledge base, only based on the input context. Specifically, with the help of a knowledge base, we introduce two auxiliary training objectives: 1) Interpret Masked Word, which conjectures the meaning of the masked entity given the context; 2) Hypernym Generation, which predicts the hypernym of the entity based on the context. Experiment results on two dialogue corpus verify the effectiveness of our methods under both knowledge available and unavailable settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_180", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_180.flac", "type": "audio", "duration": 477.056}, {"from": "Assistant", "value": "Multi-turn response selection models have recently shown comparable performance to humans in several benchmark datasets. However, in the real environment, these models often have weaknesses, such as making incorrect predictions based heavily on superficial patterns without a comprehensive understanding of the context. For example, these models often give a high score to the wrong response candidate containing several keywords related to the context but using the inconsistent tense. In this study, we analyze the weaknesses of the open-domain Korean Multi-turn response selection models and publish an adversarial dataset to evaluate these weaknesses. We also suggest a strategy to build a robust model in this adversarial environment.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_181", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_181.flac", "type": "audio", "duration": 676.537}, {"from": "Assistant", "value": "Conversation disentanglement aims to separate intermingled messages into detached sessions, which is a fundamental task in understanding multi-party conversations. Existing work on conversation disentanglement relies heavily upon human-annotated datasets, which is expensive to obtain in practice. In this work, we explore training a conversation disentanglement model without referencing any human annotations. Our method is built upon the deep co-training algorithm, which consists of two neural networks: a message-pair classifier and a session classifier. The former is responsible of retrieving local relations between two messages while the latter categorizes a message to a session by capturing context-aware information. Both the two networks are initialized respectively with pseudo data built from the unannotated corpus. During the deep co-training process, we use the session classifier as a reinforcement learning component to learn a session assigning policy by maximizing the local rewards given by the message-pair classifier. For the message-pair classifier, we enrich its training data by retrieving message pairs with high confidence from the disentangled sessions predicted by the session classifier. Experimental results on the large Movie Dialogue Dataset demonstrate that our proposed approach achieves competitive performance compared to previous supervised methods. Further experiments show that the predicted disentangled conversations can promote the performance on the downstream task of multi-party response selection.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_182", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_182.flac", "type": "audio", "duration": 543.254}, {"from": "Assistant", "value": "Consistency Identification has obtained remarkable success on open-domain dialogue, which can be used for preventing inconsistent response generation. However, in contrast to the rapid development in open-domain dialogue, few efforts have been made to the task-oriented dialogue direction. In this paper, we argue thatconsistency problemis more urgent in task-oriented domain. To facilitate the research, we introduce CI-ToD, a novel dataset forConsistencyIdentification inTask-orientedDialog system. In addition, we not only annotate the single label to enable the model to judge whether the system response is contradictory, but also provide more fine-grained labels (i.e., Dialogue History Inconsistency, User Query Inconsistency and Knowledge Base Inconsistency) to encourage model to know what inconsistent sources lead to it. Empirical results show that state-of-the-art methods only achieve 51.3%, which is far behind the human performance of 93.2%, indicating that there is ample room for improving consistency identification ability. Finally, we conduct exhaustive experiments and qualitative analysis to comprehend key challenges and provide guidance for future directions. All datasets and models are publicly available athttps://github.com/yizhen20133868/CI-ToD.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_183", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_183.flac", "type": "audio", "duration": 406.047}, {"from": "Assistant", "value": "Grounded dialogue models generate responses that are grounded on certain concepts. Limited by the distribution of grounded dialogue data, models trained on such data face thetransferabilitychallenges in terms of the data distribution and the type of grounded concepts. To address the challenges, we propose thegrounded minimal editingframework, which minimally edits existing responses to be grounded on the given concept. Focusing on personas, we propose Grounded Minimal Editor (GME), which learns to edit by disentangling and recombining persona-related and persona-agnostic parts of the response. To evaluate persona-grounded minimal editing, we present the PersonaMi-nEdit dataset, and experimental results show that GME outperforms competitive baselines by a large margin. To evaluate the transferability, we experiment on the test set of BlendedSkillTalk and show that GME can edit dialogue models’ responses to largely improve their persona consistency while preserving the use of knowledge and empathy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_184", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_184.flac", "type": "audio", "duration": 682.574}, {"from": "Assistant", "value": "Generating informative and appropriate responses is challenging but important for building human-like dialogue systems. Although various knowledge-grounded conversation models have been proposed, these models have limitations in utilizing knowledge that infrequently occurs in the training data, not to mention integrating unseen knowledge into conversation generation. In this paper, we propose an Entity-Agnostic Representation Learning (EARL) method to introduce knowledge graphs to informative conversation generation. Unlike traditional approaches that parameterize the specific representation for each entity, EARL utilizes the context of conversations and the relational structure of knowledge graphs to learn the category representation for entities, which is generalized to incorporating unseen entities in knowledge graphs into conversation generation. Automatic and manual evaluations demonstrate that our model can generate more informative, coherent, and natural responses than baseline models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_185", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_185.flac", "type": "audio", "duration": 637.376}, {"from": "Assistant", "value": "Learning sentence embeddings from dialogues has drawn increasing attention due to its low annotation cost and high domain adaptability. Conventional approaches employ the siamese-network for this task, which obtains the sentence embeddings through modeling the context-response semantic relevance by applying a feed-forward network on top of the sentence encoders. However, as the semantic textual similarity is commonly measured through the element-wise distance metrics (e.g. cosine and L2 distance), such architecture yields a large gap between training and evaluating. In this paper, we propose DialogueCSE, a dialogue-based contrastive learning approach to tackle this issue. DialogueCSE first introduces a novel matching-guided embedding (MGE) mechanism, which generates a context-aware embedding for each candidate response embedding (i.e. the context-free embedding) according to the guidance of the multi-turn context-response matching matrices. Then it pairs each context-aware embedding with its corresponding context-free embedding and finally minimizes the contrastive loss across all pairs. We evaluate our model on three multi-turn dialogue datasets: the Microsoft Dialogue Corpus, the Jing Dong Dialogue Corpus, and the E-commerce Dialogue Corpus. Evaluation results show that our approach significantly outperforms the baselines across all three datasets in terms of MAP and Spearman’s correlation measures, demonstrating its effectiveness. Further quantitative experiments show that our approach achieves better performance when leveraging more dialogue context and remains robust when less training data is provided.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_186", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_186.flac", "type": "audio", "duration": 486.783}, {"from": "Assistant", "value": "Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. However, there is little attempt to combine these two types of models, which inituitively possess complementary advantages. In this paper, we propose a novel sentence ordering framework which introduces two classifiers to make better use of pairwise orderings for graph-based sentence ordering (Yin et al. 2019, 2021). Specially, given an initial sentence-entity graph, we first introduce a graph-based classifier to predict pairwise orderings between linked sentences. Then, in an iterative manner, based on the graph updated by previously predicted high-confident pairwise orderings, another classifier is used to predict the remaining uncertain pairwise orderings. At last, we adapt a GRN-based sentence ordering model (Yin et al. 2019, 2021) on the basis of final graph. Experiments on five commonly-used datasets demonstrate the effectiveness and generality of our model. Particularly, when equipped with BERT (Devlin et al. 2019) and FHDecoder (Yin et al. 2020), our model achieves state-of-the-art performance. Our code is available athttps://github.com/DeepLearnXMU/IRSEG.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_187", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_187.flac", "type": "audio", "duration": 676.373}, {"from": "Assistant", "value": "Implicit discourse relation recognition (IDRR) is a critical task in discourse analysis. Previous studies only regard it as a classification task and lack an in-depth understanding of the semantics of different relations. Therefore, we first view IDRR as a generation task and further propose a method joint modeling of the classification and generation. Specifically, we propose a joint model, CG-T5, to recognize the relation label and generate the target sentence containing the meaning of relations simultaneously. Furthermore, we design three target sentence forms, including the question form, for the generation model to incorporate prior knowledge. To address the issue that large discourse units are hardly embedded into the target sentence, we also propose a target sentence construction mechanism that automatically extracts core sentences from those large discourse units. Experimental results both on Chinese MCDTB and English PDTB datasets show that our model CG-T5 achieves the best performance against several state-of-the-art systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_188", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_188.flac", "type": "audio", "duration": 627.055}, {"from": "Assistant", "value": "Discourse segmentation and sentence-level discourse parsing play important roles for various NLP tasks to consider textual coherence. Despite recent achievements in both tasks, there is still room for improvement due to the scarcity of labeled data. To solve the problem, we propose a language model-based generative classifier (LMGC) for using more information from labels by treating the labels as an input while enhancing label representations by embedding descriptions for each label. Moreover, since this enables LMGC to make ready the representations for labels, unseen in the pre-training step, we can effectively use a pre-trained language model in LMGC. Experimental results on the RST-DT dataset show that our LMGC achieved the state-of-the-art F1 score of 96.72 in discourse segmentation. It further achieved the state-of-the-art relation F1 scores of 84.69 with gold EDU boundaries and 81.18 with automatically segmented boundaries, respectively, in sentence-level discourse parsing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_189", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_189.flac", "type": "audio", "duration": 606.761}, {"from": "Assistant", "value": "Multimodal Transformers achieve superior performance in multimodal learning tasks. However, the quadratic complexity of the self-attention mechanism in Transformers limits their deployment in low-resource devices and makes their inference and training computationally expensive. We propose multimodal Sparse Phased Transformer (SPT) to alleviate the problem of self-attention complexity and memory footprint. SPT uses a sampling function to generate a sparse attention matrix and compress a long sequence to a shorter sequence of hidden states. SPT concurrently captures interactions between the hidden states of different modalities at every layer. To further improve the efficiency of our method, we use Layer-wise parameter sharing and Factorized Co-Attention that share parameters between Cross Attention Blocks, with minimal impact on task performance. We evaluate our model with three sentiment analysis datasets and achieve comparable or superior performance compared with the existing methods, with a 90% reduction in the number of parameters. We conclude that (SPT) along with parameter sharing can capture multimodal interactions with reduced model size and improved sample efficiency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_190", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_190.flac", "type": "audio", "duration": 636.018}, {"from": "Assistant", "value": "Hierarchical multi-label text classification (HMTC) deals with the challenging task where an instance can be assigned to multiple hierarchically structured categories at the same time. The majority of prior studies either focus on reducing the HMTC task into a flat multi-label problem ignoring the vertical category correlations or exploiting the dependencies across different hierarchical levels without considering the horizontal correlations among categories at the same level, which inevitably leads to fundamental information loss. In this paper, we propose a novel HMTC framework that considers both vertical and horizontal category correlations. Specifically, we first design a loosely coupled graph convolutional neural network as the representation extractor to obtain representations for words, documents, and, more importantly, level-wise representations for categories, which are not considered in previous works. Then, the learned category representations are adopted to capture the vertical dependencies among levels of category hierarchy and model the horizontal correlations. Finally, based on the document embeddings and category embeddings, we design a hybrid algorithm to predict the categories of the entire hierarchical structure. Extensive experiments conducted on real-world HMTC datasets validate the effectiveness of the proposed framework with significant improvements over the baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_192", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_192.flac", "type": "audio", "duration": 689.052}, {"from": "Assistant", "value": "In natural language processing (NLP), state-of-the-art (SOTA) semi-supervised learning (SSL) frameworks have shown great performance on deep pre-trained language models such as BERT, and are expected to significantly reduce the demand for manual labeling. However, our empirical studies indicate that these frameworks are not suitable for lightweight models such as TextCNN, LSTM and etc. In this work, we develop a new SSL framework called FLiText, which stands for Faster and Lighter semi-supervised Text classification. FLiText introduces an inspirer network together with the consistency regularization framework, which leverages a generalized regular constraint on the lightweight models for efficient SSL. As a result, FLiText obtains new SOTA performance for lightweight models across multiple SSL benchmarks on text classification. Compared with existing SOTA SSL methods on TextCNN, FLiText improves the accuracy of lightweight model TextCNN from 51.00% to 90.49% on IMDb, 39.8% to 58.06% on Yelp-5, and from 55.3% to 65.08% on Yahoo! Answer. In addition, compared with the fully supervised method on the full dataset, FLiText just uses less than 1% of labeled data to improve the accuracy by 6.59%, 3.94%, and 3.22% on the datasets of IMDb, Yelp-5, and Yahoo! Answer respectively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_193", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_193.flac", "type": "audio", "duration": 474.496}, {"from": "Assistant", "value": "Bias is pervasive for NLP models, motivating the development of automatic debiasing techniques. Evaluation of NLP debiasing methods has largely been limited to binary attributes in isolation, e.g., debiasing with respect to binary gender or race, however many corpora involve multiple such attributes, possibly with higher cardinality. In this paper we argue that a truly fair model must consider ‘gerrymandering’ groups which comprise not only single attributes, but also intersectional groups. We evaluate a form of bias-constrained model which is new to NLP, as well an extension of the iterative nullspace projection technique which can handle multiple identities.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_194", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_194.flac", "type": "audio", "duration": 704.935}, {"from": "Assistant", "value": "Definition generation techniques aim to generate a definition of a target word or phrase given a context. In previous studies, researchers have faced various issues such as the out-of-vocabulary problem and over/under-specificity problems. Over-specific definitions present narrow word meanings, whereas under-specific definitions present general and context-insensitive meanings. Herein, we propose a method for definition generation with appropriate specificity. The proposed method addresses the aforementioned problems by leveraging a pre-trained encoder-decoder model, namely Text-to-Text Transfer Transformer, and introducing a re-ranking mechanism to model specificity in definitions. Experimental results on standard evaluation datasets indicate that our method significantly outperforms the previous state-of-the-art method. Moreover, manual evaluation confirms that our method effectively addresses the over/under-specificity problems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_195", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_195.flac", "type": "audio", "duration": 674.168}, {"from": "Assistant", "value": "Unsupervised style transfer models are mainly based on an inductive learning approach, which represents the style as embeddings, decoder parameters, or discriminator parameters and directly applies these general rules to the test cases. However, the lacking of parallel corpus hinders the ability of these inductive learning methods on this task. As a result, it is likely to cause severe inconsistent style expressions, like ‘the salad is rude’. To tackle this problem, we propose a novel transductive learning approach in this paper, based on a retrieval-based context-aware style representation. Specifically, an attentional encoder-decoder with a retriever framework is utilized. It involves top-K relevant sentences in the target style in the transfer process. In this way, we can learn a context-aware style embedding to alleviate the above inconsistency problem. In this paper, both sparse (BM25) and dense retrieval functions (MIPS) are used, and two objective functions are designed to facilitate joint learning. Experimental results show that our method outperforms several strong baselines. The proposed transductive learning approach is general and effective to the task of unsupervised style transfer, and we will apply it to the other two typical methods in the future.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_196", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_196.flac", "type": "audio", "duration": 348.053}, {"from": "Assistant", "value": "Recently graph-based methods have been adopted for Abstractive Text Summarization. However, existing graph-based methods only consider either word relations or structure information, which neglect the correlation between them. To simultaneously capture the word relations and structure information from sentences, we propose a novel Dual Graph network for Abstractive Sentence Summarization. Specifically, we first construct semantic scenario graph and semantic word relation graph based on FrameNet, and subsequently learn their representations and design graph fusion method to enhance their correlation and obtain better semantic representation for summary generation. Experimental results show our model outperforms existing state-of-the-art methods on two popular benchmark datasets, i.e., Gigaword and DUC 2004.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_197", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_197.flac", "type": "audio", "duration": 600.043}, {"from": "Assistant", "value": "Natural language generation (NLG) tasks on pro-drop languages are known to suffer from zero pronoun (ZP) problems, and the problems remain challenging due to the scarcity of ZP-annotated NLG corpora. In this case, we propose a highly adaptive two-stage approach to couple context modeling with ZP recovering to mitigate the ZP problem in NLG tasks. Notably, we frame the recovery process in a task-supervised fashion where the ZP representation recovering capability is learned during the NLG task learning process, thus our method does not require NLG corpora annotated with ZPs. For system enhancement, we learn an adversarial bot to adjust our model outputs to alleviate the error propagation caused by mis-recovered ZPs. Experiments on three document-level NLG tasks, i.e., machine translation, question answering, and summarization, show that our approach can improve the performance to a great extent, and the improvement on pronoun translation is very impressive.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_198", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_198.flac", "type": "audio", "duration": 604.693}, {"from": "Assistant", "value": "Although exposure bias has been widely studied in some NLP tasks, it faces its unique challenges in dialogue response generation, the representative one-to-various generation scenario. In real human dialogue, there are many appropriate responses for the same context, not only with different expressions, but also with different topics. Therefore, due to the much bigger gap between various ground-truth responses and the generated synthetic response, exposure bias is more challenging in dialogue generation task. What’s more, as MLE encourages the model to only learn the common words among different ground-truth responses, but ignores the interesting and specific parts, exposure bias may further lead to the common response generation problem, such as “I don’t know” and “HaHa?” In this paper, we propose a novel adaptive switching mechanism, which learns to automatically transit between ground-truth learning and generated learning regarding the word-level matching score, such as the cosine similarity. Experimental results on both Chinese STC dataset and English Reddit dataset, show that our adaptive method achieves a significant improvement in terms of metric-based evaluation and human evaluation, as compared with the state-of-the-art exposure bias approaches. Further analysis on NMT task also shows that our model can achieve a significant improvement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_199", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_199.flac", "type": "audio", "duration": 577.045}, {"from": "Assistant", "value": "A long-standing issue with paraphrase generation is the lack of reliable supervision signals. In this paper, we propose a new unsupervised paradigm for paraphrase generation based on the assumption that the probabilities of generating two sentences with the same meaning given the same context should be the same. Inspired by this fundamental idea, we propose a pipelined system which consists of paraphrase candidate generation based on contextual language models, candidate filtering using scoring functions, and paraphrase model training based on the selected candidates. The proposed paradigm offers merits over existing paraphrase generation methods: (1) using the context regularizer on meanings, the model is able to generate massive amounts of high-quality paraphrase pairs; (2) the combination of the huge amount of paraphrase candidates and further diversity-promoting filtering yields paraphrases with more lexical and syntactic diversity; and (3) using human-interpretable scoring functions to select paraphrase pairs from candidates, the proposed framework provides a channel for developers to intervene with the data generation process, leading to a more controllable model. Experimental results across different tasks and datasets demonstrate that the proposed paradigm significantly outperforms existing paraphrase approaches in both supervised and unsupervised setups.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_200", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_200.flac", "type": "audio", "duration": 731.057}, {"from": "Assistant", "value": "Generating long text conditionally depending on the short input text has recently attracted more and more research efforts. Most existing approaches focus more on introducing extra knowledge to supplement the short input text, but ignore the coherence issue of the generated texts. To address aforementioned research issue, this paper proposes a novel two-stage approach to generate coherent long text. Particularly, we first build a document-level path for each output text with each sentence embedding as its node, and a revised self-organising map (SOM) is proposed to cluster similar nodes of a family of document-level paths to construct the directed semantic graph. Then, three subgraph alignment methods are proposed to extract the maximum matching paths or subgraphs. These directed subgraphs are considered to well preserve extra but relevant content to the short input text, and then they are decoded by the employed pre-trained model to generate coherent long text. Extensive experiments have been performed on three real-world datasets, and the promising results demonstrate that the proposed approach is superior to the state-of-the-art approaches w.r.t. a number of evaluation criteria.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_201", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_201.flac", "type": "audio", "duration": 578.084}, {"from": "Assistant", "value": "Natural question generation (QG) aims to generate questions from a passage, and generated questions are answered from the passage. Most models with state-of-the-art performance model the previously generated text at each decoding step. However, (1) they ignore the rich structure information that is hidden in the previously generated text. (2) they ignore the impact of copied words on the passage. We perceive that information in previously generated words serves as auxiliary information in subsequent generation. To address these problems, we design the Iterative Graph Network-based Decoder (IGND) to model the previous generation using a Graph Neural Network at each decoding step. Moreover, our graph model captures dependency relations in the passage that boost the generation. Experimental results demonstrate that our model outperforms the state-of-the-art models with sentence-level QG tasks on SQuAD and MARCO datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_202", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_202.flac", "type": "audio", "duration": 605.056}, {"from": "Assistant", "value": "Generating high quality question-answer pairs is a hard but meaningful task. Although previous works have achieved great results on answer-aware question generation, it is difficult to apply them into practical application in the education field. This paper for the first time addresses the question-answer pair generation task on the real-world examination data, and proposes a new unified framework on RACE. To capture the important information of the input passage we first automatically generate (rather than extracting) keyphrases, thus this task is reduced to keyphrase-question-answer triplet joint generation. Accordingly, we propose a multi-agent communication model to generate and optimize the question and keyphrases iteratively, and then apply the generated question and keyphrases to guide the generation of answers. To establish a solid benchmark, we build our model on the strong generative pre-training model. Experimental results show that our model makes great breakthroughs in the question-answer pair generation task. Moreover, we make a comprehensive analysis on our model, suggesting new directions for this challenging task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_203", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_203.flac", "type": "audio", "duration": 406.059}, {"from": "Assistant", "value": "Previous works on syntactically controlled paraphrase generation heavily rely on large-scale parallel paraphrase data that is not easily available for many languages and domains. In this paper, we take this research direction to the extreme and investigate whether it is possible to learn syntactically controlled paraphrase generation with nonparallel data. We propose a syntactically-informed unsupervised paraphrasing model based on conditional variational auto-encoder (VAE) which can generate texts in a specified syntactic structure. Particularly, we design a two-stage learning method to effectively train the model using non-parallel data. The conditional VAE is trained to reconstruct the input sentence according to the given input and its syntactic structure. Furthermore, to improve the syntactic controllability and semantic consistency of the pre-trained conditional VAE, we fine-tune it using syntax controlling and cycle reconstruction learning objectives, and employ Gumbel-Softmax to combine these new learning objectives. Experiment results demonstrate that the proposed model trained only on non-parallel data is capable of generating diverse paraphrases with specified syntactic structure. Additionally, we validate the effectiveness of our method for generating syntactically adversarial examples on the sentiment analysis task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_204", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_204.flac", "type": "audio", "duration": 578.502}, {"from": "Assistant", "value": "Few-shot relation extraction (FSRE) focuses on recognizing novel relations by learning with merely a handful of annotated instances. Meta-learning has been widely adopted for such a task, which trains on randomly generated few-shot tasks to learn generic data representations. Despite impressive results achieved, existing models still perform suboptimally when handling hard FSRE tasks, where the relations are fine-grained and similar to each other. We argue this is largely because existing models do not distinguish hard tasks from easy ones in the learning process. In this paper, we introduce a novel approach based on contrastive learning that learns better representations by exploiting relation label information. We further design a method that allows the model to adaptively learn how to focus on hard tasks. Experiments on two standard datasets demonstrate the effectiveness of our method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_205", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_205.flac", "type": "audio", "duration": 463.981}, {"from": "Assistant", "value": "Entity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing. Recent progress in entity retrieval shows that the dual-encoder structure is a powerful and efficient framework to nominate candidates if entities are only identified by descriptions. However, they ignore the property that meanings of entity mentions diverge in different contexts and are related to various portions of descriptions, which are treated equally in previous works. In this work, we propose Multi-View Entity Representations (MuVER), a novel approach for entity retrieval that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method. Our method achieves the state-of-the-art performance on ZESHEL and improves the quality of candidates on three standard Entity Linking datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_206", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_206.flac", "type": "audio", "duration": 731.011}, {"from": "Assistant", "value": "Event detection (ED) aims at identifying event instances of specified types in given texts, which has been formalized as a sequence labeling task. As far as we know, existing neural-based ED models make decisions relying entirely on the contextual semantic features of each word in the inputted text, which we find is easy to be confused by the varied contexts in the test stage. To this end, we come up with the idea of introducing a set of statistical features from word-event co-occurrence frequencies in the entire training set to cooperate with contextual features. Specifically, we propose a Semantic and Statistic-Joint Discriminative Network (SS-JDN) consisting of a semantic feature extractor, a statistical feature extractor, and a joint event discriminator. In experiments, SS-JDN effectively exceeds ten recent strong baselines on ACE2005 and KBP2015 datasets. Further, we perform extensive experiments to comprehensively probe SS-JDN.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_207", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_207.flac", "type": "audio", "duration": 603.022}, {"from": "Assistant", "value": "Event factuality indicates the degree of certainty about whether an event occurs in the real world. Existing studies mainly focus on identifying event factuality at sentence level, which easily leads to conflicts between different mentions of the same event. To this end, we study the problem of document-level event factuality identification, which determines the event factuality from the view of a document. For this task, we need to consider two important characteristics: Local Uncertainty and Global Structure, which can be utilized to improve performance. In this paper, we propose an Uncertain Local-to-Global Network (ULGN) to make use of these two characteristics. Specifically, we devise a Local Uncertainty Estimation module to model the uncertainty of local information. Moreover, we propose an Uncertain Information Aggregation module to leverage the global structure for integrating the local information. Experimental results demonstrate the effectiveness of our proposed method, outperforming the previous state-of-the-art model by 8.4% and 11.45% of F1 score on two widely used datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_208", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_208.flac", "type": "audio", "duration": 441.323}, {"from": "Assistant", "value": "Table filling based relational triple extraction methods are attracting growing research interests due to their promising performance and their abilities on extracting triples from complex sentences. However, this kind of methods are far from their full potential because most of them only focus on using local features but ignore the global associations of relations and of token pairs, which increases the possibility of overlooking some important information during triple extraction. To overcome this deficiency, we propose a global feature-oriented triple extraction model that makes full use of the mentioned two kinds of global associations. Specifically, we first generate a table feature for each relation. Then two kinds of global associations are mined from the generated table features. Next, the mined global associations are integrated into the table feature of each relation. This “generate-mine-integrate” process is performed multiple times so that the table feature of each relation is refined step by step. Finally, each relation’s table is filled based on its refined table feature, and all triples linked to this relation are extracted based on its filled table. We evaluate the proposed model on three benchmark datasets. Experimental results show our model is effective and it achieves state-of-the-art results on all of these datasets. The source code of our work is available at:https://github.com/neukg/GRTE.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_209", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_209.flac", "type": "audio", "duration": 676.053}, {"from": "Assistant", "value": "This paper studies the keyphrase generation (KG) task for scenarios where structure plays an important role. For example, a scientific publication consists of a short title and a long body, where the title can be used for de-emphasizing unimportant details in the body. Similarly, for short social media posts (, tweets), scarce context can be augmented from titles, though often missing. Our contribution is generating/augmenting structure then injecting these information in the encoding, using existing keyphrases of other documents, complementing missing/incomplete titles. We propose novel structure-augmented document encoding approaches that consist of the following two phases: The first phase, generating structure, extends the given document with related but absent keyphrases, augmenting missing context. The second phase, encoding structure, builds a graph of keyphrases and the given document to obtain the structure-aware representation of the augmented text. Our empirical results validate that our proposed structure augmentation and augmentation-aware encoding/decoding can improve KG for both scenarios, outperforming the state-of-the-art.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_210", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_210.flac", "type": "audio", "duration": 660.203}, {"from": "Assistant", "value": "Auxiliary information from multiple sources has been demonstrated to be effective in zero-shot fine-grained entity typing (ZFET). However, there lacks a comprehensive understanding about how to make better use of the existing information sources and how they affect the performance of ZFET. In this paper, we empirically study three kinds of auxiliary information: context consistency, type hierarchy and background knowledge (e.g., prototypes and descriptions) of types, and propose a multi-source fusion model (MSF) targeting these sources. The performance obtains up to 11.42% and 22.84% absolute gains over state-of-the-art baselines on BBN and Wiki respectively with regard to macro F1 scores. More importantly, we further discuss the characteristics, merits and demerits of each information source and provide an intuitive understanding of the complementarity among them.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_212", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_212.flac", "type": "audio", "duration": 608.64}, {"from": "Assistant", "value": "Neural relation extraction models have shown promising results in recent years; however, the model performance drops dramatically given only a few training samples. Recent works try leveraging the advance in few-shot learning to solve the low resource problem, where they train label-agnostic models to directly compare the semantic similarities among context sentences in the embedding space. However, the label-aware information, i.e., the relation label that contains the semantic knowledge of the relation itself, is often neglected for prediction. In this work, we propose a framework considering both label-agnostic and label-aware semantic mapping information for low resource relation extraction. We show that incorporating the above two types of mapping information in both pretraining and fine-tuning can significantly improve the model performance on low-resource relation extraction tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_213", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_213.flac", "type": "audio", "duration": 536.043}, {"from": "Assistant", "value": "The encoder–decoder framework achieves state-of-the-art results in keyphrase generation (KG) tasks by predicting both present keyphrases that appear in the source document and absent keyphrases that do not. However, relying solely on the source document can result in generating uncontrollable and inaccurate absent keyphrases. To address these problems, we propose a novel graph-based method that can capture explicit knowledge from related references. Our model first retrieves some document-keyphrases pairs similar to the source document from a pre-defined index as references. Then a heterogeneous graph is constructed to capture relations with different levels of granularity of the source document and its retrieved references. To guide the decoding process, a hierarchical attention and copy mechanism is introduced, which directly copies appropriate words from both source document and its references based on their relevance and significance. The experimental results on multiple KG benchmarks show that the proposed model achieves significant improvements against other baseline models, especially with regard to the absent keyphrase prediction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_214", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_214.flac", "type": "audio", "duration": 510.059}, {"from": "Assistant", "value": "Implicit event argument extraction (EAE) is a crucial document-level information extraction task that aims to identify event arguments beyond the sentence level. Despite many efforts for this task, the lack of enough training data has long impeded the study. In this paper, we take a new perspective to address the data sparsity issue faced by implicit EAE, by bridging the task with machine reading comprehension (MRC). Particularly, we devise two data augmentation regimes via MRC, including: 1) implicit knowledge transfer, which enables knowledge transfer from other tasks, by building a unified training framework in the MRC formulation, and 2) explicit data augmentation, which can explicitly generate new training examples, by treating MRC models as an annotator. The extensive experiments have justified the effectiveness of our approach — it not only obtains state-of-the-art performance on two benchmarks, but also demonstrates superior results in a data-low scenario.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_215", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_215.flac", "type": "audio", "duration": 557.047}, {"from": "Assistant", "value": "Keyphrase extraction is a fundamental task in Natural Language Processing, which usually contains two main parts: candidate keyphrase extraction and keyphrase importance estimation. From the view of human understanding documents, we typically measure the importance of phrase according to its syntactic accuracy, information saliency, and concept consistency simultaneously. However, most existing keyphrase extraction approaches only focus on the part of them, which leads to biased results. In this paper, we propose a new approach to estimate the importance of keyphrase from multiple perspectives (called asKIEMP) and further improve the performance of keyphrase extraction. Specifically,KIEMPestimates the importance of phrase with three modules: a chunking module to measure its syntactic accuracy, a ranking module to check its information saliency, and a matching module to judge the concept (i.e., topic) consistency between phrase and the whole document. These three modules are seamlessly jointed together via an end-to-end multi-task learning model, which is helpful for three parts to enhance each other and balance the effects of three perspectives. Experimental results on six benchmark datasets show thatKIEMPoutperforms the existing state-of-the-art keyphrase extraction approaches in most cases.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_216", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_216.flac", "type": "audio", "duration": 679.044}, {"from": "Assistant", "value": "Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce. Existing works either utilize self-training scheme to generate pseudo labels that will cause the gradual drift problem, or leverage meta-learning scheme which does not solicit feedback explicitly. To alleviate selection bias due to the lack of feedback loops in existing LRE learning paradigms, we developed a Gradient Imitation Reinforcement Learning method to encourage pseudo label data to imitate the gradient descent direction on labeled data and bootstrap its optimization capability through trial and error. We also propose a framework called GradLRE, which handles two major scenarios in low-resource relation extraction. Besides the scenario where unlabeled data is sufficient, GradLRE handles the situation where no unlabeled data is available, by exploiting a contextualized augmentation method to generate data. Experimental results on two public datasets demonstrate the effectiveness of GradLRE on low resource relation extraction when comparing with baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_217", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_217.flac", "type": "audio", "duration": 714.571}, {"from": "Assistant", "value": "Taxonomies are symbolic representations of hierarchical relationships between terms or entities. While taxonomies are useful in broad applications, manually updating or maintaining them is labor-intensive and difficult to scale in practice. Conventional supervised methods for this enrichment task fail to find optimal parents of new terms in low-resource settings where only small taxonomies are available because of overfitting to hierarchical relationships in the taxonomies. To tackle the problem of low-resource taxonomy enrichment, we propose Musubu, an efficient framework for taxonomy enrichment in low-resource settings with pretrained language models (LMs) as knowledge bases to compensate for the shortage of information. Musubu leverages an LM-based classifier to determine whether or not inputted term pairs have hierarchical relationships. Musubu also utilizes Hearst patterns to generate queries to leverage implicit knowledge from the LM efficiently for more accurate prediction. We empirically demonstrate the effectiveness of our method in extensive experiments on taxonomies from both a SemEval task and real-world retailer datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_218", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_218.flac", "type": "audio", "duration": 641.045}, {"from": "Assistant", "value": "Previous works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i.e.,semantic entity), while the relations in-between are largely unexplored. In this paper, we adapt the popular dependency parsing model, the biaffine parser, to this entity relation extraction task. Being different from the original dependency parsing model which recognizes dependency relations between words, we identify relations between groups of words with layout information instead. We have compared different representations of the semantic entity, different VRD encoders, and different relation decoders. For the model training, we explore multi-task learning to combine entity labeling and relation extraction tasks; and for the evaluation, we conduct experiments on different datasets with filtering and augmentation. The results demonstrate that our proposed model achieves 65.96% F1 score on the FUNSD dataset. As for the real-world application, our model has been applied to the in-house customs data, achieving reliable performance in the production setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_219", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_219.flac", "type": "audio", "duration": 727.403}, {"from": "Assistant", "value": "Joint entity and relation extraction is challenging due to the complex interaction of interaction between named entity recognition and relation extraction. Although most existing works tend to jointly train these two tasks through a shared network, they fail to fully utilize the interdependence between entity types and relation types. In this paper, we design a novel synchronous dual network (SDN) with cross-type attention via separately and interactively considering the entity types and relation types. On the one hand, SDN adopts two isomorphic bi-directional type-attention LSTM to encode the entity type enhanced representations and the relation type enhanced representations, respectively. On the other hand, SDN explicitly models the interdependence between entity types and relation types via cross-type attention mechanism. In addition, we also propose a new multi-task learning strategy via modeling the interaction of two types of information. Experiments on NYT and WebNLG datasets verify the effectiveness of the proposed model, achieving state-of-the-art performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_220", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_220.flac", "type": "audio", "duration": 721.212}, {"from": "Assistant", "value": "Dense retrieval requires high-quality text sequence embeddings to support effective search in the representation space. Autoencoder-based language models are appealing in dense retrieval as they train the encoder to output high-quality embedding that can reconstruct the input texts. However, in this paper, we provide theoretical analyses and show empirically that an autoencoder language model with a low reconstruction loss may not provide good sequence representations because the decoder may take shortcuts by exploiting language patterns. To address this, we propose a new self-learning method that pre-trains the autoencoder using aweakdecoder, with restricted capacity and attention flexibility to push the encoder to provide better text representations. Our experiments on web search, news recommendation, and open domain question answering show that our pre-trained model significantly boosts the effectiveness and few-shot ability of dense retrieval models. Our code is available athttps://github.com/microsoft/SEED-Encoder/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_221", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_221.flac", "type": "audio", "duration": 573.208}, {"from": "Assistant", "value": "Recent studies have shown that prompts improve the performance of large pre-trained language models for few-shot text classification. Yet, it is unclear how the prompting knowledge can be transferred across similar NLP tasks for the purpose of mutual reinforcement. Based on continuous prompt embeddings, we propose TransPrompt, a transferable prompting framework for few-shot learning across similar tasks. In TransPrompt, we employ a multi-task meta-knowledge acquisition procedure to train a meta-learner that captures cross-task transferable knowledge. Two de-biasing techniques are further designed to make it more task-agnostic and unbiased towards any tasks. After that, the meta-learner can be adapted to target tasks with high accuracy. Extensive experiments show that TransPrompt outperforms single-task and cross-task strong baselines over multiple NLP tasks and datasets. We further show that the meta-learner can effectively improve the performance on previously unseen tasks; and TransPrompt also outperforms strong fine-tuning baselines when learning with full training sets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_222", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_222.flac", "type": "audio", "duration": 527.04}, {"from": "Assistant", "value": "Weakly-supervised text classification has received much attention in recent years for it can alleviate the heavy burden of annotating massive data. Among them, keyword-driven methods are the mainstream where user-provided keywords are exploited to generate pseudo-labels for unlabeled texts. However, existing methods treat keywords independently, thus ignore the correlation among them, which should be useful if properly exploited. In this paper, we propose a novel framework called ClassKG to explore keyword-keyword correlation on keyword graph by GNN. Our framework is an iterative process. In each iteration, we first construct a keyword graph, so the task of assigning pseudo labels is transformed to annotating keyword subgraphs. To improve the annotation quality, we introduce a self-supervised task to pretrain a subgraph annotator, and then finetune it. With the pseudo labels generated by the subgraph annotator, we then train a text classifier to classify the unlabeled texts. Finally, we re-extract keywords from the classified texts. Extensive experiments on both long-text and short-text datasets show that our method substantially outperforms the existing ones.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_223", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_223.flac", "type": "audio", "duration": 590.059}, {"from": "Assistant", "value": "News recommendation is critical for personalized news access. Most existing news recommendation methods rely on centralized storage of users’ historical news click behavior data, which may lead to privacy concerns and hazards. Federated Learning is a privacy-preserving framework for multiple clients to collaboratively train models without sharing their private data. However, the computation and communication cost of directly learning many existing news recommendation models in a federated way are unacceptable for user clients. In this paper, we propose an efficient federated learning framework for privacy-preserving news recommendation. Instead of training and communicating the whole model, we decompose the news recommendation model into a large news model maintained in the server and a light-weight user model shared on both server and clients, where news representations and user model are communicated between server and clients. More specifically, the clients request the user model and news representations from the server, and send their locally computed gradients to the server for aggregation. The server updates its global user model with the aggregated gradients, and further updates its news model to infer updated news representations. Since the local gradients may contain private information, we propose a secure aggregation method to aggregate gradients in a privacy-preserving way. Experiments on two real-world datasets show that our method can reduce the computation and communication cost on clients while keep promising model performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_224", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_224.flac", "type": "audio", "duration": 662.123}, {"from": "Assistant", "value": "In various natural language processing tasks, passage retrieval and passage re-ranking are two key procedures in finding and ranking relevant information. Since both the two procedures contribute to the final performance, it is important to jointly optimize them in order to achieve mutual improvement. In this paper, we propose a novel joint training approach for dense passage retrieval and passage reranking. A major contribution is that we introduce the dynamic listwise distillation, where we design a unified listwise training approach for both the retriever and the re-ranker. During the dynamic distillation, the retriever and the re-ranker can be adaptively improved according to each other’s relevance information. We also propose a hybrid data augmentation strategy to construct diverse training instances for listwise training approach. Extensive experiments show the effectiveness of our approach on both MSMARCO and Natural Questions datasets. Our code is available athttps://github.com/PaddlePaddle/RocketQA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_225", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_225.flac", "type": "audio", "duration": 434.731}, {"from": "Assistant", "value": "Passage retrieval and ranking is a key task in open-domain question answering and information retrieval. Current effective approaches mostly rely on pre-trained deep language model-based retrievers and rankers. These methods have been shown to effectively model the semantic matching between queries and passages, also in presence of keyword mismatch, i.e. passages that are relevant to a query but do not contain important query keywords. In this paper we consider the Dense Retriever (DR), a passage retrieval method, and the BERT re-ranker, a popular passage re-ranking method. In this context, we formally investigate how these models respond and adapt to a specific type of keyword mismatch – that caused by keyword typos occurring in queries. Through empirical investigation, we find that typos can lead to a significant drop in retrieval and ranking effectiveness. We then propose a simple typos-aware training framework for DR and BERT re-ranker to address this issue. Our experimental results on the MS MARCO passage ranking dataset show that, with our proposed typos-aware training, DR and BERT re-ranker can become robust to typos in queries, resulting in significantly improved effectiveness compared to models trained without appropriately accounting for typos.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_226", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_226.flac", "type": "audio", "duration": 462.82}, {"from": "Assistant", "value": "Cross-lingual entity alignment (EA) aims to find the equivalent entities between crosslingual KGs (Knowledge Graphs), which is a crucial step for integrating KGs. Recently, many GNN-based EA methods are proposed and show decent performance improvements on several public datasets. However, existing GNN-based EA methods inevitably inherit poor interpretability and low efficiency from neural networks. Motivated by the isomorphic assumption of GNN-based methods, we successfully transform the cross-lingual EA problem into an assignment problem. Based on this re-definition, we propose a frustratingly Simple but Effective Unsupervised entity alignment method (SEU) without neural networks. Extensive experiments have been conducted to show that our proposed unsupervised approach even beats advanced supervised methods across all public datasets while having high efficiency, interpretability, and stability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_227", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_227.flac", "type": "audio", "duration": 479.04}, {"from": "Assistant", "value": "Recent work has shown that dense passage retrieval techniques achieve better ranking accuracy in open-domain question answering compared to sparse retrieval techniques such as BM25, but at the cost of large space and memory requirements. In this paper, we analyze the redundancy present in encoded dense vectors and show that the default dimension of 768 is unnecessarily large. To improve space efficiency, we propose a simple unsupervised compression pipeline that consists of principal component analysis (PCA), product quantization, and hybrid search. We further investigate other supervised baselines and find surprisingly that unsupervised PCA outperforms them in some settings. We perform extensive experiments on five question answering datasets and demonstrate that our best pipeline achieves good accuracy–space trade-offs, for example,48×compression with less than 3% drop in top-100 retrieval accuracy on average or96×compression with less than 4% drop. Code and data are available athttp://pyserini.io/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_228", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_228.flac", "type": "audio", "duration": 478.052}, {"from": "Assistant", "value": "Most recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to incorporate syntax-driven contextual information to improve model performance, with little attention paid to the limitation where high-quality dependency parsers in most cases unavailable, especially for in-domain scenarios. To address this limitation, in this paper, we propose attentive graph convolutional networks (A-GCN) to improve neural RE methods with an unsupervised manner to build the context graph, without relying on the existence of a dependency parser. Specifically, we construct the graph from n-grams extracted from a lexicon built from pointwise mutual information (PMI) and apply attention over the graph. Therefore, different word pairs from the contexts within and across n-grams are weighted in the model and facilitate RE accordingly. Experimental results with further analyses on two English benchmark datasets for RE demonstrate the effectiveness of our approach, where state-of-the-art performance is observed on both datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_229", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_229.flac", "type": "audio", "duration": 944.286}, {"from": "Assistant", "value": "Pimentel et al. (2020) recently analysed probing from an information-theoretic perspective. They argue that probing should be seen as approximating a mutual information. This led to the rather unintuitive conclusion that representations encode exactly the same information about a target task as the original sentences. The mutual information, however, assumes the true probability distribution of a pair of random variables is known, leading to unintuitive results in settings where it is not. This paper proposes a new framework to measure what we term Bayesian mutual information, which analyses information from the perspective of Bayesian agents—allowing for more intuitive findings in scenarios with finite data. For instance, under Bayesian MI we have that data can add information, processing can help, and information can hurt, which makes it more intuitive for machine learning applications. Finally, we apply our framework to probing where we believe Bayesian mutual information naturally operationalises ease of extraction by explicitly limiting the available background knowledge to solve a task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_230", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_230.flac", "type": "audio", "duration": 760.221}, {"from": "Assistant", "value": "A possible explanation for the impressive performance of masked language model (MLM) pre-training is that such models have learned to represent the syntactic structures prevalent in classical NLP pipelines. In this paper, we propose a different explanation: MLMs succeed on downstream tasks almost entirely due to their ability to model higher-order word co-occurrence statistics. To demonstrate this, we pre-train MLMs on sentences with randomly shuffled word order, and show that these models still achieve high accuracy after fine-tuning on many downstream tasks—including tasks specifically designed to be challenging for models that ignore word order. Our models perform surprisingly well according to some parametric syntactic probes, indicating possible deficiencies in how we test representations for syntactic information. Overall, our results show that purely distributional information largely explains the success of pre-training, and underscore the importance of curating challenging evaluation datasets that require deeper linguistic knowledge.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_231", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_231.flac", "type": "audio", "duration": 228.16}, {"from": "Assistant", "value": "We demonstrate that, hidden within one-layer randomly weighted neural networks, there exist subnetworks that can achieve impressive performance, without ever modifying the weight initializations, on machine translation tasks. To find subnetworks for one-layer randomly weighted neural networks, we apply different binary masks to the same weight matrix to generate different layers. Hidden within a one-layer randomly weighted Transformer, we find that subnetworks that can achieve 29.45/17.29 BLEU on IWSLT14/WMT14. Using a fixed pre-trained embedding layer, the previously found subnetworks are smaller than, but can match 98%/92% (34.14/25.24 BLEU) of the performance of, a trained Transformersmall/baseon IWSLT14/WMT14. Furthermore, we demonstrate the effectiveness of larger and deeper transformers in this setting, as well as the impact of different initialization methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_232", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_232.flac", "type": "audio", "duration": 711.459}, {"from": "Assistant", "value": "Pre-trained self-supervised models such as BERT have achieved striking success in learning sequence representations, especially for natural language processing. These models typically corrupt the given sequences with certain types of noise, such as masking, shuffling, or substitution, and then try to recover the original input. However, such pre-training approaches are prone to learning representations that are covariant with the noise, leading to the discrepancy between the pre-training and fine-tuning stage. To remedy this, we present ContrAstive Pre-Training (CAPT) to learn noise invariant sequence representations. The proposed CAPT encourages the consistency between representations of the original sequence and its corrupted version via unsupervised instance-wise training signals. In this way, it not only alleviates the pretrain-finetune discrepancy induced by the noise of pre-training, but also aids the pre-trained model in better capturing global semantics of the input via more effective sentence-level supervision. Different from most prior work that focuses on a particular modality, comprehensive empirical evidence on 11 natural language understanding and cross-modal tasks illustrates that CAPT is applicable for both language and vision-language tasks, and obtains surprisingly consistent improvement, including 0.6% absolute gain on GLUE benchmarks and 0.8% absolute increment on NLVR2.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_233", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_233.flac", "type": "audio", "duration": 491.822}, {"from": "Assistant", "value": "Lifelong Learning (LL) black-box models are dynamic in that they keep learning from new tasks and constantly update their parameters. Owing to the need to utilize information from previously seen tasks, and capture commonalities in potentially diverse data, it is hard for automatic explanation methods to explain the outcomes of these models. In addition, existing explanation methods, e.g., LIME, which are computationally expensive when explaining a static black-box model, are even more inefficient in the LL setting. In this paper, we propose a novel Lifelong Explanation (LLE) approach that continuously trains a student explainer under the supervision of a teacher – an arbitrary explanation algorithm – on different tasks undertaken in LL. We also leverage the Experience Replay (ER) mechanism to prevent catastrophic forgetting in the student explainer. Our experiments comparing LLE to three baselines on text classification tasks show that LLE can enhance the stability of the explanations for all seen tasks and maintain the same level of faithfulness to the black-box model as the teacher, while being up to 10ˆ2 times faster at test time. Our ablation study shows that the ER mechanism in our LLE approach enhances the learning capabilities of the student explainer. Our code is available athttps://github.com/situsnow/LLE.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_234", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_234.flac", "type": "audio", "duration": 761.057}, {"from": "Assistant", "value": "Are pairs of words that tend to occur together also likely to stand in a linguistic dependency? This empirical question is motivated by a long history of literature in cognitive science, psycholinguistics, and NLP. In this work we contribute an extensive analysis of the relationship between linguistic dependencies and statistical dependence between words. Improving on previous work, we introduce the use of large pretrained language models to compute contextualized estimates of the pointwise mutual information between words (CPMI). For multiple models and languages, we extract dependency trees which maximize CPMI, and compare to gold standard linguistic dependencies. Overall, we find that CPMI dependencies achieve an unlabelled undirected attachment score of at most≈ 0.5. While far above chance, and consistently above a non-contextualized PMI baseline, this score is generally comparable to a simple baseline formed by connecting adjacent words. We analyze which kinds of linguistic dependencies are best captured in CPMI dependencies, and also find marked differences between the estimates of the large pretrained language models, illustrating how their different training schemes affect the type of dependencies they capture.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_235", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_235.flac", "type": "audio", "duration": 480.049}, {"from": "Assistant", "value": "In computational linguistics, it has been shown that hierarchical structures make language models (LMs) more human-like. However, the previous literature has been agnostic about a parsing strategy of the hierarchical models. In this paper, we investigated whether hierarchical structures make LMs more human-like, and if so, which parsing strategy is most cognitively plausible. In order to address this question, we evaluated three LMs against human reading times in Japanese with head-final left-branching structures: Long Short-Term Memory (LSTM) as a sequential model and Recurrent Neural Network Grammars (RNNGs) with top-down and left-corner parsing strategies as hierarchical models. Our computational modeling demonstrated that left-corner RNNGs outperformed top-down RNNGs and LSTM, suggesting that hierarchical and left-corner architectures are more cognitively plausible than top-down or sequential architectures. In addition, the relationships between the cognitive plausibility and (i) perplexity, (ii) parsing, and (iii) beam size will also be discussed.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_236", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_236.flac", "type": "audio", "duration": 591.018}, {"from": "Assistant", "value": "Transformer models are permutation equivariant. To supply the order and type information of the input tokens, position and segment embeddings are usually added to the input. Recent works proposed variations of positional encodings with relative position encodings achieving better performance. Our analysis shows that the gain actually comes from moving positional information to attention layer from the input. Motivated by this, we introduce Decoupled Positional Attention for Transformers (DIET), a simple yet effective mechanism to encode position and segment information into the Transformer models. The proposed method has faster training and inference time, while achieving competitive performance on GLUE, XTREME and WMT benchmarks. We further generalize our method to long-range transformers and show performance gain.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_237", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_237.flac", "type": "audio", "duration": 435.281}, {"from": "Assistant", "value": "Relative position embedding (RPE) is a successful method to explicitly and efficaciously encode position information into Transformer models. In this paper, we investigate the potential problems in Shaw-RPE and XL-RPE, which are the most representative and prevalent RPEs, and propose two novel RPEs called Low-level Fine-grained High-level Coarse-grained (LFHC) RPE and Gaussian Cumulative Distribution Function (GCDF) RPE. LFHC-RPE is an improvement of Shaw-RPE, which enhances the perception ability at medium and long relative positions. GCDF-RPE utilizes the excellent properties of the Gaussian function to amend the prior encoding mechanism in XL-RPE. Experimental results on nine authoritative datasets demonstrate the effectiveness of our methods empirically. Furthermore, GCDF-RPE achieves the best overall performance among five different RPEs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_238", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_238.flac", "type": "audio", "duration": 649.764}, {"from": "Assistant", "value": "Mixup is a recent regularizer for current deep classification networks. Through training a neural network on convex combinations of pairs of examples and their labels, it imposes locally linear constraints on the model’s input space. However, such strict linear constraints often lead to under-fitting which degrades the effects of regularization. Noticeably, this issue is getting more serious when the resource is extremely limited. To address these issues, we propose the Adversarial Mixing Policy (AMP), organized in a “min-max-rand” formulation, to relax the Locally Linear Constraints in Mixup. Specifically, AMP adds a small adversarial perturbation to the mixing coefficients rather than the examples. Thus, slight non-linearity is injected in-between the synthetic examples and synthetic labels. By training on these data, the deep networks are further regularized, and thus achieve a lower predictive error rate. Experiments on five text classification benchmarks and five backbone models have empirically shown that our methods reduce the error rate over Mixup variants in a significant margin (up to 31.3%), especially in low-resource conditions (up to 17.5%).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_239", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_239.flac", "type": "audio", "duration": 472.967}, {"from": "Assistant", "value": "It is difficult to rank and evaluate the performance of grammatical error correction (GEC) systems, as a sentence can be rewritten in numerous correct ways. A number of GEC metrics have been used to evaluate proposed GEC systems; however, each system relies on either a comparison with one or more reference texts—in what is known as the gold standard for reference-based metrics—or a separate annotated dataset to fine-tune the reference-less metric. Reference-based systems have a low correlation with human judgement, cannot capture all the ways in which a sentence can be corrected, and require substantial work to develop a test dataset. We propose a reference-less GEC evaluation system that is strongly correlated with human judgement, solves the issues related to the use of a reference, and does not need another annotated dataset for fine-tuning. The proposed system relies solely on commonly available tools. Additionally, currently available reference-less metrics do not work properly when part of a sentence is repeated as opposed to reference-based metrics. In our proposed system, we look to address issues inherent in reference-less metrics and reference-based metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_240", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_240.flac", "type": "audio", "duration": 443.942}, {"from": "Assistant", "value": "Current language models are usually trained using a self-supervised scheme, where the main focus is learning representations at the word or sentence level. However, there has been limited progress in generating useful discourse-level representations. In this work, we propose to use ideas from predictive coding theory to augment BERT-style language models with a mechanism that allows them to learn suitable discourse-level representations. As a result, our proposed approach is able to predict future sentences using explicit top-down connections that operate at the intermediate layers of the network. By experimenting with benchmarks designed to evaluate discourse-related knowledge using pre-trained sentence representations, we demonstrate that our approach improves performance in 6 out of 11 tasks by excelling in discourse relationship detection.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_243", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_243.flac", "type": "audio", "duration": 632.93}, {"from": "Assistant", "value": "In this work, we explore “prompt tuning,” a simple yet effective mechanism for learning “soft prompts” to condition frozen language models to perform specific downstream tasks. Unlike the discrete text prompts used by GPT-3, soft prompts are learned through backpropagation and can be tuned to incorporate signals from any number of labeled examples. Our end-to-end learned approach outperforms GPT-3’s few-shot learning by a large margin. More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with scale: as models exceed billions of parameters, our method “closes the gap” and matches the strong performance of model tuning (where all model weights are tuned). This finding is especially relevant because large models are costly to share and serve and the ability to reuse one frozen model for multiple downstream tasks can ease this burden. Our method can be seen as a simplification of the recently proposed “prefix tuning” of Li and Liang (2021) and we provide a comparison to this and other similar approaches. Finally, we show that conditioning a frozen model with soft prompts confers benefits in robustness to domain transfer and enables efficient “prompt ensembling.” We release code and model checkpoints to reproduce our experiments.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_244", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_244.flac", "type": "audio", "duration": 705.329}, {"from": "Assistant", "value": "We propose a deep generative model that performs typography analysis and font reconstruction by learning disentangled manifolds of both font style and character shape. Our approach enables us to massively scale up the number of character types we can effectively model compared to previous methods. Specifically, we infer separate latent variables representing character and font via a pair of inference networks which take as input sets of glyphs that either all share a character type, or belong to the same font. This design allows our model to generalize to characters that were not observed during training time, an important task in light of the relative sparsity of most fonts. We also put forward a new loss, adapted from prior work that measures likelihood using an adaptive distribution in a projected space, resulting in more natural images without requiring a discriminator. We evaluate on the task of font reconstruction over various datasets representing character types of many languages, and compare favorably to modern style transfer systems according to both automatic and manually-evaluated metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_245", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_245.flac", "type": "audio", "duration": 472.043}, {"from": "Assistant", "value": "Text-Based Games (TBGs) have emerged as important testbeds for reinforcement learning (RL) in the natural language domain. Previous methods using LSTM-based action policies are uninterpretable and often overfit the training games showing poor performance to unseen test games. We present SymboLic Action policy for Textual Environments (SLATE), that learns interpretable action policy rules from symbolic abstractions of textual observations for improved generalization. We outline a method for end-to-end differentiable symbolic rule learning and show that such symbolic policies outperform previous state-of-the-art methods in text-based RL for the coin collector environment from 5-10x fewer training games. Additionally, our method provides human-understandable policy rules that can be readily verified for their logical consistency and can be easily debugged.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_246", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_246.flac", "type": "audio", "duration": 419.051}, {"from": "Assistant", "value": "Inspired by mutual information (MI) based feature selection in SVMs and logistic regression, in this paper, we propose MI-based layer-wise pruning: for each layer of a multi-layer neural network, neurons with higher values of MI with respect to preserved neurons in the upper layer are preserved. Starting from the top softmax layer, layer-wise pruning proceeds in a top-down fashion until reaching the bottom word embedding layer. The proposed pruning strategy offers merits over weight-based pruning techniques: (1) it avoids irregular memory access since representations and matrices can be squeezed into their smaller but dense counterparts, leading to greater speedup; (2) in a manner of top-down pruning, the proposed method operates from a more global perspective based on training signals in the top layer, and prunes each layer by propagating the effect of global signals through layers, leading to better performances at the same sparsity level. Extensive experiments show that at the same sparsity level, the proposed strategy offers both greater speedup and higher performances than weight-based pruning methods (e.g., magnitude pruning, movement pruning).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_247", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_247.flac", "type": "audio", "duration": 717.099}, {"from": "Assistant", "value": "Short text classification is a fundamental task in natural language processing. It is hard due to the lack of context information and labeled data in practice. In this paper, we propose a new method called SHINE, which is based on graph neural network (GNN), for short text classification. First, we model the short text dataset as a hierarchical heterogeneous graph consisting of word-level component graphs which introduce more semantic and syntactic information. Then, we dynamically learn a short document graph that facilitates effective label propagation among similar short texts. Thus, comparing with existing GNN-based methods, SHINE can better exploit interactions between nodes of the same types and capture similarities between short texts. Extensive experiments on various benchmark short text datasets show that SHINE consistently outperforms state-of-the-art methods, especially with fewer labels.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_249", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_249.flac", "type": "audio", "duration": 459.051}, {"from": "Assistant", "value": "Masked language modeling (MLM), a self-supervised pretraining objective, is widely used in natural language processing for learning text representations. MLM trains a model to predict a random sample of input tokens that have been replaced by a [MASK] placeholder in a multi-class setting over the entire vocabulary. When pretraining, it is common to use alongside MLM other auxiliary objectives on the token or sequence level to improve downstream performance (e.g. next sentence prediction). However, no previous work so far has attempted in examining whether other simpler linguistically intuitive or not objectives can be used standalone as main pretraining objectives. In this paper, we explore five simple pretraining objectives based on token-level classification tasks as replacements of MLM. Empirical results on GLUE and SQUAD show that our proposed methods achieve comparable or better performance to MLM using a BERT-BASE architecture. We further validate our methods using smaller models, showing that pretraining a model with 41% of the BERT-BASE’s parameters, BERT-MEDIUM results in only a 1% drop in GLUE scores with our best objective.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_250", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_250.flac", "type": "audio", "duration": 653.061}, {"from": "Assistant", "value": "On many natural language processing tasks, large pre-trained language models (PLMs) have shown overwhelming performances compared with traditional neural network methods. Nevertheless, their huge model size and low inference speed have hindered the deployment on resource-limited devices in practice. In this paper, we target to compress PLMs with knowledge distillation, and propose a hierarchical relational knowledge distillation (HRKD) method to capture both hierarchical and domain relational information. Specifically, to enhance the model capability and transferability, we leverage the idea of meta-learning and set up domain-relational graphs to capture the relational information across different domains. And to dynamically select the most representative prototypes for each domain, we propose a hierarchical compare-aggregate mechanism to capture hierarchical relationships. Extensive experiments on public multi-domain datasets demonstrate the superior performance of our HRKD method as well as its strong few-shot learning ability. For reproducibility, we release the code athttps://github.com/cheneydon/hrkd.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_251", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_251.flac", "type": "audio", "duration": 721.049}, {"from": "Assistant", "value": "Recent studies have shown that deep neural network-based models are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversarial word-substitution attacks for neural NLP models. However, there is a lack of systematic study on comparing different defense approaches under the same attacking setting. In this paper, we seek to fill the gap of systematic studies through comprehensive researches on understanding the behavior of neural text classifiers trained by various defense methods under representative adversarial attacks. In addition, we propose an effective method to further improve the robustness of neural text classifiers against such attacks, and achieved the highest accuracy on both clean and adversarial examples on AGNEWS and IMDB datasets by a significant margin. We hope this study could provide useful clues for future research on text adversarial defense. Codes are available athttps://github.com/RockyLzy/TextDefender.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_252", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_252.flac", "type": "audio", "duration": 609.059}, {"from": "Assistant", "value": "Difficult samples of the minority class in imbalanced text classification are usually hard to be classified as they are embedded into an overlapping semantic region with the majority class. In this paper, we propose a Mutual Information constrained Semantically Oversampling framework (MISO) that can generate anchor instances to help the backbone network determine the re-embedding position of a non-overlapping representation for each difficult sample. MISO consists of (1) a semantic fusion module that learns entangled semantics among difficult and majority samples with an adaptive multi-head attention mechanism, (2) a mutual information loss that forces our model to learn new representations of entangled semantics in the non-overlapping region of the minority class, and (3) a coupled adversarial encoder-decoder that fine-tunes disentangled semantic representations to remain their correlations with the minority class, and then using these disentangled semantic representations to generate anchor instances for each difficult sample. Experiments on a variety of imbalanced text classification tasks demonstrate that anchor instances help classifiers achieve significant improvements over strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_253", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_253.flac", "type": "audio", "duration": 526.048}, {"from": "Assistant", "value": "Multi-label document classification, associating one document instance with a set of relevant labels, is attracting more and more research attention. Existing methods explore the incorporation of information beyond text, such as document metadata or label structure. These approaches however either simply utilize the semantic information of metadata or employ the predefined parent-child label hierarchy, ignoring the heterogeneous graphical structures of metadata and labels, which we believe are crucial for accurate multi-label document classification. Therefore, in this paper, we propose a novel neural network based approach for multi-label document classification, in which two heterogeneous graphs are constructed and learned using heterogeneous graph transformers. One is metadata heterogeneous graph, which models various types of metadata and their topological relations. The other is label heterogeneous graph, which is constructed based on both the labels’ hierarchy and their statistical dependencies. Experimental results on two benchmark datasets show the proposed approach outperforms several state-of-the-art baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_254", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_254.flac", "type": "audio", "duration": 669.06}, {"from": "Assistant", "value": "Recent research has investigated quantum NLP, designing algorithms that process natural language in quantum computers, and also quantum-inspired algorithms that improve NLP performance on classical computers. In this survey, we review representative methods at the intersection of NLP and quantum physics in the past ten years, categorizing them according to the use of quantum theory, the linguistic targets that are modeled, and the downstream application. The literature review ends with a discussion on the key factors to the success that has been achieved by existing work, as well as challenges ahead, with the goal of better understanding the promises and further directions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_255", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_255.flac", "type": "audio", "duration": 714.014}, {"from": "Assistant", "value": "Sequence labeling aims to predict a fine-grained sequence of labels for the text. However, such formulation hinders the effectiveness of supervised methods due to the lack of token-level annotated data. This is exacerbated when we meet a diverse range of languages. In this work, we explore multilingual sequence labeling with minimal supervision using a single unified model for multiple languages. Specifically, we propose a Meta Teacher-Student (MetaTS) Network, a novel meta learning method to alleviate data scarcity by leveraging large multilingual unlabeled data. Prior teacher-student frameworks of self-training rely on rigid teaching strategies, which may hardly produce high-quality pseudo-labels for consecutive and interdependent tokens. On the contrary, MetaTS allows the teacher to dynamically adapt its pseudo-annotation strategies by the student’s feedback on the generated pseudo-labeled data of each language and thus mitigate error propagation from noisy pseudo-labels. Extensive experiments on both public and real-world multilingual sequence labeling datasets empirically demonstrate the effectiveness of MetaTS.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_256", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_256.flac", "type": "audio", "duration": 478.169}, {"from": "Assistant", "value": "Neural Machine Translation (NMT) has shown a strong ability to utilize local context to disambiguate the meaning of words. However, it remains a challenge for NMT to leverage broader context information like topics. In this paper, we propose heterogeneous ways of embedding topic information at the sentence level into an NMT model to improve translation performance. Specifically, the topic information can be incorporated as pre-encoder topic embedding, post-encoder topic embedding, and decoder topic embedding to increase the likelihood of selecting target words from the same topic of the source sentence. Experimental results show that NMT models with the proposed topic knowledge embedding outperform the baselines on the English -> German and English -> French translation tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_257", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_257.flac", "type": "audio", "duration": 632.21}, {"from": "Assistant", "value": "Compared to monolingual models, cross-lingual models usually require a more expressive vocabulary to represent all languages adequately. We find that many languages are under-represented in recent cross-lingual language models due to the limited vocabulary capacity. To this end, we propose an algorithm VoCap to determine the desired vocabulary capacity of each language. However, increasing the vocabulary size significantly slows down the pre-training speed. In order to address the issues, we propose k-NN-based target sampling to accelerate the expensive softmax. Our experiments show that the multilingual vocabulary learned with VoCap benefits cross-lingual language model pre-training. Moreover, k-NN-based target sampling mitigates the side-effects of increasing the vocabulary size while achieving comparable performance and faster pre-training speed. The code and the pretrained multilingual vocabularies are available athttps://github.com/bozheng-hit/VoCapXLM.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_258", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_258.flac", "type": "audio", "duration": 417.773}, {"from": "Assistant", "value": "Recent research questions the importance of the dot-product self-attention in Transformer models and shows that most attention heads learn simple positional patterns. In this paper, we push further in this research line and propose a novel substitute mechanism for self-attention: Recurrent AtteNtion (RAN) . RAN directly learns attention weights without any token-to-token interaction and further improves their capacity by layer-to-layer interaction. Across an extensive set of experiments on 10 machine translation tasks, we find that RAN models are competitive and outperform their Transformer counterpart in certain scenarios, with fewer parameters and inference time. Particularly, when apply RAN to the decoder of Transformer, there brings consistent improvements by about +0.5 BLEU on 6 translation tasks and +1.0 BLEU on Turkish-English translation task. In addition, we conduct extensive analysis on the attention weights of RAN to confirm their reasonableness. Our RAN is a promising alternative to build more effective and efficient NMT models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_259", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_259.flac", "type": "audio", "duration": 716.057}, {"from": "Assistant", "value": "Lack of training data presents a grand challenge to scaling out spoken language understanding (SLU) to low-resource languages. Although various data augmentation approaches have been proposed to synthesize training data in low-resource target languages, the augmented data sets are often noisy, and thus impede the performance of SLU models. In this paper we focus on mitigating noise in augmented data. We develop a denoising training approach. Multiple models are trained with data produced by various augmented methods. Those models provide supervision signals to each other. The experimental results show that our method outperforms the existing state of the art by 3.05 and 4.24 percentage points on two benchmark datasets, respectively. The code will be made open sourced on github.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_260", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_260.flac", "type": "audio", "duration": 720.538}, {"from": "Assistant", "value": "Multi-head self-attention recently attracts enormous interest owing to its specialized functions, significant parallelizable computation, and flexible extensibility. However, very recent empirical studies show that some self-attention heads make little contribution and can be pruned as redundant heads. This work takes a novel perspective of identifying and then vitalizing redundant heads. We propose a redundant head enlivening (RHE) method to precisely identify redundant heads, and then vitalize their potential by learning syntactic relations and prior knowledge in the text without sacrificing the roles of important heads. Two novel syntax-enhanced attention (SEA) mechanisms: a dependency mask bias and a relative local-phrasal position bias, are introduced to revise self-attention distributions for syntactic enhancement in machine translation. The importance of individual heads is dynamically evaluated during the redundant heads identification, on which we apply SEA to vitalize redundant heads while maintaining the strength of important heads. Experimental results on widely adopted WMT14 and WMT16 English to German and English to Czech language machine translation validate the RHE effectiveness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_261", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_261.flac", "type": "audio", "duration": 715.012}, {"from": "Assistant", "value": "Machine translation usually relies on parallel corpora to provide parallel signals for training. The advent of unsupervised machine translation has brought machine translation away from this reliance, though performance still lags behind traditional supervised machine translation. In unsupervised machine translation, the model seeks symmetric language similarities as a source of weak parallel signal to achieve translation. Chomsky’s Universal Grammar theory postulates that grammar is an innate form of knowledge to humans and is governed by universal principles and constraints. Therefore, in this paper, we seek to leverage such shared grammar clues to provide more explicit language parallel signals to enhance the training of unsupervised machine translation models. Through experiments on multiple typical language pairs, we demonstrate the effectiveness of our proposed approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_262", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_262.flac", "type": "audio", "duration": 719.04}, {"from": "Assistant", "value": "Recently a number of approaches have been proposed to improve translation performance for document-level neural machine translation (NMT). However, few are focusing on the subject of lexical translation consistency. In this paper we apply “one translation per discourse” in NMT, and aim to encourage lexical translation consistency for document-level NMT. This is done by first obtaining a word link for each source word in a document, which tells the positions where the source word appears. Then we encourage the translation of those words within a link to be consistent in two ways. On the one hand, when encoding sentences within a document we properly share context information of those words. On the other hand, we propose an auxiliary loss function to better constrain that their translation should be consistent. Experimental results on Chinese↔English and English→French translation tasks show that our approach not only achieves state-of-the-art performance in BLEU scores, but also greatly improves lexical consistency in translation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_264", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_264.flac", "type": "audio", "duration": 684.565}, {"from": "Assistant", "value": "Scheduled sampling is widely used to mitigate the exposure bias problem for neural machine translation. Its core motivation is to simulate the inference scene during training by replacing ground-truth tokens with predicted tokens, thus bridging the gap between training and inference. However, vanilla scheduled sampling is merely based on training steps and equally treats all decoding steps. Namely, it simulates an inference scene with uniform error rates, which disobeys the real inference scene, where larger decoding steps usually have higher error rates due to error accumulations. To alleviate the above discrepancy, we propose scheduled sampling methods based on decoding steps, increasing the selection chance of predicted tokens with the growth of decoding steps. Consequently, we can more realistically simulate the inference scene during training, thus better bridging the gap between training and inference. Moreover, we investigate scheduled sampling based on both training steps and decoding steps for further improvements. Experimentally, our approaches significantly outperform the Transformer baseline and vanilla scheduled sampling on three large-scale WMT tasks. Additionally, our approaches also generalize well to the text summarization task on two popular benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_265", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_265.flac", "type": "audio", "duration": 691.051}, {"from": "Assistant", "value": "Non-autoregressive neural machine translation, which decomposes the dependence on previous target tokens from the inputs of the decoder, has achieved impressive inference speedup but at the cost of inferior accuracy. Previous works employ iterative decoding to improve the translation by applying multiple refinement iterations. However, a serious drawback is that these approaches expose the serious weakness in recognizing the erroneous translation pieces. In this paper, we propose an architecture named RewriteNAT to explicitly learn to rewrite the erroneous translation pieces. Specifically, RewriteNAT utilizes a locator module to locate the erroneous ones, which are then revised into the correct ones by a revisor module. Towards keeping the consistency of data distribution with iterative decoding, an iterative training strategy is employed to further improve the capacity of rewriting. Extensive experiments conducted on several widely-used benchmarks show that RewriteNAT can achieve better performance while significantly reducing decoding time, compared with previous iterative decoding strategies. In particular, RewriteNAT can obtain competitive results with autoregressive translation on WMT14 En-De, En-Fr and WMT16 Ro-En translation benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_266", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_266.flac", "type": "audio", "duration": 471.339}, {"from": "Assistant", "value": "Position representation is crucial for building position-aware representations in Transformers. Existing position representations suffer from a lack of generalization to test data with unseen lengths or high computational cost. We investigate shifted absolute position embedding (SHAPE) to address both issues. The basic idea of SHAPE is to achieve shift invariance, which is a key property of recent successful position representations, by randomly shifting absolute positions during training. We demonstrate that SHAPE is empirically comparable to its counterpart while being simpler and faster.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_267", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_267.flac", "type": "audio", "duration": 694.044}, {"from": "Assistant", "value": "Quality estimation (QE) of machine translation (MT) aims to evaluate the quality of machine-translated sentences without references and is important in practical applications of MT. Training QE models require massive parallel data with hand-crafted quality annotations, which are time-consuming and labor-intensive to obtain. To address the issue of the absence of annotated training data, previous studies attempt to develop unsupervised QE methods. However, very few of them can be applied to both sentence- and word-level QE tasks, and they may suffer from noises in the synthetic data. To reduce the negative impact of noises, we propose a self-supervised method for both sentence- and word-level QE, which performs quality estimation by recovering the masked target words. Experimental results show that our method outperforms previous unsupervised methods on several QE tasks in different language pairs and domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_268", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_268.flac", "type": "audio", "duration": 752.048}, {"from": "Assistant", "value": "This paper considers the unsupervised domain adaptation problem for neural machine translation (NMT), where we assume the access to only monolingual text in either the source or target language in the new domain. We propose a cross-lingual data selection method to extract in-domain sentences in the missing language side from a large generic monolingual corpus. Our proposed method trains an adaptive layer on top of multilingual BERT by contrastive learning to align the representation between the source and target language. This then enables the transferability of the domain classifier between the languages in a zero-shot manner. Once the in-domain data is detected by the classifier, the NMT model is then adapted to the new domain by jointly learning translation and domain discrimination tasks. We evaluate our cross-lingual data selection method on NMT across five diverse domains in three language pairs, as well as a real-world scenario of translation for COVID-19. The results show that our proposed method outperforms other selection baselines up to +1.5 BLEU score.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_269", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_269.flac", "type": "audio", "duration": 715.685}, {"from": "Assistant", "value": "Rumor detection on social media puts pre-trained language models (LMs), such as BERT, and auxiliary features, such as comments, into use. However, on the one hand, rumor detection datasets in Chinese companies with comments are rare; on the other hand, intensive interaction of attention on Transformer-based models like BERT may hinder performance improvement. To alleviate these problems, we build a new Chinese microblog dataset named Weibo20 by collecting posts and associated comments from Sina Weibo and propose a new ensemble named STANKER (Stacking neTwork bAsed-on atteNtion-masKed BERT). STANKER adopts two level-grained attention-masked BERT (LGAM-BERT) models as base encoders. Unlike the original BERT, our new LGAM-BERT model takes comments as important auxiliary features and masks co-attention between posts and comments on lower-layers. Experiments on Weibo20 and three existing social media datasets showed that STANKER outperformed all compared models, especially beating the old state-of-the-art on Weibo dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_270", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_270.flac", "type": "audio", "duration": 647.605}, {"from": "Assistant", "value": "Entity Alignment (EA) aims to match equivalent entities across different Knowledge Graphs (KGs) and is an essential step of KG fusion. Current mainstream methods – neural EA models – rely on training with seed alignment, i.e., a set of pre-aligned entity pairs which are very costly to annotate. In this paper, we devise a novel Active Learning (AL) framework for neural EA, aiming to create highly informative seed alignment to obtain more effective EA models with less annotation cost. Our framework tackles two main challenges encountered when applying AL to EA: (1) How to exploit dependencies between entities within the AL strategy. Most AL strategies assume that the data instances to sample are independent and identically distributed. However, entities in KGs are related. To address this challenge, we propose a structure-aware uncertainty sampling strategy that can measure the uncertainty of each entity as well as its impact on its neighbour entities in the KG. (2) How to recognise entities that appear in one KG but not in the other KG (i.e., bachelors). Identifying bachelors would likely save annotation budget. To address this challenge, we devise a bachelor recognizer paying attention to alleviate the effect of sampling bias. Empirical results show that our proposed AL strategy can significantly improve sampling quality with good generality across different datasets, EA models and amount of bachelors.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_271", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_271.flac", "type": "audio", "duration": 439.693}, {"from": "Assistant", "value": "A real-world information extraction (IE) system for semi-structured document images often involves a long pipeline of multiple modules, whose complexity dramatically increases its development and maintenance cost. One can instead consider an end-to-end model that directly maps the input to the target output and simplify the entire process. However, such generation approach is known to lead to unstable performance if not designed carefully. Here we present our recent effort on transitioning from our existing pipeline-based IE system to an end-to-end system focusing on practical challenges that are associated with replacing and deploying the system in real, large-scale production. By carefully formulating document IE as a sequence generation task, we show that a single end-to-end IE system can be built and still achieve competent performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_272", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_272.flac", "type": "audio", "duration": 645.259}, {"from": "Assistant", "value": "The recent algorithms for math word problems (MWP) neglect to use outside knowledge not present in the problems. Most of them only capture the word-level relationship and ignore to build hierarchical reasoning like the human being for mining the contextual structure between words and sentences. In this paper, we propose aReasoning withPre-trainedKnowledge andHierarchicalStructure (RPKHS) network, which contains a pre-trained knowledge encoder and a hierarchical reasoning encoder. Firstly, our pre-trained knowledge encoder aims at reasoning the MWP by using outside knowledge from the pre-trained transformer-based models. Secondly, the hierarchical reasoning encoder is presented for seamlessly integrating the word-level and sentence-level reasoning to bridge the entity and context domain on MWP. Extensive experiments show that our RPKHS significantly outperforms state-of-the-art approaches on two large-scale commonly-used datasets, and boosts performance from 77.4% to 83.9% on Math23K, from 75.5 to 82.2% on Math23K with 5-fold cross-validation and from 83.7% to 89.8% on MAWPS. More extensive ablations are shown to demonstrate the effectiveness and interpretability of our proposed method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_273", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_273.flac", "type": "audio", "duration": 591.528}, {"from": "Assistant", "value": "Mathematical reasoning aims to infer satisfiable solutions based on the given mathematics questions. Previous natural language processing researches have proven the effectiveness of sequence-to-sequence (Seq2Seq) or related variants on mathematics solving. However, few works have been able to explore structural or syntactic information hidden in expressions (e.g., precedence and associativity). This dissertation set out to investigate the usefulness of such untapped information for neural architectures. Firstly, mathematical questions are represented in the format of graphs within syntax analysis. The structured nature of graphs allows them to represent relations of variables or operators while preserving the semantics of the expressions. Having transformed to the new representations, we proposed a graph-to-sequence neural network GraphMR, which can effectively learn the hierarchical information of graphs inputs to solve mathematics and speculate answers. A complete experimental scenario with four classes of mathematical tasks and three Seq2Seq baselines is built to conduct a comprehensive analysis, and results show that GraphMR outperforms others in hidden information learning and mathematics resolving.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_274", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_274.flac", "type": "audio", "duration": 632.326}, {"from": "Assistant", "value": "GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data. Here we address some remaining issues less reported by the GPT-3 paper, such as a non-English LM, the performances of different sized models, and the effect of recently introduced prompt optimization on in-context learning. To achieve this, we introduce HyperCLOVA, a Korean variant of 82B GPT-3 trained on a Korean-centric corpus of 560B tokens. Enhanced by our Korean-specific tokenization, HyperCLOVA with our training configuration shows state-of-the-art in-context zero-shot and few-shot learning performances on various downstream tasks in Korean. Also, we show the performance benefits of prompt-based learning and demonstrate how it can be integrated into the prompt engineering pipeline. Then we discuss the possibility of materializing the No Code AI paradigm by providing AI prototyping capabilities to non-experts of ML by introducing HyperCLOVA studio, an interactive prompt engineering interface. Lastly, we demonstrate the potential of our methods with three successful in-house applications.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_275", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_275.flac", "type": "audio", "duration": 717.056}, {"from": "Assistant", "value": "For programmers, learning the usage of APIs (Application Programming Interfaces) of a software library is important yet difficult. API recommendation tools can help developers use APIs by recommending which APIs to be used next given the APIs that have been written. Traditionally, language models such as N-gram are applied to API recommendation. However, because the software libraries keep changing and new libraries keep emerging, new APIs are common. These new APIs can be seen as OOV (out of vocabulary) words and cannot be handled well by existing API recommendation approaches due to the lack of training data. In this paper, we propose APIRecX, the first cross-library API recommendation approach, which uses BPE to split each API call in each API sequence and pre-trains a GPT based language model. It then recommends APIs by fine-tuning the pre-trained model. APIRecX can migrate the knowledge of existing libraries to a new library, and can recommend APIs that are previously regarded as OOV. We evaluate APIRecX on six libraries and the results confirm its effectiveness by comparing with two typical API recommendation approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_276", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_276.flac", "type": "audio", "duration": 647.531}, {"from": "Assistant", "value": "Knowledge graphs are essential for numerous downstream natural language processing applications, but are typically incomplete with many facts missing. This results in research efforts on multi-hop reasoning task, which can be formulated as a search process and current models typically perform short distance reasoning. However, the long-distance reasoning is also vital with the ability to connect the superficially unrelated entities. To the best of our knowledge, there lacks a general framework that approaches multi-hop reasoning in mixed long-short distance reasoning scenarios. We argue that there are two key issues for a general multi-hop reasoning model: i) where to go, and ii) when to stop. Therefore, we propose a general model which resolves the issues with three modules: 1) the local-global knowledge module to estimate the possible paths, 2) the differentiated action dropout module to explore a diverse set of paths, and 3) the adaptive stopping search module to avoid over searching. The comprehensive results on three datasets demonstrate the superiority of our model with significant improvements against baselines in both short and long distance reasoning scenarios.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_277", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_277.flac", "type": "audio", "duration": 453.611}, {"from": "Assistant", "value": "Backchannel (BC), a short reaction signal of a listener to a speaker’s utterances, helps to improve the quality of the conversation. Several studies have been conducted to predict BC in conversation; however, the utilization of advanced natural language processing techniques using lexical information presented in the utterances of a speaker has been less considered. To address this limitation, we present a BC prediction model called BPM_MT (Backchannel prediction model with multitask learning), which utilizes KoBERT, a pre-trained language model. The BPM_MT simultaneously carries out two tasks at learning: 1) BC category prediction using acoustic and lexical features, and 2) sentiment score prediction based on sentiment cues. BPM_MT exhibited 14.24% performance improvement compared to the existing baseline in the four BC categories: continuer, understanding, empathic response, and No BC. In particular, for empathic response category, a performance improvement of 17.14% was achieved.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_278", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_278.flac", "type": "audio", "duration": 888.043}, {"from": "Assistant", "value": "Precisely defining the terminology is the first step in scientific communication. Developing neural text generation models for definition generation can circumvent the labor-intensity curation, further accelerating scientific discovery. Unfortunately, the lack of large-scale terminology definition dataset hinders the process toward definition generation. In this paper, we present a large-scale terminology definition dataset Graphine covering 2,010,648 terminology definition pairs, spanning 227 biomedical subdisciplines. Terminologies in each subdiscipline further form a directed acyclic graph, opening up new avenues for developing graph-aware text generation models. We then proposed a novel graph-aware definition generation model Graphex that integrates transformer with graph neural network. Our model outperforms existing text generation models by exploiting the graph structure of terminologies. We further demonstrated how Graphine can be used to evaluate pretrained language models, compare graph representation learning methods and predict sentence granularity. We envision Graphine to be a unique resource for definition generation and many other NLP tasks in biomedicine.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_279", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_279.flac", "type": "audio", "duration": 653.056}, {"from": "Assistant", "value": "Tag recommendation relies on either a ranking function for top-k tags or an autoregressive generation method. However, the previous methods neglect one of two seemingly conflicting yet desirable characteristics of a tag set: orderlessness and inter-dependency. While the ranking approach fails to address the inter-dependency among tags when they are ranked, the autoregressive approach fails to take orderlessness into account because it is designed to utilize sequential relations among tokens. We propose a sequence-oblivious generation method for tag recommendation, in which the next tag to be generated is independent of the order of the generated tags and the order of the ground truth tags occurring in training data. Empirical results on two different domains, Instagram and Stack Overflow, show that our method is significantly superior to the previous approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_280", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_280.flac", "type": "audio", "duration": 521.045}, {"from": "Assistant", "value": "Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers. However, building such systems from scratch faces real word challenges from both imperfect product schema/knowledge and lack of training dialog data. In this work we first propose ConvSearch, an end-to-end conversational search system that deeply combines the dialog system with search. It leverages the text profile to retrieve products, which is more robust against imperfect product schema/knowledge compared with using product attributes alone. We then address the lack of data challenges by proposing an utterance transfer approach that generates dialogue utterances by using existing dialog from other domains, and leveraging the search behavior data from e-commerce retailer. With utterance transfer, we introduce a new conversational search dataset for online shopping. Experiments show that our utterance transfer method can significantly improve the availability of training dialogue data without crowd-sourcing, and the conversational search system significantly outperformed the best tested baseline.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_281", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_281.flac", "type": "audio", "duration": 361.685}, {"from": "Assistant", "value": "Spelling Error Correction (SEC) that requires high-level language understanding is a challenging but useful task. Current SEC approaches normally leverage a pre-training then fine-tuning procedure that treats data equally. By contrast, Curriculum Learning (CL) utilizes training data differently during training and has shown its effectiveness in improving both performance and training efficiency in many other NLP tasks. In NMT, a model’s performance has been shown sensitive to the difficulty of training examples, and CL has been shown effective to address this. In SEC, the data from different language learners are naturally distributed at different difficulty levels (some errors made by beginners are obvious to correct while some made by fluent speakers are hard), and we expect that designing a curriculum correspondingly for model learning may also help its training and bring about better performance. In this paper, we study how to further improve the performance of the state-of-the-art SEC method with CL, and propose a Self-Supervised Curriculum Learning (SSCL) approach. Specifically, we directly use the cross-entropy loss as criteria for: 1) scoring the difficulty of training data, and 2) evaluating the competence of the model. In our approach, CL improves the model training, which in return improves the CL measurement. In our experiments on the SIGHAN 2015 Chinese spelling check task, we show that SSCL is superior to previous norm-based and uncertainty-aware approaches, and establish a new state of the art (74.38% F1).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_282", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_282.flac", "type": "audio", "duration": 652.693}, {"from": "Assistant", "value": "Locating and fixing bugs is a time-consuming task. Most neural machine translation (NMT) based approaches for automatically bug fixing lack generality and do not make full use of the rich information in the source code. In NMT-based bug fixing, we find some predicted code identical to the input buggy code (called unchanged fix) in NMT-based approaches due to high similarity between buggy and fixed code (e.g., the difference may only appear in one particular line). Obviously, unchanged fix is not the correct fix because it is the same as the buggy code that needs to be fixed. Based on these, we propose an intuitive yet effective general framework (called Fix-Filter-Fix or Fˆ3) for bug fixing. Fˆ3 connects models with our filter mechanism to filter out the last model’s unchanged fix to the next. We propose an Fˆ3 theory that can quantitatively and accurately calculate the Fˆ3 lifting effect. To evaluate, we implement the Seq2Seq Transformer (ST) and the AST2Seq Transformer (AT) to form some basic Fˆ3 instances, called Fˆ3_ST+AT and Fˆ3_AT+ST. Comparing them with single model approaches and many model connection baselines across four datasets validates the effectiveness and generality of Fˆ3 and corroborates our findings and methodology.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_283", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_283.flac", "type": "audio", "duration": 457.045}, {"from": "Assistant", "value": "Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided. In order to achieve fast convergence and interpretability for the policy in RL, we propose a novel RL method for text-based games with a recent neuro-symbolic framework called Logical Neural Network, which can learn symbolic and interpretable rules in their differentiable network. The method is first to extract first-order logical facts from text observation and external word meaning network (ConceptNet), then train a policy in the network with directly interpretable logical operators. Our experimental results show RL training with the proposed method converges significantly faster than other state-of-the-art neuro-symbolic methods in a TextWorld benchmark.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_284", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_284.flac", "type": "audio", "duration": 355.2}, {"from": "Assistant", "value": "Biomedical Concept Normalization (BCN) is widely used in biomedical text processing as a fundamental module. Owing to numerous surface variants of biomedical concepts, BCN still remains challenging and unsolved. In this paper, we exploit biomedical concept hypernyms to facilitate BCN. We propose Biomedical Concept Normalizer with Hypernyms (BCNH), a novel framework that adopts list-wise training to make use of both hypernyms and synonyms, and also employs norm constraint on the representation of hypernym-hyponym entity pairs. The experimental results show that BCNH outperforms the previous state-of-the-art model on the NCBI dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_285", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_285.flac", "type": "audio", "duration": 678.046}, {"from": "Assistant", "value": "Integrating knowledge into text is a promising way to enrich text representation, especially in the medical field. However, undifferentiated knowledge not only confuses the text representation but also imports unexpected noises. In this paper, to alleviate this problem, we propose leveraging capsule routing to associate knowledge with medical literature hierarchically (called HiCapsRKL). Firstly, HiCapsRKL extracts two empirically designed text fragments from medical literature and encodes them into fragment representations respectively. Secondly, the capsule routing algorithm is applied to two fragment representations. Through the capsule computing and dynamic routing, each representation is processed into a new representation (denoted as caps-representation), and we integrate the caps-representations as information gain to associate knowledge with medical literature hierarchically. Finally, HiCapsRKL are validated on relevance prediction and medical literature retrieval test sets. The experimental results and analyses show that HiCapsRKLcan more accurately associate knowledge with medical literature than mainstream methods. In summary, HiCapsRKL can efficiently help selecting the most relevant knowledge to the medical literature, which may be an alternative attempt to improve knowledge-based text representation. Source code is released on GitHub.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_286", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_286.flac", "type": "audio", "duration": 648.046}, {"from": "Assistant", "value": "Recent metaphor identification approaches mainly consider the contextual text features within a sentence or introduce external linguistic features to the model. But they usually ignore the extra information that the data can provide, such as the contextual metaphor information and broader discourse information. In this paper, we propose a model augmented with hierarchical contextualized representation to extract more information from both sentence-level and discourse-level. At the sentence level, we leverage the metaphor information of words that except the target word in the sentence to strengthen the reasoning ability of our model via a novel label-enhanced contextualized representation. At the discourse level, the position-aware global memory network is adopted to learn long-range dependency among the same words within a discourse. Finally, our model combines the representations obtained from these two parts. The experiment results on two tasks of the VUA dataset show that our model outperforms every other state-of-the-art method that also does not use any external knowledge except what the pre-trained language model contains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_288", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_288.flac", "type": "audio", "duration": 720.26}, {"from": "Assistant", "value": "Our paper aims to automate the generation of medical reports from chest X-ray image inputs, a critical yet time-consuming task for radiologists. Existing medical report generation efforts emphasize producing human-readable reports, yet the generated text may not be well aligned to the clinical facts. Our generated medical reports, on the other hand, are fluent and, more importantly, clinically accurate. This is achieved by our fully differentiable and end-to-end paradigm that contains three complementary modules: taking the chest X-ray images and clinical history document of patients as inputs, our classification module produces an internal checklist of disease-related topics, referred to as enriched disease embedding; the embedding representation is then passed to our transformer-based generator, to produce the medical report; meanwhile, our generator also creates a weighted embedding representation, which is fed to our interpreter to ensure consistency with respect to disease-related topics. Empirical evaluations demonstrate very promising results achieved by our approach on commonly-used metrics concerning language fluency and clinical accuracy. Moreover, noticeable performance gains are consistently observed when additional input information is available, such as the clinical document and extra scans from different views.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_289", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_289.flac", "type": "audio", "duration": 676.288}, {"from": "Assistant", "value": "In this paper, we propose a new ranking model DR-BERT, which improves the Document Retrieval (DR) task by a task-adaptive training process and a Segmented Token Recovery Mechanism (STRM). In the task-adaptive training, we first pre-train DR-BERT to be domain-adaptive and then make the two-phase fine-tuning. In the first-phase fine-tuning, the model learns query-document matching patterns regarding different query types in a pointwise way. Next, in the second-phase fine-tuning, the model learns document-level ranking features and ranks documents with regard to a given query in a listwise manner. Such pointwise plus listwise fine-tuning enables the model to minimize errors in the document ranking by incorporating ranking-specific supervisions. Meanwhile, the model derived from pointwise fine-tuning is also used to reduce noise in the training data of the listwise fine-tuning. On the other hand, we present STRM which can compute OOV word representation and contextualization more precisely in BERT-based models. As an effective strategy in DR-BERT, STRM improves the matching perfromance of OOV words between a query and a document. Notably, our DR-BERT model keeps in the top three on the MS MARCO leaderboard since May 20, 2020.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_290", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_290.flac", "type": "audio", "duration": 385.173}, {"from": "Assistant", "value": "Scientific claim verification can help the researchers to easily find the target scientific papers with the sentence evidence from a large corpus for the given claim. Some existing works propose pipeline models on the three tasks of abstract retrieval, rationale selection and stance prediction. Such works have the problems of error propagation among the modules in the pipeline and lack of sharing valuable information among modules. We thus propose an approach, named as ARSJoint, that jointly learns the modules for the three tasks with a machine reading comprehension framework by including claim information. In addition, we enhance the information exchanges and constraints among tasks by proposing a regularization term between the sentence attention scores of abstract retrieval and the estimated outputs of rational selection. The experimental results on the benchmark dataset SciFact show that our approach outperforms the existing works.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_291", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_291.flac", "type": "audio", "duration": 744.525}, {"from": "Assistant", "value": "Domain adaption for word segmentation and POS tagging is a challenging problem for Chinese lexical processing. Self-training is one promising solution for it, which struggles to construct a set of high-quality pseudo training instances for the target domain. Previous work usually assumes a universal source-to-target adaption to collect such pseudo corpus, ignoring the different gaps from the target sentences to the source domain. In this work, we start from joint word segmentation and POS tagging, presenting a fine-grained domain adaption method to model the gaps accurately. We measure the gaps by one simple and intuitive metric, and adopt it to develop a pseudo target domain corpus based on fine-grained subdomains incrementally. A novel domain-mixed representation learning model is proposed accordingly to encode the multiple subdomains effectively. The whole process is performed progressively for both corpus construction and model training. Experimental results on a benchmark dataset show that our method can gain significant improvements over a vary of baselines. Extensive analyses are performed to show the advantages of our final domain adaption model as well.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_292", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_292.flac", "type": "audio", "duration": 743.015}, {"from": "Assistant", "value": "We develop a unified system to answer directly from text open-domain questions that may require a varying number of retrieval steps. We employ a single multi-task transformer model to perform all the necessary subtasks—retrieving supporting facts, reranking them, and predicting the answer from all retrieved documents—in an iterative fashion. We avoid crucial assumptions of previous work that do not transfer well to real-world settings, including exploiting knowledge of the fixed number of retrieval steps required to answer each question or using structured metadata like knowledge bases or web links that have limited availability. Instead, we design a system that can answer open-domain questions on any text collection without prior knowledge of reasoning complexity. To emulate this setting, we construct a new benchmark, called BeerQA, by combining existing one- and two-step datasets with a new collection of 530 questions that require three Wikipedia pages to answer, unifying Wikipedia corpora versions in the process. We show that our model demonstrates competitive performance on both existing benchmarks and this new benchmark. We make the new benchmark available athttps://beerqa.github.io/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_293", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_293.flac", "type": "audio", "duration": 718.891}, {"from": "Assistant", "value": "Information seeking is an essential step for open-domain question answering to efficiently gather evidence from a large corpus. Recently, iterative approaches have been proven to be effective for complex questions, by recursively retrieving new evidence at each step. However, almost all existing iterative approaches use predefined strategies, either applying the same retrieval function multiple times or fixing the order of different retrieval functions, which cannot fulfill the diverse requirements of various questions. In this paper, we propose a novel adaptive information-seeking strategy for open-domain question answering, namely AISO. Specifically, the whole retrieval and answer process is modeled as a partially observed Markov decision process, where three types of retrieval operations (e.g., BM25, DPR, and hyperlink) and one answer operation are defined as actions. According to the learned policy, AISO could adaptively select a proper retrieval action to seek the missing evidence at each step, based on the collected evidence and the reformulated query, or directly output the answer when the evidence set is sufficient for the question. Experiments on SQuAD Open and HotpotQA fullwiki, which serve as single-hop and multi-hop open-domain QA benchmarks, show that AISO outperforms all baseline methods with predefined strategies in terms of both retrieval and answer evaluations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_294", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_294.flac", "type": "audio", "duration": 594.361}, {"from": "Assistant", "value": "While solving math word problems automatically has received considerable attention in the NLP community, few works have addressed probability word problems specifically. In this paper, we employ and analyse various neural models for answering such word problems. In a two-step approach, the problem text is first mapped to a formal representation in a declarative language using a sequence-to-sequence model, and then the resulting representation is executed using a probabilistic programming system to provide the answer. Our best performing model incorporates general-domain contextualised word representations that were finetuned using transfer learning on another in-domain dataset. We also apply end-to-end models to this task, which bring out the importance of the two-step approach in obtaining correct solutions to probability problems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_295", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_295.flac", "type": "audio", "duration": 636.9}, {"from": "Assistant", "value": "Machine Reading Comprehension (MRC), which requires a machine to answer questions given the relevant documents, is an important way to test machines’ ability to understand human language. Multiple-choice MRC is one of the most studied tasks in MRC due to the convenience of evaluation and the flexibility of answer format. Post-hoc interpretation aims to explain a trained model and reveal how the model arrives at the prediction. One of the most important interpretation forms is to attribute model decisions to input features. Based on post-hoc interpretation methods, we assess attributions of paragraphs in multiple-choice MRC and improve the model by punishing the illogical attributions. Our method can improve model performance without any external information and model structure change. Furthermore, we also analyze how and why such a self-training method works.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_296", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_296.flac", "type": "audio", "duration": 497.046}, {"from": "Assistant", "value": "The key challenge of question answering over knowledge bases (KBQA) is the inconsistency between the natural language questions and the reasoning paths in the knowledge base (KB). Recent graph-based KBQA methods are good at grasping the topological structure of the graph but often ignore the textual information carried by the nodes and edges. Meanwhile, pre-trained language models learn massive open-world knowledge from the large corpus, but it is in the natural language form and not structured. To bridge the gap between the natural language and the structured KB, we propose three relation learning tasks for BERT-based KBQA, including relation extraction, relation matching, and relation reasoning. By relation-augmented training, the model learns to align the natural language expressions to the relations in the KB as well as reason over the missing connections in the KB. Experiments on WebQSP show that our method consistently outperforms other baselines, especially when the KB is incomplete.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_297", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_297.flac", "type": "audio", "duration": 715.291}, {"from": "Assistant", "value": "Dense retrieval methods have shown great promise over sparse retrieval methods in a range of NLP problems. Among them, dense phrase retrieval—the most fine-grained retrieval unit—is appealing because phrases can be directly used as the output for question answering and slot filling tasks. In this work, we follow the intuition that retrieving phrases naturally entails retrieving larger text blocks and study whether phrase retrieval can serve as the basis for coarse-level retrieval including passages and documents. We first observe that a dense phrase-retrieval system, without any retraining, already achieves better passage retrieval accuracy (+3-5% in top-5 accuracy) compared to passage retrievers, which also helps achieve superior end-to-end QA performance with fewer passages. Then, we provide an interpretation for why phrase-level supervision helps learn better fine-grained entailment compared to passage-level supervision, and also show that phrase retrieval can be improved to achieve competitive performance in document-retrieval tasks such as entity linking and knowledge-grounded dialogue. Finally, we demonstrate how phrase filtering and vector quantization can reduce the size of our index by 4-10x, making dense phrase retrieval a practical and versatile solution in multi-granularity retrieval.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_298", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_298.flac", "type": "audio", "duration": 682.365}, {"from": "Assistant", "value": "Many open-domain question answering problems can be cast as a textual entailment task, where a question and candidate answers are concatenated to form hypotheses. A QA system then determines if the supporting knowledge bases, regarded as potential premises, entail the hypotheses. In this paper, we investigate a neural-symbolic QA approach that integrates natural logic reasoning within deep learning architectures, towards developing effective and yet explainable question answering models. The proposed model gradually bridges a hypothesis and candidate premises following natural logic inference steps to build proof paths. Entailment scores between the acquired intermediate hypotheses and candidate premises are measured to determine if a premise entails the hypothesis. As the natural logic reasoning process forms a tree-like, hierarchical structure, we embed hypotheses and premises in a Hyperbolic space rather than Euclidean space to acquire more precise representations. Empirically, our method outperforms prior work on answering multiple-choice science questions, achieving the best results on two publicly available datasets. The natural logic inference process inherently provides evidence to help explain the prediction process.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_299", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_299.flac", "type": "audio", "duration": 720.051}, {"from": "Assistant", "value": "Conversational machine reading (CMR) requires machines to communicate with humans through multi-turn interactions between two salient dialogue states of decision making and question generation processes. In open CMR settings, as the more realistic scenario, the retrieved background knowledge would be noisy, which results in severe challenges in the information transmission. Existing studies commonly train independent or pipeline systems for the two subtasks. However, those methods are trivial by using hard-label decisions to activate question generation, which eventually hinders the model performance. In this work, we propose an effective gating strategy by smoothing the two dialogue states in only one decoder and bridge decision making and question generation to provide a richer dialogue state reference. Experiments on the OR-ShARC dataset show the effectiveness of our method, which achieves new state-of-the-art results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_300", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_300.flac", "type": "audio", "duration": 612.053}, {"from": "Assistant", "value": "The sheer volume of financial statements makes it difficult for humans to access and analyze a business’s financials. Robust numerical reasoning likewise faces unique challenges in this domain. In this work, we focus on answering deep questions over financial data, aiming to automate the analysis of a large corpus of financial documents. In contrast to existing tasks on general domain, the finance domain includes complex numerical reasoning and understanding of heterogeneous representations. To facilitate analytical progress, we propose a new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts. We also annotate the gold reasoning programs to ensure full explainability. We further introduce baselines and conduct comprehensive experiments in our dataset. The results demonstrate that popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge and in complex multi-step numerical reasoning on that knowledge. Our dataset – the first of its kind – should therefore enable significant, new community research into complex application domains. The dataset and code are publicly available athttps://github.com/czyssrs/FinQA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_301", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_301.flac", "type": "audio", "duration": 721.173}, {"from": "Assistant", "value": "Natural language (NL) explanations of model predictions are gaining popularity as a means to understand and verify decisions made by large black-box pre-trained models, for tasks such as Question Answering (QA) and Fact Verification. Recently, pre-trained sequence to sequence (seq2seq) models have proven to be very effective in jointly making predictions, as well as generating NL explanations. However, these models have many shortcomings; they can fabricate explanations even for incorrect predictions, they are difficult to adapt to long input documents, and their training requires a large amount of labeled data. In this paper, we develop FiD-Ex, which addresses these shortcomings for seq2seq models by: 1) introducing sentence markers to eliminate explanation fabrication by encouraging extractive generation, 2) using the fusion-in-decoder architecture to handle long input contexts, and 3) intermediate fine-tuning on re-structured open domain QA datasets to improve few-shot performance. FiD-Ex significantly improves over prior work in terms of explanation metrics and task accuracy on five tasks from the ERASER explainability benchmark in both fully supervised and few-shot settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_302", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_302.flac", "type": "audio", "duration": 479.936}, {"from": "Assistant", "value": "To audit the robustness of named entity recognition (NER) models, we propose RockNER, a simple yet effective method to create natural adversarial examples. Specifically, at the entity level, we replace target entities with other entities of the same semantic class in Wikidata; at the context level, we use pre-trained language models (e.g., BERT) to generate word substitutions. Together, the two levels of at- tack produce natural adversarial examples that result in a shifted distribution from the training data on which our target models have been trained. We apply the proposed method to the OntoNotes dataset and create a new benchmark named OntoRock for evaluating the robustness of existing NER models via a systematic evaluation protocol. Our experiments and analysis reveal that even the best model has a significant performance drop, and these models seem to memorize in-domain entity patterns instead of reasoning from the context. Our work also studies the effects of a few simple data augmentation methods to improve the robustness of NER models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_303", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_303.flac", "type": "audio", "duration": 715.179}, {"from": "Assistant", "value": "Recently, language models (LMs) have achieved significant performance on many NLU tasks, which has spurred widespread interest for their possible applications in the scientific and social area. However, LMs have faced much criticism of whether they are truly capable of reasoning in NLU. In this work, we propose a diagnostic method for first-order logic (FOL) reasoning with a new proposed benchmark, LogicNLI. LogicNLI is an NLI-style dataset that effectively disentangles the target FOL reasoning from commonsense inference and can be used to diagnose LMs from four perspectives: accuracy, robustness, generalization, and interpretability. Experiments on BERT, RoBERTa, and XLNet, have uncovered the weaknesses of these LMs on FOL reasoning, which motivates future exploration to enhance the reasoning ability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_304", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_304.flac", "type": "audio", "duration": 747.102}, {"from": "Assistant", "value": "Psychometric measures of ability, attitudes, perceptions, and beliefs are crucial for understanding user behavior in various contexts including health, security, e-commerce, and finance. Traditionally, psychometric dimensions have been measured and collected using survey-based methods. Inferring such constructs from user-generated text could allow timely, unobtrusive collection and analysis. In this paper we describe our efforts to construct a corpus for psychometric natural language processing (NLP) related to important dimensions such as trust, anxiety, numeracy, and literacy, in the health domain. We discuss our multi-step process to align user text with their survey-based response items and provide an overview of the resulting testbed which encompasses survey-based psychometric measures and accompanying user-generated text from 8,502 respondents. Our testbed also encompasses self-reported demographic information, including race, sex, age, income, and education - thereby affording opportunities for measuring bias and benchmarking fairness of text classification methods. We report preliminary results on use of the text to predict/categorize users’ survey response labels - and on the fairness of these models. We also discuss the important implications of our work and resulting testbed for future NLP research on psychometrics and fairness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_305", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_305.flac", "type": "audio", "duration": 475.057}, {"from": "Assistant", "value": "We present a large, challenging dataset, COUGH, for COVID-19 FAQ retrieval. Similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, Query Bank and Relevance Set. The FAQ Bank contains ~16K FAQ items scraped from 55 credible websites (e.g., CDC and WHO). For evaluation, we introduce Query Bank and Relevance Set, where the former contains 1,236 human-paraphrased queries while the latter contains ~32 human-annotated FAQ items for each query. We analyze COUGH by testing different FAQ retrieval models built on top of BM25 and BERT, among which the best model achieves 48.8 under P@5, indicating a great challenge presented by COUGH and encouraging future research for further improvement. Our COUGH dataset is available athttps://github.com/sunlab-osu/covid-faq.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_306", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_306.flac", "type": "audio", "duration": 466.048}, {"from": "Assistant", "value": "This paper presents a Chinese dataset for evaluating pretrained language models on Word Prediction given Long-term Context (Chinese WPLC). We propose both automatic and manual selection strategies tailored to Chinese to guarantee that target words in passages collected from over 69K novels can only be predicted with long-term context beyond the scope of sentences containing the target words. Dataset analysis reveals that the types of target words range from common nouns to Chinese 4-character idioms. We also observe that linguistic relations between target words and long-range context exhibit diversity, including lexical match, synonym, summary and reasoning. Experiment results show that the Chinese pretrained language model PanGu-𝛼is 45 points behind human in terms of top-1 word prediction accuracy, indicating that Chinese WPLC is a challenging dataset. The dataset is publicly available athttps://git.openi.org.cn/PCL-Platform.Intelligence/Chinese_WPLC.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_307", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_307.flac", "type": "audio", "duration": 714.965}, {"from": "Assistant", "value": "The recent success of neural language models (NLMs) on the Winograd Schema Challenge has called for further investigation of the commonsense reasoning ability of these models. Previous diagnostic datasets rely on crowd-sourcing which fails to provide coherent commonsense crucial for solving WSC problems. To better evaluate NLMs, we propose a logic-based framework that focuses on high-quality commonsense knowledge. Specifically, we identify and collect formal knowledge formulas verified by theorem provers and translate such formulas into natural language sentences. Based on these true knowledge sentences, adversarial false ones are generated. We propose a new dataset named WinoLogic with these sentences. Given a problem in WinoLogic, NLMs need to decide whether the plausible knowledge sentences could correctly solve the corresponding WSC problems in a zero-shot setting. We also ask human annotators to validate WinoLogic to ensure it is human-agreeable. Experiments show that NLMs still struggle to comprehend commonsense knowledge as humans do, indicating that their reasoning ability could have been overestimated.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_308", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_308.flac", "type": "audio", "duration": 674.447}, {"from": "Assistant", "value": "Masked language models (MLMs) have contributed to drastic performance improvements with regard to zero anaphora resolution (ZAR). To further improve this approach, in this study, we made two proposals. The first is a new pretraining task that trains MLMs on anaphoric relations with explicit supervision, and the second proposal is a new finetuning method that remedies a notorious issue, the pretrain-finetune discrepancy. Our experiments on Japanese ZAR demonstrated that our two proposals boost the state-of-the-art performance, and our detailed analysis provides new insights on the remaining challenges.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_309", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_309.flac", "type": "audio", "duration": 434.979}, {"from": "Assistant", "value": "In this paper, we propose to align sentence representations from different languages into a unified embedding space, where semantic similarities (both cross-lingual and monolingual) can be computed with a simple dot product. Pre-trained language models are fine-tuned with the translation ranking task. Existing work (Feng et al., 2020) uses sentences within the same batch as negatives, which can suffer from the issue of easy negatives. We adapt MoCo (He et al., 2020) to further improve the quality of alignment. As the experimental results show, the sentence representations produced by our model achieve the new state-of-the-art on several tasks, including Tatoeba en-zh similarity search (Artetxe andSchwenk, 2019b), BUCC en-zh bitext mining, and semantic textual similarity on 7 datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_310", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_310.flac", "type": "audio", "duration": 699.008}, {"from": "Assistant", "value": "This paper investigates continual learning for semantic parsing. In this setting, a neural semantic parser learns tasks sequentially without accessing full training data from previous tasks. Direct application of the SOTA continual learning algorithms to this problem fails to achieve comparable performance with re-training models with all seen tasks because they have not considered the special properties of structured outputs yielded by semantic parsers. Therefore, we propose TotalRecall, a continual learning method designed for neural semantic parsers from two aspects: i) a sampling method for memory replay that diversifies logical form templates and balances distributions of parse actions in a memory; ii) a two-stage training method that significantly improves generalization capability of the parsers across tasks. We conduct extensive experiments to study the research problems involved in continual semantic parsing and demonstrate that a neural semantic parser trained with TotalRecall achieves superior performance than the one trained directly with the SOTA continual learning algorithms and achieve a 3-6 times speedup compared to re-training from scratch.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_311", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_311.flac", "type": "audio", "duration": 678.046}, {"from": "Assistant", "value": "Resolving pronouns to their referents has long been studied as a fundamental natural language understanding problem. Previous works on pronoun coreference resolution (PCR) mostly focus on resolving pronouns to mentions in text while ignoring the exophoric scenario. Exophoric pronouns are common in daily communications, where speakers may directly use pronouns to refer to some objects present in the environment without introducing the objects first. Although such objects are not mentioned in the dialogue text, they can often be disambiguated by the general topics of the dialogue. Motivated by this, we propose to jointly leverage the local context and global topics of dialogues to solve the out-of-text PCR problem. Extensive experiments demonstrate the effectiveness of adding topic regularization for resolving exophoric pronouns.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_312", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_312.flac", "type": "audio", "duration": 469.056}, {"from": "Assistant", "value": "Impressive milestones have been achieved in text matching by adopting a cross-attention mechanism to capture pertinent semantic connections between two sentence representations. However, regular cross-attention focuses on word-level links between the two input sequences, neglecting the importance of contextual information. We propose a context-aware interaction network (COIN) to properly align two sequences and infer their semantic relationship. Specifically, each interaction block includes (1) a context-aware cross-attention mechanism to effectively integrate contextual information when aligning two sequences, and (2) a gate fusion layer to flexibly interpolate aligned representations. We apply multiple stacked interaction blocks to produce alignments at different levels and gradually refine the attention results. Experiments on two question matching datasets and detailed analyses demonstrate the effectiveness of our model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_313", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_313.flac", "type": "audio", "duration": 613.931}, {"from": "Assistant", "value": "As an essential form of knowledge representation, taxonomies are widely used in various downstream natural language processing tasks. However, with the continuously rising of new concepts, many existing taxonomies are unable to maintain coverage by manual expansion. In this paper, we propose TEMP, a self-supervised taxonomy expansion method, which predicts the position of new concepts by ranking the generated taxonomy-paths. For the first time, TEMP employs pre-trained contextual encoders in taxonomy construction and hypernym detection problems. Experiments prove that pre-trained contextual embeddings are able to capture hypernym-hyponym relations. To learn more detailed differences between taxonomy-paths, we train the model with dynamic margin loss by a novel dynamic margin function. Extensive evaluations exhibit that TEMP outperforms prior state-of-the-art taxonomy expansion approaches by 14.3% in accuracy and 15.8% in mean reciprocal rank on three public benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_314", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_314.flac", "type": "audio", "duration": 666.219}, {"from": "Assistant", "value": "Frame semantic parsing is a semantic analysis task based on FrameNet which has received great attention recently. The task usually involves three subtasks sequentially: (1) target identification, (2) frame classification and (3) semantic role labeling. The three subtasks are closely related while previous studies model them individually, which ignores their intern connections and meanwhile induces error propagation problem. In this work, we propose an end-to-end neural model to tackle the task jointly. Concretely, we exploit a graph-based method, regarding frame semantic parsing as a graph construction problem. All predicates and roles are treated as graph nodes, and their relations are taken as graph edges. Experiment results on two benchmark datasets of frame semantic parsing show that our method is highly competitive, resulting in better performance than pipeline models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_315", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_315.flac", "type": "audio", "duration": 719.494}, {"from": "Assistant", "value": "Recent works have shown that powerful pre-trained language models (PLM) can be fooled by small perturbations or intentional attacks. To solve this issue, various data augmentation techniques are proposed to improve the robustness of PLMs. However, it is still challenging to augment semantically relevant examples with sufficient diversity. In this work, we present Virtual Data Augmentation (VDA), a general framework for robustly fine-tuning PLMs. Based on the original token embeddings, we construct a multinomial mixture for augmenting virtual data embeddings, where a masked language model guarantees the semantic relevance and the Gaussian noise provides the augmentation diversity. Furthermore, a regularized training strategy is proposed to balance the two aspects. Extensive experiments on six datasets show that our approach is able to improve the robustness of PLMs and alleviate the performance degradation under adversarial attacks. Our codes and data are publicly available at bluehttps://github.com/RUCAIBox/VDA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_316", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_316.flac", "type": "audio", "duration": 516.053}, {"from": "Assistant", "value": "Metaphors are ubiquitous in natural language, and detecting them requires contextual reasoning about whether a semantic incongruence actually exists. Most existing work addresses this problem using pre-trained contextualized models. Despite their success, these models require a large amount of labeled data and are not linguistically-based. In this paper, we proposed a ContrAstive pre-Trained modEl (CATE) for metaphor detection with semi-supervised learning. Our model first uses a pre-trained model to obtain a contextual representation of target words and employs a contrastive objective to promote an increased distance between target words’ literal and metaphorical senses based on linguistic theories. Furthermore, we propose a simple strategy to collect large-scale candidate instances from the general corpus and generalize the model via self-training. Extensive experiments show that CATE achieves better performance against state-of-the-art baselines on several benchmark datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_317", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_317.flac", "type": "audio", "duration": 523.006}, {"from": "Assistant", "value": "Dependency parse trees are helpful for discovering the opinion words in aspect-based sentiment analysis (ABSA) (CITATION). However, the trees obtained from off-the-shelf dependency parsers are static, and could be sub-optimal in ABSA. This is because the syntactic trees are not designed for capturing the interactions between opinion words and aspect words. In this work, we aim to shorten the distance between aspects and corresponding opinion words by learning an aspect-centric tree structure. The aspect and opinion words are expected to be closer along such tree structure compared to the standard dependency parse tree. The learning process allows the tree structure to adaptively correlate the aspect and opinion words, enabling us to better identify the polarity in the ABSA task. We conduct experiments on five aspect-based sentiment datasets, and the proposed model significantly outperforms recent strong baselines. Furthermore, our thorough analysis demonstrates the average distance between aspect and opinion words are shortened by at least 19% on the standard SemEval Restaurant14 (CITATION) dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_318", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_318.flac", "type": "audio", "duration": 720.0}, {"from": "Assistant", "value": "Aspect-based sentiment analysis (ABSA) task consists of three typical subtasks: aspect term extraction, opinion term extraction, and sentiment polarity classification. These three subtasks are usually performed jointly to save resources and reduce the error propagation in the pipeline. However, most of the existing joint models only focus on the benefits of encoder sharing between subtasks but ignore the difference. Therefore, we propose a joint ABSA model, which not only enjoys the benefits of encoder sharing but also focuses on the difference to improve the effectiveness of the model. In detail, we introduce a dual-encoder design, in which a pair encoder especially focuses on candidate aspect-opinion pair classification, and the original encoder keeps attention on sequence labeling. Empirical results show that our proposed model shows robustness and significantly outperforms the previous state-of-the-art on four benchmark datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_319", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_319.flac", "type": "audio", "duration": 592.171}, {"from": "Assistant", "value": "Argument pair extraction (APE) aims to extract interactive argument pairs from two passages of a discussion. Previous work studied this task in the context of peer review and rebuttal, and decomposed it into a sequence labeling task and a sentence relation classification task. However, despite the promising performance, such an approach obtains the argument pairs implicitly by the two decomposed tasks, lacking explicitly modeling of the argument-level interactions between argument pairs. In this paper, we tackle the APE task by a mutual guidance framework, which could utilize the information of an argument in one passage to guide the identification of arguments that can form pairs with it in another passage. In this manner, two passages can mutually guide each other in the process of APE. Furthermore, we propose an inter-sentence relation graph to effectively model the inter-relations between two sentences and thus facilitates the extraction of argument pairs. Our proposed method can better represent the holistic argument-level semantics and thus explicitly capture the complex correlations between argument pairs. Experimental results show that our approach significantly outperforms the current state-of-the-art model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_320", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_320.flac", "type": "audio", "duration": 477.054}, {"from": "Assistant", "value": "Emotion inference in multi-turn conversations aims to predict the participant’s emotion in the next upcoming turn without knowing the participant’s response yet, and is a necessary step for applications such as dialogue planning. However, it is a severe challenge to perceive and reason about the future feelings of participants, due to the lack of utterance information from the future. Moreover, it is crucial for emotion inference to capture the characteristics of emotional propagation in conversations, such as persistence and contagiousness. In this study, we focus on investigating the task of emotion inference in multi-turn conversations by modeling the propagation of emotional states among participants in the conversation history, and propose an addressee-aware module to automatically learn whether the participant keeps the historical emotional state or is affected by others in the next upcoming turn. In addition, we propose an ensemble strategy to further enhance the model performance. Empirical studies on three different benchmark conversation datasets demonstrate the effectiveness of the proposed model over several strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_321", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_321.flac", "type": "audio", "duration": 603.045}, {"from": "Assistant", "value": "Aspect-based sentiment analysis (ABSA) predicts the sentiment polarity towards a particular aspect term in a sentence, which is an important task in real-world applications. To perform ABSA, the trained model is required to have a good understanding of the contextual information, especially the particular patterns that suggest the sentiment polarity. However, these patterns typically vary in different sentences, especially when the sentences come from different sources (domains), which makes ABSA still very challenging. Although combining labeled data across different sources (domains) is a promising solution to address the challenge, in practical applications, these labeled data are usually stored at different locations and might be inaccessible to each other due to privacy or legal concerns (e.g., the data are owned by different companies). To address this issue and make the best use of all labeled data, we propose a novel ABSA model with federated learning (FL) adopted to overcome the data isolation limitations and incorporate topic memory (TM) proposed to take the cases of data from diverse sources (domains) into consideration. Particularly, TM aims to identify different isolated data sources due to data inaccessibility by providing useful categorical information for localized predictions. Experimental results on a simulated environment for FL with three nodes demonstrate the effectiveness of our approach, where TM-FL outperforms different baselines including some well-designed FL frameworks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_322", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_322.flac", "type": "audio", "duration": 684.053}, {"from": "Assistant", "value": "As an important task in opinion mining, comparative opinion mining aims to identify comparative sentences from product reviews, extract the comparative elements, and obtain the corresponding comparative opinion tuples. However, most previous studies simply regarded comparative tuple extraction as comparative element extraction, but ignored the fact that many comparative sentences may contain multiple comparisons. The comparative opinion tuples defined in these studies also failed to explicitly provide comparative preferences. To address these limitations, in this work we first introduce a new Comparative Opinion Quintuple Extraction (COQE) task, to identify comparative sentences from product reviews and extract all comparative opinion quintuples (Subject, Object, Comparative Aspect, Comparative Opinion, Comparative Preference). Secondly, based on the existing comparative opinion mining corpora, we make supplementary annotations and construct three datasets for the COQE task. Finally, we benchmark the COQE task by proposing a new BERT-based multi-stage approach as well as three baseline systems extended from previous methods. %The new approach significantly outperforms three baseline systems on three datasets and represents a strong benchmark for COQE. Experimental results show that the new approach significantly outperforms three baseline systems on three datasets for the COQE task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_323", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_323.flac", "type": "audio", "duration": 620.53}, {"from": "Assistant", "value": "Existing audio-language task-specific predictive approaches focus on building complicated late-fusion mechanisms. However, these models are facing challenges of overfitting with limited labels and low model generalization abilities. In this paper, we present a Cross-modal Transformer for Audio-and-Language, i.e., CTAL, which aims to learn the intra-modality and inter-modality connections between audio and language through two proxy tasks on a large amount of audio-and-language pairs: masked language modeling and masked cross-modal acoustic modeling. After fine-tuning our pre-trained model on multiple downstream audio-and-language tasks, we observe significant improvements across various tasks, such as, emotion classification, sentiment analysis, and speaker verification. On this basis, we further propose a specially-designed fusion mechanism that can be used in fine-tuning phase, which allows our pre-trained model to achieve better performance. Lastly, we demonstrate detailed ablation studies to prove that both our novel cross-modality fusion component and audio-language pre-training methods significantly contribute to the promising results. The code and pre-trained models are available athttps://github.com/tal-ai/CTAL_EMNLP2021.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_324", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_324.flac", "type": "audio", "duration": 715.244}, {"from": "Assistant", "value": "Temporal language grounding in videos aims to localize the temporal span relevant to the given query sentence. Previous methods treat it either as a boundary regression task or a span extraction task. This paper will formulate temporal language grounding into video reading comprehension and propose a Relation-aware Network (RaNet) to address it. This framework aims to select a video moment choice from the predefined answer set with the aid of coarse-and-fine choice-query interaction and choice-choice relation construction. A choice-query interactor is proposed to match the visual and textual information simultaneously in sentence-moment and token-moment levels, leading to a coarse-and-fine cross-modal interaction. Moreover, a novel multi-choice relation constructor is introduced by leveraging graph convolution to capture the dependencies among video moment choices for the best choice selection. Extensive experiments on ActivityNet-Captions, TACoS, and Charades-STA demonstrate the effectiveness of our solution. Codes will be available athttps://github.com/Huntersxsx/RaNet.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_325", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_325.flac", "type": "audio", "duration": 446.059}, {"from": "Assistant", "value": "A currently popular research area in end-to-end speech translation is the use of knowledge distillation from a machine translation (MT) task to improve the speech translation (ST) task. However, such scenario obviously only allows one way transfer, which is limited by the performance of the teacher model. Therefore, We hypothesis that the knowledge distillation-based approaches are sub-optimal. In this paper, we propose an alternative–a trainable mutual-learning scenario, where the MT and the ST models are collaboratively trained and are considered as peers, rather than teacher/student. This allows us to improve the performance of end-to-end ST more effectively than with a teacher-student paradigm. As a side benefit, performance of the MT model also improves. Experimental results show that in our mutual-learning scenario, models can effectively utilise the auxiliary information from peer models and achieve compelling results on Must-C dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_326", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_326.flac", "type": "audio", "duration": 714.688}, {"from": "Assistant", "value": "Multimodal abstractive summarization (MAS) models that summarize videos (vision modality) and their corresponding transcripts (text modality) are able to extract the essential information from massive multimodal data on the Internet. Recently, large-scale generative pre-trained language models (GPLMs) have been shown to be effective in text generation tasks. However, existing MAS models cannot leverage GPLMs’ powerful generation ability. To fill this research gap, we aim to study two research questions: 1) how to inject visual information into GPLMs without hurting their generation ability; and 2) where is the optimal place in GPLMs to inject the visual information? In this paper, we present a simple yet effective method to construct vision guided (VG) GPLMs for the MAS task using attention-based add-on layers to incorporate visual information while maintaining their original text generation ability. Results show that our best model significantly surpasses the prior state-of-the-art model by 5.7 ROUGE-1, 5.3 ROUGE-2, and 5.1 ROUGE-L scores on the How2 dataset, and our vision guidance method contributes 83.6% of the overall improvement. Furthermore, we conduct thorough ablation studies to analyze the effectiveness of various modality fusion methods and fusion locations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_327", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_327.flac", "type": "audio", "duration": 780.565}, {"from": "Assistant", "value": "Given an untrimmed video and a natural language query, Natural Language Video Localization (NLVL) aims to identify the video moment described by query. To address this task, existing methods can be roughly grouped into two groups: 1) propose-and-rank models first define a set of hand-designed moment candidates and then find out the best-matching one. 2) proposal-free models directly predict two temporal boundaries of the referential moment from frames. Currently, almost all the propose-and-rank methods have inferior performance than proposal-free counterparts. In this paper, we argue that the performance of propose-and-rank models are underestimated due to the predefined manners: 1) Hand-designed rules are hard to guarantee the complete coverage of targeted segments. 2) Densely sampled candidate moments cause redundant computation and degrade the performance of ranking process. To this end, we propose a novel model termed LPNet (Learnable Proposal Network for NLVL) with a fixed set of learnable moment proposals. The position and length of these proposals are dynamically adjusted during training process. Moreover, a boundary-aware loss has been proposed to leverage frame-level information and further improve performance. Extensive ablations on two challenging NLVL benchmarks have demonstrated the effectiveness of LPNet over existing state-of-the-art methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_328", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_328.flac", "type": "audio", "duration": 345.815}, {"from": "Assistant", "value": "In the Vision-and-Language Navigation (VLN) task an embodied agent navigates a 3D environment, following natural language instructions. A challenge in this task is how to handle ‘off the path’ scenarios where an agent veers from a reference path. Prior work supervises the agent with actions based on the shortest path from the agent’s location to the goal, but such goal-oriented supervision is often not in alignment with the instruction. Furthermore, the evaluation metrics employed by prior work do not measure how much of a language instruction the agent is able to follow. In this work, we propose a simple and effective language-aligned supervision scheme, and a new metric that measures the number of sub-instructions the agent has completed during navigation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_329", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_329.flac", "type": "audio", "duration": 747.371}, {"from": "Assistant", "value": "Healthcare is becoming a more and more important research topic recently. With the growing data in the healthcare domain, it offers a great opportunity for deep learning to improve the quality of service and reduce costs. However, the complexity of electronic health records (EHR) data is a challenge for the application of deep learning. Specifically, the data produced in the hospital admissions are monitored by the EHR system, which includes structured data like daily body temperature and unstructured data like free text and laboratory measurements. Although there are some preprocessing frameworks proposed for specific EHR data, the clinical notes that contain significant clinical value are beyond the realm of their consideration. Besides, whether these different data from various views are all beneficial to the medical tasks and how to best utilize these data remain unclear. Therefore, in this paper, we first extract the accompanying clinical notes from EHR and propose a method to integrate these data, we also comprehensively study the different models and the data leverage methods for better medical task prediction performance. The results on two prediction tasks show that our fused model with different data outperforms the state-of-the-art method without clinical notes, which illustrates the importance of our fusion method and the clinical note features.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_330", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_330.flac", "type": "audio", "duration": 446.961}, {"from": "Assistant", "value": "Sentence extractive summarization shortens a document by selecting sentences for a summary while preserving its important contents. However, constructing a coherent and informative summary is difficult using a pre-trained BERT-based encoder since it is not explicitly trained for representing the information of sentences in a document. We propose a nested tree-based extractive summarization model on RoBERTa (NeRoBERTa), where nested tree structures consist of syntactic and discourse trees in a given document. Experimental results on the CNN/DailyMail dataset showed that NeRoBERTa outperforms baseline models in ROUGE. Human evaluation results also showed that NeRoBERTa achieves significantly better scores than the baselines in terms of coherence and yields comparable scores to the state-of-the-art models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_331", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_331.flac", "type": "audio", "duration": 368.013}, {"from": "Assistant", "value": "Sentence-level extractive text summarization aims to select important sentences from a given document. However, it is very challenging to model the importance of sentences. In this paper, we propose a novel Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization, which leverages Frame semantics to model sentences from both intra-sentence level and inter-sentence level, facilitating the text summarization task. In particular, intra-sentence level semantics leverage Frames and Frame Elements to model internal semantic structure within a sentence, while inter-sentence level semantics leverage Frame-to-Frame relations to model relationships among sentences. Extensive experiments on two benchmark corpus CNN/DM and NYT demonstrate that our model outperforms six state-of-the-art methods significantly.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_332", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_332.flac", "type": "audio", "duration": 698.967}, {"from": "Assistant", "value": "Code summarization aims to generate concise natural language descriptions of source code, which can help improve program comprehension and maintenance. Recent studies show that syntactic and structural information extracted from abstract syntax trees (ASTs) is conducive to summary generation. However, existing approaches fail to fully capture the rich information in ASTs because of the large size/depth of ASTs. In this paper, we propose a novel model CAST that hierarchically splits and reconstructs ASTs. First, we hierarchically split a large AST into a set of subtrees and utilize a recursive neural network to encode the subtrees. Then, we aggregate the embeddings of subtrees by reconstructing the split ASTs to get the representation of the complete AST. Finally, AST representation, together with source code embedding obtained by a vanilla code token encoder, is used for code summarization. Extensive experiments, including the ablation study and the human evaluation, on benchmarks have demonstrated the power of CAST. To facilitate reproducibility, our code and data are available athttps://github.com/DeepSoftwareAnalytics/CAST.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_333", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_333.flac", "type": "audio", "duration": 650.048}, {"from": "Assistant", "value": "Most of existing extractive multi-document summarization (MDS) methods score each sentence individually and extract salient sentences one by one to compose a summary, which have two main drawbacks: (1) neglecting both the intra and cross-document relations between sentences; (2) neglecting the coherence and conciseness of the whole summary. In this paper, we propose a novel MDS framework (SgSum) to formulate the MDS task as a sub-graph selection problem, in which source documents are regarded as a relation graph of sentences (e.g., similarity graph or discourse graph) and the candidate summaries are its sub-graphs. Instead of selecting salient sentences, SgSum selects a salient sub-graph from the relation graph as the summary. Comparing with traditional methods, our method has two main advantages: (1) the relations between sentences are captured by modeling both the graph structure of the whole document set and the candidate sub-graphs; (2) directly outputs an integrate summary in the form of sub-graph which is more informative and coherent. Extensive experiments on MultiNews and DUC datasets show that our proposed method brings substantial improvements over several strong baselines. Human evaluation results also demonstrate that our model can produce significantly more coherent and informative summaries compared with traditional MDS methods. Moreover, the proposed architecture has strong transfer ability from single to multi-document input, which can reduce the resource bottleneck in MDS tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_334", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_334.flac", "type": "audio", "duration": 717.739}, {"from": "Assistant", "value": "Sentence fusion is a conditional generation task that merges several related sentences into a coherent one, which can be deemed as a summary sentence. The importance of sentence fusion has long been recognized by communities in natural language generation, especially in text summarization. It remains challenging for a state-of-the-art neural abstractive summarization model to generate a well-integrated summary sentence. In this paper, we explore the effective sentence fusion method in the context of text summarization. We propose to build an event graph from the input sentences to effectively capture and organize related events in a structured way and use the constructed event graph to guide sentence fusion. In addition to make use of the attention over the content of sentences and graph nodes, we further develop a graph flow attention mechanism to control the fusion process via the graph structure. When evaluated on sentence fusion data built from two summarization datasets, CNN/DaliyMail and Multi-News, our model shows to achieve state-of-the-art performance in terms of Rouge and other metrics like fusion rate and faithfulness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_335", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_335.flac", "type": "audio", "duration": 476.053}, {"from": "Assistant", "value": "This paper explores a variant of automatic headline generation methods, where a generated headline is required to include a given phrase such as a company or a product name. Previous methods using Transformer-based models generate a headline including a given phrase by providing the encoder with additional information corresponding to the given phrase. However, these methods cannot always include the phrase in the generated headline. Inspired by previous RNN-based methods generating token sequences in backward and forward directions from the given phrase, we propose a simple Transformer-based method that guarantees to include the given phrase in the high-quality generated headline. We also consider a new headline generation strategy that takes advantage of the controllable generation order of Transformer. Our experiments with the Japanese News Corpus demonstrate that our methods, which are guaranteed to include the phrase in the generated headline, achieve ROUGE scores comparable to previous Transformer-based methods. We also show that our generation strategy performs better than previous strategies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_336", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_336.flac", "type": "audio", "duration": 489.899}, {"from": "Assistant", "value": "The copying mechanism has had considerable success in abstractive summarization, facilitating models to directly copy words from the input text to the output summary. Existing works mostly employ encoder-decoder attention, which applies copying at each time step independently of the former ones. However, this may sometimes lead to incomplete copying. In this paper, we propose a novel copying scheme named Correlational Copying Network (CoCoNet) that enhances the standard copying mechanism by keeping track of the copying history. It thereby takes advantage of prior copying distributions and, at each time step, explicitly encourages the model to copy the input word that is relevant to the previously copied one. In addition, we strengthen CoCoNet through pre-training with suitable corpora that simulate the copying behaviors. Experimental results show that CoCoNet can copy more accurately and achieves new state-of-the-art performances on summarization benchmarks, including CNN/DailyMail for news summarization and SAMSum for dialogue summarization. The code and checkpoint will be publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_337", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_337.flac", "type": "audio", "duration": 494.051}, {"from": "Assistant", "value": "Neural abstractive summarization systems have gained significant progress in recent years. However, abstractive summarization often produce inconsisitent statements or false facts. How to automatically generate highly abstract yet factually correct summaries? In this paper, we proposed an efficient weak-supervised adversarial data augmentation approach to form the factual consistency dataset. Based on the artificial dataset, we train an evaluation model that can not only make accurate and robust factual consistency discrimination but is also capable of making interpretable factual errors tracing by backpropagated gradient distribution on token embeddings. Experiments and analysis conduct on public annotated summarization and factual consistency datasets demonstrate our approach effective and reasonable.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_340", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_340.flac", "type": "audio", "duration": 720.608}, {"from": "Assistant", "value": "Question Generation (QG) is the task of generating a plausible question for a given <passage, answer> pair. Template-based QG uses linguistically-informed heuristics to transform declarative sentences into interrogatives, whereas supervised QG uses existing Question Answering (QA) datasets to train a system to generate a question given a passage and an answer. A disadvantage of the heuristic approach is that the generated questions are heavily tied to their declarative counterparts. A disadvantage of the supervised approach is that they are heavily tied to the domain/language of the QA dataset used as training data. In order to overcome these shortcomings, we propose a distantly-supervised QG method which uses questions generated heuristically from summaries as a source of training data for a QG system. We make use of freely available news summary data, transforming declarative summary sentences into appropriate questions using heuristics informed by dependency parsing, named entity recognition and semantic role labeling. The resulting questions are then combined with the original news articles to train an end-to-end neural QG model. We extrinsically evaluate our approach using unsupervised QA: our QG model is used to generate synthetic QA pairs for training a QA model. Experimental results show that, trained with only 20k English Wikipedia-based synthetic QA pairs, the QA model substantially outperforms previous unsupervised models on three in-domain datasets (SQuAD1.1, Natural Questions, TriviaQA) and three out-of-domain datasets (NewsQA, BioASQ, DuoRC), demonstrating the transferability of the approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_341", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_341.flac", "type": "audio", "duration": 720.98}, {"from": "Assistant", "value": "Multi-hop Question Answering (QA) is a challenging task because it requires precise reasoning with entity relations at every step towards the answer. The relations can be represented in terms of labels in knowledge graph (e.g., spouse) or text in text corpus (e.g., they have been married for 26 years). Existing models usually infer the answer by predicting the sequential relation path or aggregating the hidden graph features. The former is hard to optimize, and the latter lacks interpretability. In this paper, we propose TransferNet, an effective and transparent model for multi-hop QA, which supports both label and text relations in a unified framework. TransferNet jumps across entities at multiple steps. At each step, it attends to different parts of the question, computes activated scores for relations, and then transfer the previous entity scores along activated relations in a differentiable way. We carry out extensive experiments on three datasets and demonstrate that TransferNet surpasses the state-of-the-art models by a large margin. In particular, on MetaQA, it achieves 100% accuracy in 2-hop and 3-hop questions. By qualitative analysis, we show that TransferNet has transparent and interpretable intermediate results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_342", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_342.flac", "type": "audio", "duration": 718.976}, {"from": "Assistant", "value": "Weakly-supervised table question-answering (TableQA) models have achieved state-of-art performance by using pre-trained BERT transformer to jointly encoding a question and a table to produce structured query for the question. However, in practical settings TableQA systems are deployed over table corpora having topic and word distributions quite distinct from BERT’s pretraining corpus. In this work we simulate the practical topic shift scenario by designing novel challenge benchmarks WikiSQL-TS and WikiTable-TS, consisting of train-dev-test splits in five distinct topic groups, based on the popular WikiSQL and WikiTable-Questions datasets. We empirically show that, despite pre-training on large open-domain text, performance of models degrades significantly when they are evaluated on unseen topics. In response, we propose T3QA (Topic Transferable Table Question Answering) a pragmatic adaptation framework for TableQA comprising of: (1) topic-specific vocabulary injection into BERT, (2) a novel text-to-text transformer generator (such as T5, GPT2) based natural language question generation pipeline focused on generating topic-specific training data, and (3) a logical form re-ranker. We show that T3QA provides a reasonably good baseline for our topic shift benchmarks. We believe our topic split benchmarks will lead to robust TableQA solutions that are better suited for practical deployment", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_343", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_343.flac", "type": "audio", "duration": 707.048}, {"from": "Assistant", "value": "Web search is an essential way for humans to obtain information, but it’s still a great challenge for machines to understand the contents of web pages. In this paper, we introduce the task of web-based structural reading comprehension. Given a web page and a question about it, the task is to find an answer from the web page. This task requires a system not only to understand the semantics of texts but also the structure of the web page. Moreover, we proposed WebSRC, a novel Web-based Structural Reading Comprehension dataset. WebSRC consists of 400K question-answer pairs, which are collected from 6.4K web pages with corresponding HTML source code, screenshots, and metadata. Each question in WebSRC requires a certain structural understanding of a web page to answer, and the answer is either a text span on the web page or yes/no. We evaluate various strong baselines on our dataset to show the difficulty of our task. We also investigate the usefulness of structural information and visual features. Our dataset and baselines have been publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_344", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_344.flac", "type": "audio", "duration": 454.809}, {"from": "Assistant", "value": "Current NLP datasets targeting ambiguity can be solved by a native speaker with relative ease. We present Cryptonite, a large-scale dataset based on cryptic crosswords, which is both linguistically complex and naturally sourced. Each example in Cryptonite is a cryptic clue, a short phrase or sentence with a misleading surface reading, whose solving requires disambiguating semantic, syntactic, and phonetic wordplays, as well as world knowledge. Cryptic clues pose a challenge even for experienced solvers, though top-tier experts can solve them with almost 100% accuracy. Cryptonite is a challenging task for current models; fine-tuning T5-Large on 470k cryptic clues achieves only 7.6% accuracy, on par with the accuracy of a rule-based clue solver (8.6%).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_345", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_345.flac", "type": "audio", "duration": 480.049}, {"from": "Assistant", "value": "Recently, end-to-end (E2E) trained models for question answering over knowledge graphs (KGQA) have delivered promising results using only a weakly supervised dataset. However, these models are trained and evaluated in a setting where hand-annotated question entities are supplied to the model, leaving the important and non-trivial task of entity resolution (ER) outside the scope of E2E learning. In this work, we extend the boundaries of E2E learning for KGQA to include the training of an ER component. Our model only needs the question text and the answer entities to train, and delivers a stand-alone QA model that does not require an additional ER component to be supplied during runtime. Our approach is fully differentiable, thanks to its reliance on a recent method for building differentiable KGs (Cohen et al., 2020). We evaluate our E2E trained model on two public datasets and show that it comes close to baseline models that use hand-annotated entities.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_346", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_346.flac", "type": "audio", "duration": 471.62}, {"from": "Assistant", "value": "Most of the existing Knowledge-based Question Answering (KBQA) methods first learn to map the given question to a query graph, and then convert the graph to an executable query to find the answer. The query graph is typically expanded progressively from the topic entity based on a sequence prediction model. In this paper, we propose a new solution to query graph generation that works in the opposite manner: we start with the entire knowledge base and gradually shrink it to the desired query graph. This approach improves both the efficiency and the accuracy of query graph generation, especially for complex multi-hop questions. Experimental results show that our method achieves state-of-the-art performance on ComplexWebQuestion (CWQ) dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_347", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_347.flac", "type": "audio", "duration": 788.053}, {"from": "Assistant", "value": "Despite the recent advances in applying pre-trained language models to generate high-quality texts, generating long passages that maintain long-range coherence is yet challenging for these models. In this paper, we propose DiscoDVT, a discourse-aware discrete variational Transformer to tackle the incoherence issue. DiscoDVT learns a discrete variable sequence that summarizes the global structure of the text and then applies it to guide the generation process at each decoding step. To further embed discourse-aware information into the discrete latent representations, we introduce an auxiliary objective to model the discourse relations within the text. We conduct extensive experiments on two open story generation datasets and demonstrate that the latent codes learn meaningful correspondence to the discourse structures that guide the model to generate long texts with better long-range coherence.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_348", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_348.flac", "type": "audio", "duration": 738.604}, {"from": "Assistant", "value": "There is an increasing interest in the use of mathematical word problem (MWP) generation in educational assessment. Different from standard natural question generation, MWP generation needs to maintain the underlying mathematical operations between quantities and variables, while at the same time ensuring the relevance between the output and the given topic. To address above problem, we develop an end-to-end neural model to generate diverse MWPs in real-world scenarios from commonsense knowledge graph and equations. The proposed model (1) learns both representations from edge-enhanced Levi graphs of symbolic equations and commonsense knowledge; (2) automatically fuses equation and commonsense knowledge information via a self-planning module when generating the MWPs. Experiments on an educational gold-standard set and a large-scale generated MWP set show that our approach is superior on the MWP generation task, and it outperforms the SOTA models in terms of both automatic evaluation metrics, i.e., BLEU-4, ROUGE-L, Self-BLEU, and human evaluation metrics, i.e., equation relevance, topic relevance, and language coherence. To encourage reproducible results, we make our code and MWP dataset public available athttps://github.com/tal-ai/MaKE_EMNLP2021.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_349", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_349.flac", "type": "audio", "duration": 556.048}, {"from": "Assistant", "value": "Style transfer aims to rewrite a source text in a different target style while preserving its content. We propose a novel approach to this task that leverages generic resources, and without using any task-specific parallel (source–target) data outperforms existing unsupervised approaches on the two most popular style transfer tasks: formality transfer and polarity swap. In practice, we adopt a multi-step procedure which builds on a generic pre-trained sequence-to-sequence model (BART). First, we strengthen the model’s ability to rewrite by further pre-training BART on both an existing collection of generic paraphrases, as well as on synthetic pairs created using a general-purpose lexical resource. Second, through an iterative back-translation approach, we train two models, each in a transfer direction, so that they can provide each other with synthetically generated pairs, dynamically in the training process. Lastly, we let our best resulting model generate static synthetic pairs to be used in a supervised training regime. Besides methodology and state-of-the-art results, a core contribution of this work is a reflection on the nature of the two tasks we address, and how their differences are highlighted by their response to our approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_350", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_350.flac", "type": "audio", "duration": 685.012}, {"from": "Assistant", "value": "Paraphrases refer to texts that convey the same meaning with different expression forms. Pivot-based methods, also known as the round-trip translation, have shown promising results in generating high-quality paraphrases. However, existing pivot-based methods all rely on language as the pivot, where large-scale, high-quality parallel bilingual texts are required. In this paper, we explore the feasibility of using semantic and syntactic representations as the pivot for paraphrase generation. Concretely, we transform a sentence into a variety of different semantic or syntactic representations (including AMR, UD, and latent semantic representation), and then decode the sentence back from the semantic representations. We further explore a pretraining-based approach to compress the pipeline process into an end-to-end framework. We conduct experiments comparing different approaches with different kinds of pivots. Experimental results show that taking AMR as pivot can obtain paraphrases with better quality than taking language as the pivot. The end-to-end framework can reduce semantic shift when language is used as the pivot. Besides, several unsupervised pivot-based methods can generate paraphrases with similar quality as the supervised sequence-to-sequence model, which indicates that parallel data of paraphrases may not be necessary for paraphrase generation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_351", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_351.flac", "type": "audio", "duration": 717.009}, {"from": "Assistant", "value": "Pretrained language models (PLM) have recently advanced graph-to-text generation, where the input graph is linearized into a sequence and fed into the PLM to obtain its representation. However, efficiently encoding the graph structure in PLMs is challenging because such models were pretrained on natural language, and modeling structured data may lead to catastrophic forgetting of distributional knowledge. In this paper, we propose StructAdapt, an adapter method to encode graph structure into PLMs. Contrary to prior work, StructAdapt effectively models interactions among the nodes based on the graph connectivity, only training graph structure-aware adapter parameters. In this way, we incorporate task-specific knowledge while maintaining the topological structure of the graph. We empirically show the benefits of explicitly encoding graph structure into PLMs using StructAdapt, outperforming the state of the art on two AMR-to-text datasets, training only 5.1% of the PLM parameters.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_352", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_352.flac", "type": "audio", "duration": 722.605}, {"from": "Assistant", "value": "We propose to tackle data-to-text generation tasks by directly splicing together retrieved segments of text from “neighbor” source-target pairs. Unlike recent work that conditions on retrieved neighbors but generates text token-by-token, left-to-right, we learn a policy that directly manipulates segments of neighbor text, by inserting or replacing them in partially constructed generations. Standard techniques for training such a policy require an oracle derivation for each generation, and we prove that finding the shortest such derivation can be reduced to parsing under a particular weighted context-free grammar. We find that policies learned in this way perform on par with strong baselines in terms of automatic and human evaluation, but allow for more interpretable and controllable generation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_355", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_355.flac", "type": "audio", "duration": 667.016}, {"from": "Assistant", "value": "Although paths of user interests shift in knowledge graphs (KGs) can benefit conversational recommender systems (CRS), explicit reasoning on KGs has not been well considered in CRS, due to the complex of high-order and incomplete paths. We propose CRFR, which effectively does explicit multi-hop reasoning on KGs with a conversational context-based reinforcement learning model. Considering the incompleteness of KGs, instead of learning single complete reasoning path, CRFR flexibly learns multiple reasoning fragments which are likely contained in the complete paths of interests shift. A fragments-aware unified model is then designed to fuse the fragments information from item-oriented and concept-oriented KGs to enhance the CRS response with entities and words from the fragments. Extensive experiments demonstrate CRFR’s SOTA performance on recommendation, conversation and conversation interpretability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_356", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_356.flac", "type": "audio", "duration": 853.056}, {"from": "Assistant", "value": "In this paper, we provide a bilingual parallel human-to-human recommendation dialog dataset (DuRecDial 2.0) to enable researchers to explore a challenging task of multilingual and cross-lingual conversational recommendation. The difference between DuRecDial 2.0 and existing conversational recommendation datasets is that the data item (Profile, Goal, Knowledge, Context, Response) in DuRecDial 2.0 is annotated in two languages, both English and Chinese, while other datasets are built with the setting of a single language. We collect 8.2k dialogs aligned across English and Chinese languages (16.5k dialogs and 255k utterances in total) that are annotated by crowdsourced workers with strict quality control procedure. We then build monolingual, multilingual, and cross-lingual conversational recommendation baselines on DuRecDial 2.0. Experiment results show that the use of additional English data can bring performance improvement for Chinese conversational recommendation, indicating the benefits of DuRecDial 2.0. Finally, this dataset provides a challenging testbed for future studies of monolingual, multilingual, and cross-lingual conversational recommendation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_357", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_357.flac", "type": "audio", "duration": 704.215}, {"from": "Assistant", "value": "We propose a novel problem within end-to-end learning of task oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their problem (e.g., car not starting). Such dialogs are grounded in domain-specific flowcharts, which the agent is supposed to follow during the conversation. Our task exposes novel technical challenges for neural TOD, such as grounding an utterance to the flowchart without explicit annotation, referring to additional manual pages when user asks a clarification question, and ability to follow unseen flowcharts at test time. We release a dataset (FLODIAL) consisting of 2,738 dialogs grounded on 12 different troubleshooting flowcharts. We also design a neural model, FLONET, which uses a retrieval-augmented generation architecture to train the dialog agent. Our experiments find that FLONET can do zero-shot transfer to unseen flowcharts, and sets a strong baseline for future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_358", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_358.flac", "type": "audio", "duration": 718.059}, {"from": "Assistant", "value": "We present a model to predict fine-grained emotions along the continuous dimensions of valence, arousal, and dominance (VAD) with a corpus with categorical emotion annotations. Our model is trained by minimizing the EMD (Earth Mover’s Distance) loss between the predicted VAD score distribution and the categorical emotion distributions sorted along VAD, and it can simultaneously classify the emotion categories and predict the VAD scores for a given sentence. We use pre-trained RoBERTa-Large and fine-tune on three different corpora with categorical labels and evaluate on EmoBank corpus with VAD scores. We show that our approach reaches comparable performance to that of the state-of-the-art classifiers in categorical emotion classification and shows significant positive correlations with the ground truth VAD scores. Also, further training with supervision of VAD labels leads to improved performance especially when dataset is small. We also present examples of predictions of appropriate emotion words that are not part of the original annotations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_359", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_359.flac", "type": "audio", "duration": 701.01}, {"from": "Assistant", "value": "Fine-grained classification involves dealing with datasets with larger number of classes with subtle differences between them. Guiding the model to focus on differentiating dimensions between these commonly confusable classes is key to improving performance on fine-grained tasks. In this work, we analyse the contrastive fine-tuning of pre-trained language models on two fine-grained text classification tasks, emotion classification and sentiment analysis. We adaptively embed class relationships into a contrastive objective function to help differently weigh the positives and negatives, and in particular, weighting closely confusable negatives more than less similar negative examples. We find that Label-aware Contrastive Loss outperforms previous contrastive methods, in the presence of larger number and/or more confusable classes, and helps models to produce output distributions that are more differentiated.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_360", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_360.flac", "type": "audio", "duration": 625.28}, {"from": "Assistant", "value": "Aspect terms extraction (ATE) and aspect sentiment classification (ASC) are two fundamental and fine-grained sub-tasks in aspect-level sentiment analysis (ALSA). In the textual analysis, joint extracting both aspect terms and sentiment polarities has been drawn much attention due to the better applications than individual sub-task. However, in the multi-modal scenario, the existing studies are limited to handle each sub-task independently, which fails to model the innate connection between the above two objectives and ignores the better applications. Therefore, in this paper, we are the first to jointly perform multi-modal ATE (MATE) and multi-modal ASC (MASC), and we propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-modal aspect-level sentiment analysis (MALSA). Specifically, we first build an auxiliary text-image relation detection module to control the proper exploitation of visual information. Second, we adopt the hierarchical framework to bridge the multi-modal connection between MATE and MASC, as well as separately visual guiding for each sub module. Finally, we can obtain all aspect-level sentiment polarities dependent on the jointly extracted specific aspects. Extensive experiments show the effectiveness of our approach against the joint textual approaches, pipeline and collapsed multi-modal approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_361", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_361.flac", "type": "audio", "duration": 778.325}, {"from": "Assistant", "value": "Aspect category sentiment analysis has attracted increasing research attention. The dominant methods make use of pre-trained language models by learning effective aspect category-specific representations, and adding specific output layers to its pre-trained representation. We consider a more direct way of making use of pre-trained language models, by casting the ACSA tasks into natural language generation tasks, using natural language sentences to represent the output. Our method allows more direct use of pre-trained knowledge in seq2seq language models by directly following the task setting during pre-training. Experiments on several benchmarks show that our method gives the best reported results, having large advantages in few-shot and zero-shot settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_362", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_362.flac", "type": "audio", "duration": 398.245}, {"from": "Assistant", "value": "Both the issues of data deficiencies and semantic consistency are important for data augmentation. Most of previous methods address the first issue, but ignore the second one. In the cases of aspect-based sentiment analysis, violation of the above issues may change the aspect and sentiment polarity. In this paper, we propose a semantics-preservation data augmentation approach by considering the importance of each word in a textual sequence according to the related aspects and sentiments. We then substitute the unimportant tokens with two replacement strategies without altering the aspect-level polarity. Our approach is evaluated on several publicly available sentiment analysis datasets and the real-world stock price/risk movement prediction scenarios. Experimental results show that our methodology achieves better performances in all datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_363", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_363.flac", "type": "audio", "duration": 430.057}, {"from": "Assistant", "value": "Sentiment analysis systems have been shown to exhibit sensitivity to protected attributes. Round-trip translation, on the other hand, has been shown to normalize text. We explore the impact of round-trip translation on the demographic parity of sentiment classifiers and show how round-trip translation consistently improves classification fairness at test time (reducing up to 47% of between-group gaps). We also explore the idea of retraining sentiment classifiers on round-trip-translated data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_364", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_364.flac", "type": "audio", "duration": 476.011}, {"from": "Assistant", "value": "Humor detection has gained attention in recent years due to the desire to understand user-generated content with figurative language. However, substantial individual and cultural differences in humor perception make it very difficult to collect a large-scale humor dataset with reliable humor labels. We propose CHoRaL, a framework to generate perceived humor labels on Facebook posts, using the naturally available user reactions to these posts with no manual annotation needed. CHoRaL provides both binary labels and continuous scores of humor and non-humor. We present the largest dataset to date with labeled humor on 785K posts related to COVID-19. Additionally, we analyze the expression of COVID-related humor in social media by extracting lexico-semantic and affective features from the posts, and build humor detection models with performance similar to humans. CHoRaL enables the development of large-scale humor detection models on any topic and opens a new path to the study of humor on social media.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_365", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_365.flac", "type": "audio", "duration": 703.04}, {"from": "Assistant", "value": "Dialogue summarization has drawn much attention recently. Especially in the customer service domain, agents could use dialogue summaries to help boost their works by quickly knowing customer’s issues and service progress. These applications require summaries to contain the perspective of a single speaker and have a clear topic flow structure, while neither are available in existing datasets. Therefore, in this paper, we introduce a novel Chinese dataset for Customer Service Dialogue Summarization (CSDS). CSDS improves the abstractive summaries in two aspects: (1) In addition to the overall summary for the whole dialogue, role-oriented summaries are also provided to acquire different speakers’ viewpoints. (2) All the summaries sum up each topic separately, thus containing the topic-level structure of the dialogue. We define tasks in CSDS as generating the overall summary and different role-oriented summaries for a given dialogue. Next, we compare various summarization methods on CSDS, and experiment results show that existing methods are prone to generate redundant and incoherent summaries. Besides, the performance becomes much worse when analyzing the performance on role-oriented summaries and topic structures. We hope that this study could benchmark Chinese dialogue summarization and benefit further studies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_366", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_366.flac", "type": "audio", "duration": 510.016}, {"from": "Assistant", "value": "Existing relation extraction (RE) methods typically focus on extracting relational facts between entity pairs within single sentences or documents. However, a large quantity of relational facts in knowledge bases can only be inferred across documents in practice. In this work, we present the problem of cross-document RE, making an initial step towards knowledge acquisition in the wild. To facilitate the research, we construct the first human-annotated cross-document RE dataset CodRED. Compared to existing RE datasets, CodRED presents two key challenges: Given two entities, (1) it requires finding the relevant documents that can provide clues for identifying their relations; (2) it requires reasoning over multiple documents to extract the relational facts. We conduct comprehensive experiments to show that CodRED is challenging to existing RE methods including strong BERT-based models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_367", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_367.flac", "type": "audio", "duration": 725.902}, {"from": "Assistant", "value": "Enabling open-domain dialogue systems to ask clarifying questions when appropriate is an important direction for improving the quality of the system response. Namely, for cases when a user request is not specific enough for a conversation system to provide an answer right away, it is desirable to ask a clarifying question to increase the chances of retrieving a satisfying answer. To address the problem of ‘asking clarifying questions in open-domain dialogues’: (1) we collect and release a new dataset focused on open-domain single- and multi-turn conversations, (2) we benchmark several state-of-the-art neural baselines, and (3) we propose a pipeline consisting of offline and online steps for evaluating the quality of clarifying questions in various dialogues. These contributions are suitable as a foundation for further research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_368", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_368.flac", "type": "audio", "duration": 479.724}, {"from": "Assistant", "value": "Standard train-dev-test splits used to benchmark multiple models against each other are ubiquitously used in Natural Language Processing (NLP). In this setup, the train data is used for training the model, the development set for evaluating different versions of the proposed model(s) during development, and the test set to confirm the answers to the main research question(s). However, the introduction of neural networks in NLP has led to a different use of these standard splits; the development set is now often used for model selection during the training procedure. Because of this, comparing multiple versions of the same model during development leads to overestimation on the development data. As an effect, people have started to compare an increasing amount of models on the test data, leading to faster overfitting and “expiration” of our test sets. We propose to use a tune-set when developing neural network methods, which can be used for model picking so that comparing the different versions of a new model can safely be done on the development data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_369", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_369.flac", "type": "audio", "duration": 450.049}, {"from": "Assistant", "value": "We introduce a high-quality and large-scale Vietnamese-English parallel dataset of 3.02M sentence pairs, which is 2.9M pairs larger than the benchmark Vietnamese-English machine translation corpus IWSLT15. We conduct experiments comparing strong neural baselines and well-known automatic translation engines on our dataset and find that in both automatic and human evaluations: the best performance is obtained by fine-tuning the pre-trained sequence-to-sequence denoising auto-encoder mBART. To our best knowledge, this is the first large-scale Vietnamese-English machine translation study. We hope our publicly available dataset and study can serve as a starting point for future research and applications on Vietnamese-English machine translation. We release our dataset at:https://github.com/VinAIResearch/PhoMT", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_371", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_371.flac", "type": "audio", "duration": 531.017}, {"from": "Assistant", "value": "Recently, the textual adversarial attack models become increasingly popular due to their successful in estimating the robustness of NLP models. However, existing works have obvious deficiencies. (1)They usually consider only a single granularity of modification strategies (e.g. word-level or sentence-level), which is insufficient to explore the holistic textual space for generation; (2) They need to query victim models hundreds of times to make a successful attack, which is highly inefficient in practice. To address such problems, in this paper we propose MAYA, a Multi-grAnularitY Attack model to effectively generate high-quality adversarial samples with fewer queries to victim models. Furthermore, we propose a reinforcement-learning based method to train a multi-granularity attack agent through behavior cloning with the expert knowledge from our MAYA algorithm to further reduce the query times. Additionally, we also adapt the agent to attack black-box models that only output labels without confidence scores. We conduct comprehensive experiments to evaluate our attack models by attacking BiLSTM, BERT and RoBERTa in two different black-box attack settings and three benchmark datasets. Experimental results show that our models achieve overall better attacking performance and produce more fluent and grammatical adversarial samples compared to baseline models. Besides, our adversarial attack agent significantly reduces the query times in both attack settings. Our codes are released athttps://github.com/Yangyi-Chen/MAYA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_372", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_372.flac", "type": "audio", "duration": 658.564}, {"from": "Assistant", "value": "Similarity measures are a vital tool for understanding how language models represent and process language. Standard representational similarity measures such as cosine similarity and Euclidean distance have been successfully used in static word embedding models to understand how words cluster in semantic space. Recently, these measures have been applied to embeddings from contextualized models such as BERT and GPT-2. In this work, we call into question the informativity of such measures for contextualized language models. We find that a small number of rogue dimensions, often just 1-3, dominate these measures. Moreover, we find a striking mismatch between the dimensions that dominate similarity measures and those which are important to the behavior of the model. We show that simple postprocessing techniques such as standardization are able to correct for rogue dimensions and reveal underlying representational quality. We argue that accounting for rogue dimensions is essential for any similarity-based analysis of contextual language models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_373", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_373.flac", "type": "audio", "duration": 716.8}, {"from": "Assistant", "value": "Transformer architecture has become ubiquitous in the natural language processing field. To interpret the Transformer-based models, their attention patterns have been extensively analyzed. However, the Transformer architecture is not only composed of the multi-head attention; other components can also contribute to Transformers’ progressive performance. In this study, we extended the scope of the analysis of Transformers from solely the attention patterns to the whole attention block, i.e., multi-head attention, residual connection, and layer normalization. Our analysis of Transformer-based masked language models shows that the token-to-token interaction performed via attention has less impact on the intermediate representations than previously assumed. These results provide new intuitive explanations of existing reports; for example, discarding the learned attention patterns tends not to adversely affect the performance. The codes of our experiments are publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_374", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_374.flac", "type": "audio", "duration": 456.017}, {"from": "Assistant", "value": "Adversarial attacks and backdoor attacks are two common security threats that hang over deep learning. Both of them harness task-irrelevant features of data in their implementation. Text style is a feature that is naturally irrelevant to most NLP tasks, and thus suitable for adversarial and backdoor attacks. In this paper, we make the first attempt to conduct adversarial and backdoor attacks based on text style transfer, which is aimed at altering the style of a sentence while preserving its meaning. We design an adversarial attack method and a backdoor attack method, and conduct extensive experiments to evaluate them. Experimental results show that popular NLP models are vulnerable to both adversarial and backdoor attacks based on text style transfer—the attack success rates can exceed 90% without much effort. It reflects the limited ability of NLP models to handle the feature of text style that has not been widely realized. In addition, the style transfer-based adversarial and backdoor attack methods show superiority to baselines in many aspects. All the code and data of this paper can be obtained athttps://github.com/thunlp/StyleAttack.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_375", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_375.flac", "type": "audio", "duration": 479.422}, {"from": "Assistant", "value": "Using data from English cloze tests, in which subjects also self-reported their gender, age, education, and race, we examine performance differences of pretrained language models across demographic groups, defined by these (protected) attributes. We demonstrate wide performance gaps across demographic groups and show that pretrained language models systematically disfavor young non-white male speakers; i.e., not only do pretrained language models learn social biases (stereotypical associations) – pretrained language models also learn sociolectal biases, learning to speak more like some than like others. We show, however, that, with the exception of BERT models, larger pretrained language models reduce some the performance gaps between majority and minority groups.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_376", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_376.flac", "type": "audio", "duration": 427.061}, {"from": "Assistant", "value": "State-of-the-art contextual embeddings are obtained from large language models available only for a few languages. For others, we need to learn representations using a multilingual model. There is an ongoing debate on whether multilingual embeddings can be aligned in a space shared across many languages. The novel Orthogonal Structural Probe (Limisiewicz and Mareček, 2021) allows us to answer this question for specific linguistic features and learn a projection based only on mono-lingual annotated datasets. We evaluate syntactic (UD) and lexical (WordNet) structural information encoded inmBERT’s contextual representations for nine diverse languages. We observe that for languages closely related to English, no transformation is needed. The evaluated information is encoded in a shared cross-lingual embedding space. For other languages, it is beneficial to apply orthogonal transformation learned separately for each language. We successfully apply our findings to zero-shot and few-shot cross-lingual parsing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_377", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_377.flac", "type": "audio", "duration": 480.374}, {"from": "Assistant", "value": "Many recent works have demonstrated that unsupervised sentence representations of neural networks encode syntactic information by observing that neural language models are able to predict the agreement between a verb and its subject. We take a critical look at this line of research by showing that it is possible to achieve high accuracy on this agreement task with simple surface heuristics, indicating a possible flaw in our assessment of neural networks’ syntactic ability. Our fine-grained analyses of results on the long-range French object-verb agreement show that contrary to LSTMs, Transformers are able to capture a non-trivial amount of grammatical structure.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_378", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_378.flac", "type": "audio", "duration": 517.056}, {"from": "Assistant", "value": "Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types. In this paper, we argue that the implicitly entailed extrinsic and intrinsic dependencies between labels can provide critical knowledge to tackle the above challenges. To this end, we propose Label Reasoning Network(LRN), which sequentially reasons fine-grained entity labels by discovering and exploiting label dependencies knowledge entailed in the data. Specifically, LRN utilizes an auto-regressive network to conduct deductive reasoning and a bipartite attribute graph to conduct inductive reasoning between labels, which can effectively model, learn and reason complex label dependencies in a sequence-to-set, end-to-end manner. Experiments show that LRN achieves the state-of-the-art performance on standard ultra fine-grained entity typing benchmarks, and can also resolve the long tail label problem effectively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_379", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_379.flac", "type": "audio", "duration": 615.933}, {"from": "Assistant", "value": "Span extraction, aiming to extract text spans (such as words or phrases) from plain text, is a fundamental process in Information Extraction. Recent works introduce the label knowledge to enhance the text representation by formalizing the span extraction task into a question answering problem (QA Formalization), which achieves state-of-the-art performance. However, such a QA Formalization does not fully exploit the label knowledge and causes a dramatic decrease in efficiency of training/inference. To address those problems, we introduce a fresh paradigm to integrate label knowledge and further propose a novel model to explicitly and efficiently integrate label knowledge into text representations. Specifically, it encodes texts and label annotations independently and then integrates label knowledge into text representation with an elaborate-designed semantics fusion module. We conduct extensive experiments on three typical span extraction tasks: flat NER, nested NER, and event detection. The empirical results show that 1) our model achieves a new state-of-the-art performance on four benchmarks, and 2) reduces training time and inference time by 76% and 77% on average, respectively, compared with the QA Formalization paradigm.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_380", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_380.flac", "type": "audio", "duration": 440.533}, {"from": "Assistant", "value": "Automatically extracting interpersonal relationships of conversation interlocutors can enrich personal knowledge bases to enhance personalized search, recommenders and chatbots. To infer speakers’ relationships from dialogues we propose PRIDE, a neural multi-label classifier, based on BERT and Transformer for creating a conversation representation. PRIDE utilizes dialogue structure and augments it with external knowledge about speaker features and conversation style. Unlike prior works, we address multi-label prediction of fine-grained relationships. We release large-scale datasets, based on screenplays of movies and TV shows, with directed relationships of conversation participants. Extensive experiments on both datasets show superior performance of PRIDE compared to the state-of-the-art baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_381", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_381.flac", "type": "audio", "duration": 436.053}, {"from": "Assistant", "value": "Recent transformer-based approaches demonstrate promising results on relational scientific information extraction. Existing datasets focus on high-level description of how research is carried out. Instead we focus on the subtleties of how experimental associations are presented by building SciClaim, a dataset of scientific claims drawn from Social and Behavior Science (SBS), PubMed, and CORD-19 papers. Our novel graph annotation schema incorporates not only coarse-grained entity spans as nodes and relations as edges between them, but also fine-grained attributes that modify entities and their relations, for a total of 12,738 labels in the corpus. By including more label types and more than twice the label density of previous datasets, SciClaim captures causal, comparative, predictive, statistical, and proportional associations over experimental variables along with their qualifications, subtypes, and evidence. We extend work in transformer-based joint entity and relation extraction to effectively infer our schema, showing the promise of fine-grained knowledge graphs in scientific claims and beyond.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_382", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_382.flac", "type": "audio", "duration": 709.045}, {"from": "Assistant", "value": "Relating entities and events in text is a key component of natural language understanding. Cross-document coreference resolution, in particular, is important for the growing interest in multi-document analysis tasks. In this work we propose a new model that extends the efficient sequential prediction paradigm for coreference resolution to cross-document settings and achieves competitive results for both entity and event coreference while providing strong evidence of the efficacy of both sequential models and higher-order inference in cross-document settings. Our model incrementally composes mentions into cluster representations and predicts links between a mention and the already constructed clusters, approximating a higher-order model. In addition, we conduct extensive ablation studies that provide new insights into the importance of various inputs and representation types in coreference.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_383", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_383.flac", "type": "audio", "duration": 522.453}, {"from": "Assistant", "value": "Infusing factual knowledge into pre-trained models is fundamental for many knowledge-intensive tasks. In this paper, we proposed Mixture-of-Partitions (MoP), an infusion approach that can handle a very large knowledge graph (KG) by partitioning it into smaller sub-graphs and infusing their specific knowledge into various BERT models using lightweight adapters. To leverage the overall factual knowledge for a target task, these sub-graph adapters are further fine-tuned along with the underlying BERT through a mixture layer. We evaluate our MoP with three biomedical BERTs (SciBERT, BioBERT, PubmedBERT) on six downstream tasks (inc. NLI, QA, Classification), and the results show that our MoP consistently enhances the underlying BERTs in task performance, and achieves new SOTA performances on five evaluated datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_384", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_384.flac", "type": "audio", "duration": 708.053}, {"from": "Assistant", "value": "We present models which complete missing text given transliterations of ancient Mesopotamian documents, originally written on cuneiform clay tablets (2500 BCE - 100 CE). Due to the tablets’ deterioration, scholars often rely on contextual cues to manually fill in missing parts in the text in a subjective and time-consuming process. We identify that this challenge can be formulated as a masked language modelling task, used mostly as a pretraining objective for contextualized language models. Following, we develop several architectures focusing on the Akkadian language, the lingua franca of the time. We find that despite data scarcity (1M tokens) we can achieve state of the art performance on missing tokens prediction (89% hit@5) using a greedy decoding scheme and pretraining on data from other languages and different time periods. Finally, we conduct human evaluations showing the applicability of our models in assisting experts to transcribe texts in extinct languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_385", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_385.flac", "type": "audio", "duration": 445.962}, {"from": "Assistant", "value": "During the fine-tuning phase of transfer learning, the pretrained vocabulary remains unchanged, while model parameters are updated. The vocabulary generated based on the pretrained data is suboptimal for downstream data when domain discrepancy exists. We propose to consider the vocabulary as an optimizable parameter, allowing us to update the vocabulary by expanding it with domain specific vocabulary based on a tokenization statistic. Furthermore, we preserve the embeddings of the added words from overfitting to downstream data by utilizing knowledge learned from a pretrained language model with a regularization term. Our method achieved consistent performance improvements on diverse domains (i.e., biomedical, computer science, news, and reviews).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_386", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_386.flac", "type": "audio", "duration": 590.623}, {"from": "Assistant", "value": "Developing robust NLP models that perform well on many, even small, slices of data is a significant but important challenge, with implications from fairness to general reliability. To this end, recent research has explored how models rely on spurious correlations, and how counterfactual data augmentation (CDA) can mitigate such issues. In this paper we study how and why modeling counterfactuals over multiple attributes can go significantly further in improving model performance. We propose RDI, a context-aware methodology which takes into account the impact of secondary attributes on the model’s predictions and increases sensitivity for secondary attributes over reweighted counterfactually augmented data. By implementing RDI in the context of toxicity detection, we find that accounting for secondary attributes can significantly improve robustness, with improvements in sliced accuracy on the original dataset up to 7% compared to existing robustness methods. We also demonstrate that RDI generalizes to the coreference resolution task and provide guidelines to extend this to other tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_387", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_387.flac", "type": "audio", "duration": 720.051}, {"from": "Assistant", "value": "Statistical language modeling and translation with transformers have found many successful applications in program understanding and generation tasks, setting high benchmarks for tools in modern software development environments. The finite context window of these neural models means, however, that they will be unable to leverage the entire relevant context of large files and packages for any given task. While there are many efforts to extend the context window, we introduce an architecture-independent approach for leveraging the syntactic hierarchies of source code for incorporating entire file-level context into a fixed-length window. Using concrete syntax trees of each source file we extract syntactic hierarchies and integrate them into context window by selectively removing from view more specific, less relevant scopes for a given task. We evaluate this approach on code generation tasks and joint translation of natural language and source code in Python programming language, achieving a new state-of-the-art in code completion and summarization for Python in the CodeXGLUE benchmark. We also introduce new CodeXGLUE benchmarks for user-experience-motivated tasks: code completion with normalized literals, method body completion/code summarization conditioned on file-level context.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_388", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_388.flac", "type": "audio", "duration": 476.055}, {"from": "Assistant", "value": "Pre-trained language models (LMs) have become ubiquitous in solving various natural language processing (NLP) tasks. There has been increasing interest in what knowledge these LMs contain and how we can extract that knowledge, treating LMs as knowledge bases (KBs). While there has been much work on probing LMs in the general domain, there has been little attention to whether these powerful LMs can be used as domain-specific KBs. To this end, we create the BioLAMA benchmark, which is comprised of 49K biomedical factual knowledge triples for probing biomedical LMs. We find that biomedical LMs with recently proposed probing methods can achieve up to 18.51% Acc@5 on retrieving biomedical knowledge. Although this seems promising given the task difficulty, our detailed analyses reveal that most predictions are highly correlated with prompt templates without any subjects, hence producing similar results on each relation and hindering their capabilities to be used as domain-specific KBs. We hope that BioLAMA can serve as a challenging benchmark for biomedical factual probing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_389", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_389.flac", "type": "audio", "duration": 697.063}, {"from": "Assistant", "value": "Reading order detection is the cornerstone to understanding visually-rich documents (e.g., receipts and forms). Unfortunately, no existing work took advantage of advanced deep learning models because it is too laborious to annotate a large enough dataset. We observe that the reading order of WORD documents is embedded in their XML metadata; meanwhile, it is easy to convert WORD documents to PDFs or images. Therefore, in an automated manner, we construct ReadingBank, a benchmark dataset that contains reading order, text, and layout information for 500,000 document images covering a wide spectrum of document types. This first-ever large-scale dataset unleashes the power of deep neural networks for reading order detection. Specifically, our proposed LayoutReader captures the text and layout information for reading order prediction using the seq2seq model. It performs almost perfectly in reading order detection and significantly improves both open-source and commercial OCR engines in ordering text lines in their results in our experiments. The dataset and models are publicly available athttps://aka.ms/layoutreader.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_390", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_390.flac", "type": "audio", "duration": 694.059}, {"from": "Assistant", "value": "Visual Dialog is assumed to require the dialog history to generate correct responses during a dialog. However, it is not clear from previous work how dialog history is needed for visual dialog. In this paper we define what it means for a visual question to require dialog history and we release a subset of the Guesswhat?! questions for which their dialog history completely changes their responses. We propose a novel interpretable representation that visually grounds dialog history: the Region under Discussion. It constrains the image’s spatial features according to a semantic representation of the history inspired by the information structure notion of Question under Discussion.We evaluate the architecture on task-specific multimodal models and the visual transformer model LXMERT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_391", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_391.flac", "type": "audio", "duration": 695.723}, {"from": "Assistant", "value": "This paper introduces a novel approach to learn visually grounded meaning representations of words as low-dimensional node embeddings on an underlying graph hierarchy. The lower level of the hierarchy models modality-specific word representations, conditioned to another modality, through dedicated but communicating graphs, while the higher level puts these representations together on a single graph to learn a representation jointly from both modalities. The topology of each graph models similarity relations among words, and is estimated jointly with the graph embedding. The assumption underlying this model is that words sharing similar meaning correspond to communities in an underlying graph in a low-dimensional space. We named this model Hierarchical Multi-Modal Similarity Graph Embedding (HM-SGE). Experimental results validate the ability of HM-SGE to simulate human similarity judgments and concept categorization, outperforming the state of the art.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_392", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_392.flac", "type": "audio", "duration": 710.379}, {"from": "Assistant", "value": "We aim to automatically identify human action reasons in online videos. We focus on the widespread genre of lifestyle vlogs, in which people perform actions while verbally describing them. We introduce and make publicly available the WhyAct dataset, consisting of 1,077 visual actions manually annotated with their reasons. We describe a multimodal model that leverages visual and textual information to automatically infer the reasons corresponding to an action presented in the video.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_393", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_393.flac", "type": "audio", "duration": 710.856}, {"from": "Assistant", "value": "Recent work has shown that monolingual masked language models learn to represent data-driven notions of language variation which can be used for domain-targeted training data selection. Dataset genre labels are already frequently available, yet remain largely unexplored in cross-lingual setups. We harness this genre metadata as a weak supervision signal for targeted data selection in zero-shot dependency parsing. Specifically, we project treebank-level genre information to the finer-grained sentence level, with the goal to amplify information implicitly stored in unsupervised contextualized representations. We demonstrate that genre is recoverable from multilingual contextual embeddings and that it provides an effective signal for training data selection in cross-lingual, zero-shot scenarios. For 12 low-resource language treebanks, six of which are test-only, our genre-specific methods significantly outperform competitive baselines as well as recent embedding-based methods for data selection. Moreover, genre-based data selection provides new state-of-the-art results for three of these target languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_394", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_394.flac", "type": "audio", "duration": 589.056}, {"from": "Assistant", "value": "We explore the link between the extent to which syntactic relations are preserved in translation and the ease of correctly constructing a parse tree in a zero-shot setting. While previous work suggests such a relation, it tends to focus on the macro level and not on the level of individual edges—a gap we aim to address. As a test case, we take the transfer of Universal Dependencies (UD) parsing from English to a diverse set of languages and conduct two sets of experiments. In one, we analyze zero-shot performance based on the extent to which English source edges are preserved in translation. In another, we apply three linguistically motivated transformations to UD, creating more cross-lingually stable versions of it, and assess their zero-shot parsability. In order to compare parsing performance across different schemes, we perform extrinsic evaluation on the downstream task of cross-lingual relation extraction (RE) using a subset of a standard English RE benchmark translated to Russian and Korean. In both sets of experiments, our results suggest a strong relation between cross-lingual stability and zero-shot parsing performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_395", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_395.flac", "type": "audio", "duration": 681.045}, {"from": "Assistant", "value": "For over thirty years, researchers have developed and analyzed methods for latent tree induction as an approach for unsupervised syntactic parsing. Nonetheless, modern systems still do not perform well enough compared to their supervised counterparts to have any practical use as structural annotation of text. In this work, we present a technique that uses distant supervision in the form of span constraints (i.e. phrase bracketing) to improve performance in unsupervised constituency parsing. Using a relatively small number of span constraints we can substantially improve the output from DIORA, an already competitive unsupervised parsing system. Compared with full parse tree annotation, span constraints can be acquired with minimal effort, such as with a lexicon derived from Wikipedia, to find exact text matches. Our experiments show span constraints based on entities improves constituency parsing on English WSJ Penn Treebank by more than 5 F1. Furthermore, our method extends to any domain where span constraints are easily attainable, and as a case study we demonstrate its effectiveness by parsing biomedical text from the CRAFT dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_396", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_396.flac", "type": "audio", "duration": 224.769}, {"from": "Assistant", "value": "The Internet is home to thousands of communities, each with their own unique worldview and associated ideological differences. With new communities constantly emerging and serving as ideological birthplaces, battlegrounds, and bunkers, it is critical to develop a framework for understanding worldviews and ideological distinction. Most existing work, however, takes a predetermined view based on political polarization: the “right vs. left” dichotomy of U.S. politics. In reality, both political polarization – and worldviews more broadly – transcend one-dimensional difference, and deserve a more complete analysis. Extending the ability of word embedding models to capture the semantic and cultural characteristics of their training corpora, we propose a novel method for discovering the multifaceted ideological and worldview characteristics of communities. Using over 1B comments collected from the largest communities on Reddit.com representing ~40% of Reddit activity, we demonstrate the efficacy of this approach to uncover complex ideological differences across multiple axes of polarization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_397", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_397.flac", "type": "audio", "duration": 687.04}, {"from": "Assistant", "value": "Dialogue models trained on human conversations inadvertently learn to generate toxic responses. In addition to producing explicitly offensive utterances, these models can also implicitly insult a group or individual by aligning themselves with an offensive statement. To better understand the dynamics of contextually offensive language, we investigate the stance of dialogue model responses in offensive Reddit conversations. Specifically, we create ToxiChat, a crowd-annotated dataset of 2,000 Reddit threads and model responses labeled with offensive language and stance. Our analysis reveals that 42% of human responses agree with toxic comments, whereas only 13% agree with safe comments. This undesirable behavior is learned by neural dialogue models, such as DialoGPT, which we show are two times more likely to agree with offensive comments. To enable automatic detection of offensive language, we fine-tuned transformer-based classifiers on ToxiChat that achieve 0.71 F1 for offensive labels and 0.53 Macro-F1 for stance labels. Finally, we quantify the effectiveness of controllable text generation (CTG) methods to mitigate the tendency of neural dialogue models to agree with offensive comments. Compared to the baseline, our best CTG model achieves a 19% reduction in agreement with offensive comments and produces 29% fewer offensive replies. Our work highlights the need for further efforts to characterize and analyze inappropriate behavior in dialogue models, in order to help make them safer.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_398", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_398.flac", "type": "audio", "duration": 667.527}, {"from": "Assistant", "value": "Recent work in open-domain conversational agents has demonstrated that significant improvements in humanness and user preference can be achieved via massive scaling in both pre-training data and model size (Adiwardana et al., 2020; Roller et al., 2020). However, if we want to build agents with human-like abilities, we must expand beyond handling just text. A particularly important topic is the ability to see images and communicate about what is perceived. With the goal of getting humans to engage in multi-modal dialogue, we investigate combining components from state-of-the-art open-domain dialogue agents with those from state-of-the-art vision models. We study incorporating different image fusion schemes and domain-adaptive pre-training and fine-tuning strategies, and show that our best resulting model outperforms strong existing models in multi-modal dialogue while simultaneously performing as well as its predecessor (text-only) BlenderBot (Roller et al., 2020) in text-based conversation. We additionally investigate and incorporate safety components in our final model, and show that such efforts do not diminish model performance with respect to human preference.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_399", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_399.flac", "type": "audio", "duration": 683.851}, {"from": "Assistant", "value": "With the early success of query-answer assistants such as Alexa and Siri, research attempts to expand system capabilities of handling service automation are now abundant. However, preliminary systems have quickly found the inadequacy in relying on simple classification techniques to effectively accomplish the automation task. The main challenge is that the dialogue often involves complexity in user’s intents (or purposes) which are multiproned, subject to spontaneous change, and difficult to track. Furthermore, public datasets have not considered these complications and the general semantic annotations are lacking which may result in zero-shot problem. Motivated by the above, we propose a Label-Aware BERT Attention Network (LABAN) for zero-shot multi-intent detection. We first encode input utterances with BERT and construct a label embedded space by considering embedded semantics in intent labels. An input utterance is then classified based on its projection weights on each intent embedding in this embedded space. We show that it successfully extends to few/zero-shot setting where part of intent labels are unseen in training data, by also taking account of semantics in these unseen intent labels. Experimental results show that our approach is capable of detecting many unseen intent labels correctly. It also achieves the state-of-the-art performance on five multi-intent datasets in normal cases.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_400", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_400.flac", "type": "audio", "duration": 360.32}, {"from": "Assistant", "value": "Dialogue disentanglement aims to group utterances in a long and multi-participant dialogue into threads. This is useful for discourse analysis and downstream applications such as dialogue response selection, where it can be the first step to construct a clean context/response set. Unfortunately, labeling allreply-tolinks takes quadratic effort w.r.t the number of utterances: an annotator must check all preceding utterances to identify the one to which the current utterance is a reply. In this paper, we are the first to propose azero-shotdialogue disentanglement solution. Firstly, we train a model on a multi-participant response selection dataset harvested from the web which is not annotated; we then apply the trained model to perform zero-shot dialogue disentanglement. Without any labeled data, our model can achieve a cluster F1 score of 25. We also fine-tune the model using various amounts of labeled data. Experiments show that with only 10% of the data, we achieve nearly the same performance of using the full dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_401", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_401.flac", "type": "audio", "duration": 717.613}, {"from": "Assistant", "value": "Next generation task-oriented dialog systems need to understand conversational contexts with their perceived surroundings, to effectively help users in the real-world multimodal environment. Existing task-oriented dialog datasets aimed towards virtual assistance fall short and do not situate the dialog in the user’s multimodal context. To overcome, we present a new dataset for Situated and Interactive Multimodal Conversations, SIMMC 2.0, which includes 11K task-oriented user<->assistant dialogs (117K utterances) in the shopping domain, grounded in immersive and photo-realistic scenes. The dialogs are collection using a two-phase pipeline: (1) A novel multimodal dialog simulator generates simulated dialog flows, with an emphasis on diversity and richness of interactions, (2) Manual paraphrasing of generating utterances to draw from natural language distribution. We provide an in-depth analysis of the collected dataset, and describe in detail the four main benchmark tasks we propose for SIMMC 2.0. Our baseline model, powered by the state-of-the-art language model, shows promising results, and highlights new challenges and directions for the community to study.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_402", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_402.flac", "type": "audio", "duration": 692.053}, {"from": "Assistant", "value": "The task of dialogue rewriting aims to reconstruct the latest dialogue utterance by copying the missing content from the dialogue context. Until now, the existing models for this task suffer from the robustness issue, i.e., performances drop dramatically when testing on a different dataset. We address this robustness issue by proposing a novel sequence-tagging-based model so that the search space is significantly reduced, yet the core of this task is still well covered. As a common issue of most tagging models for text generation, the model’s outputs may lack fluency. To alleviate this issue, we inject the loss signal from BLEU or GPT-2 under a REINFORCE framework. Experiments show huge improvements of our model over the current state-of-the-art systems when transferring to another dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_403", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_403.flac", "type": "audio", "duration": 703.054}, {"from": "Assistant", "value": "Recent state-of-the-art approaches in open-domain dialogue include training end-to-end deep-learning models to learn various conversational features like emotional content of response, symbolic transitions of dialogue contexts in a knowledge graph and persona of the agent and the user, among others. While neural models have shown reasonable results, modelling the cognitive processes that humans use when conversing with each other may improve the agent’s quality of responses. A key element of natural conversation is to tailor one’s response such that it accounts for concepts that the speaker and listener may or may not know and the contextual relevance of all prior concepts used in conversation. We show that a rich representation and explicit modeling of these psychological processes can improve predictions made by existing neural network models. In this work, we propose a novel probabilistic approach using Markov Random Fields (MRF) to augment existing deep-learning methods for improved next utterance prediction. Using human and automatic evaluations, we show that our augmentation approach significantly improves the performance of existing state-of-the-art retrieval models for open-domain conversational agents.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_404", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_404.flac", "type": "audio", "duration": 601.049}, {"from": "Assistant", "value": "Task-oriented conversational systems often use dialogue state tracking to represent the user’s intentions, which involves filling in values of pre-defined slots. Many approaches have been proposed, often using task-specific architectures with special-purpose classifiers. Recently, good results have been obtained using more general architectures based on pretrained language models. Here, we introduce a new variation of the language modeling approach that uses schema-driven prompting to provide task-aware history encoding that is used for both categorical and non-categorical slots. We further improve performance by augmenting the prompting with schema descriptions, a naturally occurring source of in-domain knowledge. Our purely generative system achieves state-of-the-art performance on MultiWOZ 2.2 and achieves competitive performance on two other benchmarks: MultiWOZ 2.1 and M2M. The data and code will be available athttps://github.com/chiahsuan156/DST-as-Prompting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_405", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_405.flac", "type": "audio", "duration": 714.78}, {"from": "Assistant", "value": "Coreference resolution is key to many natural language processing tasks and yet has been relatively unexplored in Sign Language Processing. In signed languages, space is primarily used to establish reference. Solving coreference resolution for signed languages would not only enable higher-level Sign Language Processing systems, but also enhance our understanding of language in different modalities and of situated references, which are key problems in studying grounded language. In this paper, we: (1) introduce Signed Coreference Resolution (SCR), a new challenge for coreference modeling and Sign Language Processing; (2) collect an annotated corpus of German Sign Language with gold labels for coreference together with an annotation software for the task; (3) explore features of hand gesture, iconicity, and spatial situated properties and move forward to propose a set of linguistically informed heuristics and unsupervised models for the task; (4) put forward several proposals about ways to address the complexities of this challenge effectively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_406", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_406.flac", "type": "audio", "duration": 717.102}, {"from": "Assistant", "value": "We develop a novel approach for confidently accelerating inference in the large and expensive multilayer Transformers that are now ubiquitous in natural language processing (NLP). Amortized or approximate computational methods increase efficiency, but can come with unpredictable performance costs. In this work, we present CATs – Confident Adaptive Transformers – in which we simultaneously increase computational efficiency, while guaranteeing a specifiable degree of consistency with the original model with high confidence. Our method trains additional prediction heads on top of intermediate layers, and dynamically decides when to stop allocating computational effort to each input using a meta consistency classifier. To calibrate our early prediction stopping rule, we formulate a unique extension of conformal prediction. We demonstrate the effectiveness of this approach on four classification and regression tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_407", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_407.flac", "type": "audio", "duration": 448.981}, {"from": "Assistant", "value": "Recently, pre-trained language models (LMs) have achieved strong performance when fine-tuned on difficult benchmarks like SuperGLUE. However, performance can suffer when there are very few labeled examples available for fine-tuning. Pattern Exploiting Training (PET) is a recent approach that leverages patterns for few-shot learning. However, PET uses task-specific unlabeled data. In this paper, we focus on few-shot learning without any unlabeled data and introduce ADAPET, which modifies PET’s objective to provide denser supervision during fine-tuning. As a result, ADAPET outperforms PET on SuperGLUE without any task-specific unlabeled data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_408", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_408.flac", "type": "audio", "duration": 711.018}, {"from": "Assistant", "value": "Unsupervised Data Augmentation (UDA) is a semisupervised technique that applies a consistency loss to penalize differences between a model’s predictions on (a) observed (unlabeled) examples; and (b) corresponding ‘noised’ examples produced via data augmentation. While UDA has gained popularity for text classification, open questions linger over which design decisions are necessary and how to extend the method to sequence labeling tasks. In this paper, we re-examine UDA and demonstrate its efficacy on several sequential tasks. Our main contribution is an empirical study of UDA to establish which components of the algorithm confer benefits in NLP. Notably, although prior work has emphasized the use of clever augmentation techniques including back-translation, we find that enforcing consistency between predictions assigned to observed and randomly substituted words often yields comparable (or greater) benefits compared to these more complex perturbation models. Furthermore, we find that applying UDA’s consistency loss affords meaningful gains without any unlabeled data at all, i.e., in a standard supervised setting. In short, UDA need not be unsupervised to realize much of its noted benefits, and does not require complex data augmentation to be effective.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_409", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_409.flac", "type": "audio", "duration": 699.051}, {"from": "Assistant", "value": "Recent work has demonstrated that pre-training in-domain language models can boost performance when adapting to a new domain. However, the costs associated with pre-training raise an important question: given a fixed budget, what steps should an NLP practitioner take to maximize performance? In this paper, we study domain adaptation under budget constraints, and approach it as a customer choice problem between data annotation and pre-training. Specifically, we measure the annotation cost of three procedural text datasets and the pre-training cost of three in-domain language models. Then we evaluate the utility of different combinations of pre-training and data annotation under varying budget constraints to assess which combination strategy works best. We find that, for small budgets, spending all funds on annotation leads to the best performance; once the budget becomes large enough, a combination of data annotation and in-domain pre-training works more optimally. We therefore suggest that task-specific data annotation should be part of an economical strategy when adapting an NLP model to a new domain.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_410", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_410.flac", "type": "audio", "duration": 635.051}, {"from": "Assistant", "value": "Warning: this paper contains content that may be offensive or upsetting. Commonsense knowledge bases (CSKB) are increasingly used for various natural language processing tasks. Since CSKBs are mostly human-generated and may reflect societal biases, it is important to ensure that such biases are not conflated with the notion of commonsense. Here we focus on two widely used CSKBs, ConceptNet and GenericsKB, and establish the presence of bias in the form of two types of representational harms, overgeneralization of polarized perceptions and representation disparity across different demographic groups in both CSKBs. Next, we find similar representational harms for downstream models that use ConceptNet. Finally, we propose a filtering-based approach for mitigating such harms, and observe that our filtered-based approach can reduce the issues in both resources and models but leads to a performance drop, leaving room for future work to build fairer and stronger commonsense models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_411", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_411.flac", "type": "audio", "duration": 714.688}, {"from": "Assistant", "value": "Language representations are known to carry stereotypical biases and, as a result, lead to biased predictions in downstream tasks. While existing methods are effective at mitigating biases by linear projection, such methods are too aggressive: they not only remove bias, but also erase valuable information from word embeddings. We develop new measures for evaluating specific information retention that demonstrate the tradeoff between bias removal and information retention. To address this challenge, we propose OSCaR (Orthogonal Subspace Correction and Rectification), a bias-mitigating method that focuses on disentangling biased associations between concepts instead of removing concepts wholesale. Our experiments on gender biases show that OSCaR is a well-balanced approach that ensures that semantic information is retained in the embeddings and bias is also effectively mitigated.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_412", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_412.flac", "type": "audio", "duration": 722.048}, {"from": "Assistant", "value": "Generating paragraphs of diverse contents is important in many applications. Existing generation models produce similar contents from homogenized contexts due to the fixed left-to-right sentence order. Our idea is permuting the sentence orders to improve the content diversity of multi-sentence paragraph. We propose a novel framework PermGen whose objective is to maximize the expected log-likelihood of output paragraph distributions with respect to all possible sentence orders. PermGen uses hierarchical positional embedding and designs new procedures for training, and decoding in the sentence-permuted generation. Experiments on three paragraph generation benchmarks demonstrate PermGen generates more diverse outputs with a higher quality than existing models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_413", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_413.flac", "type": "audio", "duration": 711.367}, {"from": "Assistant", "value": "Prior studies on text-to-text generation typically assume that the model could figure out what to attend to in the input and what to include in the output via seq2seq learning, with only the parallel training data and no additional guidance. However, it remains unclear whether current models can preserve important concepts in the source input, as seq2seq learning does not have explicit focus on the concepts and commonly used evaluation metrics also treat them equally important as other tokens. In this paper, we present a systematic analysis that studies whether current seq2seq models, especially pre-trained language models, are good enough for preserving important input concepts and to what extent explicitly guiding generation with the concepts as lexical constraints is beneficial. We answer the above questions by conducting extensive analytical experiments on four representative text-to-text generation tasks. Based on the observations, we then propose a simple yet effective framework to automatically extract, denoise, and enforce important input concepts as lexical constraints. This new method performs comparably or better than its unconstrained counterpart on automatic metrics, demonstrates higher coverage for concept preservation, and receives better ratings in the human evaluation. Our code is available athttps://github.com/morningmoni/EDE.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_414", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_414.flac", "type": "audio", "duration": 691.931}, {"from": "Assistant", "value": "This paper focuses on paraphrase generation,which is a widely studied natural language generation task in NLP. With the development of neural models, paraphrase generation research has exhibited a gradual shift to neural methods in the recent years. This has provided architectures for contextualized representation of an input text and generating fluent, diverseand human-like paraphrases. This paper surveys various approaches to paraphrase generation with a main focus on neural methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_415", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_415.flac", "type": "audio", "duration": 718.059}, {"from": "Assistant", "value": "Exposure bias has been regarded as a central problem for auto-regressive language models (LM). It claims that teacher forcing would cause the test-time generation to be incrementally distorted due to the training-generation discrepancy. Although a lot of algorithms have been proposed to avoid teacher forcing and therefore alleviate exposure bias, there is little work showing how serious the exposure bias problem actually is. In this work, we focus on the task of open-ended language generation, propose metrics to quantify the impact of exposure bias in the aspects of quality, diversity, and consistency. Our key intuition is that if we feed ground-truth data prefixes (instead of prefixes generated by the model itself) into the model and ask it to continue the generation, the performance should become much better because the training-generation discrepancy in the prefix is removed. Both automatic and human evaluations are conducted in our experiments. On the contrary to the popular belief in exposure bias, we find that the the distortion induced by the prefix discrepancy is limited, and does not seem to be incremental during the generation. Moreover, our analysis reveals an interesting self-recovery ability of the LM, which we hypothesize to be countering the harmful effects from exposure bias.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_416", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_416.flac", "type": "audio", "duration": 690.048}, {"from": "Assistant", "value": "Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with self-contained, summary-centric questions and length-constrained, article-summarizing answers. We begin by collecting a new dataset of news articles with questions as titles and pairing them with summaries of varying length. This dataset is used to learn a QA pair generation model producing summaries as answers that balance brevity with sufficiency jointly with their corresponding questions. We then reinforce the QA pair generation process with a differentiable reward function to mitigate exposure bias, a common problem in natural language generation. Both automatic metrics and human evaluation demonstrate these QA pairs successfully capture the central gists of the articles and achieve high answer accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_417", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_417.flac", "type": "audio", "duration": 720.0}, {"from": "Assistant", "value": "Paraphrase generation has benefited extensively from recent progress in the designing of training objectives and model architectures. However, previous explorations have largely focused on supervised methods, which require a large amount of labeled data that is costly to collect. To address this drawback, we adopt a transfer learning approach and propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking (DB). To enforce a surface form dissimilar from the input, whenever the language model emits a token contained in the source sequence, DB prevents the model from outputting the subsequent source token for the next generation step. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair (QQP) and the ParaNMT datasets and is robust to domain shift between the two datasets of distinct distributions. We also demonstrate that our model transfers to paraphrasing in other languages without any additional finetuning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_419", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_419.flac", "type": "audio", "duration": 705.817}, {"from": "Assistant", "value": "The task of news article image captioning aims to generate descriptive and informative captions for news article images. Unlike conventional image captions that simply describe the content of the image in general terms, news image captions follow journalistic guidelines and rely heavily on named entities to describe the image content, often drawing context from the whole article they are associated with. In this work, we propose a new approach to this task, motivated by caption guidelines that journalists follow. Our approach, Journalistic Guidelines Aware News Image Captioning (JoGANIC), leverages the structure of captions to improve the generation quality and guide our representation design. Experimental results, including detailed ablation studies, on two large-scale publicly available datasets show that JoGANIC substantially outperforms state-of-the-art methods both on caption generation and named entity related metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_420", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_420.flac", "type": "audio", "duration": 671.253}, {"from": "Assistant", "value": "We propose to control paraphrase generation through carefully chosen target syntactic structures to generate more proper and higher quality paraphrases. Our model, AESOP, leverages a pretrained language model and adds deliberately chosen syntactical control via a retrieval-based selection module to generate fluent paraphrases. Experiments show that AESOP achieves state-of-the-art performances on semantic preservation and syntactic conformation on two benchmark datasets with ground-truth syntactic control from human-annotated exemplars. Moreover, with the retrieval-based target syntax selection module, AESOP generates paraphrases with even better qualities than the current best model using human-annotated target syntactic parses according to human evaluation. We further demonstrate the effectiveness of AESOP to improve classification models’ robustness to syntactic perturbation by data augmentation on two GLUE tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_421", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_421.flac", "type": "audio", "duration": 766.485}, {"from": "Assistant", "value": "Many NLG tasks such as summarization, dialogue response, or open domain question answering, focus primarily on a source text in order to generate a target response. This standard approach falls short, however, when a user’s intent or context of work is not easily recoverable based solely on that source text– a scenario that we argue is more of the rule than the exception. In this work, we argue that NLG systems in general should place a much higher level of emphasis on making use of additional context, and suggest that relevance (as used in Information Retrieval) be thought of as a crucial tool for designing user-oriented text-generating tasks. We further discuss possible harms and hazards around such personalization, and argue that value-sensitive design represents a crucial path forward through these challenges.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_422", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_422.flac", "type": "audio", "duration": 769.254}, {"from": "Assistant", "value": "Event schemas encode knowledge of stereotypical structures of events and their connections. As events unfold, schemas are crucial to act as a scaffolding. Previous work on event schema induction focuses either on atomic events or linear temporal event sequences, ignoring the interplay between events via arguments and argument relations. We introduce a new concept of Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations. In addition, we propose a Temporal Event Graph Model that predicts event instances following the temporal complex event schema. To build and evaluate such schemas, we release a new schema learning corpus containing 6,399 documents accompanied with event graphs, and we have manually constructed gold-standard schemas. Intrinsic evaluations by schema matching and instance graph perplexity, prove the superior quality of our probabilistic graph schema library compared to linear representations. Extrinsic evaluation on schema-guided future event prediction further demonstrates the predictive power of our event graph model, significantly outperforming human schemas and baselines by more than 17.8% on HITS@1.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_424", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_424.flac", "type": "audio", "duration": 706.049}, {"from": "Assistant", "value": "Scientific literature analysis needs fine-grained named entity recognition (NER) to provide a wide range of information for scientific discovery. For example, chemistry research needs to study dozens to hundreds of distinct, fine-grained entity types, making consistent and accurate annotation difficult even for crowds of domain experts. On the other hand, domain-specific ontologies and knowledge bases (KBs) can be easily accessed, constructed, or integrated, which makes distant supervision realistic for fine-grained chemistry NER. In distant supervision, training labels are generated by matching mentions in a document with the concepts in the knowledge bases (KBs). However, this kind of KB-matching suffers from two major challenges: incomplete annotation and noisy annotation. We propose ChemNER, an ontology-guided, distantly-supervised method for fine-grained chemistry NER to tackle these challenges. It leverages the chemistry type ontology structure to generate distant labels with novel methods of flexible KB-matching and ontology-guided multi-type disambiguation. It significantly improves the distant label generation for the subsequent sequence labeling model training. We also provide an expert-labeled, chemistry NER dataset with 62 fine-grained chemistry types (e.g., chemical compounds and chemical reactions). Experimental results show that ChemNER is highly effective, outperforming substantially the state-of-the-art NER methods (with .25 absolute F1 score improvement).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_425", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_425.flac", "type": "audio", "duration": 516.017}, {"from": "Assistant", "value": "Academic neural models for coreference resolution (coref) are typically trained on a single dataset, OntoNotes, and model improvements are benchmarked on that same dataset. However, real-world applications of coref depend on the annotation guidelines and the domain of the target dataset, which often differ from those of OntoNotes. We aim to quantify transferability of coref models based on the number of annotated documents available in the target dataset. We examine eleven target datasets and find that continued training is consistently effective and especially beneficial when there are few target documents. We establish new benchmarks across several datasets, including state-of-the-art results on PreCo.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_426", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_426.flac", "type": "audio", "duration": 692.303}, {"from": "Assistant", "value": "Document-level entity-based extraction (EE), aiming at extracting entity-centric information such as entity roles and entity relations, is key to automatic knowledge acquisition from text corpora for various domains. Most document-level EE systems build extractive models, which struggle to model long-term dependencies among entities at the document level. To address this issue, we propose a generative framework for two document-level EE tasks: role-filler entity extraction (REE) and relation extraction (RE). We first formulate them as a template generation problem, allowing models to efficiently capture cross-entity dependencies, exploit label semantics, and avoid the exponential computation complexity of identifying N-ary relations. A novel cross-attention guided copy mechanism, TopK Copy, is incorporated into a pre-trained sequence-to-sequence model to enhance the capabilities of identifying key information in the input document. Experiments done on the MUC-4 and SciREX dataset show new state-of-the-art results on REE (+3.26%), binary RE (+4.8%), and 4-ary RE (+2.7%) in F1 score.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_427", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_427.flac", "type": "audio", "duration": 615.829}, {"from": "Assistant", "value": "We address the sampling bias and outlier issues in few-shot learning for event detection, a subtask of information extraction. We propose to model the relations between training tasks in episodic few-shot learning by introducing cross-task prototypes. We further propose to enforce prediction consistency among classifiers across tasks to make the model more robust to outliers. Our extensive experiment shows a consistent improvement on three few-shot learning datasets. The findings suggest that our model is more robust when labeled data of novel event types is limited. The source code is available athttp://github.com/laiviet/fsl-proact.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_428", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_428.flac", "type": "audio", "duration": 692.523}, {"from": "Assistant", "value": "Traditional supervised Information Extraction (IE) methods can extract structured knowledge elements from unstructured data, but it is limited to a pre-defined target ontology. In reality, the ontology of interest may change over time, adding emergent new types or more fine-grained subtypes. We propose a new lifelong learning framework to address this challenge. We focus on lifelong event detection as an exemplar case and propose a new problem formulation that is also generalizable to other IE tasks. In event detection and more general IE tasks, rich correlations or semantic relatedness exist among hierarchical knowledge element types. In our proposed framework, knowledge is being transferred between learned old event types and new event types. Specifically, we update old knowledge with new event types’ mentions using a self-training loss. In addition, we aggregate old event types’ representations based on their similarities with new event types to initialize the new event types’ representations. Experimental results show that our framework outperforms competitive baselines with a 5.1% absolute gain in the F1 score. Moreover, our proposed framework can boost the F1 score for over 30% absolute gain on some new long-tail rare event types with few training instances. Our knowledge transfer module improves performance on both learned event types and new event types under the lifelong learning setting, showing that it helps consolidate old knowledge and improve novel knowledge acquisition.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_429", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_429.flac", "type": "audio", "duration": 681.045}, {"from": "Assistant", "value": "Extracting relations across large text spans has been relatively underexplored in NLP, but it is particularly important for high-value domains such as biomedicine, where obtaining high recall of the latest findings is crucial for practical applications. Compared to conventional information extraction confined to short text spans, document-level relation extraction faces additional challenges in both inference and learning. Given longer text spans, state-of-the-art neural architectures are less effective and task-specific self-supervision such as distant supervision becomes very noisy. In this paper, we propose decomposing document-level relation extraction into relation detection and argument resolution, taking inspiration from Davidsonian semantics. This enables us to incorporate explicit discourse modeling and leverage modular self-supervision for each sub-problem, which is less noise-prone and can be further refined end-to-end via variational EM. We conduct a thorough evaluation in biomedical machine reading for precision oncology, where cross-paragraph relation mentions are prevalent. Our method outperforms prior state of the art, such as multi-scale learning and graph neural networks, by over 20 absolute F1 points. The gain is particularly pronounced among the most challenging relation instances whose arguments never co-occur in a paragraph.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_431", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_431.flac", "type": "audio", "duration": 657.045}, {"from": "Assistant", "value": "Existing work on Fine-grained Entity Typing (FET) typically trains automatic models on the datasets obtained by using Knowledge Bases (KB) as distant supervision. However, the reliance on KB means this training setting can be hampered by the lack of or the incompleteness of the KB. To alleviate this limitation, we propose a novel setting for training FET models: FET without accessing any knowledge base. Under this setting, we propose a two-step framework to train FET models. In the first step, we automatically create pseudo data with fine-grained labels from a large unlabeled dataset. Then a neural network model is trained based on the pseudo data, either in an unsupervised way or using self-training under the weak guidance from a coarse-grained Named Entity Recognition (NER) model. Experimental results show that our method achieves competitive performance with respect to the models trained on the original KB-supervised datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_432", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_432.flac", "type": "audio", "duration": 678.742}, {"from": "Assistant", "value": "Recent literatures have shown that knowledge graph (KG) learning models are highly vulnerable to adversarial attacks. However, there is still a paucity of vulnerability analyses of cross-lingual entity alignment under adversarial attacks. This paper proposes an adversarial attack model with two novel attack techniques to perturb the KG structure and degrade the quality of deep cross-lingual entity alignment. First, an entity density maximization method is employed to hide the attacked entities in dense regions in two KGs, such that the derived perturbations are unnoticeable. Second, an attack signal amplification method is developed to reduce the gradient vanishing issues in the process of adversarial attacks for further improving the attack effectiveness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_433", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_433.flac", "type": "audio", "duration": 459.058}, {"from": "Assistant", "value": "In recent years, few-shot models have been applied successfully to a variety of NLP tasks. Han et al. (2018) introduced a few-shot learning framework for relation classification, and since then, several models have surpassed human performance on this task, leading to the impression that few-shot relation classification is solved. In this paper we take a deeper look at the efficacy of strong few-shot classification models in the more common relation extraction setting, and show that typical few-shot evaluation metrics obscure a wide variability in performance across relations. In particular, we find that state of the art few-shot relation classification models overly rely on entity type information, and propose modifications to the training routine to encourage models to better discriminate between relations involving similar entity types.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_434", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_434.flac", "type": "audio", "duration": 641.045}, {"from": "Assistant", "value": "Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models. However, most existing techniques focus on augmenting in-domain data in low-resource scenarios where annotated data is quite limited. In this work, we take this research direction to the opposite and study cross-domain data augmentation for the NER task. We investigate the possibility of leveraging data from high-resource domains by projecting it into the low-resource domains. Specifically, we propose a novel neural architecture to transform the data representation from a high-resource to a low-resource domain by learning the patterns (e.g. style, noise, abbreviations, etc.) in the text that differentiate them and a shared feature space where both domains are aligned. We experiment with diverse datasets and show that transforming the data to the low-resource domain representation achieves significant improvements over only using data from high-resource domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_435", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_435.flac", "type": "audio", "duration": 729.365}, {"from": "Assistant", "value": "In recent years pre-trained language models (PLM) such as BERT have proven to be very effective in diverse NLP tasks such as Information Extraction, Sentiment Analysis and Question Answering. Trained with massive general-domain text, these pre-trained language models capture rich syntactic, semantic and discourse information in the text. However, due to the differences between general and specific domain text (e.g., Wikipedia versus clinic notes), these models may not be ideal for domain-specific tasks (e.g., extracting clinical relations). Furthermore, it may require additional medical knowledge to understand clinical text properly. To solve these issues, in this research, we conduct a comprehensive examination of different techniques to add medical knowledge into a pre-trained BERT model for clinical relation extraction. Our best model outperforms the state-of-the-art systems on the benchmark i2b2/VA 2010 clinical relation extraction dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_436", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_436.flac", "type": "audio", "duration": 649.045}, {"from": "Assistant", "value": "While pre-trained language models (PTLMs) have achieved noticeable success on many NLP tasks, they still struggle for tasks that require event temporal reasoning, which is essential for event-centric applications. We present a continual pre-training approach that equips PTLMs with targeted knowledge about event temporal relations. We design self-supervised learning objectives to recover masked-out event and temporal indicators and to discriminate sentences from their corrupted counterparts (where event or temporal indicators got replaced). By further pre-training a PTLM with these objectives jointly, we reinforce its attention to event and temporal information, yielding enhanced capability on event temporal reasoning. This **E**ffective **CON**tinual pre-training framework for **E**vent **T**emporal reasoning (ECONET) improves the PTLMs’ fine-tuning performances across five relation extraction and question answering tasks and achieves new or on-par state-of-the-art performances in most of our downstream tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_437", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_437.flac", "type": "audio", "duration": 455.488}, {"from": "Assistant", "value": "Recent information extraction approaches have relied on training deep neural models. However, such models can easily overfit noisy labels and suffer from performance degradation. While it is very costly to filter noisy labels in large learning resources, recent studies show that such labels take more training steps to be memorized and are more frequently forgotten than clean labels, therefore are identifiable in training. Motivated by such properties, we propose a simple co-regularization framework for entity-centric information extraction, which consists of several neural models with identical structures but different parameter initialization. These models are jointly optimized with the task-specific losses and are regularized to generate similar predictions based on an agreement loss, which prevents overfitting on noisy labels. Extensive experiments on two widely used but noisy benchmarks for information extraction, TACRED and CoNLL03, demonstrate the effectiveness of our framework. We release our code to the community for future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_438", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_438.flac", "type": "audio", "duration": 643.051}, {"from": "Assistant", "value": "Machine learning-based prediction of material properties is often hampered by the lack of sufficiently large training data sets. The majority of such measurement data is embedded in scientific literature and the ability to automatically extract these data is essential to support the development of reliable property prediction methods. In this work, we describe a methodology for developing an automatic property extraction framework using material solubility as the target property. We create a training and evaluation data set containing tags for solubility-related entities using a combination of regular expressions and manual tagging. We then compare five entity recognition models leveraging both token-level and span-level architectures on the task of classifying solute names, solubility values, and solubility units. Additionally, we explore a novel pretraining approach that leverages automated chemical name and quantity extraction tools to generate large datasets that do not rely on intensive manual tagging. Finally, we perform an analysis to identify the causes of classification errors.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_439", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_439.flac", "type": "audio", "duration": 702.059}, {"from": "Assistant", "value": "The task of Event Detection (ED) in Information Extraction aims to recognize and classify trigger words of events in text. The recent progress has featured advanced transformer-based language models (e.g., BERT) as a critical component in state-of-the-art models for ED. However, the length limit for input texts is a barrier for such ED models as they cannot encode long-range document-level context that has been shown to be beneficial for ED. To address this issue, we propose a novel method to model document-level context for ED that dynamically selects relevant sentences in the document for the event prediction of the target sentence. The target sentence will be then augmented with the selected sentences and consumed entirely by transformer-based language models for improved representation learning for ED. To this end, the REINFORCE algorithm is employed to train the relevant sentence selection for ED. Several information types are then introduced to form the reward function for the training process, including ED performance, sentence similarity, and discourse relations. Our extensive experiments on multiple benchmark datasets reveal the effectiveness of the proposed model, leading to new state-of-the-art performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_440", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_440.flac", "type": "audio", "duration": 702.443}, {"from": "Assistant", "value": "Previous work on crosslingual Relation and Event Extraction (REE) suffers from the monolingual bias issue due to the training of models on only the source language data. An approach to overcome this issue is to use unlabeled data in the target language to aid the alignment of crosslingual representations, i.e., via fooling a language discriminator. However, as this approach does not condition on class information, a target language example of a class could be incorrectly aligned to a source language example of a different class. To address this issue, we propose a novel crosslingual alignment method that leverages class information of REE tasks for representation learning. In particular, we propose to learn two versions of representation vectors for each class in an REE task based on either source or target language examples. Representation vectors for corresponding classes will then be aligned to achieve class-aware alignment for crosslingual representations. In addition, we propose to further align representation vectors for language-universal word categories (i.e., parts of speech and dependency relations). As such, a novel filtering mechanism is presented to facilitate the learning of word category representations from contextualized representations on input texts based on adversarial learning. We conduct extensive crosslingual experiments with English, Chinese, and Arabic over REE tasks. The results demonstrate the benefits of the proposed method that significantly advances the state-of-the-art performance in these settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_441", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_441.flac", "type": "audio", "duration": 697.063}, {"from": "Assistant", "value": "Traditional event extraction methods require predefined event types and their corresponding annotations to learn event extractors. These prerequisites are often hard to be satisfied in real-world applications. This work presents a corpus-based open-domain event type induction method that automatically discovers a set of event types from a given corpus. As events of the same type could be expressed in multiple ways, we propose to represent each event type as a cluster of <predicate sense, object head> pairs. Specifically, our method (1) selects salient predicates and object heads, (2) disambiguates predicate senses using only a verb sense dictionary, and (3) obtains event types by jointly embedding and clustering <predicate sense, object head> pairs in a latent spherical space. Our experiments, on three datasets from different domains, show our method can discover salient and high-quality event types, according to both automatic and human evaluations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_442", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_442.flac", "type": "audio", "duration": 692.048}, {"from": "Assistant", "value": "Cross-domain Named Entity Recognition (NER) transfers the NER knowledge from high-resource domains to the low-resource target domain. Due to limited labeled resources and domain shift, cross-domain NER is a challenging task. To address these challenges, we propose a progressive domain adaptation Knowledge Distillation (KD) approach – PDALN. It achieves superior domain adaptability by employing three components: (1) Adaptive data augmentation techniques, which alleviate cross-domain gap and label sparsity simultaneously; (2) Multi-level Domain invariant features, derived from a multi-grained MMD (Maximum Mean Discrepancy) approach, to enable knowledge transfer across domains; (3) Advanced KD schema, which progressively enables powerful pre-trained language models to perform domain adaptation. Extensive experiments on four benchmarks show that PDALN can effectively adapt high-resource domains to low-resource target domains, even if they are diverse in terms and writing styles. Comparison with other baselines indicates the state-of-the-art performance of PDALN.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_443", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_443.flac", "type": "audio", "duration": 445.973}, {"from": "Assistant", "value": "Large-scale document retrieval systems often utilize two styles of neural network models which live at two different ends of the joint computation vs. accuracy spectrum. The first style is dual encoder (or two-tower) models, where the query and document representations are computed completely independently and combined with a simple dot product operation. The second style is cross-attention models, where the query and document features are concatenated in the input layer and all computation is based on the joint query-document representation. Dual encoder models are typically used for retrieval and deep re-ranking, while cross-attention models are typically used for shallow re-ranking. In this paper, we present a lightweight architecture that explores this joint cost vs. accuracy trade-off based on multi-vector attention (MVA). We thoroughly evaluate our method on the MS-MARCO passage retrieval dataset and show how to efficiently trade off retrieval accuracy with joint computation and offline document storage cost. We show that a highly compressed document representation and inexpensive joint computation can be achieved through a combination of learned pooling tokens and aggressive downprojection. Our code and model checkpoints are open-source and available on GitHub.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_444", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_444.flac", "type": "audio", "duration": 480.421}, {"from": "Assistant", "value": "The goal of question answering (QA) is to answer _any_ question. However, major QA datasets have skewed distributions over gender, profession, and nationality. Despite that skew, an analysis of model accuracy reveals little evidence that accuracy is lower for people based on gender or nationality; instead, there is more variation on professions (question topic) and question ambiguity. But QA’s lack of representation could itself hide evidence of bias, necessitating QA datasets that better represent global diversity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_445", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_445.flac", "type": "audio", "duration": 477.936}, {"from": "Assistant", "value": "Commonsense reasoning benchmarks have been largely solved by fine-tuning language models. The downside is that fine-tuning may cause models to overfit to task-specific data and thereby forget their knowledge gained during pre-training. Recent works only propose lightweight model updates as models may already possess useful knowledge from past experience, but a challenge remains in understanding what parts and to what extent models should be refined for a given task. In this paper, we investigate what models learn from commonsense reasoning datasets. We measure the impact of three different adaptation methods on the generalization and accuracy of models. Our experiments with two models show that fine-tuning performs best, by learning both the content and the structure of the task, but suffers from overfitting and limited generalization to novel answers. We observe that alternative adaptation methods like prefix-tuning have comparable accuracy, but generalize better to unseen answers and are more robust to adversarial splits.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_446", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_446.flac", "type": "audio", "duration": 715.051}, {"from": "Assistant", "value": "Feed-forward layers constitute two-thirds of a transformer model’s parameters, yet their role in the network remains under-explored. We show that feed-forward layers in transformer-based language models operate as key-value memories, where each key correlates with textual patterns in the training examples, and each value induces a distribution over the output vocabulary. Our experiments show that the learned patterns are human-interpretable, and that lower layers tend to capture shallow patterns, while upper layers learn more semantic ones. The values complement the keys’ input patterns by inducing output distributions that concentrate probability mass on tokens likely to appear immediately after each pattern, particularly in the upper layers. Finally, we demonstrate that the output of a feed-forward layer is a composition of its memories, which is subsequently refined throughout the model’s layers via residual connections to produce the final output distribution.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_447", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_447.flac", "type": "audio", "duration": 700.011}, {"from": "Assistant", "value": "When a model attribution technique highlights a particular part of the input, a user might understand this highlight as making a statement about counterfactuals (Miller, 2019): if that part of the input were to change, the model’s prediction might change as well. This paper investigates how well different attribution techniques align with this assumption on realistic counterfactuals in the case of reading comprehension (RC). RC is a particularly challenging test case, as token-level attributions that have been extensively studied in other NLP tasks such as sentiment analysis are less suitable to represent the reasoning that RC models perform. We construct counterfactual sets for three different RC settings, and through heuristics that can connect attribution methods’ outputs to high-level model behavior, we can evaluate how useful different attribution methods and even different formats are for understanding counterfactuals. We find that pairwise attributions are better suited to RC than token-level attributions across these different RC settings, with our best performance coming from a modification that we propose to an existing pairwise attribution method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_448", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_448.flac", "type": "audio", "duration": 695.573}, {"from": "Assistant", "value": "After a neural sequence model encounters an unexpected token, can its behavior be predicted? We show that RNN and transformer language models exhibit structured, consistent generalization in out-of-distribution contexts. We begin by introducing two idealized models of generalization in next-word prediction: a lexical context model in which generalization is consistent with the last word observed, and a syntactic context model in which generalization is consistent with the global structure of the input. In experiments in English, Finnish, Mandarin, and random regular languages, we demonstrate that neural language models interpolate between these two forms of generalization: their predictions are well-approximated by a log-linear combination of lexical and syntactic predictive distributions. We then show that, in some languages, noise mediates the two forms of generalization: noise applied to input tokens encourages syntactic generalization, while noise in history representations encourages lexical generalization. Finally, we offer a preliminary theoretical explanation of these results by proving that the observed interpolation behavior is expected in log-linear models with a particular feature correlation structure. These results help explain the effectiveness of two popular regularization schemes and show that aspects of sequence model generalization can be understood and controlled.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_449", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_449.flac", "type": "audio", "duration": 702.059}, {"from": "Assistant", "value": "Much of the progress in contemporary NLP has come from learning representations, such as masked language model (MLM) contextual embeddings, that turn challenging problems into simple classification tasks. But how do we quantify and explain this effect? We adapt general tools from computational learning theory to fit the specific characteristics of text datasets and present a method to evaluate the compatibility between representations and tasks. Even though many tasks can be easily solved with simple bag-of-words (BOW) representations, BOW does poorly on hard natural language inference tasks. For one such task we find that BOW cannot distinguish between real and randomized labelings, while pre-trained MLM representations show 72x greater distinction between real and random labelings than BOW. This method provides a calibrated, quantitative measure of the difficulty of a classification-based NLP task, enabling comparisons between representations without requiring empirical evaluations that may be sensitive to initializations and hyperparameters. The method provides a fresh perspective on the patterns in a dataset and the alignment of those patterns with specific labels.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_450", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_450.flac", "type": "audio", "duration": 689.563}, {"from": "Assistant", "value": "As NLP systems become better at detecting opinions and beliefs from text, it is important to ensure not only that models are accurate but also that they arrive at their predictions in ways that align with human reasoning. In this work, we present a method for imparting human-like rationalization to a stance detection model using crowdsourced annotations on a small fraction of the training data. We show that in a data-scarce setting, our approach can improve the reasoning of a state-of-the-art classifier—particularly for inputs containing challenging phenomena such as sarcasm—at no cost in predictive performance. Furthermore, we demonstrate that attention weights surpass a leading attribution method in providing faithful explanations of our model’s predictions, thus serving as a computationally cheap and reliable source of attributions for our model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_451", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_451.flac", "type": "audio", "duration": 687.125}, {"from": "Assistant", "value": "Multi-task learning with transformer encoders (MTL) has emerged as a powerful technique to improve performance on closely-related tasks for both accuracy and efficiency while a question still remains whether or not it would perform as well on tasks that are distinct in nature. We first present MTL results on five NLP tasks, POS, NER, DEP, CON, and SRL, and depict its deficiency over single-task learning. We then conduct an extensive pruning analysis to show that a certain set of attention heads get claimed by most tasks during MTL, who interfere with one another to fine-tune those heads for their own objectives. Based on this finding, we propose the Stem Cell Hypothesis to reveal the existence of attention heads naturally talented for many tasks that cannot be jointly trained to create adequate embeddings for all of those tasks. Finally, we design novel parameter-free probes to justify our hypothesis and demonstrate how attention heads are transformed across the five tasks during MTL through label analysis.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_452", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_452.flac", "type": "audio", "duration": 708.046}, {"from": "Assistant", "value": "We study the problem of generating counterfactual text for a classifier as a means for understanding and debugging classification. Given a textual input and a classification model, we aim to minimally alter the text to change the model’s prediction. White-box approaches have been successfully applied to similar problems in vision where one can directly optimize the continuous input. Optimization-based approaches become difficult in the language domain due to the discrete nature of text. We bypass this issue by directly optimizing in the latent space and leveraging a language model to generate candidate modifications from optimized latent representations. We additionally use Shapley values to estimate the combinatoric effect of multiple changes. We then use these estimates to guide a beam search for the final counterfactual text. We achieve favorable performance compared to recent white-box and black-box baselines using human and automatic evaluations. Ablation studies show that both latent optimization and the use of Shapley values improve success rate and the quality of the generated counterfactuals.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_453", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_453.flac", "type": "audio", "duration": 454.528}, {"from": "Assistant", "value": "Contextualized representations based on neural language models have furthered the state of the art in various NLP tasks. Despite its great success, the nature of such representations remains a mystery. In this paper, we present an empirical property of these representations—”average” approximates “first principal component”. Specifically, experiments show that the average of these representations shares almost the same direction as the first principal component of the matrix whose columns are these representations. We believe this explains why the average representation is always a simple yet strong baseline. Our further examinations show that this property also holds in more challenging scenarios, for example, when the representations are from a model right after its random initialization. Therefore, we conjecture that this property is intrinsic to the distribution of representations and not necessarily related to the input structure. We realize that these representations empirically follow a normal distribution for each dimension, and by assuming this is true, we demonstrate that the empirical property can be in fact derived mathematically.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_454", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_454.flac", "type": "audio", "duration": 712.62}, {"from": "Assistant", "value": "Prior work has shown that structural supervision helps English language models learn generalizations about syntactic phenomena such as subject-verb agreement. However, it remains unclear if such an inductive bias would also improve language models’ ability to learn grammatical dependencies in typologically different languages. Here we investigate this question in Mandarin Chinese, which has a logographic, largely syllable-based writing system; different word order; and sparser morphology than English. We train LSTMs, Recurrent Neural Network Grammars, Transformer language models, and Transformer-parameterized generative parsing models on two Mandarin Chinese datasets of different sizes. We evaluate the models’ ability to learn different aspects of Mandarin grammar that assess syntactic and semantic relationships. We find suggestive evidence that structural supervision helps with representing syntactic state across intervening content and improves performance in low-data settings, suggesting that the benefits of hierarchical inductive biases in acquiring dependency relationships may extend beyond English.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_455", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_455.flac", "type": "audio", "duration": 714.803}, {"from": "Assistant", "value": "A key problem in multi-task learning (MTL) research is how to select high-quality auxiliary tasks automatically. This paper presents GradTS, an automatic auxiliary task selection method based on gradient calculation in Transformer-based models. Compared to AUTOSEM, a strong baseline method, GradTS improves the performance of MT-DNN with a bert-base-cased backend model, from 0.33% to 17.93% on 8 natural language understanding (NLU) tasks in the GLUE benchmarks. GradTS is also time-saving since (1) its gradient calculations are based on single-task experiments and (2) the gradients are re-used without additional experiments when the candidate task set changes. On the 8 GLUE classification tasks, for example, GradTS costs on average 21.32% less time than AUTOSEM with comparable GPU consumption. Further, we show the robustness of GradTS across various task settings and model selections, e.g. mixed objectives among candidate tasks. The efficiency and efficacy of GradTS in these case studies illustrate its general applicability in MTL research without requiring manual task filtering or costly parameter tuning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_456", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_456.flac", "type": "audio", "duration": 742.017}, {"from": "Assistant", "value": "Codifying commonsense knowledge in machines is a longstanding goal of artificial intelligence. Recently, much progress toward this goal has been made with automatic knowledge base (KB) construction techniques. However, such techniques focus primarily on the acquisition of positive (true) KB statements, even though negative (false) statements are often also important for discriminative reasoning over commonsense KBs. As a first step toward the latter, this paper proposes NegatER, a framework that ranks potential negatives in commonsense KBs using a contextual language model (LM). Importantly, as most KBs do not contain negatives, NegatER relies only on the positive knowledge in the LM and does not require ground-truth negative examples. Experiments demonstrate that, compared to multiple contrastive data augmentation approaches, NegatER yields negatives that are more grammatical, coherent, and informative—leading to statistically significant accuracy improvements in a challenging KB completion task and confirming that the positive knowledge in LMs can be “re-purposed” to generate negative knowledge.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_457", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_457.flac", "type": "audio", "duration": 721.003}, {"from": "Assistant", "value": "In order to alleviate the huge demand for annotated datasets for different tasks, many recent natural language processing datasets have adopted automated pipelines for fast-tracking usable data. However, model training with such datasets poses a challenge because popular optimization objectives are not robust to label noise induced in the annotation generation process. Several noise-robust losses have been proposed and evaluated on tasks in computer vision, but they generally use a single dataset-wise hyperparamter to control the strength of noise resistance. This work proposes novel instance-adaptive training frameworks to change single dataset-wise hyperparameters of noise resistance in such losses to be instance-wise. Such instance-wise noise resistance hyperparameters are predicted by special instance-level label quality predictors, which are trained along with the main classification models. Experiments on noisy and corrupted NLP datasets show that proposed instance-adaptive training frameworks help increase the noise-robustness provided by such losses, promoting the use of the frameworks and associated losses in NLP models trained with noisy data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_458", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_458.flac", "type": "audio", "duration": 703.054}, {"from": "Assistant", "value": "Multilingual neural machine translation (MNMT) learns to translate multiple language pairs with a single model, potentially improving both the accuracy and the memory-efficiency of deployed models. However, the heavy data imbalance between languages hinders the model from performing uniformly across language pairs. In this paper, we propose a new learning objective for MNMT based on distributionally robust optimization, which minimizes the worst-case expected loss over the set of language pairs. We further show how to practically optimize this objective for large translation corpora using an iterated best response scheme, which is both effective and incurs negligible additional computational cost compared to standard empirical risk minimization. We perform extensive experiments on three sets of languages from two datasets and show that our method consistently outperforms strong baseline methods in terms of average and per-language performance under both many-to-one and one-to-many translation settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_459", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_459.flac", "type": "audio", "duration": 577.062}, {"from": "Assistant", "value": "Transformers that are pre-trained on multilingual corpora, such as, mBERT and XLM-RoBERTa, have achieved impressive cross-lingual transfer capabilities. In the zero-shot transfer setting, only English training data is used, and the fine-tuned model is evaluated on another target language. While this works surprisingly well, substantial variance has been observed in target language performance between different fine-tuning runs, and in the zero-shot setup, no target-language development data is available to select among multiple fine-tuned models. Prior work has relied on English dev data to select among models that are fine-tuned with different learning rates, number of steps and other hyperparameters, often resulting in suboptimal choices. In this paper, we show that it is possible to select consistently better models when small amounts of annotated data are available in auxiliary pivot languages. We propose a machine learning approach to model selection that uses the fine-tuned model’s own internal representations to predict its cross-lingual capabilities. In extensive experiments we find that this method consistently selects better models than English validation data across twenty five languages (including eight low-resource languages), and often achieves results that are comparable to model selection using target language development data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_460", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_460.flac", "type": "audio", "duration": 677.047}, {"from": "Assistant", "value": "Natural Language Processing (NLP) is increasingly relying on general end-to-end systems that need to handle many different linguistic phenomena and nuances. For example, a Natural Language Inference (NLI) system has to recognize sentiment, handle numbers, perform coreference, etc. Our solutions to complex problems are still far from perfect, so it is important to create systems that can learn to correct mistakes quickly, incrementally, and with little training data. In this work, we propose a continual few-shot learning (CFL) task, in which a system is challenged with a difficult phenomenon and asked to learn to correct mistakes with only a few (10 to 15) training examples. To this end, we first create benchmarks based on previously annotated data: two NLI (ANLI and SNLI) and one sentiment analysis (IMDB) datasets. Next, we present various baselines from diverse paradigms (e.g., memory-aware synapses and Prototypical networks) and compare them on few-shot learning and continual few-shot learning setups. Our contributions are in creating a benchmark suite and evaluation protocol for continual few-shot learning on the text classification tasks, and making several interesting observations on the behavior of similarity-based methods. We hope that our work serves as a useful starting point for future work on this important topic.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_461", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_461.flac", "type": "audio", "duration": 654.06}, {"from": "Assistant", "value": "Non-parametric neural language models (NLMs) learn predictive distributions of text utilizing an external datastore, which allows them to learn through explicitly memorizing the training datapoints. While effective, these models often require retrieval from a large datastore at test time, significantly increasing the inference overhead and thus limiting the deployment of non-parametric NLMs in practical applications. In this paper, we take the recently proposed k-nearest neighbors language model as an example, exploring methods to improve its efficiency along various dimensions. Experiments on the standard WikiText-103 benchmark and domain-adaptation datasets show that our methods are able to achieve up to a 6x speed-up in inference speed while retaining comparable performance. The empirical analysis we present may provide guidelines for future research seeking to develop or deploy more efficient non-parametric NLMs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_462", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_462.flac", "type": "audio", "duration": 702.059}, {"from": "Assistant", "value": "Despite their recent successes in tackling many NLP tasks, large-scale pre-trained language models do not perform as well in few-shot settings where only a handful of training examples are available. To address this shortcoming, we propose STraTA, which stands for Self-Training with Task Augmentation, an approach that builds on two key ideas for effective leverage of unlabeled data. First, STraTA uses task augmentation, a novel technique that synthesizes a large amount of data for auxiliary-task fine-tuning from target-task unlabeled texts. Second, STraTA performs self-training by further fine-tuning the strong base model created by task augmentation on a broad distribution of pseudo-labeled data. Our experiments demonstrate that STraTA can substantially improve sample efficiency across 12 few-shot benchmarks. Remarkably, on the SST-2 sentiment dataset, STraTA, with only 8 training examples per class, achieves comparable results to standard fine-tuning with 67K training examples. Our analyses reveal that task augmentation and self-training are both complementary and independently effective.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_463", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_463.flac", "type": "audio", "duration": 700.053}, {"from": "Assistant", "value": "The paradigm of pre-training followed by finetuning has become a standard procedure for NLP tasks, with a known problem of domain shift between the pre-training and downstream corpus. Previous works have tried to mitigate this problem with additional pre-training, either on the downstream corpus itself when it is large enough, or on a manually curated unlabeled corpus of a similar domain. In this paper, we address the problem for the case when the downstream corpus is too small for additional pre-training. We propose TADPOLE, a task adapted pre-training framework based on data selection techniques adapted fromDomain Adaptation. We formulate the data selection as an anomaly detection problem that unlike existing methods works well when the downstream corpus is limited in size. It results in a scalable and efficient unsupervised technique that eliminates the need for any manual data curation. We evaluate our framework on eight tasks across four different domains: Biomedical, Computer Science, News, and Movie reviews, and compare its performance against competitive baseline techniques from the area of Domain Adaptation. Our framework outperforms all the baseline methods. On small datasets with less than 5K training examples, we get a gain of 1.82% in performance with additional pre-training for only 5% steps compared to the originally pre-trained models. It also compliments some of the other techniques such as data augmentation known for boosting performance when downstream corpus is small; highest performance is achieved when data augmentation is combined with task adapted pre-training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_464", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_464.flac", "type": "audio", "duration": 561.045}, {"from": "Assistant", "value": "We propose the first general-purpose gradient-based adversarial attack against transformer models. Instead of searching for a single adversarial example, we search for a distribution of adversarial examples parameterized by a continuous-valued matrix, hence enabling gradient-based optimization. We empirically demonstrate that our white-box attack attains state-of-the-art attack performance on a variety of natural language tasks, outperforming prior work in terms of adversarial success rate with matching imperceptibility as per automated and human evaluation. Furthermore, we show that a powerful black-box transfer attack, enabled by sampling from the adversarial distribution, matches or exceeds existing methods, while only requiring hard-label outputs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_465", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_465.flac", "type": "audio", "duration": 630.059}, {"from": "Assistant", "value": "The research community has proposed copious modifications to the Transformer architecture since it was introduced over three years ago, relatively few of which have seen widespread adoption. In this paper, we comprehensively evaluate many of these modifications in a shared experimental setting that covers most of the common uses of the Transformer in natural language processing. Surprisingly, we find that most modifications do not meaningfully improve performance. Furthermore, most of the Transformer variants we found beneficial were either developed in the same codebase that we used or are relatively minor changes. We conjecture that performance improvements may strongly depend on implementation details and correspondingly make some recommendations for improving the generality of experimental results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_467", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_467.flac", "type": "audio", "duration": 720.043}, {"from": "Assistant", "value": "Many recent successes in sentence representation learning have been achieved by simply fine-tuning on the Natural Language Inference (NLI) datasets with triplet loss or siamese loss. Nevertheless, they share a common weakness: sentences in a contradiction pair are not necessarily from different semantic categories. Therefore, optimizing the semantic entailment and contradiction reasoning objective alone is inadequate to capture the high-level semantic structure. The drawback is compounded by the fact that the vanilla siamese or triplet losses only learn from individual sentence pairs or triplets, which often suffer from bad local optima. In this paper, we propose PairSupCon, an instance discrimination based approach aiming to bridge semantic entailment and contradiction understanding with high-level categorical concept encoding. We evaluate PairSupCon on various downstream tasks that involve understanding sentence semantics at different granularities. We outperform the previous state-of-the-art method with 10%–13% averaged improvement on eight clustering tasks, and 5%–6% averaged improvement on seven semantic textual similarity (STS) tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_468", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_468.flac", "type": "audio", "duration": 412.971}, {"from": "Assistant", "value": "We propose pre-finetuning, an additional large-scale learning stage between language model pre-training and fine-tuning. Pre-finetuning is massively multi-task learning (around 50 datasets, over 4.8 million total labeled examples), and is designed to encourage learning of representations that generalize better to many different tasks. We show that pre-finetuning consistently improves performance for pretrained discriminators (e.g. RoBERTa) and generation models (e.g. BART) on a wide range of tasks (sentence prediction, commonsense reasoning, MRC, etc.), while also significantly improving sample efficiency during fine-tuning. We also show that large-scale multi-tasking is crucial; pre-finetuning can hurt performance when few tasks are used up until a critical point (usually above 15) after which performance improves linearly in the number of tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_469", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_469.flac", "type": "audio", "duration": 720.683}, {"from": "Assistant", "value": "Meta-learning considers the problem of learning an efficient learning process that can leverage its past experience to accurately solve new tasks. However, the efficacy of meta-learning crucially depends on the distribution of tasks available for training, and this is often assumed to be known a priori or constructed from limited supervised datasets. In this work, we aim to provide task distributions for meta-learning by considering self-supervised tasks automatically proposed from unlabeled text, to enable large-scale meta-learning in NLP. We design multiple distributions of self-supervised tasks by considering important aspects of task diversity, difficulty, type, domain, and curriculum, and investigate how they affect meta-learning performance. Our analysis shows that all these factors meaningfully alter the task distribution, some inducing significant improvements in downstream few-shot accuracy of the meta-learned models. Empirically, results on 20 downstream tasks show significant improvements in few-shot learning – adding up to +4.2% absolute accuracy (on average) to the previous unsupervised meta-learning method, and perform comparably to supervised methods on the FewRel 2.0 benchmark.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_470", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_470.flac", "type": "audio", "duration": 479.724}, {"from": "Assistant", "value": "Language agnostic and semantic-language information isolation is an emerging research direction for multilingual representations models. We explore this problem from a novel angle of geometric algebra and semantic space. A simple but highly effective method “Language Information Removal (LIR)” factors out language identity information from semantic related components in multilingual representations pre-trained on multi-monolingual data. A post-training and model-agnostic method, LIR only uses simple linear operations, e.g. matrix factorization and orthogonal projection. LIR reveals that for weak-alignment multilingual systems, the principal components of semantic spaces primarily encodes language identity information. We first evaluate the LIR on a cross-lingual question answer retrieval task (LAReQA), which requires the strong alignment for the multilingual embedding space. Experiment shows that LIR is highly effectively on this task, yielding almost 100% relative improvement in MAP for weak-alignment models. We then evaluate the LIR on Amazon Reviews and XEVAL dataset, with the observation that removing language information is able to improve the cross-lingual transfer performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_471", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_471.flac", "type": "audio", "duration": 719.702}, {"from": "Assistant", "value": "In cross-lingual language models, representations for many different languages live in the same space. Here, we investigate the linguistic and non-linguistic factors affecting sentence-level alignment in cross-lingual pretrained language models for 101 languages and 5,050 language pairs. Using BERT-based LaBSE and BiLSTM-based LASER as our models, and the Bible as our corpus, we compute a task-based measure of cross-lingual alignment in the form of bitext retrieval performance, as well as four intrinsic measures of vector space alignment and isomorphism. We then examine a range of linguistic, quasi-linguistic, and training-related features as potential predictors of these alignment metrics. The results of our analyses show that word order agreement and agreement in morphological complexity are two of the strongest linguistic predictors of cross-linguality. We also note in-family training data as a stronger predictor than language-specific training data across the board. We verify some of our linguistic findings by looking at the effect of morphological segmentation on English-Inuktitut alignment, in addition to examining the effect of word order agreement on isomorphism for 66 zero-shot language pairs from a different corpus. We make the data and code for our experiments publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_472", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_472.flac", "type": "audio", "duration": 480.049}, {"from": "Assistant", "value": "The availability of corpora has led to significant advances in training semantic parsers in English. Unfortunately, for languages other than English, annotated data is limited and so is the performance of the developed parsers. Recently, pretrained multilingual models have been proven useful for zero-shot cross-lingual transfer in many NLP tasks. What else does it require to apply a parser trained in English to other languages for zero-shot cross-lingual semantic parsing? Will simple language-independent features help? To this end, we experiment with six Discourse Representation Structure (DRS) semantic parsers in English, and generalize them to Italian, German and Dutch, where there are only a small number of manually annotated parses available. Extensive experiments show that despite its simplicity, adding Universal Dependency (UD) relations and Universal POS tags (UPOS) as model-agnostic features achieves surprisingly strong improvement on all parsers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_473", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_473.flac", "type": "audio", "duration": 472.062}, {"from": "Assistant", "value": "Simultaneous translation is vastly different from full-sentence translation, in the sense that it starts translation before the source sentence ends, with only a few words delay. However, due to the lack of large-scale, high-quality simultaneous translation datasets, most such systems are still trained on conventional full-sentence bitexts. This is far from ideal for the simultaneous scenario due to the abundance of unnecessary long-distance reorderings in those bitexts. We propose a novel method that rewrites the target side of existing full-sentence corpora into simultaneous-style translation. Experiments on Zh→En and Ja→En simultaneous translation show substantial improvements (up to +2.7 BLEU) with the addition of these generated pseudo-references.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_474", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_474.flac", "type": "audio", "duration": 713.579}, {"from": "Assistant", "value": "Sentence-level Quality estimation (QE) of machine translation is traditionally formulated as a regression task, and the performance of QE models is typically measured by Pearson correlation with human labels. Recent QE models have achieved previously-unseen levels of correlation with human judgments, but they rely on large multilingual contextualized language models that are computationally expensive and make them infeasible for real-world applications. In this work, we evaluate several model compression techniques for QE and find that, despite their popularity in other NLP tasks, they lead to poor performance in this regression setting. We observe that a full model parameterization is required to achieve SoTA results in a regression task. However, we argue that the level of expressiveness of a model in a continuous range is unnecessary given the downstream applications of QE, and show that reframing QE as a classification problem and evaluating QE models using classification metrics would better reflect their actual performance in real-world applications.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_475", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_475.flac", "type": "audio", "duration": 660.096}, {"from": "Assistant", "value": "Recent advances in neural machine translation (NMT) have pushed the quality of machine translation systems to the point where they are becoming widely adopted to build competitive systems. However, there is still a large number of languages that are yet to reap the benefits of NMT. In this paper, we provide the first large-scale case study of the practical application of MT in the Turkic language family in order to realize the gains of NMT for Turkic languages under high-resource to extremely low-resource scenarios. In addition to presenting an extensive analysis that identifies the bottlenecks towards building competitive systems to ameliorate data scarcity, our study has several key contributions, including, i) a large parallel corpus covering 22 Turkic languages consisting of common public datasets in combination with new datasets of approximately 1.4 million parallel sentences, ii) bilingual baselines for 26 language pairs, iii) novel high-quality test sets in three different translation domains and iv) human evaluation scores. All models, scripts, and data will be released to the public.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_476", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_476.flac", "type": "audio", "duration": 654.779}, {"from": "Assistant", "value": "Word embeddings are powerful representations that form the foundation of many natural language processing architectures, both in English and in other languages. To gain further insight into word embeddings, we explore their stability (e.g., overlap between the nearest neighbors of a word in different embedding spaces) in diverse languages. We discuss linguistic properties that are related to stability, drawing out insights about correlations with affixing, language gender systems, and other features. This has implications for embedding use, particularly in research that uses them to study language trends.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_477", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_477.flac", "type": "audio", "duration": 712.249}, {"from": "Assistant", "value": "Current approaches to incorporating terminology constraints in machine translation (MT) typically assume that the constraint terms are provided in their correct morphological forms. This limits their application to real-world scenarios where constraint terms are provided as lemmas. In this paper, we introduce a modular framework for incorporating lemma constraints in neural MT (NMT) in which linguistic knowledge and diverse types of NMT models can be flexibly applied. It is based on a novel cross-lingual inflection module that inflects the target lemma constraints based on the source context. We explore linguistically motivated rule-based and data-driven neural-based inflection modules and design English-German health and English-Lithuanian news test suites to evaluate them in domain adaptation and low-resource MT settings. Results show that our rule-based inflection module helps NMT models incorporate lemma constraints more accurately than a neural module and outperforms the existing end-to-end approach with lower training costs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_478", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_478.flac", "type": "audio", "duration": 449.045}, {"from": "Assistant", "value": "We observe that the development cross-entropy loss of supervised neural machine translation models scales like a power law with the amount of training data and the number of non-embedding parameters in the model. We discuss some practical implications of these results, such as predicting BLEU achieved by large scale models and predicting the ROI of labeling data in low-resource language pairs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_479", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_479.flac", "type": "audio", "duration": 396.968}, {"from": "Assistant", "value": "This paper asks whether extrapolating the hidden space distribution of text examples from one class onto another is a valid inductive bias for data augmentation. To operationalize this question, I propose a simple data augmentation protocol called “good-enough example extrapolation” (GE3). GE3 is lightweight and has no hyperparameters. Applied to three text classification datasets for various data imbalance scenarios, GE3 improves performance more than upsampling and other hidden-space data augmentation methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_480", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_480.flac", "type": "audio", "duration": 693.023}, {"from": "Assistant", "value": "Paraphrase generation is a longstanding NLP task that has diverse applications on downstream NLP tasks. However, the effectiveness of existing efforts predominantly relies on large amounts of golden labeled data. Though unsupervised endeavors have been proposed to alleviate this issue, they may fail to generate meaningful paraphrases due to the lack of supervision signals. In this work, we go beyond the existing paradigms and propose a novel approach to generate high-quality paraphrases with data of weak supervision. Specifically, we tackle the weakly-supervised paraphrase generation problem by: (1) obtaining abundant weakly-labeled parallel sentences via retrieval-based pseudo paraphrase expansion; and (2) developing a meta-learning framework to progressively select valuable samples for fine-tuning a pre-trained language model BART on the sentential paraphrasing task. We demonstrate that our approach achieves significant improvements over existing unsupervised approaches, and is even comparable in performance with supervised state-of-the-arts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_481", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_481.flac", "type": "audio", "duration": 697.216}, {"from": "Assistant", "value": "Multi-label document classification (MLDC) problems can be challenging, especially for long documents with a large label set and a long-tail distribution over labels. In this paper, we present an effective convolutional attention network for the MLDC problem with a focus on medical code prediction from clinical documents. Our innovations are three-fold: (1) we utilize a deep convolution-based encoder with the squeeze-and-excitation networks and residual networks to aggregate the information across the document and learn meaningful document representations that cover different ranges of texts; (2) we explore multi-layer and sum-pooling attention to extract the most informative features from these multi-scale representations; (3) we combine binary cross entropy loss and focal loss to improve performance for rare labels. We focus our evaluation study on MIMIC-III, a widely used dataset in the medical domain. Our models outperform prior work on medical coding and achieve new state-of-the-art results on multiple metrics. We also demonstrate the language independent nature of our approach by applying it to two non-English datasets. Our model outperforms prior best model and a multilingual Transformer model by a substantial margin.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_482", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_482.flac", "type": "audio", "duration": 733.216}, {"from": "Assistant", "value": "Recent work learns contextual representations of source code by reconstructing tokens from their context. For downstream semantic understanding tasks like code clone detection, these representations should ideally capture program functionality. However, we show that the popular reconstruction-based RoBERTa model is sensitive to source code edits, even when the edits preserve semantics. We propose ContraCode: a contrastive pre-training task that learns code functionality, not form. ContraCode pre-trains a neural network to identify functionally similar variants of a program among many non-equivalent distractors. We scalably generate these variants using an automated source-to-source compiler as a form of data augmentation. Contrastive pre-training outperforms RoBERTa on an adversarial code clone detection benchmark by 39% AUROC. Surprisingly, improved adversarial robustness translates to better accuracy over natural code; ContraCode improves summarization and TypeScript type inference accuracy by 2 to 13 percentage points over competitive baselines. All source is available athttps://github.com/parasj/contracode.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_483", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_483.flac", "type": "audio", "duration": 728.062}, {"from": "Assistant", "value": "While large-scale pretrained language models have significantly improved writing assistance functionalities such as autocomplete, more complex and controllable writing assistants have yet to be explored. We leverage advances in language modeling to build an interactive writing assistant that generates and rephrases text according to fine-grained author specifications. Users provide input to our Intent-Guided Assistant (IGA) in the form of text interspersed with tags that correspond to specific rhetorical directives (e.g., adding description or contrast, or rephrasing a particular sentence). We fine-tune a language model on a dataset heuristically-labeled with author intent, which allows IGA to fill in these tags with generated text that users can subsequently edit to their liking. A series of automatic and crowdsourced evaluations confirm the quality of IGA’s generated outputs, while a small-scale user study demonstrates author preference for IGA over baseline methods in a creative writing task. We release our dataset, code, and demo to spur further research into AI-assisted writing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_484", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_484.flac", "type": "audio", "duration": 561.343}, {"from": "Assistant", "value": "We study the problem of generating arithmetic math word problems (MWPs) given a math equation that specifies the mathematical computation and a context that specifies the problem scenario. Existing approaches are prone to generating MWPs that are either mathematically invalid or have unsatisfactory language quality. They also either ignore the context or require manual specification of a problem template, which compromises the diversity of the generated MWPs. In this paper, we develop a novel MWP generation approach that leverages i) pre-trained language models and a context keyword selection model to improve the language quality of generated MWPs and ii) an equation consistency constraint for math equations to improve the mathematical validity of the generated MWPs. Extensive quantitative and qualitative experiments on three real-world MWP datasets demonstrate the superior performance of our approach compared to various baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_485", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_485.flac", "type": "audio", "duration": 975.531}, {"from": "Assistant", "value": "Irrespective of the success of the deep learning-based mixed-domain transfer learning approach for solving various Natural Language Processing tasks, it does not lend a generalizable solution for detecting misinformation from COVID-19 social media data. Due to the inherent complexity of this type of data, caused by its dynamic (context evolves rapidly), nuanced (misinformation types are often ambiguous), and diverse (skewed, fine-grained, and overlapping categories) nature, it is imperative for an effective model to capture both the local and global context of the target domain. By conducting a systematic investigation, we show that: (i) the deep Transformer-based pre-trained models, utilized via the mixed-domain transfer learning, are only good at capturing the local context, thus exhibits poor generalization, and (ii) a combination of shallow network-based domain-specific models and convolutional neural networks can efficiently extract local as well as global context directly from the target data in a hierarchical fashion, enabling it to offer a more generalizable solution.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_486", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_486.flac", "type": "audio", "duration": 716.373}, {"from": "Assistant", "value": "Health and medical researchers often give clinical and policy recommendations to inform health practice and public health policy. However, no current health information system supports the direct retrieval of health advice. This study fills the gap by developing and validating an NLP-based prediction model for identifying health advice in research publications. We annotated a corpus of 6,000 sentences extracted from structured abstracts in PubMed publications as ‘“strong advice”, “weak advice”, or “no advice”, and developed a BERT-based model that can predict, with a macro-averaged F1-score of 0.93, whether a sentence gives strong advice, weak advice, or not. The prediction model generalized well to sentences in both unstructured abstracts and discussion sections, where health advice normally appears. We also conducted a case study that applied this prediction model to retrieve specific health advice on COVID-19 treatments from LitCovid, a large COVID research literature portal, demonstrating the usefulness of retrieving health advice sentences as an advanced research literature navigation function for health researchers and the general public.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_487", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_487.flac", "type": "audio", "duration": 672.055}, {"from": "Assistant", "value": "Automatic short answer grading (ASAG) is the task of assessing students’ short natural language responses to objective questions. It is a crucial component of new education platforms, and could support more wide-spread use of constructed response questions to replace cognitively less challenging multiple choice questions. We propose a Semantic Feature-wise transformation Relation Network (SFRN) that exploits the multiple components of ASAG datasets more effectively. SFRN captures relational knowledge among the questions (Q), reference answers or rubrics (R), and labeled student answers (A). A relation network learns vector representations for the elements of QRA triples, then combines the learned representations using learned semantic feature-wise transformations. We apply translation-based data augmentation to address the two problems of limited training data, and high data skew for multi-class ASAG tasks. Our model has up to 11% performance improvement over state-of-the-art results on the benchmark SemEval-2013 datasets, and surpasses custom approaches designed for a Kaggle challenge, demonstrating its generality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_488", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_488.flac", "type": "audio", "duration": 714.06}, {"from": "Assistant", "value": "Quantitatively measuring the impact-related aspects of scientific, engineering, and technological (SET) innovations is a fundamental problem with broad applications. Traditional citation-based measures for assessing the impact of innovations and related entities do not take into account the content of the publications. This limits their ability to provide rigorous quality-related metrics because they cannot account for the reasons that led to a citation. We present approaches to estimate content-aware bibliometrics to quantitatively measure the scholarly impact of a publication. Our approaches assess the impact of a cited publication by the extent to which the cited publication informs the citing publication. We introduce a new metric, called “Content Informed Index” (CII), that uses the content of the paper as a source of distant-supervision, to quantify how much the cited-node informs the citing-node. We evaluate the weights estimated by our approach on three manually annotated datasets, where the annotations quantify the extent of information in the citation. Particularly, we evaluate how well the ranking imposed by our approach associates with the ranking imposed by the manual annotations. CII achieves up to 103% improvement in performance as compared to the second-best performing approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_489", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_489.flac", "type": "audio", "duration": 701.057}, {"from": "Assistant", "value": "Natural Language Understanding (NLU) is an established component within a conversational AI or digital assistant system, and it is responsible for producing semantic understanding of a user request. We propose a scalable and automatic approach for improving NLU in a large-scale conversational AI system by leveraging implicit user feedback, with an insight that user interaction data and dialog context have rich information embedded from which user satisfaction and intention can be inferred. In particular, we propose a domain-agnostic framework for curating new supervision data for improving NLU from live production traffic. With an extensive set of experiments, we show the results of applying the framework and improving NLU for a large-scale production system across 10 domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_490", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_490.flac", "type": "audio", "duration": 721.003}, {"from": "Assistant", "value": "How can we generate concise explanations for multi-hop Reading Comprehension (RC)? The current strategies of identifying supporting sentences can be seen as an extractive question-focused summarization of the input text. However, these extractive explanations are not necessarily concise i.e. not minimally sufficient for answering a question. Instead, we advocate for an abstractive approach, where we propose to generate a question-focused, abstractive summary of input paragraphs and then feed it to an RC system. Given a limited amount of human-annotated abstractive explanations, we train the abstractive explainer in a semi-supervised manner, where we start from the supervised model and then train it further through trial and error maximizing a conciseness-promoted reward function. Our experiments demonstrate that the proposed abstractive explainer can generate more compact explanations than an extractive explainer with limited supervision (only 2k instances) while maintaining sufficiency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_491", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_491.flac", "type": "audio", "duration": 650.048}, {"from": "Assistant", "value": "The task of learning from only a few examples (called a few-shot setting) is of key importance and relevance to a real-world setting. For question answering (QA), the current state-of-the-art pre-trained models typically need fine-tuning on tens of thousands of examples to obtain good results. Their performance degrades significantly in a few-shot setting (< 100 examples). To address this, we propose a simple fine-tuning framework that leverages pre-trained text-to-text models and is directly aligned with their pre-training framework. Specifically, we construct the input as a concatenation of the question, a mask token representing the answer span and a context. Given this input, the model is fine-tuned using the same objective as that of its pre-training objective. Through experimental studies on various few-shot configurations, we show that this formulation leads to significant gains on multiple QA benchmarks (an absolute gain of 34.2 F1 points on average when there are only 16 training examples). The gains extend further when used with larger models (Eg:- 72.3 F1 on SQuAD using BART-large with only 32 examples) and translate well to a multilingual setting . On the multilingual TydiQA benchmark, our model outperforms the XLM-Roberta-large by an absolute margin of upto 40 F1 points and an average of 33 F1 points in a few-shot setting (<= 64 training examples). We conduct detailed ablation studies to analyze factors contributing to these gains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_492", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_492.flac", "type": "audio", "duration": 707.048}, {"from": "Assistant", "value": "In the context of neural passage retrieval, we study three promising techniques: synthetic data generation, negative sampling, and fusion. We systematically investigate how these techniques contribute to the performance of the retrieval system and how they complement each other. We propose a multi-stage framework comprising of pre-training with synthetic data, fine-tuning with labeled data, and negative sampling at both stages. We study six negative sampling strategies and apply them to the fine-tuning stage and, as a noteworthy novelty, to the synthetic data that we use for pre-training. Also, we explore fusion methods that combine negatives from different strategies. We evaluate our system using two passage retrieval tasks for open-domain QA and using MS MARCO. Our experiments show that augmenting the negative contrast in both stages is effective to improve passage retrieval accuracy and, importantly, they also show that synthetic data generation and negative sampling have additive benefits. Moreover, using the fusion of different kinds allows us to reach performance that establishes a new state-of-the-art level in two of the tasks we evaluated.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_493", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_493.flac", "type": "audio", "duration": 313.685}, {"from": "Assistant", "value": "Our goal is to deliver a new task and leaderboard to stimulate research on question answering and pre-trained language models (PTLMs) to understand a significant instructional document, e.g., an introductory college textbook or a manual. PTLMs have shown great success in many question-answering tasks, given significant supervised training, but much less so in zero-shot settings. We propose a new task that includes two college-level introductory texts in the social sciences (American Government 2e) and humanities (U.S. History), hundreds of true/false statements based on review questions written by the textbook authors, validation/development tests based on the first eight chapters of the textbooks, blind tests based on the remaining textbook chapters, and baseline results given state-of-the-art PTLMs. Since the questions are balanced, random performance should be ~50%. T5, fine-tuned with BoolQ achieves the same performance, suggesting that the textbook’s content is not pre-represented in the PTLM. Taking the exam closed book, but having read the textbook (i.e., adding the textbook to T5’s pre-training), yields at best minor improvement (56%), suggesting that the PTLM may not have “understood” the textbook (or perhaps misunderstood the questions). Performance is better (~60%) when the exam is taken open-book (i.e., allowing the machine to automatically retrieve a paragraph and use it to answer the question).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_494", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_494.flac", "type": "audio", "duration": 694.044}, {"from": "Assistant", "value": "We present ReasonBert, a pre-training method that augments language models with the ability to reason over long-range relations and multiple, possibly hybrid contexts. Unlike existing pre-training methods that only harvest learning signals from local contexts of naturally occurring texts, we propose a generalized notion of distant supervision to automatically connect multiple pieces of text and tables to create pre-training examples that require long-range reasoning. Different types of reasoning are simulated, including intersecting multiple pieces of evidence, bridging from one piece of evidence to another, and detecting unanswerable cases. We conduct a comprehensive evaluation on a variety of extractive question answering datasets ranging from single-hop to multi-hop and from text-only to table-only to hybrid that require various reasoning capabilities and show that ReasonBert achieves remarkable improvement over an array of strong baselines. Few-shot experiments further demonstrate that our pre-training method substantially improves sample efficiency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_495", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_495.flac", "type": "audio", "duration": 480.049}, {"from": "Assistant", "value": "Many datasets have been created for training reading comprehension models, and a natural question is whether we can combine them to build models that (1) perform better on all of the training datasets and (2) generalize and transfer better to new datasets. Prior work has addressed this goal by training one network simultaneously on multiple datasets, which works well on average but is prone to over- or under-fitting different sub- distributions and might transfer worse compared to source models with more overlap with the target dataset. Our approach is to model multi-dataset question answering with an ensemble of single-dataset experts, by training a collection of lightweight, dataset-specific adapter modules (Houlsby et al., 2019) that share an underlying Transformer model. We find that these Multi-Adapter Dataset Experts (MADE) outperform all our baselines in terms of in-distribution accuracy, and simple methods based on parameter-averaging lead to better zero-shot generalization and few-shot transfer performance, offering a strong and versatile starting point for building new reading comprehension systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_496", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_496.flac", "type": "audio", "duration": 473.06}, {"from": "Assistant", "value": "Open-domain question answering has exploded in popularity recently due to the success of dense retrieval models, which have surpassed sparse models using only a few supervised training examples. However, in this paper, we demonstrate current dense models are not yet the holy grail of retrieval. We first construct EntityQuestions, a set of simple, entity-rich questions based on facts from Wikidata (e.g., “Where was Arve Furset born?”), and observe that dense retrievers drastically under-perform sparse methods. We investigate this issue and uncover that dense retrievers can only generalize to common entities unless the question pattern is explicitly observed during training. We discuss two simple solutions towards addressing this critical problem. First, we demonstrate that data augmentation is unable to fix the generalization problem. Second, we argue a more robust passage encoder helps facilitate better question adaptation using specialized question encoders. We hope our work can shed light on the challenges in creating a robust, universal dense retriever that works well across different input distributions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_497", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_497.flac", "type": "audio", "duration": 701.01}, {"from": "Assistant", "value": "Question Answering (QA) tasks requiring information from multiple documents often rely on a retrieval model to identify relevant information for reasoning. The retrieval model is typically trained to maximize the likelihood of the labeled supporting evidence. However, when retrieving from large text corpora such as Wikipedia, the correct answer can often be obtained from multiple evidence candidates. Moreover, not all such candidates are labeled as positive during annotation, rendering the training signal weak and noisy. This problem is exacerbated when the questions are unanswerable or when the answers are Boolean, since the model cannot rely on lexical overlap to make a connection between the answer and supporting evidence. We develop a new parameterization of set-valued retrieval that handles unanswerable queries, and we show that marginalizing over this set during training allows a model to mitigate false negatives in supporting evidence annotations. We test our method on two multi-document QA datasets, IIRC and HotpotQA. On IIRC, we show that joint modeling with marginalization improves model performance by 5.5 F1 points and achieves a new state-of-the-art performance of 50.5 F1. We also show that retrieval marginalization results in 4.1 QA F1 improvement over a non-marginalized baseline on HotpotQA in the fullwiki setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_498", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_498.flac", "type": "audio", "duration": 539.376}, {"from": "Assistant", "value": "We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as machine reading comprehension task based on a single given document or passage. In this work, we aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents. To facilitate such task, we introduce a new dataset that contains dialogues grounded in multiple documents from four different domains. We also explore modeling the dialogue-based and document-based contexts in the dataset. We present strong baseline approaches and various experimental results, aiming to support further research efforts on such a task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_499", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_499.flac", "type": "audio", "duration": 780.051}, {"from": "Assistant", "value": "Code-switching is the communication phenomenon where the speakers switch between different languages during a conversation. With the widespread adoption of conversational agents and chat platforms, code-switching has become an integral part of written conversations in many multi-lingual communities worldwide. Therefore, it is essential to develop techniques for understanding and summarizing these conversations. Towards this objective, we introduce the task of abstractive summarization of Hindi-English (Hi-En) code-switched conversations. We also develop the first code-switched conversation summarization dataset -GupShup, which contains over 6,800 Hi-En conversations and their corresponding human-annotated summaries in English (En) and Hi-En. We present a detailed account of the entire data collection and annotation process. We analyze the dataset using various code-switching statistics. We train state-of-the-art abstractive summarization models and report their performances using both automated metrics and human evaluation. Our results show that multi-lingual mBART and multi-view seq2seq models obtain the best performances on this new dataset. We also conduct an extensive qualitative analysis to provide insight into the models and some of their shortcomings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_500", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_500.flac", "type": "audio", "duration": 703.04}, {"from": "Assistant", "value": "An important task in NLP applications such as sentence simplification is the ability to take a long, complex sentence and split it into shorter sentences, rephrasing as necessary. We introduce a novel dataset and a new model for this ‘split and rephrase’ task. Our BiSECT training data consists of 1 million long English sentences paired with shorter, meaning-equivalent English sentences. We obtain these by extracting 1-2 sentence alignments in bilingual parallel corpora and then using machine translation to convert both sides of the corpus into the same language. BiSECT contains higher quality training examples than the previous Split and Rephrase corpora, with sentence splits that require more significant modifications. We categorize examples in our corpus and use these categories in a novel model that allows us to target specific regions of the input sentence to be split and edited. Moreover, we show that models trained on BiSECT can perform a wider variety of split operations and improve upon previous state-of-the-art approaches in automatic and human evaluations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_501", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_501.flac", "type": "audio", "duration": 480.043}, {"from": "Assistant", "value": "This survey/position paper discusses ways to improve coverage of resources such as WordNet. Rapp estimated correlations, rho, between corpus statistics and pyscholinguistic norms. rho improves with quantity (corpus size) and quality (balance). 1M words is enough for simple estimates (unigram frequencies), but at least 100x more is required for good estimates of word associations and embeddings. Given such estimates, WordNet’s coverage is remarkable. WordNet was developed on SemCor, a small sample (200k words) from the Brown Corpus. Knowledge Graph Completion (KGC) attempts to learn missing links from subsets. But Rapp’s estimates of sizes suggest it would be more profitable to collect more data than to infer missing information that is not there.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_502", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_502.flac", "type": "audio", "duration": 657.775}, {"from": "Assistant", "value": "This paper presents a novel training method, Conditional Masked Language Modeling (CMLM), to effectively learn sentence representations on large scale unlabeled corpora. CMLM integrates sentence representation learning into MLM training by conditioning on the encoded vectors of adjacent sentences. Our English CMLM model achieves state-of-the-art performance on SentEval, even outperforming models learned using supervised signals. As a fully unsupervised learning method, CMLM can be conveniently extended to a broad range of languages and domains. We find that a multilingual CMLM model co-trained with bitext retrieval (BR) and natural language inference (NLI) tasks outperforms the previous state-of-the-art multilingual models by a large margin, e.g. 10% improvement upon baseline models on cross-lingual semantic search. We explore the same language bias of the learned representations, and propose a simple, post-training and model agnostic approach to remove the language identifying information from the representation while still retaining sentence semantics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_503", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_503.flac", "type": "audio", "duration": 695.04}, {"from": "Assistant", "value": "Although recent developments in neural architectures and pre-trained representations have greatly increased state-of-the-art model performance on fully-supervised semantic role labeling (SRL), the task remains challenging for languages where supervised SRL training data are not abundant. Cross-lingual learning can improve performance in this setting by transferring knowledge from high-resource languages to low-resource ones. Moreover, we hypothesize that annotations of syntactic dependencies can be leveraged to further facilitate cross-lingual transfer. In this work, we perform an empirical exploration of the helpfulness of syntactic supervision for crosslingual SRL within a simple multitask learning scheme. With comprehensive evaluations across ten languages (in addition to English) and three SRL benchmark datasets, including both dependency- and span-based SRL, we show the effectiveness of syntactic supervision in low-resource scenarios.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_504", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_504.flac", "type": "audio", "duration": 506.453}, {"from": "Assistant", "value": "Enthymemes are defined as arguments where a premise or conclusion is left implicit. We tackle the task of generating theimplicit premise in an enthymeme, which requires not only an understanding of the stated conclusion and premise but also additional inferences that could depend on commonsense knowledge. The largest available dataset for enthymemes (Habernal et al., 2018) consists of 1.7k samples, which is not large enough to train a neural text generation model. To address this issue, we take advantage of a similar task and dataset: Abductive reasoning in narrative text (Bhagavatula et al., 2020). However, we show that simply using a state-of-the-art seq2seq model fine-tuned on this data might not generate meaningful implicit premises associated with the given enthymemes. We demonstrate that encoding discourse-aware commonsense during fine-tuning improves the quality of the generated implicit premises and outperforms all other baselines both in automatic and human evaluations on three different datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_505", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_505.flac", "type": "audio", "duration": 720.051}, {"from": "Assistant", "value": "Systematic compositionality is an essential mechanism in human language, allowing the recombination of known parts to create novel expressions. However, existing neural models have been shown to lack this basic ability in learning symbolic structures. Motivated by the failure of a Transformer model on the SCAN compositionality challenge (Lake and Baroni, 2018), which requires parsing a command into actions, we propose two auxiliary sequence prediction tasks as additional training supervision. These automatically-generated sequences are more representative of the underlying compositional symbolic structures of the input data. During inference, the model jointly predicts the next action and the next tokens in the auxiliary sequences at each step. Experiments on the SCAN dataset show that our method encourages the Transformer to understand compositional structures of the command, improving its accuracy on multiple challenging splits from ≤ 10% to 100%. With only 418 (5%) training instances, our approach still achieves 97.8% accuracy on the MCD1 split. Therefore, we argue that compositionality can be induced in Transformers given minimal but proper guidance. We also show that a better result is achieved using less contextualized vectors as the attention’s query, providing insights into architecture choices in achieving systematic compositionality. Finally, we show positive generalization results on the grounded-SCAN task (Ruis et al., 2020).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_506", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_506.flac", "type": "audio", "duration": 720.0}, {"from": "Assistant", "value": "An interpretable system for open-domain reasoning needs to express its reasoning process in a transparent form. Natural language is an attractive representation for this purpose — it is both highly expressive and easy for humans to understand. However, manipulating natural language statements in logically consistent ways is hard: models must cope with variation in how meaning is expressed while remaining precise. In this paper, we describe ParaPattern, a method for building models to generate deductive inferences from diverse natural language inputs without direct human supervision. We train BART-based models (Lewis et al., 2020) to generate the result of applying a particular logical operation to one or more premise statements. Crucially, we develop a largely automated pipeline for constructing suitable training examples from Wikipedia. We evaluate our models using out-of-domain sentence compositions from the QASC (Khot et al., 2020) and EntailmentBank (Dalvi et al., 2021) datasets as well as targeted perturbation sets. Our results show that our models are substantially more accurate and flexible than baseline systems. ParaPattern achieves 85% validity on examples of the ‘substitution’ operation from EntailmentBank without the use of any in-domain training data, matching the performance of a model fine-tuned for EntailmentBank. The full source code for our method is publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_507", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_507.flac", "type": "audio", "duration": 714.06}, {"from": "Assistant", "value": "Predicting linearized Abstract Meaning Representation (AMR) graphs using pre-trained sequence-to-sequence Transformer models has recently led to large improvements on AMR parsing benchmarks. These parsers are simple and avoid explicit modeling of structure but lack desirable properties such as graph well-formedness guarantees or built-in graph-sentence alignments. In this work we explore the integration of general pre-trained sequence-to-sequence language models and a structure-aware transition-based approach. We depart from a pointer-based transition system and propose a simplified transition set, designed to better exploit pre-trained language models for structured fine-tuning. We also explore modeling the parser state within the pre-trained encoder-decoder architecture and different vocabulary strategies for the same purpose. We provide a detailed comparison with recent progress in AMR parsing and show that the proposed parser retains the desirable properties of previous transition-based approaches, while being simpler and reaching the new parsing state of the art for AMR 2.0, without the need for graph re-categorization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_508", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_508.flac", "type": "audio", "duration": 633.408}, {"from": "Assistant", "value": "Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence. Existing cognitive science literature on defeasible reasoning suggests that a person forms a “mental model” of the problem scenario before answering questions. Our research goal asks whether neural models can similarly benefit from envisioning the question scenario before answering a defeasible query. Our approach is, given a question, to have a model first create a graph of relevant influences, and then leverage that graph as an additional input when answering the question. Our system, CURIOUS, achieves a new state-of-the-art on three different defeasible reasoning datasets. This result is significant as it illustrates that performance can be improved by guiding a system to “think about” a question and explicitly model the scenario, rather than answering reflexively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_509", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_509.flac", "type": "audio", "duration": 472.294}, {"from": "Assistant", "value": "For many business applications, we often seek to analyze sentiments associated with any arbitrary aspects of commercial products, despite having a very limited amount of labels or even without any labels at all. However, existing aspect target sentiment classification (ATSC) models are not trainable if annotated datasets are not available. Even with labeled data, they fall short of reaching satisfactory performance. To address this, we propose simple approaches that better solve ATSC with natural language prompts, enabling the task under zero-shot cases and enhancing supervised settings, especially for few-shot cases. Under the few-shot setting for SemEval 2014 Task 4 laptop domain, our method of reformulating ATSC as an NLI task outperforms supervised SOTA approaches by up to 24.13 accuracy points and 33.14 macro F1 points. Moreover, we demonstrate that our prompts could handle implicitly stated aspects as well: our models reach about 77% accuracy on detecting sentiments for aspect categories (e.g., food), which do not necessarily appear within the text, even though we trained the models only with explicitly mentioned aspect terms (e.g., fajitas) from just 16 reviews - while the accuracy of the no-prompt baseline is only around 65%.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_510", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_510.flac", "type": "audio", "duration": 499.051}, {"from": "Assistant", "value": "People convey their intention and attitude through linguistic styles of the text that they write. In this study, we investigate lexicon usages across styles throughout two lenses: human perception and machine word importance, since words differ in the strength of the stylistic cues that they provide. To collect labels of human perception, we curate a new dataset, Hummingbird, on top of benchmarking style datasets. We have crowd workers highlight the representative words in the text that makes them think the text has the following styles: politeness, sentiment, offensiveness, and five emotion types. We then compare these human word labels with word importance derived from a popular fine-tuned style classifier like BERT. Our results show that the BERT often finds content words not relevant to the target style as important words used in style prediction, but humans do not perceive the same way even though for some styles (e.g., positive sentiment and joy) human- and machine-identified words share significant overlap for some styles.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_511", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_511.flac", "type": "audio", "duration": 650.56}, {"from": "Assistant", "value": "Stance detection determines whether the author of a text is in favor of, against or neutral to a specific target and provides valuable insights into important events such as legalization of abortion. Despite significant progress on this task, one of the remaining challenges is the scarcity of annotations. Besides, most previous works focused on a hard-label training in which meaningful similarities among categories are discarded during training. To address these challenges, first, we evaluate a multi-target and a multi-dataset training settings by training one model on each dataset and datasets of different domains, respectively. We show that models can learn more universal representations with respect to targets in these settings. Second, we investigate the knowledge distillation in stance detection and observe that transferring knowledge from a teacher model to a student model can be beneficial in our proposed training settings. Moreover, we propose an Adaptive Knowledge Distillation (AKD) method that applies instance-specific temperature scaling to the teacher and student predictions. Results show that the multi-dataset model performs best on all datasets and it can be further improved by the proposed AKD, outperforming the state-of-the-art by a large margin. We publicly release our code.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_512", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_512.flac", "type": "audio", "duration": 704.377}, {"from": "Assistant", "value": "Visual question answering (VQA) is challenging not only because the model has to handle multi-modal information, but also because it is just so hard to collect sufficient training examples — there are too many questions one can ask about an image. As a result, a VQA model trained solely on human-annotated examples could easily over-fit specific question styles or image contents that are being asked, leaving the model largely ignorant about the sheer diversity of questions. Existing methods address this issue primarily by introducing an auxiliary task such as visual grounding, cycle consistency, or debiasing. In this paper, we take a drastically different approach. We found that many of the “unknowns” to the learned VQA model are indeed “known” in the dataset implicitly. For instance, questions asking about the same object in different images are likely paraphrases; the number of detected or annotated objects in an image already provides the answer to the “how many” question, even if the question has not been annotated for that image. Building upon these insights, we present a simple data augmentation pipeline SimpleAug to turn this “known” knowledge into training examples for VQA. We show that these augmented examples can notably improve the learned VQA models’ performance, not only on the VQA-CP dataset with language prior shifts but also on the VQA v2 dataset without such shifts. Our method further opens up the door to leverage weakly-labeled or unlabeled images in a principled way to enhance VQA models. Our code and data are publicly available athttps://github.com/heendung/simpleAUG.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_513", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_513.flac", "type": "audio", "duration": 480.043}, {"from": "Assistant", "value": "Phrase grounding aims to map textual phrases to their associated image regions, which can be a prerequisite for multimodal reasoning and can benefit tasks requiring identifying objects based on language. With pre-trained vision-and-language models achieving impressive performance across tasks, it remains unclear if we can directly utilize their learned embeddings for phrase grounding without fine-tuning. To this end, we propose a method to extract matched phrase-region pairs from pre-trained vision-and-language embeddings and propose four fine-tuning objectives to improve the model phrase grounding ability using image-caption data without any supervised grounding signals. Experiments on two representative datasets demonstrate the effectiveness of our objectives, outperforming baseline models in both weakly-supervised and supervised phrase grounding settings. In addition, we evaluate the aligned embeddings on several other downstream tasks and show that we can achieve better phrase grounding without sacrificing representation generality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_514", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_514.flac", "type": "audio", "duration": 720.004}, {"from": "Assistant", "value": "While Automatic Speech Recognition has been shown to be vulnerable to adversarial attacks, defenses against these attacks are still lagging. Existing, naive defenses can be partially broken with an adaptive attack. In classification tasks, the Randomized Smoothing paradigm has been shown to be effective at defending models. However, it is difficult to apply this paradigm to ASR tasks, due to their complexity and the sequential nature of their outputs. Our paper overcomes some of these challenges by leveraging speech-specific tools like enhancement and ROVER voting to design an ASR model that is robust to perturbations. We apply adaptive versions of state-of-the-art attacks, such as the Imperceptible ASR attack, to our model, and show that our strongest defense is robust to all attacks that use inaudible noise, and can only be broken with very high distortion.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_515", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_515.flac", "type": "audio", "duration": 651.285}, {"from": "Assistant", "value": "The combination of gestures, intonations, and textual content plays a key role in argument delivery. However, the current literature mostly considers textual content while assessing the quality of an argument, and it is limited to datasets containing short sequences (18-48 words). In this paper, we study argument quality assessment in a multimodal context, and experiment on DBATES, a publicly available dataset of long debate videos. First, we propose a set of interpretable debate centric features such as clarity, content variation, body movement cues, and pauses, inspired by theories of argumentation quality. Second, we design the Multimodal ARgument Quality assessor (MARQ) – a hierarchical neural network model that summarizes the multimodal signals on long sequences and enriches the multimodal embedding with debate centric features. Our proposed MARQ model achieves an accuracy of 81.91% on the argument quality prediction task and outperforms established baseline models with an error rate reduction of 22.7%. Through ablation studies, we demonstrate the importance of multimodal cues in modeling argument quality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_516", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_516.flac", "type": "audio", "duration": 707.648}, {"from": "Assistant", "value": "Neural module networks (NMN) are a popular approach for grounding visual referring expressions. Prior implementations of NMN use pre-defined and fixed textual inputs in their module instantiation. This necessitates a large number of modules as they lack the ability to share weights and exploit associations between similar textual contexts (e.g. “dark cube on the left” vs. “black cube on the left”). In this work, we address these limitations and evaluate the impact of contextual clues in improving the performance of NMN models. First, we address the problem of fixed textual inputs by parameterizing the module arguments. This substantially reduce the number of modules in NMN by up to 75% without any loss in performance. Next we propose a method to contextualize our parameterized model to enhance the module’s capacity in exploiting the visiolinguistic associations. Our model outperforms the state-of-the-art NMN model on CLEVR-Ref+ dataset with +8.1% improvement in accuracy on the single-referent test set and +4.3% on the full test set. Additionally, we demonstrate that contextualization provides +11.2% and +1.7% improvements in accuracy over prior NMN models on CLOSURE and NLVR2. We further evaluate the impact of our contextualization by constructing a contrast set for CLEVR-Ref+, which we call CC-Ref+. We significantly outperform the baselines by as much as +10.4% absolute accuracy on CC-Ref+, illustrating the generalization skills of our approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_517", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_517.flac", "type": "audio", "duration": 770.368}, {"from": "Assistant", "value": "Knowledge-based visual question answering (VQA) requires answering questions with external knowledge in addition to the content of images. One dataset that is mostly used in evaluating knowledge-based VQA is OK-VQA, but it lacks a gold standard knowledge corpus for retrieval. Existing work leverage different knowledge bases (e.g., ConceptNet and Wikipedia) to obtain external knowledge. Because of varying knowledge bases, it is hard to fairly compare models’ performance. To address this issue, we collect a natural language knowledge base that can be used for any VQA system. Moreover, we propose a Visual Retriever-Reader pipeline to approach knowledge-based VQA. The visual retriever aims to retrieve relevant knowledge, and the visual reader seeks to predict answers based on given knowledge. We introduce various ways to retrieve knowledge using text and images and two reader styles: classification and extraction. Both the retriever and reader are trained with weak supervision. Our experimental results show that a good retriever can significantly improve the reader’s performance on the OK-VQA challenge. The code and corpus are provided inhttps://github.com/luomancs/retriever_reader_for_okvqa.git.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_518", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_518.flac", "type": "audio", "duration": 668.053}, {"from": "Assistant", "value": "Communication between human and mobile agents is getting increasingly important as such agents are widely deployed in our daily lives. Vision-and-Dialogue Navigation is one of the tasks that evaluate the agent’s ability to interact with humans for assistance and navigate based on natural language responses. In this paper, we explore the Navigation from Dialogue History (NDH) task, which is based on the Cooperative Vision-and-Dialogue Navigation (CVDN) dataset, and present a state-of-the-art model which is built upon Vision-Language transformers. However, despite achieving competitive performance, we find that the agent in the NDH task is not evaluated appropriately by the primary metric – Goal Progress. By analyzing the performance mismatch between Goal Progress and other metrics (e.g., normalized Dynamic Time Warping) from our state-of-the-art model, we show that NDH’s sub-path based task setup (i.e., navigating partial trajectory based on its correspondent subset of the full dialogue) does not provide the agent with enough supervision signal towards the goal region. Therefore, we propose a new task setup called NDH-Full which takes the full dialogue and the whole navigation path as one instance. We present a strong baseline model and show initial results on this new task. We further describe several approaches that we try, in order to improve the model performance (based on curriculum learning, pre-training, and data-augmentation), suggesting potential useful training methods on this new NDH-Full task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_519", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_519.flac", "type": "audio", "duration": 623.642}, {"from": "Assistant", "value": "Timeline Summarization identifies major events from a news collection and describes them following temporal order, with key dates tagged. Previous methods generally generate summaries separately for each date after they determine the key dates of events. These methods overlook the events’ intra-structures (arguments) and inter-structures (event-event connections). Following a different route, we propose to represent the news articles as an event-graph, thus the summarization becomes compressing the whole graph to its salient sub-graph. The key hypothesis is that the events connected through shared arguments and temporal order depict the skeleton of a timeline, containing events that are semantically related, temporally coherent and structurally salient in the global event graph. A time-aware optimal transport distance is then introduced for learning the compression model in an unsupervised manner. We show that our approach significantly improves on the state of the art on three real-world datasets, including two public standard benchmarks and our newly collected Timeline100 dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_520", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_520.flac", "type": "audio", "duration": 735.051}, {"from": "Assistant", "value": "With the explosive growth of livestream broadcasting, there is an urgent need for new summarization technology that enables us to create a preview of streamed content and tap into this wealth of knowledge. However, the problem is nontrivial due to the informal nature of spoken language. Further, there has been a shortage of annotated datasets that are necessary for transcript summarization. In this paper, we present StreamHover, a framework for annotating and summarizing livestream transcripts. With a total of over 500 hours of videos annotated with both extractive and abstractive summaries, our benchmark dataset is significantly larger than currently existing annotated corpora. We explore a neural extractive summarization model that leverages vector-quantized variational autoencoder to learn latent vector representations of spoken utterances and identify salient utterances from the transcripts to form summaries. We show that our model generalizes better and improves performance over strong baselines. The results of this study provide an avenue for future research to improve summarization solutions for efficient browsing of livestreams.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_521", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_521.flac", "type": "audio", "duration": 718.565}, {"from": "Assistant", "value": "Part of speech (POS) tagging is a familiar NLP task. State of the art taggers routinely achieve token-level accuracies of over 97% on news body text, evidence that the problem is well understood. However, the register of English news headlines, “headlinese”, is very different from the register of long-form text, causing POS tagging models to underperform on headlines. In this work, we automatically annotate news headlines with POS tags by projecting predicted tags from corresponding sentences in news bodies. We train a multi-domain POS tagger on both long-form and headline text and show that joint training on both registers improves over training on just one or naïvely concatenating training sets. We evaluate on a newly-annotated corpus of over 5,248 English news headlines from the Google sentence compression corpus, and show that our model yields a 23% relative error reduction per token and 19% per headline. In addition, we demonstrate that better headline POS tags can improve the performance of a syntax-based open information extraction system. We make POSH, the POS-tagged Headline corpus, available to encourage research in improved NLP models for news headlines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_emnlp-main_523", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_emnlp-main_523.flac", "type": "audio", "duration": 678.208}, {"from": "Assistant", "value": "Recently, it has been argued that encoder-decoder models can be made more interpretable by replacing the softmax function in the attention with its sparse variants. In this work, we introduce a novel, simple method for achieving sparsity in attention: we replace the softmax activation with a ReLU, and show that sparsity naturally emerges from such a formulation. Training stability is achieved with layer normalization with either a specialized initialization or an additional gating function. Our model, which we call Rectified Linear Attention (ReLA), is easy to implement and more efficient than previously proposed sparse attention mechanisms. We apply ReLA to the Transformer and conduct experiments on five machine translation tasks. ReLA achieves translation performance comparable to several strong baselines, with training and decoding speed similar to that of the vanilla attention. Our analysis shows that ReLA delivers high sparsity rate and head diversity, and the induced cross attention achieves better accuracy with respect to source-target word alignment than recent sparsified softmax-based models. Intriguingly, ReLA heads also learn to attend to nothing (i.e. ‘switch off’) for some queries, which is not possible with sparsified softmax alternatives.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1001", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1001.flac", "type": "audio", "duration": 1199.851}, {"from": "Assistant", "value": "We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining. Medical records which are written by clinicians from different specialties usually contain quite different terminologies and writing styles. The difference of specialties and the cost of human annotation makes it particularly difficult to train a universal medical NER system. In this paper, we propose a label-aware double transfer learning framework (La-DTL) for cross-specialty NER, so that a medical NER system designed for one specialty could be conveniently applied to another one with minimal annotation efforts. The transferability is guaranteed by two components: (i) we propose label-aware MMD for feature representation transfer, and (ii) we perform parameter transfer with a theoretical upper bound which is also label aware. We conduct extensive experiments on 12 cross-specialty NER tasks. The experimental results demonstrate that La-DTL provides consistent accuracy improvement over strong baselines. Besides, the promising experimental results on non-medical NER scenarios indicate that La-DTL is potential to be seamlessly adapted to a wide range of NER tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1002", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1002.flac", "type": "audio", "duration": 1291.968}, {"from": "Assistant", "value": "The task of Fine-grained Entity Type Classification (FETC) consists of assigning types from a hierarchy to entity mentions in text. Existing methods rely on distant supervision and are thus susceptible to noisy labels that can be out-of-context or overly-specific for the training sentence. Previous methods that attempt to address these issues do so with heuristics or with the help of hand-crafted features. Instead, we propose an end-to-end solution with a neural network model that uses a variant of cross-entropy loss function to handle out-of-context labels, and hierarchical loss normalization to cope with overly-specific ones. Also, previous work solve FETC a multi-label classification followed by ad-hoc post-processing. In contrast, our solution is more elegant: we use public word embeddings to train a single-label that jointly learns representations for entity mentions and their context. We show experimentally that our approach is robust against noise and consistently outperforms the state-of-the-art on established benchmarks for the task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1003", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1003.flac", "type": "audio", "duration": 1198.507}, {"from": "Assistant", "value": "Semi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed instances. Due to the lack of labeled data, a key challenge in bootstrapping is semantic drift: if a false positive instance is added during an iteration, then all following iterations are contaminated. We introduce BREX, a new bootstrapping method that protects against such contamination by highly effective confidence assessment. This is achieved by using entity and template seeds jointly (as opposed to just one as in previous work), by expanding entities and templates in parallel and in a mutually constraining fashion in each iteration and by introducing higherquality similarity measures for templates. Experimental results show that BREX achieves an F1 that is 0.13 (0.87 vs. 0.74) better than the state of the art for four relationships.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1004", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1004.flac", "type": "audio", "duration": 1103.872}, {"from": "Assistant", "value": "What makes some types of languages more probable than others? For instance, we know that almost all spoken languages contain the vowel phoneme /i/; why should that be? The field of linguistic typology seeks to answer these questions and, thereby, divine the mechanisms that underlie human language. In our work, we tackle the problem of vowel system typology, i.e., we propose a generative probability model of which vowels a language contains. In contrast to previous work, we work directly with the acoustic information—the first two formant values—rather than modeling discrete sets of symbols from the international phonetic alphabet. We develop a novel generative probability model and report results on over 200 languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1005", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1005.flac", "type": "audio", "duration": 1068.565}, {"from": "Assistant", "value": "Morphological segmentation for polysynthetic languages is challenging, because a word may consist of many individual morphemes and training data can be extremely scarce. Since neural sequence-to-sequence (seq2seq) models define the state of the art for morphological segmentation in high-resource settings and for (mostly) European languages, we first show that they also obtain competitive performance for Mexican polysynthetic languages in minimal-resource settings. We then propose two novel multi-task training approaches—one with, one without need for external unlabeled resources—, and two corresponding data augmentation methods, improving over the neural baseline for all languages. Finally, we explore cross-lingual transfer as a third way to fortify our neural model and show that we can train one single multi-lingual model for related languages while maintaining comparable or even improved performance, thus reducing the amount of parameters by close to 75%. We provide our morphological segmentation datasets for Mexicanero, Nahuatl, Wixarika and Yorem Nokki for future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1006", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1006.flac", "type": "audio", "duration": 915.883}, {"from": "Assistant", "value": "Recently, neural machine translation (NMT) has emerged as a powerful alternative to conventional statistical approaches. However, its performance drops considerably in the presence of morphologically rich languages (MRLs). Neural engines usually fail to tackle the large vocabulary and high out-of-vocabulary (OOV) word rate of MRLs. Therefore, it is not suitable to exploit existing word-based models to translate this set of languages. In this paper, we propose an extension to the state-of-the-art model of Chung et al. (2016), which works at the character level and boosts the decoder with target-side morphological information. In our architecture, an additional morphology table is plugged into the model. Each time the decoder samples from a target vocabulary, the table sends auxiliary signals from the most relevant affixes in order to enrich the decoder’s current state and constrain it to provide better predictions. We evaluated our model to translate English into German, Russian, and Turkish as three MRLs and observed significant improvements.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1007", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1007.flac", "type": "audio", "duration": 962.411}, {"from": "Assistant", "value": "In conversational speech, the acoustic signal provides cues that help listeners disambiguate difficult parses. For automatically parsing spoken utterances, we introduce a model that integrates transcribed text and acoustic-prosodic features using a convolutional neural network over energy and pitch trajectories coupled with an attention-based recurrent neural network that accepts text and prosodic features. We find that different types of acoustic-prosodic features are individually helpful, and together give statistically significant improvements in parse and disfluency detection F1 scores over a strong text-only baseline. For this study with known sentence boundaries, error analyses show that the main benefit of acoustic-prosodic features is in sentences with disfluencies, attachment decisions are most improved, and transcription errors obscure gains from prosody.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1008", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1008.flac", "type": "audio", "duration": 1006.677}, {"from": "Assistant", "value": "We explore multitask models for neural translation of speech, augmenting them in order to reflect two intuitive notions. First, we introduce a model where the second task decoder receives information from the decoder of the first task, since higher-level intermediate representations should provide useful information. Second, we apply regularization that encourages transitivity and invertibility. We show that the application of these notions on jointly trained models improves performance on the tasks of low-resource speech transcription and translation. It also leads to better performance when using attention information for word discovery over unsegmented input.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1009", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1009.flac", "type": "audio", "duration": 978.261}, {"from": "Assistant", "value": "This work examines the rhetorical techniques that speakers employ during political campaigns. We introduce a new corpus of speeches from campaign events in the months leading up to the 2016 U.S. presidential election and develop new models for predicting moments of audience applause. In contrast to existing datasets, we tackle the challenge of working with transcripts that derive from uncorrected closed captioning, using associated audio recordings to automatically extract and align labels for instances of audience applause. In prediction experiments, we find that lexical features carry the most information, but that a variety of features are predictive, including prosody, long-term contextual dependencies, and theoretically motivated features designed to capture rhetorical techniques.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1027", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1027.flac", "type": "audio", "duration": 1041.941}, {"from": "Assistant", "value": "Can attention- or gradient-based visualization techniques be used to infer token-level labels for binary sequence tagging problems, using networks trained only on sentence-level labels? We construct a neural network architecture based on soft attention, train it as a binary sentence classifier and evaluate against token-level annotation on four different datasets. Inferring token labels from a network provides a method for quantitatively evaluating what the model is learning, along with generating useful feedback in assistance systems. Our results indicate that attention-based methods are able to predict token-level labels more accurately, compared to gradient-based methods, sometimes even rivaling the supervised oracle network.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1028", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1028.flac", "type": "audio", "duration": 1100.544}, {"from": "Assistant", "value": "Information about the meaning of mathematical variables in text is useful in NLP/IR tasks such as symbol disambiguation, topic modeling and mathematical information retrieval (MIR). We introducevariable typing, the task of assigning onemathematical type(multi-word technical terms referring to mathematical concepts) to each variable in a sentence of mathematical text. As part of this work, we also introduce a new annotated data set composed of 33,524 data points extracted from scientific documents published on arXiv. Our intrinsic evaluation demonstrates that our data set is sufficient to successfully train and evaluate current classifiers from three different model architectures. The best performing model is evaluated on an extrinsic task: MIR, by producing atyped formula index. Our results show that the best performing MIR models make use of our typed index, compared to a formula index only containing raw symbols, thereby demonstrating the usefulness of variable typing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1029", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1029.flac", "type": "audio", "duration": 977.92}, {"from": "Assistant", "value": "Machine Learning has been the quintessential solution for many AI problems, but learning models are heavily dependent on specific training data. Some learning models can be incorporated with prior knowledge using a Bayesian setup, but these learning models do not have the ability to access any organized world knowledge on demand. In this work, we propose to enhance learning models with world knowledge in the form of Knowledge Graph (KG) fact triples for Natural Language Processing (NLP) tasks. Our aim is to develop a deep learning model that can extract relevant prior support facts from knowledge graphs depending on the task using attention mechanism. We introduce a convolution-based model for learning representations of knowledge graph entity and relation clusters in order to reduce the attention space. We show that the proposed method is highly scalable to the amount of prior information that has to be processed and can be applied to any generic NLP task. Using this method we show significant improvement in performance for text classification with 20Newsgroups (News20) & DBPedia datasets, and natural language inference with Stanford Natural Language Inference (SNLI) dataset. We also demonstrate that a deep learning model can be trained with substantially less amount of labeled training data, when it has access to organized world knowledge in the form of a knowledge base.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1030", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1030.flac", "type": "audio", "duration": 1168.619}, {"from": "Assistant", "value": "Building a taxonomy from the ground up involves several sub-tasks: selecting terms to include, predicting semantic relations between terms, and selecting a subset of relational instances to keep, given constraints on the taxonomy graph. Methods for this final step – taxonomic organization – vary both in terms of the constraints they impose, and whether they enable discovery of synonymous terms. It is hard to isolate the impact of these factors on the quality of the resulting taxonomy because organization methods are rarely compared directly. In this paper, we present a head-to-head comparison of six taxonomic organization algorithms that vary with respect to their structural and transitivity constraints, and treatment of synonymy. We find that while transitive algorithms out-perform their non-transitive counterparts, the top-performing transitive algorithm is prohibitively slow for taxonomies with as few as 50 entities. We propose a simple modification to a non-transitive optimum branching algorithm to explicitly incorporate synonymy, resulting in a method that is substantially faster than the best transitive algorithm while giving complementary performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1031", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1031.flac", "type": "audio", "duration": 1016.768}, {"from": "Assistant", "value": "We explore two solutions to the problem of mistranslating rare words in neural machine translation. First, we argue that the standard output layer, which computes the inner product of a vector representing the context with all possible output word embeddings, rewards frequent words disproportionately, and we propose to fix the norms of both vectors to a constant value. Second, we integrate a simple lexical module which is jointly trained with the rest of the model. We evaluate our approaches on eight language pairs with data sizes ranging from 100k to 8M words, and achieve improvements of up to +4.3 BLEU, surpassing phrase-based translation in nearly all settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1032", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1032.flac", "type": "audio", "duration": 1070.912}, {"from": "Assistant", "value": "In this paper, we propose a new universal machine translation approach focusing on languages with a limited amount of parallel data. Our proposed approach utilizes a transfer-learning approach to share lexical and sentence level representations across multiple source languages into one target language. The lexical part is shared through a Universal Lexical Representation to support multi-lingual word-level sharing. The sentence-level sharing is represented by a model of experts from all source languages that share the source encoders with all other languages. This enables the low-resource language to utilize the lexical and sentence representations of the higher resource languages. Our approach is able to achieve 23 BLEU on Romanian-English WMT2016 using a tiny parallel corpus of 6k sentences, compared to the 18 BLEU of strong baseline system which uses multi-lingual training and back-translation. Furthermore, we show that the proposed approach can achieve almost 20 BLEU on the same dataset through fine-tuning a pre-trained multi-lingual system in a zero-shot setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1033", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1033.flac", "type": "audio", "duration": 921.024}, {"from": "Assistant", "value": "There has been much recent work on training neural attention models at the sequence-level using either reinforcement learning-style methods or by optimizing the beam. In this paper, we survey a range of classical objective functions that have been widely used to train linear models for structured prediction and apply them to neural sequence to sequence models. Our experiments show that these losses can perform surprisingly well by slightly outperforming beam search optimization in a like for like setup. We also report new state of the art results on both IWSLT’14 German-English translation as well as Gigaword abstractive summarization. On the large WMT’14 English-French task, sequence-level training achieves 41.5 BLEU which is on par with the state of the art.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1034", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1034.flac", "type": "audio", "duration": 1102.016}, {"from": "Assistant", "value": "Dirichlet Multinomial Regression (DMR) and other supervised topic models can incorporate arbitrary document-level features to inform topic priors. However, their ability to model corpora are limited by the representation and selection of these features – a choice the topic modeler must make. Instead, we seek models that can learn the feature representations upon which to condition topic selection. We present deep Dirichlet Multinomial Regression (dDMR), a generative topic model that simultaneously learns document feature representations and topics. We evaluate dDMR on three datasets: New York Times articles with fine-grained tags, Amazon product reviews with product images, and Reddit posts with subreddit identity. dDMR learns representations that outperform DMR and LDA according to heldout perplexity and are more effective at downstream predictive tasks as the number of topics grows. Additionally, human subjects judge dDMR topics as being more representative of associated document features. Finally, we find that supervision leads to faster convergence as compared to an LDA baseline and that dDMR’s model fit is less sensitive to training parameters than DMR.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1035", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1035.flac", "type": "audio", "duration": 1109.525}, {"from": "Assistant", "value": "Millions of conversations are generated every day on social media platforms. With limited attention, it is challenging for users to select which discussions they would like to participate in. Here we propose a new method for microblog conversation recommendation. While much prior work has focused on post-level recommendation, we exploit both the conversational context, and user content and behavior preferences. We propose a statistical model that jointly captures: (1) topics for representing user interests and conversation content, and (2) discourse modes for describing user replying behavior and conversation dynamics. Experimental results on two Twitter datasets demonstrate that our system outperforms methods that only model content without considering discourse.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1036", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1036.flac", "type": "audio", "duration": 1073.024}, {"from": "Assistant", "value": "Arguing without committing a fallacy is one of the main requirements of an ideal debate. But even when debating rules are strictly enforced and fallacious arguments punished, arguers often lapse into attacking the opponent by an ad hominem argument. As existing research lacks solid empirical investigation of the typology of ad hominem arguments as well as their potential causes, this paper fills this gap by (1) performing several large-scale annotation studies, (2) experimenting with various neural architectures and validating our working hypotheses, such as controversy or reasonableness, and (3) providing linguistic insights into triggers of ad hominem using explainable neural network architectures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1037", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1037.flac", "type": "audio", "duration": 1095.68}, {"from": "Assistant", "value": "In this paper, we study the problem of parsing structured knowledge graphs from textual descriptions. In particular, we consider the scene graph representation that considers objects together with their attributes and relations: this representation has been proved useful across a variety of vision and language applications. We begin by introducing an alternative but equivalent edge-centric view of scene graphs that connect to dependency parses. Together with a careful redesign of label and action space, we combine the two-stage pipeline used in prior work (generic dependency parsing followed by simple post-processing) into one, enabling end-to-end training. The scene graphs generated by our learned neural dependency parser achieve an F-score similarity of 49.67% to ground truth graphs on our evaluation set, surpassing best previous approaches by 5%. We further demonstrate the effectiveness of our learned parser on image retrieval applications.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1038", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1038.flac", "type": "audio", "duration": 1174.592}, {"from": "Assistant", "value": "We investigate grounded sentence representations, where we train a sentence encoder to predict the image features of a given caption—i.e., we try to “imagine” how a sentence would be depicted visually—and use the resultant features as sentence representations. We examine the quality of the learned representations on a variety of standard sentence representation quality benchmarks, showing improved performance for grounded models over non-grounded ones. In addition, we thoroughly analyze the extent to which grounding contributes to improved performance, and show that the system also learns improved word embeddings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1039", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1039.flac", "type": "audio", "duration": 1143.36}, {"from": "Assistant", "value": "The present work investigates whether different quantification mechanisms (set comparison, vague quantification, and proportional estimation) can be jointly learned from visual scenes by a multi-task computational model. The motivation is that, in humans, these processes underlie the same cognitive, non-symbolic ability, which allows an automatic estimation and comparison of set magnitudes. We show that when information about lower-complexity tasks is available, the higher-level proportional task becomes more accurate than when performed in isolation. Moreover, the multi-task model is able to generalize to unseen combinations of target/non-target objects. Consistently with behavioral evidence showing the interference of absolute number in the proportional task, the multi-task model no longer works when asked to provide the number of target objects in the scene.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1040", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1040.flac", "type": "audio", "duration": 1228.395}, {"from": "Assistant", "value": "Visual question answering (Visual QA) has attracted a lot of attention lately, seen essentially as a form of (visual) Turing test that artificial intelligence should strive to achieve. In this paper, we study a crucial component of this task: how can we design good datasets for the task? We focus on the design of multiple-choice based datasets where the learner has to select the right answer from a set of candidate ones including the target (i.e., the correct one) and the decoys (i.e., the incorrect ones). Through careful analysis of the results attained by state-of-the-art learning models and human annotators on existing datasets, we show that the design of the decoy answers has a significant impact on how and what the learning models learn from the datasets. In particular, the resulting learner can ignore the visual information, the question, or both while still doing well on the task. Inspired by this, we propose automatic procedures to remedy such design deficiencies. We apply the procedures to re-construct decoy answers for two popular Visual QA datasets as well as to create a new Visual QA dataset from the Visual Genome project, resulting in the largest dataset for this task. Extensive empirical studies show that the design deficiencies have been alleviated in the remedied datasets and the performance on them is likely a more faithful indicator of the difference among learning models. The datasets are released and publicly available viahttp://www.teds.usc.edu/website_vqa/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1055", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1055.flac", "type": "audio", "duration": 1085.824}, {"from": "Assistant", "value": "Previously, neural methods in grammatical error correction (GEC) did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) baselines. We demonstrate parallels between neural GEC and low-resource neural MT and successfully adapt several methods from low-resource MT to neural GEC. We further establish guidelines for trustable results in neural GEC and propose a set of model-independent methods for neural GEC that can be easily applied in most GEC settings. Proposed methods include adding source-side noise, domain-adaptation techniques, a GEC-specific training-objective, transfer learning with monolingual data, and ensembling of independently trained GEC models and language models. The combined effects of these methods result in better than state-of-the-art neural GEC models that outperform previously best neural GEC systems by more than 10% M² on the CoNLL-2014 benchmark and 5.9% on the JFLEG test set. Non-neural state-of-the-art systems are outperformed by more than 2% on the CoNLL-2014 benchmark and by 4% on JFLEG.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1056", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1056.flac", "type": "audio", "duration": 1039.317}, {"from": "Assistant", "value": "Cross-lingual Hypernymy Detection involves determining if a word in one language (“fruit”) is a hypernym of a word in another language (“pomme” i.e. apple in French). The ability to detect hypernymy cross-lingually can aid in solving cross-lingual versions of tasks such as textual entailment and event coreference. We propose BiSparse-Dep, a family of unsupervised approaches for cross-lingual hypernymy detection, which learns sparse, bilingual word embeddings based on dependency contexts. We show that BiSparse-Dep can significantly improve performance on this task, compared to approaches based only on lexical context. Our approach is also robust, showing promise for low-resource settings: our dependency-based embeddings can be learned using a parser trained on related languages, with negligible loss in performance. We also crowd-source a challenging dataset for this task on four languages – Russian, French, Arabic, and Chinese. Our embeddings and datasets are publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1057", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1057.flac", "type": "audio", "duration": 1119.829}, {"from": "Assistant", "value": "Translation-based methods for grammar correction that directly map noisy, ungrammatical text to their clean counterparts are able to correct a broad range of errors; however, such techniques are bottlenecked by the need for a large parallel corpus of noisy and clean sentence pairs. In this paper, we consider synthesizing parallel data by noising a clean monolingual corpus. While most previous approaches introduce perturbations using features computed from local context windows, we instead develop error generation processes using a neural sequence transduction model trained to translate clean examples to their noisy counterparts. Given a corpus of clean examples, we propose beam search noising procedures to synthesize additional noisy examples that human evaluators were nearly unable to discriminate from nonsynthesized examples. Surprisingly, when trained on additional data synthesized using our best-performing noising scheme, our model approaches the same performance as when trained on additional nonsynthesized data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1059", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1059.flac", "type": "audio", "duration": 1065.067}, {"from": "Assistant", "value": "Answering complex questions is a time-consuming activity for humans that requires reasoning and integration of information. Recent work on reading comprehension made headway in answering simple questions, but tackling complex questions is still an ongoing research challenge. Conversely, semantic parsers have been successful at handling compositionality, but only when the information resides in a target knowledge-base. In this paper, we present a novel framework for answering broad and complex questions, assuming answering simple questions is possible using a search engine and a reading comprehension model. We propose to decompose complex questions into a sequence of simple questions, and compute the final answer from the sequence of answers. To illustrate the viability of our approach, we create a new dataset of complex questions, ComplexWebQuestions, and present a model that decomposes questions and interacts with the web to compute an answer. We empirically demonstrate that question decomposition improves performance from 20.8 precision@1 to 27.5 precision@1 on this new dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1060", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1060.flac", "type": "audio", "duration": 980.011}, {"from": "Assistant", "value": "We introduce MeSys, a meaning-based approach, for solving English math word problems (MWPs) via understanding and reasoning in this paper. It first analyzes the text, transforms both body and question parts into their corresponding logic forms, and then performs inference on them. The associated context of each quantity is represented with proposed role-tags (e.g., nsubj, verb, etc.), which provides the flexibility for annotating an extracted math quantity with its associated context information (i.e., the physical meaning of this quantity). Statistical models are proposed to select the operator and operands. A noisy dataset is designed to assess if a solver solves MWPs mainly via understanding or mechanical pattern matching. Experimental results show that our approach outperforms existing systems on both benchmark datasets and the noisy dataset, which demonstrates that the proposed approach understands the meaning of each quantity in the text more.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1063", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1063.flac", "type": "audio", "duration": 894.635}, {"from": "Assistant", "value": "Current measures for evaluating text simplification systems focus on evaluating lexical text aspects, neglecting its structural aspects. In this paper we propose the first measure to address structural aspects of text simplification, called SAMSA. It leverages recent advances in semantic parsing to assess simplification quality by decomposing the input based on its semantic structure and comparing it to the output. SAMSA provides a reference-less automatic evaluation procedure, avoiding the problems that reference-based methods face due to the vast space of valid simplifications for a given sentence. Our human evaluation experiments show both SAMSA’s substantial correlation with human judgments, as well as the deficiency of existing reference-based measures in evaluating structural simplification.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1064", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1064.flac", "type": "audio", "duration": 1162.347}, {"from": "Assistant", "value": "A major proportion of a text summary includes important entities found in the original text. These entities build up the topic of the summary. Moreover, they hold commonsense information once they are linked to a knowledge base. Based on these observations, this paper investigates the usage of linked entities to guide the decoder of a neural text summarizer to generate concise and better summaries. To this end, we leverage on an off-the-shelf entity linking system (ELS) to extract linked entities and propose Entity2Topic (E2T), a module easily attachable to a sequence-to-sequence model that transforms a list of entities into a vector representation of the topic of the summary. Current available ELS’s are still not sufficiently effective, possibly introducing unresolved ambiguities and irrelevant entities. We resolve the imperfections of the ELS by (a) encoding entities with selective disambiguation, and (b) pooling entity vectors using firm attention. By applying E2T to a simple sequenceto-sequence model with attention mechanism as base model, we see significant improvements of the performance in the Gigaword (sentence to title) and CNN (long document to multi-sentence highlights) summarization datasets by at least 2 ROUGE points.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1065", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1065.flac", "type": "audio", "duration": 1040.213}, {"from": "Assistant", "value": "We present NEWSROOM, a summarization dataset of 1.3 million articles and summaries written by authors and editors in newsrooms of 38 major news publications. Extracted from search and social media metadata between 1998 and 2017, these high-quality summaries demonstrate high diversity of summarization styles. In particular, the summaries combine abstractive and extractive strategies, borrowing words and phrases from articles at varying rates. We analyze the extraction strategies used in NEWSROOM summaries against other datasets to quantify the diversity and difficulty of our new data, and train existing methods on the data to evaluate its utility and challenges. The dataset is available online at summari.es.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1066", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1066.flac", "type": "audio", "duration": 1020.501}, {"from": "Assistant", "value": "Traditional approaches to semantic parsing (SP) work by training individual models for each available parallel dataset of text-meaning pairs. In this paper, we explore the idea of polyglot semantic translation, or learning semantic parsing models that are trained on multiple datasets and natural languages. In particular, we focus on translating text to code signature representations using the software component datasets of Richardson and Kuhn (2017b,a). The advantage of such models is that they can be used for parsing a wide variety of input natural languages and output programming languages, or mixed input languages, using a single unified model. To facilitate modeling of this type, we develop a novel graph-based decoding framework that achieves state-of-the-art performance on the above datasets, and apply this method to two other benchmark SP tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1067", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1067.flac", "type": "audio", "duration": 1099.349}, {"from": "Assistant", "value": "We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME. We also present a substantial expansion of the It Happened portion of the Universal Decompositional Semantics dataset, yielding the largest event factuality dataset to date. We report model results on this extended factuality dataset as well.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1092", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1092.flac", "type": "audio", "duration": 886.187}, {"from": "Assistant", "value": "This work exploits translation data as a source of semantically relevant learning signal for models of word representation. In particular, we exploit equivalence through translation as a form of distributional context and jointly learn how to embed and align with a deep generative model. Our EmbedAlign model embeds words in their complete observed context and learns by marginalisation of latent lexical alignments. Besides, it embeds words as posterior probability densities, rather than point estimates, which allows us to compare words in context using a measure of overlap between distributions (e.g. KL divergence). We investigate our model’s performance on a range of lexical semantics tasks achieving competitive results on several standard benchmarks including natural language inference, paraphrasing, and text similarity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1093", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1093.flac", "type": "audio", "duration": 977.493}, {"from": "Assistant", "value": "Word embedding is a key component in many downstream applications in processing natural languages. Existing approaches often assume the existence of a large collection of text for learning effective word embedding. However, such a corpus may not be available for some low-resource languages. In this paper, we study how to effectively learn a word embedding model on a corpus with only a few million tokens. In such a situation, the co-occurrence matrix is sparse as the co-occurrences of many word pairs are unobserved. In contrast to existing approaches often only sample a few unobserved word pairs as negative samples, we argue that the zero entries in the co-occurrence matrix also provide valuable information. We then design a Positive-Unlabeled Learning (PU-Learning) approach to factorize the co-occurrence matrix and validate the proposed approaches in four different languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1094", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1094.flac", "type": "audio", "duration": 1127.232}, {"from": "Assistant", "value": "Public debate forums provide a common platform for exchanging opinions on a topic of interest. While recent studies in natural language processing (NLP) have provided empirical evidence that the language of the debaters and their patterns of interaction play a key role in changing the mind of a reader, research in psychology has shown that prior beliefs can affect our interpretation of an argument and could therefore constitute a competing alternative explanation for resistance to changing one’s stance. To study the actual effect of language use vs. prior beliefs on persuasion, we provide a new dataset and propose a controlled setting that takes into consideration two reader-level factors: political and religious ideology. We find that prior beliefs affected by these reader-level factors play a more important role than language use effects and argue that it is important to account for them in NLP studies of persuasion.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1095", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1095.flac", "type": "audio", "duration": 1131.797}, {"from": "Assistant", "value": "We address the detection of abusive words. The task is to identify such words among a set of negative polar expressions. We propose novel features employing information from both corpora and lexical resources. These features are calibrated on a small manually annotated base lexicon which we use to produce a large lexicon. We show that the word-level information we learn cannot be equally derived from a large dataset of annotated microposts. We demonstrate the effectiveness of our (domain-independent) lexicon in the cross-domain detection of abusive microposts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1096", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1096.flac", "type": "audio", "duration": 1102.101}, {"from": "Assistant", "value": "Understanding how social power structures affect the way we interact with one another is of great interest to social scientists who want to answer fundamental questions about human behavior, as well as to computer scientists who want to build automatic methods to infer the social contexts of interactions. In this paper, we employ advancements in extra-propositional semantics extraction within NLP to study how author commitment reflects the social context of an interactions. Specifically, we investigate whether the level of commitment expressed by individuals in an organizational interaction reflects the hierarchical power structures they are part of. We find that subordinates use significantly more instances of non-commitment than superiors. More importantly, we also find that subordinates attribute propositions to other agents more often than superiors do — an aspect that has not been studied before. Finally, we show that enriching lexical features with commitment labels captures important distinctions in social meanings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1098", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1098.flac", "type": "audio", "duration": 978.155}, {"from": "Assistant", "value": "Dynamic topic modeling facilitates the identification of topical trends over time in temporal collections of unstructured documents. We introduce a novel unsupervised neural dynamic topic model named as Recurrent Neural Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each time influence the topic discovery in the subsequent time steps. We account for the temporal ordering of documents by explicitly modeling a joint distribution of latent topical dependencies over time, using distributional estimators with temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP research, we demonstrate that compared to state-of-the art topic models, RNNRSM shows better generalization, topic interpretation, evolution and trends. We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1099", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1099.flac", "type": "audio", "duration": 945.579}, {"from": "Assistant", "value": "Multilingual topic models enable document analysis across languages through coherent multilingual summaries of the data. However, there is no standard and effective metric to evaluate the quality of multilingual topics. We introduce a new intrinsic evaluation of multilingual topic models that correlates well with human judgments of multilingual topic coherence as well as performance in downstream applications. Importantly, we also study evaluation for low-resource languages. Because standard metrics fail to accurately measure topic quality when robust external resources are unavailable, we propose an adaptation model that improves the accuracy and reliability of these metrics in low-resource settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1101", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1101.flac", "type": "audio", "duration": 949.952}, {"from": "Assistant", "value": "This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. At 433k examples, this resource is one of the largest corpora available for natural language inference (a.k.a. recognizing textual entailment), improving upon available resources in both its coverage and difficulty. MultiNLI accomplishes this by offering data from ten distinct genres of written and spoken English, making it possible to evaluate systems on nearly the full complexity of the language, while supplying an explicit setting for evaluating cross-genre domain adaptation. In addition, an evaluation using existing machine learning models designed for the Stanford NLI corpus shows that it represents a substantially more difficult task than does that corpus, despite the two showing similar levels of inter-annotator agreement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1102", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1102.flac", "type": "audio", "duration": 1051.733}, {"from": "Assistant", "value": "Recognizing lexical semantic relations between word pairs is an important task for many applications of natural language processing. One of the mainstream approaches to this task is to exploit the lexico-syntactic paths connecting two target words, which reflect the semantic relations of word pairs. However, this method requires that the considered words co-occur in a sentence. This requirement is hardly satisfied because of Zipf’s law, which states that most content words occur very rarely. In this paper, we propose novel methods with a neural model of P(path|w1,w2) to solve this problem. Our proposed model of P (path|w1, w2 ) can be learned in an unsupervised manner and can generalize the co-occurrences of word pairs and dependency paths. This model can be used to augment the path data of word pairs that do not co-occur in the corpus, and extract features capturing relational information from word pairs. Our experimental results demonstrate that our methods improve on previous neural approaches based on dependency paths and successfully solve the focused problem.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1103", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1103.flac", "type": "audio", "duration": 1091.029}, {"from": "Assistant", "value": "We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entailment (LE), also known as the IS-A or hyponymy-hypernymy relation. By injecting external linguistic constraints (e.g., WordNet links) into the initial vector space, the LE specialisation procedure brings true hyponymy-hypernymy pairs closer together in the transformed Euclidean space. The proposed asymmetric distance measure adjusts the norms of word vectors to reflect the actual WordNet-style hierarchy of concepts. Simultaneously, a joint objective enforces semantic similarity using the symmetric cosine distance, yielding a vector space specialised for both lexical relations at once. LEAR specialisation achieves state-of-the-art performance in the tasks of hypernymy directionality, hypernymy detection, and graded lexical entailment, demonstrating the effectiveness and robustness of the proposed asymmetric specialisation model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1104", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1104.flac", "type": "audio", "duration": 966.784}, {"from": "Assistant", "value": "Abstract Meaning Representation (AMR) research has mostly focused on English. We show that it is possible to use AMR annotations for English as a semantic representation for sentences written in other languages. We exploit an AMR parser for English and parallel corpora to learn AMR parsers for Italian, Spanish, German and Chinese. Qualitative analysis show that the new parsers overcome structural differences between the languages. We further propose a method to evaluate the parsers that does not require gold standard data in the target languages. This method highly correlates with the gold standard evaluation, obtaining a Pearson correlation coefficient of 0.95.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1105", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1105.flac", "type": "audio", "duration": 1066.283}, {"from": "Assistant", "value": "Sentences with gapping, such as Paul likes coffee and Mary tea, lack an overt predicate to indicate the relation between two or more arguments. Surface syntax representations of such sentences are often produced poorly by parsers, and even if correct, not well suited to downstream natural language understanding tasks such as relation extraction that are typically designed to extract information from sentences with canonical clause structure. In this paper, we present two methods for parsing to a Universal Dependencies graph representation that explicitly encodes the elided material with additional nodes and edges. We find that both methods can reconstruct elided material from dependency trees with high accuracy when the parser correctly predicts the existence of a gap. We further demonstrate that one of our methods can be applied to other languages based on a case study on Swedish.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1106", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1106.flac", "type": "audio", "duration": 1118.357}, {"from": "Assistant", "value": "Abstract Meaning Representation (AMR) annotations are often assumed to closely mirror dependency syntax, but AMR explicitly does not require this, and the assumption has never been tested. To test it, we devise an expressive framework to align AMR graphs to dependency graphs, which we use to annotate 200 AMRs. Our annotation explains how 97% of AMR edges are evoked by words or syntax. Previously existing AMR alignment frameworks did not allow for mapping AMR onto syntax, and as a consequence they explained at most 23%. While we find that there are indeed many cases where AMR annotations closely mirror syntax, there are also pervasive differences. We use our annotations to test a baseline AMR-to-syntax aligner, finding that this task is more difficult than AMR-to-string alignment; and to pinpoint errors in an AMR parser. We make our data and code freely available for further research on AMR parsing and generation, and the relationship of AMR to syntax.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1107", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1107.flac", "type": "audio", "duration": 926.805}, {"from": "Assistant", "value": "We present a graph-based Tree Adjoining Grammar (TAG) parser that uses BiLSTMs, highway connections, and character-level CNNs. Our best end-to-end parser, which jointly performs supertagging, POS tagging, and parsing, outperforms the previously reported best results by more than 2.2 LAS and UAS points. The graph-based parsing architecture allows for global inference and rich feature representations for TAG parsing, alleviating the fundamental trade-off between transition-based and graph-based parsing systems. We also demonstrate that the proposed parser achieves state-of-the-art performance in the downstream tasks of Parsing Evaluation using Textual Entailments (PETE) and Unbounded Dependency Recovery. This provides further support for the claim that TAG is a viable formalism for problems that require rich structural analysis of sentences.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1108", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1108.flac", "type": "audio", "duration": 1040.704}, {"from": "Assistant", "value": "Recurrent neural networks (RNNs) achieved impressive results in a variety of linguistic processing tasks, suggesting that they can induce non-trivial properties of language. We investigate to what extent RNNs learn to track abstract hierarchical syntactic structure. We test whether RNNs trained with a generic language modeling objective in four languages (Italian, English, Hebrew, Russian) can predict long-distance number agreement in various constructions. We include in our evaluation nonsensical sentences where RNNs cannot rely on semantic or lexical cues (“The colorless green ideas I ate with the chair sleep furiously”), and, for Italian, we compare model performance to human intuitions. Our language-model-trained RNNs make reliable predictions about long-distance agreement, and do not lag much behind human performance. We thus bring support to the hypothesis that RNNs are not just shallow-pattern extractors, but they also acquire deeper grammatical competence.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1131", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1131.flac", "type": "audio", "duration": 1000.875}, {"from": "Assistant", "value": "Entity mentions embedded in longer entity mentions are referred to as nested entities. Most named entity recognition (NER) systems deal only with the flat entities and ignore the inner nested ones, which fails to capture finer-grained semantic information in underlying texts. To address this issue, we propose a novel neural model to identify nested entities by dynamically stacking flat NER layers. Each flat NER layer is based on the state-of-the-art flat NER model that captures sequential context representation with bidirectional Long Short-Term Memory (LSTM) layer and feeds it to the cascaded CRF layer. Our model merges the output of the LSTM layer in the current flat NER layer to build new representation for detected entities and subsequently feeds them into the next flat NER layer. This allows our model to extract outer entities by taking full advantage of information encoded in their corresponding inner entities, in an inside-to-outside way. Our model dynamically stacks the flat NER layers until no outer entities are extracted. Extensive evaluation shows that our dynamic model outperforms state-of-the-art feature-based systems on nested NER, achieving 74.7% and 72.2% on GENIA and ACE2005 datasets, respectively, in terms of F-score.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1132", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1132.flac", "type": "audio", "duration": 945.92}, {"from": "Assistant", "value": "We present a novel deep learning architecture to address the natural language inference (NLI) task. Existing approaches mostly rely on simple reading mechanisms for independent encoding of the premise and hypothesis. Instead, we propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis during encoding and inference. We also introduce a sophisticated ensemble strategy to combine our proposed models, which noticeably improves final predictions. Finally, we demonstrate how the results can be improved further with an additional preprocessing step. Our evaluation shows that DR-BiLSTM obtains the best single model and ensemble model results achieving the new state-of-the-art scores on the Stanford NLI dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1133", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1133.flac", "type": "audio", "duration": 1635.136}, {"from": "Assistant", "value": "We introduce KBGAN, an adversarial learning framework to improve the performances of a wide range of existing knowledge graph embedding models. Because knowledge graphs typically only contain positive facts, sampling useful negative training examples is a nontrivial task. Replacing the head or tail entity of a fact with a uniformly randomly selected entity is a conventional method for generating negative facts, but the majority of the generated negative facts can be easily discriminated from positive facts, and will contribute little towards the training. Inspired by generative adversarial networks (GANs), we use one knowledge graph embedding model as a negative sample generator to assist the training of our desired model, which acts as the discriminator in GANs. This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks. In experiments, we adversarially train two translation-based models, TRANSE and TRANSD, each with assistance from one of the two probability-based models, DISTMULT and COMPLEX. We evaluate the performances of KBGAN on the link prediction task, using three knowledge base completion datasets: FB15k-237, WN18 and WN18RR. Experimental results show that adversarial training substantially improves the performances of target embedding models under various settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1134", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1134.flac", "type": "audio", "duration": 1031.168}, {"from": "Assistant", "value": "An essential step in FrameNet Semantic Role Labeling is the Frame Identification (FrameId) task, which aims at disambiguating a situation around a predicate. Whilst current FrameId methods rely on textual representations only, we hypothesize that FrameId can profit from a richer understanding of the situational context. Such contextual information can be obtained from common sense knowledge, which is more present in images than in text. In this paper, we extend a state-of-the-art FrameId system in order to effectively leverage multimodal representations. We conduct a comprehensive evaluation on the English FrameNet and its German counterpart SALSA. Our analysis shows that for the German data, textual representations are still competitive with multimodal ones. However on the English data, our multimodal FrameId approach outperforms its unimodal counterpart, setting a new state of the art. Its benefits are particularly apparent in dealing with ambiguous and rare instances, the main source of errors of current systems. For research purposes, we release (a) the implementation of our system, (b) our evaluation splits for SALSA 2.0, and (c) the embeddings for synsets and IMAGINED words.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1135", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1135.flac", "type": "audio", "duration": 982.549}, {"from": "Assistant", "value": "We present a new approach to learning a semantic parser from multiple datasets, even when the target semantic formalisms are drastically different and the underlying corpora do not overlap. We handle such “disjoint” data by treating annotations for unobserved formalisms as latent structured variables. Building on state-of-the-art baselines, we show improvements both in frame-semantic parsing and semantic dependency parsing by modeling them jointly.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1136", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1136.flac", "type": "audio", "duration": 1149.248}, {"from": "Assistant", "value": "Recognizing that even correct translations are not always semantically equivalent, we automatically detect meaning divergences in parallel sentence pairs with a deep neural model of bilingual semantic similarity which can be trained for any parallel corpus without any manual annotation. We show that our semantic model detects divergences more accurately than models based on surface features derived from word alignments, and that these divergences matter for neural machine translation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1137", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1137.flac", "type": "audio", "duration": 1101.376}, {"from": "Assistant", "value": "A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and associated texts. In this paper we aim to bootstrap generators from large scale datasets where the data (e.g., DBPedia facts) and related texts (e.g., Wikipedia abstracts) are loosely aligned. We tackle this challenging task by introducing a special-purpose content selection mechanism. We use multi-instance learning to automatically discover correspondences between data and text pairs and show how these can be used to enhance the content signal while training an encoder-decoder architecture. Experimental results demonstrate that models trained with content-specific objectives improve upon a vanilla encoder-decoder which solely relies on soft attention.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1138", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1138.flac", "type": "audio", "duration": 1153.899}, {"from": "Assistant", "value": "Supervised training of abstractive language generation models results in learning conditional probabilities over language sequences based on the supervised training signal. When the training signal contains a variety of writing styles, such models may end up learning an ‘average’ style that is directly influenced by the training data make-up and cannot be controlled by the needs of an application. We describe a family of model architectures capable of capturing both generic language characteristics via shared model parameters, as well as particular style characteristics via private model parameters. Such models are able to generate language according to a specific learned style, while still taking advantage of their power to model generic language phenomena. Furthermore, we describe an extension that uses a mixture of output distributions from all learned styles to perform on-the-fly style adaptation based on the textual input alone. Experimentally, we find that the proposed models consistently outperform models that encapsulate single-style or average-style language generation capabilities.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1162", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1162.flac", "type": "audio", "duration": 864.299}, {"from": "Assistant", "value": "Variational autoencoders (VAE) combined with hierarchical RNNs have emerged as a powerful framework for conversation modeling. However, they suffer from the notorious degeneration problem, where the decoders learn to ignore latent variables and reduce to vanilla RNNs. We empirically show that this degeneracy occurs mostly due to two reasons. First, the expressive power of hierarchical RNN decoders is often high enough to model the data using only its decoding distributions without relying on the latent variables. Second, the conditional VAE structure whose generation process is conditioned on a context, makes the range of training targets very sparse; that is, the RNN decoders can easily overfit to the training data ignoring the latent variables. To solve the degeneration problem, we propose a novel model named Variational Hierarchical Conversation RNNs (VHCR), involving two key ideas of (1) using a hierarchical structure of latent variables, and (2) exploiting an utterance drop regularization. With evaluations on two datasets of Cornell Movie Dialog and Ubuntu Dialog Corpus, we show that our VHCR successfully utilizes latent variables and outperforms state-of-the-art models for conversation generation. Moreover, it can perform several new utterance control tasks, thanks to its hierarchical latent structure.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1163", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1163.flac", "type": "audio", "duration": 1164.864}, {"from": "Assistant", "value": "Virtual agents are becoming a prominent channel of interaction in customer service. Not all customer interactions are smooth, however, and some can become almost comically bad. In such instances, a human agent might need to step in and salvage the conversation. Detecting bad conversations is important since disappointing customer service may threaten customer loyalty and impact revenue. In this paper, we outline an approach to detecting such egregious conversations, using behavioral cues from the user, patterns in agent responses, and user-agent interaction. Using logs of two commercial systems, we show that using these features improves the detection F1-score by around 20% over using textual features alone. In addition, we show that those features are common across two quite different domains and, arguably, universal.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1164", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1164.flac", "type": "audio", "duration": 1207.339}, {"from": "Assistant", "value": "An enormous amount of conversation occurs online every day, such as on chat platforms where multiple conversations may take place concurrently. Interleaved conversations lead to difficulties in not only following discussions but also retrieving relevant information from simultaneous messages. Conversation disentanglement aims to separate intermingled messages into detached conversations. In this paper, we propose to leverage representation learning for conversation disentanglement. A Siamese hierarchical convolutional neural network (SHCNN), which integrates local and more global representations of a message, is first presented to estimate the conversation-level similarity between closely posted messages. With the estimated similarity scores, our algorithm for conversation identification by similarity ranking (CISIR) then derives conversations based on high-confidence message pairs and pairwise redundancy. Experiments were conducted with four publicly available datasets of conversations from Reddit and IRC channels. The experimental results show that our approach significantly outperforms comparative baselines in both pairwise similarity estimation and conversation disentanglement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1165", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1165.flac", "type": "audio", "duration": 951.083}, {"from": "Assistant", "value": "Inferring missing links in knowledge graphs (KG) has attracted a lot of attention from the research community. In this paper, we tackle a practical query answering task involving predicting the relation of a given entity pair. We frame this prediction problem as an inference problem in a probabilistic graphical model and aim at resolving it from a variational inference perspective. In order to model the relation between the query entity pair, we assume that there exists an underlying latent variable (paths connecting two nodes) in the KG, which carries the equivalent semantics of their relations. However, due to the intractability of connections in large KGs, we propose to use variation inference to maximize the evidence lower bound. More specifically, our framework (Diva) is composed of three modules, i.e. a posterior approximator, a prior (path finder), and a likelihood (path reasoner). By using variational inference, we are able to incorporate them closely into a unified architecture and jointly optimize them to perform KG reasoning. With active interactions among these sub-modules, Diva is better at handling noise and coping with more complex reasoning scenarios. In order to evaluate our method, we conduct the experiment of the link prediction task on multiple datasets and achieve state-of-the-art performances on both datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1166", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1166.flac", "type": "audio", "duration": 1078.955}, {"from": "Assistant", "value": "Recognizing temporal relations among events and time expressions has been an essential but challenging task in natural language processing. Conventional annotation of judging temporal relations puts a heavy load on annotators. In reality, the existing annotated corpora include annotations on only “salient” event pairs, or on pairs in a fixed window of sentences. In this paper, we propose a new approach to obtain temporal relations from absolute time value (a.k.a. time anchors), which is suitable for texts containing rich temporal information such as news articles. We start from time anchors for events and time expressions, and temporal relation annotations are induced automatically by computing relative order of two time anchors. This proposal shows several advantages over the current methods for temporal relation annotation: it requires less annotation effort, can induce inter-sentence relations easily, and increases informativeness of temporal relations. We compare the empirical statistics and automatic recognition results with our data against a previous temporal relation corpus. We also reveal that our data contributes to a significant improvement of the downstream time anchor prediction task, demonstrating 14.1 point increase in overall accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1167", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1167.flac", "type": "audio", "duration": 880.149}, {"from": "Assistant", "value": "Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in a Knowledge Graph (KG). Degree of connectivity of an entity in the KG directly affects an EL system’s ability to correctly link mentions in text to the entity in KG. This causes many EL systems to perform well for entities well connected to other entities in KG, bringing into focus the role of KG density in EL. In this paper, we propose Entity Linking using Densified Knowledge Graphs (ELDEN). ELDEN is an EL system which first densifies the KG with co-occurrence statistics from a large text corpus, and then uses the densified KG to train entity embeddings. Entity similarity measured using these trained entity embeddings result in improved EL. ELDEN outperforms state-of-the-art EL system on benchmark datasets. Due to such densification, ELDEN performs well for sparsely connected entities in the KG too. ELDEN’s approach is simple, yet effective. We have made ELDEN’s code and data publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1168", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1168.flac", "type": "audio", "duration": 1013.845}, {"from": "Assistant", "value": "In this paper, we propose to study the problem of court view generation from the fact description in a criminal case. The task aims to improve the interpretability of charge prediction systems and help automatic legal document generation. We formulate this task as a text-to-text natural language generation (NLG) problem. Sequence-to-sequence model has achieved cutting-edge performances in many NLG tasks. However, due to the non-distinctions of fact descriptions, it is hard for Seq2Seq model to generate charge-discriminative court views. In this work, we explore charge labels to tackle this issue. We propose a label-conditioned Seq2Seq model with attention for this problem, to decode court views conditioned on encoded charge labels. Experimental results show the effectiveness of our method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1169", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1169.flac", "type": "audio", "duration": 992.341}, {"from": "Assistant", "value": "We consider the task of text attribute transfer: transforming a sentence to alter a specific attribute (e.g., sentiment) while preserving its attribute-independent content (e.g., “screen is just the right size” to “screen is too small”). Our training data includes only sentences labeled with their attribute (e.g., positive and negative), but not pairs of sentences that only differ in the attributes, so we must learn to disentangle attributes from attribute-independent content in an unsupervised way. Previous work using adversarial methods has struggled to produce high-quality outputs. In this paper, we propose simpler methods motivated by the observation that text attributes are often marked by distinctive phrases (e.g., “too small”). Our strongest method extracts content words by deleting phrases associated with the sentence’s original attribute value, retrieves new phrases associated with the target attribute, and uses a neural model to fluently combine these into a final output. Based on human evaluation, our best method generates grammatical and appropriate responses on 22% more inputs than the best previous system, averaged over three attribute transfer datasets: altering sentiment of reviews on Yelp, altering sentiment of reviews on Amazon, and altering image captions to be more romantic or humorous.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1170", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1170.flac", "type": "audio", "duration": 1054.997}, {"from": "Assistant", "value": "We propose syntactically controlled paraphrase networks (SCPNs) and use them to generate adversarial examples. Given a sentence and a target syntactic form (e.g., a constituency parse), SCPNs are trained to produce a paraphrase of the sentence with the desired syntax. We show it is possible to create training data for this task by first doing backtranslation at a very large scale, and then using a parser to label the syntactic transformations that naturally occur during this process. Such data allows us to train a neural encoder-decoder model with extra inputs to specify the target syntax. A combination of automated and human evaluations show that SCPNs generate paraphrases that follow their target specifications without decreasing paraphrase quality when compared to baseline (uncontrolled) paraphrase systems. Furthermore, they are more capable of generating syntactically adversarial examples that both (1) “fool” pretrained models and (2) improve the robustness of these models to syntactic variation when used to augment their training data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1171", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1171.flac", "type": "audio", "duration": 1077.419}, {"from": "Assistant", "value": "Sentiment analysis is used as a proxy to measure human emotion, where the objective is to categorize text according to some predefined notion of sentiment. Sentiment analysis datasets are typically constructed with gold-standard sentiment labels, assigned based on the results of manual annotations. When working with such annotations, it is common for dataset constructors to discard “noisy” or “controversial” data where there is significant disagreement on the proper label. In datasets constructed for the purpose of Twitter sentiment analysis (TSA), these controversial examples can compose over 30% of the originally annotated data. We argue that the removal of such data is a problematic trend because, when performing real-time sentiment classification of short-text, an automated system cannot know a priori which samples would fall into this category of disputed sentiment. We therefore propose the notion of a “complicated” class of sentiment to categorize such text, and argue that its inclusion in the short-text sentiment analysis framework will improve the quality of automated sentiment analysis systems as they are implemented in real-world settings. We motivate this argument by building and analyzing a new publicly available TSA dataset of over 7,000 tweets annotated with 5x coverage, named MTSA. Our analysis of classifier performance over our dataset offers insights into sentiment analysis dataset and model design, how current techniques would perform in the real world, and how researchers should handle difficult data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1172", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1172.flac", "type": "audio", "duration": 970.517}, {"from": "Assistant", "value": "We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of tasks with disparate label spaces. We outperform strong single and multi-task baselines and achieve a new state of the art for aspect-based and topic-based sentiment analysis.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1173", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1173.flac", "type": "audio", "duration": 1059.435}, {"from": "Assistant", "value": "Predicting the emotional value of lexical items is a well-known problem in sentiment analysis. While research has focused on polarity for quite a long time, meanwhile this early focus has been shifted to more expressive emotion representation models (such as Basic Emotions or Valence-Arousal-Dominance). This change resulted in a proliferation of heterogeneous formats and, in parallel, often small-sized, non-interoperable resources (lexicons and corpus annotations). In particular, the limitations in size hampered the application of deep learning methods in this area because they typically require large amounts of input data. We here present a solution to get around this language data bottleneck by rephrasing word emotion induction as a multi-task learning problem. In this approach, the prediction of each independent emotion dimension is considered as an individual task and hidden layers are shared between these dimensions. We investigate whether multi-task learning is more advantageous than single-task learning for emotion prediction by comparing our model against a wide range of alternative emotion and polarity induction methods featuring 9 typologically diverse languages and a total of 15 conditions. Our model turns out to outperform each one of them. Against all odds, the proposed deep learning approach yields the largest gain on the smallest data sets, merely composed of one thousand samples.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1174", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1174.flac", "type": "audio", "duration": 899.968}, {"from": "Assistant", "value": "We often talk about events that impact us positively or negatively. For example “I got a job” is good news, but “I lost my job” is bad news. When we discuss an event, we not only understand its affective polarity but also the reason why the event is beneficial or detrimental. For example, getting or losing a job has affective polarity primarily because it impacts us financially. Our work aims to categorize affective events based upon human need categories that often explain people’s motivations and desires: PHYSIOLOGICAL, HEALTH, LEISURE, SOCIAL, FINANCIAL, COGNITION, and FREEDOM. We create classification models based on event expressions as well as models that use contexts surrounding event mentions. We also design a co-training model that learns from unlabeled data by simultaneously training event expression and event context classifiers in an iterative learning process. Our results show that co-training performs well, producing substantially better results than the individual classifiers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1175", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1175.flac", "type": "audio", "duration": 1015.957}, {"from": "Assistant", "value": "Reasoning is a crucial part of natural language argumentation. To comprehend an argument, one must analyze its warrant, which explains why its claim follows from its premises. As arguments are highly contextualized, warrants are usually presupposed and left implicit. Thus, the comprehension does not only require language understanding and logic skills, but also depends on common sense. In this paper we develop a methodology for reconstructing warrants systematically. We operationalize it in a scalable crowdsourcing process, resulting in a freely licensed dataset with warrants for 2k authentic arguments from news comments. On this basis, we present a new challenging task, the argument reasoning comprehension task. Given an argument with a claim and a premise, the goal is to choose the correct implicit warrant from two options. Both warrants are plausible and lexically close, but lead to contradicting claims. A solution to this task will define a substantial step towards automatic warrant reconstruction. However, experiments with several neural attention and language models reveal that current approaches do not suffice.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1176", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1176.flac", "type": "audio", "duration": 1028.075}, {"from": "Assistant", "value": "We explore deception detection in interview dialogues. We analyze a set of linguistic features in both truthful and deceptive responses to interview questions. We also study the perception of deception, identifying characteristics of statements that are perceived as truthful or deceptive by interviewers. Our analysis show significant differences between truthful and deceptive question responses, as well as variations in deception patterns across gender and native language. This analysis motivated our selection of features for machine learning experiments aimed at classifying globally deceptive speech. Our best classification performance is 72.74% F1-Score (about 17% better than human performance), which is achieved using a combination of linguistic features and individual traits.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1177", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1177.flac", "type": "audio", "duration": 1286.4}, {"from": "Assistant", "value": "We show that explicit pragmatic inference aids in correctly generating and following natural language instructions for complex, sequential tasks. Our pragmatics-enabled models reason about why speakers produce certain instructions, and about how listeners will react upon hearing them. Like previous pragmatic models, we use learned base listener and speaker models to build a pragmatic speaker that uses the base listener to simulate the interpretation of candidate descriptions, and a pragmatic listener that reasons counterfactually about alternative descriptions. We extend these models to tasks with sequential structure. Evaluation of language generation and interpretation shows that pragmatic inference improves state-of-the-art listener models (at correctly interpreting human instructions) and speaker models (at producing instructions correctly interpreted by humans) in diverse settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1178", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1178.flac", "type": "audio", "duration": 1025.899}, {"from": "Assistant", "value": "Election manifestos document the intentions, motives, and views of political parties. They are often used for analysing a party’s fine-grained position on a particular issue, as well as for coarse-grained positioning of a party on the left–right spectrum. In this paper we propose a two-stage model for automatically performing both levels of analysis over manifestos. In the first step we employ a hierarchical multi-task structured deep model to predict fine- and coarse-grained positions, and in the second step we perform post-hoc calibration of coarse-grained positions using probabilistic soft logic. We empirically show that the proposed model outperforms state-of-art approaches at both granularities using manifestos from twelve countries, written in ten different languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1179", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1179.flac", "type": "audio", "duration": 1053.824}, {"from": "Assistant", "value": "Natural Language Inference is a challenging task that has received substantial attention, and state-of-the-art models now achieve impressive test set performance in the form of accuracy scores. Here, we go beyond this single evaluation metric to examine robustness to semantically-valid alterations to the input data. We identify three factors - insensitivity, polarity and unseen pairs - and compare their impact on three SNLI models under a variety of conditions. Our results demonstrate a number of strengths and weaknesses in the models’ ability to generalise to new in-domain instances. In particular, while strong performance is possible on unseen hypernyms, unseen antonyms are more challenging for all the models. More generally, the models suffer from an insensitivity to certain small but semantically significant alterations, and are also often influenced by simple statistical correlations between words and training labels. Overall, we show that evaluations of NLI models can benefit from studying the influence of factors intrinsic to the models or found in the dataset used.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1202", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1202.flac", "type": "audio", "duration": 1131.733}, {"from": "Assistant", "value": "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1203", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1203.flac", "type": "audio", "duration": 1157.824}, {"from": "Assistant", "value": "We propose a context-dependent model to map utterances within an interaction to executable formal queries. To incorporate interaction history, the model maintains an interaction-level encoder that updates after each turn, and can copy sub-sequences of previously predicted queries during generation. Our approach combines implicit and explicit modeling of references between utterances. We evaluate our model on the ATIS flight planning interactions, and demonstrate the benefits of modeling context and explicit references.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1204", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1204.flac", "type": "audio", "duration": 1125.227}, {"from": "Assistant", "value": "We introduce an approach to neural text generation that explicitly represents entities mentioned in the text. Entity representations are vectors that are updated as the text proceeds; they are designed specifically for narrative text like fiction or news stories. Our experiments demonstrate that modeling entities offers a benefit in two automatic evaluations: mention generation (in which a model chooses which entity to mention next and which words to use in the mention) and selection between a correct next sentence and a distractor from later in the same story. We also conduct a human evaluation on automatically generated text in story contexts; this study supports our emphasis on entities and suggests directions for further research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-1205", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-1205.flac", "type": "audio", "duration": 973.483}, {"from": "Assistant", "value": "We investigate the computational complexity of various problems for simple recurrent neural networks (RNNs) as formal models for recognizing weighted languages. We focus on the single-layer, ReLU-activation, rational-weight RNNs with softmax, which are commonly used in natural language processing applications. We show that most problems for such RNNs are undecidable, including consistency, equivalence, minimization, and the determination of the highest-weighted string. However, for consistent RNNs the last problem becomes decidable, although the solution length can surpass all computable bounds. If additionally the string is limited to polynomial length, the problem becomes NP-complete. In summary, this shows that approximations and heuristic algorithms are necessary in practical applications of those RNNs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-2021", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-2021.flac", "type": "audio", "duration": 1051.392}, {"from": "Assistant", "value": "This paper introduces rank-based training of structured prediction energy networks (SPENs). Our method samples from output structures using gradient descent and minimizes the ranking violation of the sampled structures with respect to a scalar scoring function defined with domain knowledge. We have successfully trained SPEN for citation field extraction without any labeled data instances, where the only source of supervision is a simple human-written scoring function. Such scoring functions are often easy to provide; the SPEN then furnishes an efficient structured prediction inference procedure.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-2022", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-2022.flac", "type": "audio", "duration": 990.4}, {"from": "Assistant", "value": "Political identity is often manifested in language variation, but the relationship between the two is still relatively unexplored from a quantitative perspective. This study examines the use of Catalan, a language local to the semi-autonomous region of Catalonia in Spain, on Twitter in discourse related to the 2017 independence referendum. We corroborate prior findings that pro-independence tweets are more likely to include the local language than anti-independence tweets. We also find that Catalan is used more often in referendum-related discourse than in other contexts, contrary to prior findings on language variation. This suggests a strong role for the Catalan language in the expression of Catalonian political identity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-2046", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-2046.flac", "type": "audio", "duration": 1183.424}, {"from": "Assistant", "value": "We combine two of the most popular approaches to automated Grammatical Error Correction (GEC): GEC based on Statistical Machine Translation (SMT) and GEC based on Neural Machine Translation (NMT). The hybrid system achieves new state-of-the-art results on the CoNLL-2014 and JFLEG benchmarks. This GEC system preserves the accuracy of SMT output and, at the same time, generates more fluent sentences as it typical for NMT. Our analysis shows that the created systems are closer to reaching human-level performance than any other GEC system reported so far.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-2047", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-2047.flac", "type": "audio", "duration": 1023.403}, {"from": "Assistant", "value": "We examine the problem of question answering over knowledge graphs, focusing on simple questions that can be answered by the lookup of a single fact. Adopting a straightforward decomposition of the problem into entity detection, entity linking, relation prediction, and evidence combination, we explore simple yet strong baselines. On the popular SimpleQuestions dataset, we find that basic LSTMs and GRUs plus a few heuristics yield accuracies that approach the state of the art, and techniques that do not use neural networks also perform reasonably well. These results show that gains from sophisticated deep learning techniques proposed in the literature are quite modest and that some previous models exhibit unnecessary complexity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-2048", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-2048.flac", "type": "audio", "duration": 1133.483}, {"from": "Assistant", "value": "Entrainment has been shown to occur for various linguistic features individually. Motivated by cognitive theories regarding linguistic entrainment, we analyze speakers’ overall entrainment behaviors and search for an underlying structure. We consider various measures of both acoustic-prosodic and lexical entrainment, measuring the latter with a novel application of two previously introduced methods in addition to a standard high-frequency word measure. We present a negative result of our search, finding no meaningful correlations, clusters, or principal components in various entrainment measures, and discuss practical and theoretical implications.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-2049", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-2049.flac", "type": "audio", "duration": 1158.656}, {"from": "Assistant", "value": "Distributional data tells us that a man can swallow candy, but not that a man can swallow a paintball, since this is never attested. However both are physically plausible events. This paper introduces the task of semantic plausibility: recognizing plausible but possibly novel events. We present a new crowdsourced dataset of semantic plausibility judgments of single events such as man swallow paintball. Simple models based on distributional representations perform poorly on this task, despite doing well on selection preference, but injecting manually elicited knowledge about entity properties provides a substantial performance boost. Our error analysis shows that our new dataset is a great testbed for semantic plausibility models: more sophisticated knowledge representation and propagation could address many of the remaining errors.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-2069", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-2069.flac", "type": "audio", "duration": 1046.485}, {"from": "Assistant", "value": "We address the task of detecting foiled image captions, i.e. identifying whether a caption contains a word that has been deliberately replaced by a semantically similar word, thus rendering it inaccurate with respect to the image being described. Solving this problem should in principle require a fine-grained understanding of images to detect subtle perturbations in captions. In such contexts, encoding sufficiently descriptive image information becomes a key challenge. In this paper, we demonstrate that it is possible to solve this task using simple, interpretable yet powerful representations based on explicit object information over multilayer perceptron models. Our models achieve state-of-the-art performance on a recently published dataset, with scores exceeding those achieved by humans on the task. We also measure the upper-bound performance of our models using gold standard annotations. Our study and analysis reveals that the simpler model performs well even without image information, suggesting that the dataset contains strong linguistic bias.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-2070", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-2070.flac", "type": "audio", "duration": 1045.163}, {"from": "Assistant", "value": "We combine a neural image captioner with a Rational Speech Acts (RSA) model to make a system that is pragmatically informative: its objective is to produce captions that are not merely true but also distinguish their inputs from similar images. Previous attempts to combine RSA with neural image captioning require an inference which normalizes over the entire set of possible utterances. This poses a serious problem of efficiency, previously solved by sampling a small subset of possible utterances. We instead solve this problem by implementing a version of RSA which operates at the level of characters (“a”, “b”, “c”, ...) during the unrolling of the caption. We find that the utterance-level effect of referential captions can be obtained with only character-level decisions. Finally, we introduce an automatic method for testing the performance of pragmatic speaker models, and show that our model outperforms a non-pragmatic baseline as well as a word-level RSA captioner.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-2071", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-2071.flac", "type": "audio", "duration": 1073.387}, {"from": "Assistant", "value": "Visual reasoning with compositional natural language instructions, e.g., based on the newly-released Cornell Natural Language Visual Reasoning (NLVR) dataset, is a challenging task, where the model needs to have the ability to create an accurate mapping between the diverse phrases and the several objects placed in complex arrangements in the image. Further, this mapping needs to be processed to answer the question in the statement given the ordering and relationship of the objects across three similar images. In this paper, we propose a novel end-to-end neural model for the NLVR task, where we first use joint bidirectional attention to build a two-way conditioning between the visual information and the language phrases. Next, we use an RL-based pointer network to sort and process the varying number of unordered objects (so as to match the order of the statement phrases) in each of the three images and then pool over the three decisions. Our model achieves strong improvements (of 4-6% absolute) over the state-of-the-art on both the structured representation and raw image versions of the dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-2107", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-2107.flac", "type": "audio", "duration": 1066.069}, {"from": "Assistant", "value": "Emojis are small images that are commonly included in social media text messages. The combination of visual and textual content in the same message builds up a modern way of communication, that automatic systems are not used to deal with. In this paper we extend recent advances in emoji prediction by putting forward a multimodal approach that is able to predict emojis in Instagram posts. Instagram posts are composed of pictures together with texts which sometimes include emojis. We show that these emojis can be predicted by using the text, but also using the picture. Our main finding is that incorporating the two synergistic modalities, in a combined model, improves accuracy in an emoji prediction task. This result demonstrates that these two modalities (text and images) encode different information on the use of emojis and therefore can complement each other.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-2108", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-2108.flac", "type": "audio", "duration": 818.389}, {"from": "Assistant", "value": "We introduce a fully-differentiable approximation to higher-order inference for coreference resolution. Our approach uses the antecedent distribution from a span-ranking architecture as an attention mechanism to iteratively refine span representations. This enables the model to softly consider multiple hops in the predicted clusters. To alleviate the computational cost of this iterative process, we introduce a coarse-to-fine approach that incorporates a less accurate but more efficient bilinear factor, enabling more aggressive pruning without hurting accuracy. Compared to the existing state-of-the-art span-ranking approach, our model significantly improves accuracy on the English OntoNotes benchmark, while being far more computationally efficient.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-2109", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-2109.flac", "type": "audio", "duration": 1079.744}, {"from": "Assistant", "value": "We present a novel transition system, based on the Covington non-projective parser, introducing non-local transitions that can directly create arcs involving nodes to the left of the current focus positions. This avoids the need for long sequences of No-Arcs transitions to create long-distance arcs, thus alleviating error propagation. The resulting parser outperforms the original version and achieves the best accuracy on the Stanford Dependencies conversion of the Penn Treebank among greedy transition-based parsers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3001", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3001.flac", "type": "audio", "duration": 1099.115}, {"from": "Assistant", "value": "In recent years the use of electronic medical records has accelerated resulting in large volumes of medical data when a patient visits a healthcare facility. As a first step towards reimbursement healthcare institutions need to associate ICD-10 billing codes to these documents. This is done by trained clinical coders who may use a computer assisted solution for shortlisting of codes. In this work, we present our work to build a machine learning based scalable system for predicting ICD-10 codes from electronic medical records. We address data imbalance issues by implementing two system architectures using convolutional neural networks and logistic regression models. We illustrate the pros and cons of those system designs and show that the best performance can be achieved by leveraging the advantages of both using a system combination approach.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3002", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3002.flac", "type": "audio", "duration": 1104.747}, {"from": "Assistant", "value": "Matching a seller listed item to an appropriate product has become a fundamental and one of the most significant step for e-commerce platforms for product based experience. It has a huge impact on making the search effective, search engine optimization, providing product reviews and product price estimation etc. along with many other advantages for a better user experience. As significant and vital it has become, the challenge to tackle the complexity has become huge with the exponential growth of individual and business sellers trading millions of products everyday. We explored two approaches; classification based on shallow neural network and similarity based on deep siamese network. These models outperform the baseline by more than 5% in term of accuracy and are capable of extremely efficient training and inference.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3003", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3003.flac", "type": "audio", "duration": 1044.608}, {"from": "Assistant", "value": "Intelligent personal digital assistants (IPDAs), a popular real-life application with spoken language understanding capabilities, can cover potentially thousands of overlapping domains for natural language understanding, and the task of finding the best domain to handle an utterance becomes a challenging problem on a large scale. In this paper, we propose a set of efficient and scalable shortlisting-reranking neural models for effective large-scale domain classification for IPDAs. The shortlisting stage focuses on efficiently trimming all domains down to a list of k-best candidate domains, and the reranking stage performs a list-wise reranking of the initial k-best domains with additional contextual information. We show the effectiveness of our approach with extensive experiments on 1,500 IPDA domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3004", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3004.flac", "type": "audio", "duration": 806.037}, {"from": "Assistant", "value": "In task-oriented dialog, agents need to generate both fluent natural language responses and correct external actions like database queries and updates. Our paper makes the first attempt at evaluating state of the art models on a large real world task with human users. We show that methods that achieve state of the art performance on synthetic datasets, perform poorly in real world dialog tasks. We propose a hybrid model, where nearest neighbor is used to generate fluent responses and Seq2Seq type models ensure dialogue coherency and generate accurate external actions. The hybrid model on the customer support data achieves a 78% relative improvement in fluency, and a 200% improvement in accuracy of external calls.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3005", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3005.flac", "type": "audio", "duration": 1004.885}, {"from": "Assistant", "value": "Industrial dialogue systems such as Apple Siri and Google Now rely on large scale diverse and robust training data to enable their sophisticated conversation capability. Crowdsourcing provides a scalable and inexpensive way of data collection but collecting high quality data efficiently requires thoughtful orchestration of the crowdsourcing jobs. Prior study of this topic have focused on tasks only in the academia settings with limited scope or only provide intrinsic dataset analysis, lacking indication on how it affects the trained model performance. In this paper, we present a study of crowdsourcing methods for a user intent classification task in our deployed dialogue system. Our task requires classification of 47 possible user intents and contains many intent pairs with subtle differences. We consider different crowdsourcing job types and job prompts and analyze quantitatively the quality of the collected data and the downstream model performance on a test set of real user queries from production logs. Our observation provides insights into designing efficient crowdsourcing jobs and provide recommendations for future dialogue system data collection process.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3006", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3006.flac", "type": "audio", "duration": 1403.584}, {"from": "Assistant", "value": "End-to-end neural models show great promise towards building conversational agents that are trained from data and on-line experience using supervised and reinforcement learning. However, these models require a large corpus of dialogues to learn effectively. For goal-oriented dialogues, such datasets are expensive to collect and annotate, since each task involves a separate schema and database of entities. Further, the Wizard-of-Oz approach commonly used for dialogue collection does not provide sufficient coverage of salient dialogue flows, which is critical for guaranteeing an acceptable task completion rate in consumer-facing conversational agents. In this paper, we study a recently proposed approach for building an agent for arbitrary tasks by combining dialogue self-play and crowd-sourcing to generate fully-annotated dialogues with diverse and natural utterances. We discuss the advantages of this approach for industry applications of conversational agents, wherein an agent can be rapidly bootstrapped to deploy in front of users and further optimized via interactive learning from actual users of the system.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3012", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3012.flac", "type": "audio", "duration": 956.288}, {"from": "Assistant", "value": "We present the first real-world application of methods for improving neural machine translation (NMT) with human reinforcement, based on explicit and implicit user feedback collected on the eBay e-commerce platform. Previous work has been confined to simulation experiments, whereas in this paper we work with real logged feedback for offline bandit learning of NMT parameters. We conduct a thorough analysis of the available explicit user judgments—five-star ratings of translation quality—and show that they are not reliable enough to yield significant improvements in bandit learning. In contrast, we successfully utilize implicit task-based feedback collected in a cross-lingual search task to improve task-specific and machine translation quality metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3013", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3013.flac", "type": "audio", "duration": 1054.059}, {"from": "Assistant", "value": "We describe a batched beam decoding algorithm for NMT with LMBR n-gram posteriors, showing that LMBR techniques still yield gains on top of the best recently reported results with Transformers. We also discuss acceleration strategies for deployment, and the effect of the beam size and batching on memory and speed.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3014", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3014.flac", "type": "audio", "duration": 936.875}, {"from": "Assistant", "value": "Neural machine translation has achieved levels of fluency and adequacy that would have been surprising a short time ago. Output quality is extremely relevant for industry purposes, however it is equally important to produce results in the shortest time possible, mainly for latency-sensitive applications and to control cloud hosting costs. In this paper we show the effectiveness of translating with 8-bit quantization for models that have been trained using 32-bit floating point values. Results show that 8-bit translation makes a non-negligible impact in terms of speed with no degradation in accuracy and adequacy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3015", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3015.flac", "type": "audio", "duration": 1274.624}, {"from": "Assistant", "value": "A typical workflow to document clinical encounters entails dictating a summary, running speech recognition, and post-processing the resulting text into a formatted letter. Post-processing entails a host of transformations including punctuation restoration, truecasing, marking sections and headers, converting dates and numerical expressions, parsing lists, etc. In conventional implementations, most of these tasks are accomplished by individual modules. We introduce a novel holistic approach to post-processing that relies on machine callytranslation. We show how this technique outperforms an alternative conventional system—even learning to correct speech recognition errors during post-processing—while being much simpler to maintain.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3017", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3017.flac", "type": "audio", "duration": 1026.901}, {"from": "Assistant", "value": "This paper investigates the use of Machine Translation (MT) to bootstrap a Natural Language Understanding (NLU) system for a new language for the use case of a large-scale voice-controlled device. The goal is to decrease the cost and time needed to get an annotated corpus for the new language, while still having a large enough coverage of user requests. Different methods of filtering MT data in order to keep utterances that improve NLU performance and language-specific post-processing methods are investigated. These methods are tested in a large-scale NLU task with translating around 10 millions training utterances from English to German. The results show a large improvement for using MT data over a grammar-based and over an in-house data collection baseline, while reducing the manual effort greatly. Both filtering and post-processing approaches improve results further.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3018", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3018.flac", "type": "audio", "duration": 1040.107}, {"from": "Assistant", "value": "Fast expansion of natural language functionality of intelligent virtual agents is critical for achieving engaging and informative interactions. However, developing accurate models for new natural language domains is a time and data intensive process. We propose efficient deep neural network architectures that maximally re-use available resources through transfer learning. Our methods are applied for expanding the understanding capabilities of a popular commercial agent and are evaluated on hundreds of new domains, designed by internal or external developers. We demonstrate that our proposed methods significantly increase accuracy in low resource settings and enable rapid development of accurate models with less data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3019", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3019.flac", "type": "audio", "duration": 998.272}, {"from": "Assistant", "value": "Slot tagging, the task of detecting entities in input user utterances, is a key component of natural language understanding systems for personal digital assistants. Since each new domain requires a different set of slots, the annotation costs for labeling data for training slot tagging models increases rapidly as the number of domains grow. To tackle this, we describe Bag of Experts (BoE) architectures for model reuse for both LSTM and CRF based models. Extensive experimentation over a dataset of 10 domains drawn from data relevant to our commercial personal digital assistant shows that our BoE models outperform the baseline models with a statistically significant average margin of 5.06% in absolute F1-score when training with 2000 instances per domain, and achieve an even higher improvement of 12.16% when only 25% of the training data is used.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3020", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3020.flac", "type": "audio", "duration": 1014.464}, {"from": "Assistant", "value": "To provide better access of the inventory to buyers and better search engine optimization, e-Commerce websites are automatically generating millions of browse pages. A browse page consists of a set of slot name/value pairs within a given category, grouping multiple items which share some characteristics. These browse pages require a title describing the content of the page. Since the number of browse pages are huge, manual creation of these titles is infeasible. Previous statistical and neural approaches depend heavily on the availability of large amounts of data in a language. In this research, we apply sequence-to-sequence models to generate titles for high-resource as well as low-resource languages by leveraging transfer learning. We train these models on multi-lingual data, thereby creating one joint model which can generate titles in various different languages. Performance of the title generation system is evaluated on three different languages; English, German, and French, with a particular focus on low-resourced French language.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3021", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3021.flac", "type": "audio", "duration": 945.216}, {"from": "Assistant", "value": "As a specialized example of information extraction, part name extraction is an area that presents unique challenges. Part names are typically multi-word terms longer than two words. There is little consistency in how terms are described in noisy free text, with variations spawned by typos, ad hoc abbreviations, acronyms, and incomplete names. This makes search and analyses of parts in these data extremely challenging. In this paper, we present our algorithm, PANDA (Part Name Discovery Analytics), based on a unique method that exploits statistical, linguistic and machine learning techniques to discover part names in noisy text such as that in manufacturing quality documentation, supply chain management records, service communication logs, and maintenance reports. Experiments show that PANDA is scalable and outperforms existing techniques significantly.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3022", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3022.flac", "type": "audio", "duration": 990.165}, {"from": "Assistant", "value": "This paper introduces a meaning representation for spoken language understanding. The Alexa meaning representation language (AMRL), unlike previous approaches, which factor spoken utterances into domains, provides a common representation for how people communicate in spoken language. AMRL is a rooted graph, links to a large-scale ontology, supports cross-domain queries, fine-grained types, complex utterances and composition. A spoken language dataset has been collected for Alexa, which contains ∼20k examples across eight domains. A version of this meaning representation was released to developers at a trade show in 2016.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3024", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3024.flac", "type": "audio", "duration": 1074.347}, {"from": "Assistant", "value": "The overwhelming success of the Web and mobile technologies has enabled millions to share their opinions publicly at any time. But the same success also endangers this freedom of speech due to closing down of participatory sites misused by individuals or interest groups. We propose to support manual moderation by proactively drawing the attention of our moderators to article discussions that most likely need their intervention. To this end, we predict which articles will receive a high number of comments. In contrast to existing work, we enrich the article with metadata, extract semantic and linguistic features, and exploit annotated data from a foreign language corpus. Our logistic regression model improves F1-scores by over 80% in comparison to state-of-the-art approaches.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3025", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3025.flac", "type": "audio", "duration": 1081.963}, {"from": "Assistant", "value": "In this paper we introduce the notion of Demand-Weighted Completeness, allowing estimation of the completeness of a knowledge base with respect to how it is used. Defining an entity by its classes, we employ usage data to predict the distribution over relations for that entity. For example, instances of person in a knowledge base may require a birth date, name and nationality to be considered complete. These predicted relation distributions enable detection of important gaps in the knowledge base, and define the required facts for unseen entities. Such characterisation of the knowledge base can also quantify how usage and completeness change over time. We demonstrate a method to measure Demand-Weighted Completeness, and show that a simple neural network model performs well at this prediction task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-3026", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-3026.flac", "type": "audio", "duration": 993.557}, {"from": "Assistant", "value": "Query auto completion (QAC) systems are a standard part of search engines in industry, helping users formulate their query. Such systems update their suggestions after the user types each character, predicting the user’s intent using various signals – one of the most common being popularity. Recently, deep learning approaches have been proposed for the QAC task, to specifically address the main limitation of previous popularity-based methods: the inability to predict unseen queries. In this work we improve previous methods based on neural language modeling, with the goal of building an end-to-end system. We particularly focus on using real-world data by integrating user information for personalized suggestions when possible. We also make use of time information and study how to increase diversity in the suggestions while studying the impact on scalability. Our empirical results demonstrate a marked improvement on two separate datasets over previous best methods in both accuracy and scalability, making a step towards neural query auto-completion in production search engines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-4001", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-4001.flac", "type": "audio", "duration": 995.499}, {"from": "Assistant", "value": "Conversation is a joint social process, with participants cooperating to exchange information. This process is helped along through linguistic alignment: participants’ adoption of each other’s word use. This alignment is robust, appearing many settings, and is nearly always positive. We create an alignment model for examining alignment in Twitter conversations across antagonistic groups. This model finds that some word categories, specifically pronouns used to establish group identity and common ground, are negatively aligned. This negative alignment is observed despite other categories, which are less related to the group dynamics, showing the standard positive alignment. This suggests that alignment is strongly biased toward cooperative alignment, but that different linguistic features can show substantially different behaviors.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-4004", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-4004.flac", "type": "audio", "duration": 982.72}, {"from": "Assistant", "value": "We introduce an automatic system that performs well on two common-sense reasoning tasks, the Winograd Schema Challenge (WSC) and the Choice of Plausible Alternatives (COPA). Problem instances from these tasks require diverse, complex forms of inference and knowledge to solve. Our method uses a knowledge-hunting module to gather text from the web, which serves as evidence for candidate problem resolutions. Given an input problem, our system generates relevant queries to send to a search engine. It extracts and classifies knowledge from the returned results and weighs it to make a resolution. Our approach improves F1 performance on the WSC by 0.16 over the previous best and is competitive with the state-of-the-art on COPA, demonstrating its general applicability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-4008", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-4008.flac", "type": "audio", "duration": 858.667}, {"from": "Assistant", "value": "Igbo is a low-resource language spoken by approximately 30 million people worldwide. It is the native language of the Igbo people of south-eastern Nigeria. In Igbo language, diacritics - orthographic and tonal - play a huge role in the distinguishing the meaning and pronunciation of words. Omitting diacritics in texts often leads to lexical ambiguity. Diacritic restoration is a pre-processing task that replaces missing diacritics on words from which they have been removed. In this work, we applied embedding models to the diacritic restoration task and compared their performances to those of n-gram models. Although word embedding models have been successfully applied to various NLP tasks, it has not been used, to our knowledge, for diacritic restoration. Two classes of word embeddings models were used: those projected from the English embedding space; and those trained with Igbo bible corpus (≈ 1m). Our best result, 82.49%, is an improvement on the baseline n-gram models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-4009", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-4009.flac", "type": "audio", "duration": 860.075}, {"from": "Assistant", "value": "The aim of this thesis is to perform a Native Language Identification (NLI) task where we identify an English learner’s native language background based only on the learner’s English writing samples. We focus on the use of English grammatical morphemes across four proficiency levels. The outcome of the computational task is connected to a position in second language acquisition research that holds all learners acquire English grammatical morphemes in the same order, regardless of native language background. We use the NLI task as a tool to uncover cross-linguistic influence on the developmental trajectory of morphemes. We perform a cross-corpus evaluation across proficiency levels to increase the reliability and validity of the linguistic features that predict the native language background. We include native English data to determine the different morpheme patterns used by native versus non-native English speakers. Furthermore, we conduct a human NLI task to determine the type and magnitude of language transfer cues used by human raters versus the classifier.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-4010", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-4010.flac", "type": "audio", "duration": 867.115}, {"from": "Assistant", "value": "In this thesis proposal, we address the limitations of conventional pipeline design of task-oriented dialog systems and propose end-to-end learning solutions. We design neural network based dialog system that is able to robustly track dialog state, interface with knowledge bases, and incorporate structured query results into system responses to successfully complete task-oriented dialog. In learning such neural network based dialog systems, we propose hybrid offline training and online interactive learning methods. We introduce a multi-task learning method in pre-training the dialog agent in a supervised manner using task-oriented dialog corpora. The supervised training agent can further be improved via interacting with users and learning online from user demonstration and feedback with imitation and reinforcement learning. In addressing the sample efficiency issue with online policy learning, we further propose a method by combining the learning-from-user and learning-from-simulation approaches to improve the online interactive learning efficiency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-4011", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-4011.flac", "type": "audio", "duration": 878.357}, {"from": "Assistant", "value": "Most of the health documents, including patient education materials and discharge notes, are usually flooded with medical jargons and contain a lot of generic information about the health issue. In addition, patients are only provided with the doctor’s perspective of what happened to them in the hospital while the care procedure performed by nurses during their entire hospital stay is nowhere included. The main focus of this research is to generate personalized hospital-stay summaries for patients by combining information from physician discharge notes and nursing plan of care. It uses a metric to identify medical concepts that are Complex, extracts definitions for the concept from three external knowledge sources, and provides the simplest definition to the patient. It also takes various features of the patient into account, like their concerns and strengths, ability to understand basic health information, level of engagement in taking care of their health, and familiarity with the health issue and personalizes the content of the summaries accordingly. Our evaluation showed that the summaries contain 80% of the medical concepts that are considered as being important by both doctor and nurses. Three patient advisors (i.e. individuals who are trained in understanding patient experience extensively) verified the usability of our summaries and mentioned that they would like to get such summaries when they are discharged from hospital.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-6001", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-6001.flac", "type": "audio", "duration": 10877.035}, {"from": "Assistant", "value": "As computers and information grow a more integral part of our world, it is becoming more and more important for humans to be able to interact with their computers in complex ways. One way to do so is by programming, but the ability to understand and generate programming languages is a highly specialized skill. As a result, in the past several years there has been an increasing research interest in methods that focus on the intersection of programming and natural language, allowing users to use natural language to interact with computers in the complex ways that programs allow us to do. In this tutorial, we will focus on machine learning models of programs and natural language focused on making this goal a reality. First, we will discuss the similarities and differences between programming and natural language. Then we will discuss methods that have been designed to cover a variety of tasks in this field, including automatic explanation of programs in natural language (code-to-language), automatic generation of programs from natural language specifications (language-to-code), modeling the natural language elements of source code, and analysis of communication in collaborative programming communities. The tutorial will be aimed at NLP researchers and practitioners, aiming to describe the interesting opportunities that models at the intersection of natural and programming languages provide, and also how their techniques could provide benefit to the practice of software engineering as a whole.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-6003", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-6003.flac", "type": "audio", "duration": 10933.269}, {"from": "Assistant", "value": "In today’s information-based society, there is abundant knowledge out there carried in the form of natural language texts (e.g., news articles, social media posts, scientific publications), which spans across various domains (e.g., corporate documents, advertisements, legal acts, medical reports), which grows at an astonishing rate. Yet this knowledge is mostly inaccessible to computers and overwhelming for human experts to absorb. How to turn such massive and unstructured text data into structured, actionable knowledge, and furthermore, how to teach machines learn to reason and complete the extracted knowledge is a grand challenge to the research community. Traditional IE systems assume abundant human annotations for training high quality machine learning models, which is impractical when trying to deploy IE systems to a broad range of domains, settings and languages. In the first part of the tutorial, we introduce how to extract structured facts (i.e., entities and their relations for types of interest) from text corpora to construct knowledge bases, with a focus on methods that are weakly-supervised and domain-independent for timely knowledge base construction across various application domains. In the second part, we introduce how to leverage other knowledge, such as the distributional statistics of characters and words, the annotations for other tasks and other domains, and the linguistics and problem structures, to combat the problem of inadequate supervision, and conduct low-resource information extraction. In the third part, we describe recent advances in knowledge base reasoning. We start with the gentle introduction to the literature, focusing on path-based and embedding based methods. We then describe DeepPath, a recent attempt of using deep reinforcement learning to combine the best of both worlds for knowledge base reasoning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-6004", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-6004.flac", "type": "audio", "duration": 9903.872}, {"from": "Assistant", "value": "Incorporating linguistic, world and common sense knowledge into AI/NLP systems is currently an important research area, with several open problems and challenges. At the same time, processing and storing this knowledge in lexical resources is not a straightforward task. We propose to address these complementary goals from two methodological perspectives: the use of NLP methods to help the process of constructing and enriching lexical resources and the use of lexical resources for improving NLP applications. This tutorial may be useful for two main types of audience: those working on language resources who are interested in becoming acquainted with automatic NLP techniques, with the end goal of speeding and/or easing up the process of resource curation; and on the other hand, researchers in NLP who would like to benefit from the knowledge of lexical resources to improve their systems and models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N18-6006", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N18-6006.flac", "type": "audio", "duration": 10723.797}, {"from": "Assistant", "value": "Spoken Dialogue Systems (SDS) have great commercial potential as they promise to revolutionise the way in which humans interact with machines. The advent of deep learning led to substantial developments in this area of NLP research, and the goal of this tutorial is to familiarise the research community with the recent advances in what some call the most difficult problem in NLP. From a research perspective, the design of spoken dialogue systems provides a number of significant challenges, as these systems depend on: a) solving several difficult NLP and decision-making tasks; and b) combining these into a functional dialogue system pipeline. A key long-term goal of dialogue system research is to enable open-domain systems that can converse about arbitrary topics and assist humans with completing a wide range of tasks. Furthermore, such systems need to autonomously learn on-line to improve their performance and recover from errors using both signals from their environment and from implicit and explicit user feedback. While the design of such systems has traditionally been modular, domain and language-specific, advances in deep learning have alleviated many of the design problems. The main purpose of this tutorial is to encourage dialogue research in the NLP community by providing the research background, a survey of available resources, and giving key insights to application of state-of-the-art SDS methodology into industry-scale conversational AI systems. We plan to introduce researchers to the pipeline framework for modelling goal-oriented dialogue systems, which includes three key components: 1) Language Understanding; 2) Dialogue Management; and 3) Language Generation. The differences between goal-oriented dialogue systems and chat-bot style conversational agents will be explained in order to show the motivation behind the design of both, with the main focus on the pipeline SDS framework. For each key component, we will define the research problem, provide a brief literature review and introduce the current state-of-the-art approaches. Complementary resources (e.g. available datasets and toolkits) will also be discussed. Finally, future work, outstanding challenges, and current industry practices will be presented. All of the presented material will be made available online for future reference.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "W18-1209", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/W18-1209.flac", "type": "audio", "duration": 1205.056}, {"from": "Assistant", "value": "The positive effect of adding subword information to word embeddings has been demonstrated for predictive models. In this paper we investigate whether similar benefits can also be derived from incorporating subwords into counting models. We evaluate the impact of different types of subwords (n-grams and unsupervised morphemes), with results confirming the importance of subword information in learning representations of rare and out-of-vocabulary words.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1001", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1001.flac", "type": "audio", "duration": 1087.808}, {"from": "Assistant", "value": "Previous research shows that eye-tracking data contains information about the lexical and syntactic properties of text, which can be used to improve natural language processing models. In this work, we leverage eye movement features from three corpora with recorded gaze information to augment a state-of-the-art neural model for named entity recognition (NER) with gaze embeddings. These corpora were manually annotated with named entity labels. Moreover, we show how gaze features, generalized on word type level, eliminate the need for recorded eye-tracking data at test time. The gaze-augmented models for NER using token-level and type-level features outperform the baselines. We present the benefits of eye-tracking features by evaluating the NER models on both individual datasets as well as in cross-domain settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1002", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1002.flac", "type": "audio", "duration": 1071.147}, {"from": "Assistant", "value": "Recent work has shown that LSTMs trained on a generic language modeling objective capture syntax-sensitive generalizations such as long-distance number agreement. We have however no mechanistic understanding of how they accomplish this remarkable feat. Some have conjectured it depends on heuristics that do not truly take hierarchical structure into account. We present here a detailed study of the inner mechanics of number tracking in LSTMs at the single neuron level. We discover that long-distance number information is largely managed by two “number units”. Importantly, the behaviour of these units is partially controlled by other units independently shown to track syntactic structure. We conclude that LSTMs are, to some extent, implementing genuinely syntactic processing mechanisms, paving the way to a more general understanding of grammatical encoding in LSTMs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1003", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1003.flac", "type": "audio", "duration": 1009.792}, {"from": "Assistant", "value": "Self-training is a semi-supervised learning approach for utilizing unlabeled data to create better learners. The efficacy of self-training algorithms depends on their data sampling techniques. The majority of current sampling techniques are based on predetermined policies which may not effectively explore the data space or improve model generalizability. In this work, we tackle the above challenges by introducing a new data sampling technique based on spaced repetition that dynamically samples informative and diverse unlabeled instances with respect to individual learner and instance characteristics. The proposed model is specifically effective in the context of neural models which can suffer from overfitting and high-variance gradients when trained with small amount of labeled data. Our model outperforms current semi-supervised learning approaches developed for neural networks on publicly-available datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1004", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1004.flac", "type": "audio", "duration": 1056.0}, {"from": "Assistant", "value": "We investigate the extent to which the behavior of neural network language models reflects incremental representations of syntactic state. To do so, we employ experimental methodologies which were originally developed in the field of psycholinguistics to study syntactic representation in the human mind. We examine neural network model behavior on sets of artificial sentences containing a variety of syntactically complex structures. These sentences not only test whether the networks have a representation of syntactic state, they also reveal the specific lexical cues that networks use to update these states. We test four models: two publicly available LSTM sequence models of English (Jozefowicz et al., 2016; Gulordava et al., 2018) trained on large datasets; an RNN Grammar (Dyer et al., 2016) trained on a small, parsed dataset; and an LSTM trained on the same small corpus as the RNNG. We find evidence for basic syntactic state representations in all models, but only the models trained on large datasets are sensitive to subtle lexical cues signaling changes in syntactic state.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1005", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1005.flac", "type": "audio", "duration": 964.416}, {"from": "Assistant", "value": "Electroencephalography (EEG) recordings of brain activity taken while participants read or listen to language are widely used within the cognitive neuroscience and psycholinguistics communities as a tool to study language comprehension. Several time-locked stereotyped EEG responses to word-presentations – known collectively as event-related potentials (ERPs) – are thought to be markers for semantic or syntactic processes that take place during comprehension. However, the characterization of each individual ERP in terms of what features of a stream of language trigger the response remains controversial. Improving this characterization would make ERPs a more useful tool for studying language comprehension. We take a step towards better understanding the ERPs by finetuning a language model to predict them. This new approach to analysis shows for the first time that all of the ERPs are predictable from embeddings of a stream of language. Prior work has only found two of the ERPs to be predictable. In addition to this analysis, we examine which ERPs benefit from sharing parameters during joint training. We find that two pairs of ERPs previously identified in the literature as being related to each other benefit from joint training, while several other pairs of ERPs that benefit from joint training are suggestive of potential relationships. Extensions of this analysis that further examine what kinds of information in the model embeddings relate to each ERP have the potential to elucidate the processes involved in human language comprehension.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1006", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1006.flac", "type": "audio", "duration": 1025.6}, {"from": "Assistant", "value": "We present a simple approach to improve direct speech-to-text translation (ST) when the source language is low-resource: we pre-train the model on a high-resource automatic speech recognition (ASR) task, and then fine-tune its parameters for ST. We demonstrate that our approach is effective by pre-training on 300 hours of English ASR data to improve Spanish English ST from 10.8 to 20.2 BLEU when only 20 hours of Spanish-English ST training data are available. Through an ablation study, we find that the pre-trained encoder (acoustic model) accounts for most of the improvement, despite the fact that the shared language in these tasks is the target language text, not the source language audio. Applying this insight, we show that pre-training on ASR helps ST even when the ASR language differs from both source and target ST languages: pre-training on French ASR also improves Spanish-English ST. Finally, we show that the approach improves performance on a true low-resource task: pre-training on a combination of English ASR and French ASR improves Mboshi-French ST, where only 4 hours of data are available, from 3.5 to 7.1 BLEU.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1007", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1007.flac", "type": "audio", "duration": 1044.651}, {"from": "Assistant", "value": "In this paper, we deploy binary stochastic neural autoencoder networks as models of infant language learning in two typologically unrelated languages (Xitsonga and English). We show that the drive to model auditory percepts leads to latent clusters that partially align with theory-driven phonemic categories. We further evaluate the degree to which theory-driven phonological features are encoded in the latent bit patterns, finding that some (e.g. [+-approximant]), are well represented by the network in both languages, while others (e.g. [+-spread glottis]) are less so. Together, these findings suggest that many reliable cues to phonemic structure are immediately available to infants from bottom-up perceptual characteristics alone, but that these cues must eventually be supplemented by top-down lexical and phonotactic information to achieve adult-like phone discrimination. Our results also suggest differences in degree of perceptual availability between features, yielding testable predictions as to which features might depend more or less heavily on top-down cues during child language acquisition.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1008", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1008.flac", "type": "audio", "duration": 1074.667}, {"from": "Assistant", "value": "Disfluencies in spontaneous speech are known to be associated with prosodic disruptions. However, most algorithms for disfluency detection use only word transcripts. Integrating prosodic cues has proved difficult because of the many sources of variability affecting the acoustic correlates. This paper introduces a new approach to extracting acoustic-prosodic cues using text-based distributional prediction of acoustic cues to derive vector z-score features (innovations). We explore both early and late fusion techniques for integrating text and prosody, showing gains over a high-accuracy text-only model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1009", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1009.flac", "type": "audio", "duration": 1208.491}, {"from": "Assistant", "value": "We report on adaptation of multilingual end-to-end speech recognition models trained on as many as 100 languages. Our findings shed light on the relative importance of similarity between the target and pretraining languages along the dimensions of phonetics, phonology, language family, geographical location, and orthography. In this context, experiments demonstrate the effectiveness of two additional pretraining objectives in encouraging language-independent encoder representations: a context-independent phoneme objective paired with a language-adversarial classification objective.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1011", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1011.flac", "type": "audio", "duration": 947.712}, {"from": "Assistant", "value": "We explore the problem of Audio Captioning: generating natural language description for any kind of audio in the wild, which has been surprisingly unexplored in previous research. We contribute a large-scale dataset of 46K audio clips with human-written text pairs collected via crowdsourcing on the AudioSet dataset. Our thorough empirical studies not only show that our collected captions are indeed faithful to audio inputs but also discover what forms of audio representation and captioning models are effective for the audio captioning. From extensive experiments, we also propose two novel components that help improve audio captioning performance: the top-down multi-scale encoder and aligned semantic attention.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1012", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1012.flac", "type": "audio", "duration": 1091.2}, {"from": "Assistant", "value": "We introduce, release, and analyze a new dataset, called Humicroedit, for research in computational humor. Our publicly available data consists of regular English news headlines paired with versions of the same headlines that contain simple replacement edits designed to make them funny. We carefully curated crowdsourced editors to create funny headlines and judges to score a to a total of 15,095 edited headlines, with five judges per headline. The simple edits, usually just a single word replacement, mean we can apply straightforward analysis techniques to determine what makes our edited headlines humorous. We show how the data support classic theories of humor, such as incongruity, superiority, and setup/punchline. Finally, we develop baseline classifiers that can predict whether or not an edited headline is funny, which is a first step toward automatically generating humorous headlines as an approach to creating topical humor.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1013", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1013.flac", "type": "audio", "duration": 1197.568}, {"from": "Assistant", "value": "We present an approach for generating clarification questions with the goal of eliciting new information that would make the given textual context more complete. We propose that modeling hypothetical answers (to clarification questions) as latent variables can guide our approach into generating more useful clarification questions. We develop a Generative Adversarial Network (GAN) where the generator is a sequence-to-sequence model and the discriminator is a utility function that models the value of updating the context with the answer to the clarification question. We evaluate on two datasets, using both automatic metrics and human judgments of usefulness, specificity and relevance, showing that our approach outperforms both a retrieval-based model and ablations that exclude the utility model and the adversarial training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1014", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1014.flac", "type": "audio", "duration": 951.957}, {"from": "Assistant", "value": "Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. In this paper, we propose a copy-augmented architecture for the GEC task by copying the unchanged words from the source sentence to the target sentence. Since the GEC suffers from not having enough labeled training data to achieve high accuracy. We pre-train the copy-augmented architecture with a denoising auto-encoder using the unlabeled One Billion Benchmark and make comparisons between the fully pre-trained model and a partially pre-trained model. It is the first time copying words from the source context and fully pre-training a sequence to sequence model are experimented on the GEC task. Moreover, We add token-level and sentence-level multi-task learning for the GEC task. The evaluation results on the CoNLL-2014 test set show that our approach outperforms all recently published state-of-the-art results by a large margin.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1015", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1015.flac", "type": "audio", "duration": 329.109}, {"from": "Assistant", "value": "We propose a topic-guided variational auto-encoder (TGVAE) model for text generation. Distinct from existing variational auto-encoder (VAE) based approaches, which assume a simple Gaussian prior for latent code, our model specifies the prior as a Gaussian mixture model (GMM) parametrized by a neural topic module. Each mixture component corresponds to a latent topic, which provides a guidance to generate sentences under the topic. The neural topic module and the VAE-based neural sequence module in our model are learned jointly. In particular, a sequence of invertible Householder transformations is applied to endow the approximate posterior of the latent code with high flexibility during the model inference. Experimental results show that our TGVAE outperforms its competitors on both unconditional and conditional text generation, which can also generate semantically-meaningful sentences with various topics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1057", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1057.flac", "type": "audio", "duration": 788.736}, {"from": "Assistant", "value": "A typical conversation comprises of multiple turns between participants where they go back and forth between different topics. At each user turn, dialogue state tracking (DST) aims to estimate user’s goal by processing the current utterance. However, in many turns, users implicitly refer to the previous goal, necessitating the use of relevant dialogue history. Nonetheless, distinguishing relevant history is challenging and a popular method of using dialogue recency for that is inefficient. We, therefore, propose a novel framework for DST that identifies relevant historical context by referring to the past utterances where a particular slot-value changes and uses that together with weighted system utterance to identify the relevant context. Specifically, we use the current user utterance and the most recent system utterance to determine the relevance of a system utterance. Empirical analyses show that our method improves joint goal accuracy by 2.75% and 2.36% on WoZ 2.0 and Multi-WoZ restaurant domain datasets respectively over the previous state-of-the-art GLAD model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1060", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1060.flac", "type": "audio", "duration": 955.072}, {"from": "Assistant", "value": "We discuss the impact of data bias on abusive language detection. We show that classification scores on popular datasets reported in previous work are much lower under realistic settings in which this bias is reduced. Such biases are most notably observed on datasets that are created by focused sampling instead of random sampling. Datasets with a higher proportion of implicit abuse are more affected than datasets with a lower proportion.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1061", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1061.flac", "type": "audio", "duration": 844.331}, {"from": "Assistant", "value": "Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society. This phenomenon is pervasive and consistent across different word embedding models, causing serious concern. Several recent works tackle this problem, and propose methods for significantly reducing this gender bias in word embeddings, demonstrating convincing results. However, we argue that this removal is superficial. While the bias is indeed substantially reduced according to the provided bias definition, the actual effect is mostly hiding the bias, not removing it. The gender bias information is still reflected in the distances between “gender-neutralized” words in the debiased embeddings, and can be recovered from them. We present a series of experiments to support this claim, for two debiasing methods. We conclude that existing bias removal techniques are insufficient, and should not be trusted for providing gender-neutral modeling.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1062", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1062.flac", "type": "audio", "duration": 921.003}, {"from": "Assistant", "value": "Online texts - across genres, registers, domains, and styles - are riddled with human stereotypes, expressed in overt or subtle ways. Word embeddings, trained on these texts, perpetuate and amplify these stereotypes, and propagate biases to machine learning models that use word embeddings as features. In this work, we propose a method to debias word embeddings in multiclass settings such as race and religion, extending the work of (Bolukbasi et al., 2016) from the binary setting, such as binary gender. Next, we propose a novel methodology for the evaluation of multiclass debiasing. We demonstrate that our multiclass debiasing is robust and maintains the efficacy in standard NLP tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1063", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1063.flac", "type": "audio", "duration": 908.843}, {"from": "Assistant", "value": "The Word Embedding Association Test shows that GloVe and word2vec word embeddings exhibit human-like implicit biases based on gender, race, and other social constructs (Caliskan et al., 2017). Meanwhile, research on learning reusable text representations has begun to explore sentence-level texts, with some sentence encoders seeing enthusiastic adoption. Accordingly, we extend the Word Embedding Association Test to measure bias in sentence encoders. We then test several sentence encoders, including state-of-the-art methods such as ELMo and BERT, for the social biases studied in prior work and two important biases that are difficult or impossible to test at the word level. We observe mixed results including suspicious patterns of sensitivity that suggest the test’s assumptions may not hold in general. We conclude by proposing directions for future work on measuring bias in sentence encoders.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1064", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1064.flac", "type": "audio", "duration": 840.853}, {"from": "Assistant", "value": "In this paper, we quantify, analyze and mitigate gender bias exhibited in ELMo’s contextualized word vectors. First, we conduct several intrinsic analyses and find that (1) training data for ELMo contains significantly more male than female entities, (2) the trained ELMo embeddings systematically encode gender information and (3) ELMo unequally encodes gender information about male and female entities. Then, we show that a state-of-the-art coreference system that depends on ELMo inherits its bias and demonstrates significant bias on the WinoBias probing corpus. Finally, we explore two methods to mitigate such gender bias and show that the bias demonstrated on WinoBias can be eliminated.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1066", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1066.flac", "type": "audio", "duration": 584.661}, {"from": "Assistant", "value": "Opinion role labeling (ORL) is an important task for fine-grained opinion mining, which identifies important opinion arguments such as holder and target for a given opinion trigger. The task is highly correlative with semantic role labeling (SRL), which identifies important semantic arguments such as agent and patient for a given predicate. As predicate agents and patients usually correspond to opinion holders and targets respectively, SRL could be valuable for ORL. In this work, we propose a simple and novel method to enhance ORL by utilizing SRL, presenting semantic-aware word representations which are learned from SRL. The representations are then fed into a baseline neural ORL model as basic inputs. We verify the proposed method on a benchmark MPQA corpus. Experimental results show that the proposed method is highly effective. In addition, we compare the method with two representative methods of SRL integration as well, finding that our method can outperform the two methods significantly, achieving 1.47% higher F-scores than the better one.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1067", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1067.flac", "type": "audio", "duration": 962.347}, {"from": "Assistant", "value": "The development of a fictional plot is centered around characters who closely interact with each other forming dynamic social networks. In literature analysis, such networks have mostly been analyzed without particular relation types or focusing on roles which the characters take with respect to each other. We argue that an important aspect for the analysis of stories and their development is the emotion between characters. In this paper, we combine these aspects into a unified framework to classify emotional relationships of fictional characters. We formalize it as a new task and describe the annotation of a corpus, based on fan-fiction short stories. The extraction pipeline which we propose consists of character identification (which we treat as given by an oracle here) and the relation classification. For the latter, we provide results using several approaches previously proposed for relation identification with neural methods. The best result of 0.45 F1 is achieved with a GRU with character position indicators on the task of predicting undirected emotion relations in the associated social network graph.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1068", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1068.flac", "type": "audio", "duration": 761.877}, {"from": "Assistant", "value": "Authorship verification is the problem of inferring whether two texts were written by the same author. For this task, unmasking is one of the most robust approaches as of today with the major shortcoming of only being applicable to book-length texts. In this paper, we present a generalized unmasking approach which allows for authorship verification of texts as short as four printed pages with very high precision at an adjustable recall tradeoff. Our generalized approach therefore reduces the required material by orders of magnitude, making unmasking applicable to authorship cases of more practical proportions. The new approach is on par with other state-of-the-art techniques that are optimized for texts of this length: it achieves accuracies of 75–80%, while also allowing for easy adjustment to forensic scenarios that require higher levels of confidence in the classification.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1069", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1069.flac", "type": "audio", "duration": 872.085}, {"from": "Assistant", "value": "The automatic detection of satire vs. regular news is relevant for downstream applications (for instance, knowledge base population) and to improve the understanding of linguistic characteristics of satire. Recent approaches build upon corpora which have been labeled automatically based on article sources. We hypothesize that this encourages the models to learn characteristics for different publication sources (e.g., “The Onion” vs. “The Guardian”) rather than characteristics of satire, leading to poor generalization performance to unseen publication sources. We therefore propose a novel model for satire detection with an adversarial component to control for the confounding variable of publication source. On a large novel data set collected from German news (which we make available to the research community), we observe comparable satire classification performance and, as desired, a considerable drop in publication classification performance with adversarial training. Our analysis shows that the adversarial component is crucial for the model to learn to pay attention to linguistic properties of satire.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1070", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1070.flac", "type": "audio", "duration": 877.931}, {"from": "Assistant", "value": "Authors’ keyphrases assigned to scientific articles are essential for recognizing content and topic aspects. Most of the proposed supervised and unsupervised methods for keyphrase generation are unable to produce terms that are valuable but do not appear in the text. In this paper, we explore the possibility of considering the keyphrase string as an abstractive summary of the title and the abstract. First, we collect, process and release a large dataset of scientific paper metadata that contains 2.2 million records. Then we experiment with popular text summarization neural architectures. Despite using advanced deep learning models, large quantities of data and many days of computation, our systematic evaluation on four test datasets reveals that the explored text summarization methods could not produce better keyphrases than the simpler unsupervised methods, or the existing supervised ones.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1071", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1071.flac", "type": "audio", "duration": 783.595}, {"from": "Assistant", "value": "Neural sequence-to-sequence models are currently the dominant approach in several natural language processing tasks, but require large parallel corpora. We present a sequence-to-sequence-to-sequence autoencoder (SEQˆ3), consisting of two chained encoder-decoder pairs, with words used as a sequence of discrete latent variables. We apply the proposed model to unsupervised abstractive sentence compression, where the first and last sequences are the input and reconstructed sentences, respectively, while the middle sequence is the compressed sentence. Constraining the length of the latent word sequences forces the model to distill important information from the input. A pretrained language model, acting as a prior over the latent sequences, encourages the compressed sentences to be human-readable. Continuous relaxations enable us to sample from categorical distributions, allowing gradient-based optimization, unlike alternatives that rely on reinforcement learning. The proposed model does not require parallel text-summary pairs, achieving promising results in unsupervised sentence compression on benchmark datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1072", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1072.flac", "type": "audio", "duration": 858.027}, {"from": "Assistant", "value": "Conducting a manual evaluation is considered an essential part of summary evaluation methodology. Traditionally, the Pyramid protocol, which exhaustively compares system summaries to references, has been perceived as very reliable, providing objective scores. Yet, due to the high cost of the Pyramid method and the required expertise, researchers resorted to cheaper and less thorough manual evaluation methods, such as Responsiveness and pairwise comparison, attainable via crowdsourcing. We revisit the Pyramid approach, proposing a lightweight sampling-based version that is crowdsourcable. We analyze the performance of our method in comparison to original expert-based Pyramid evaluations, showing higher correlation relative to the common Responsiveness method. We release our crowdsourced Summary-Content-Units, along with all crowdsourcing scripts, for future evaluations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1073", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1073.flac", "type": "audio", "duration": 781.888}, {"from": "Assistant", "value": "Serial recall experiments study the ability of humans to recall words in the order in which they occurred. The following serial recall effects are generally investigated in studies with humans: word length and frequency, primacy and recency, semantic confusion, repetition, and transposition effects. In this research, we investigate neural language models in the context of these serial recall effects. Our work provides a framework to better understand and analyze neural language models and opens a new window to develop accurate language models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1074", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1074.flac", "type": "audio", "duration": 855.829}, {"from": "Assistant", "value": "Concept map-based multi-document summarization has recently been proposed as a variant of the traditional summarization task with graph-structured summaries. As shown by previous work, the grouping of coreferent concept mentions across documents is a crucial subtask of it. However, while the current state-of-the-art method suggested a new grouping method that was shown to improve the summary quality, its use of pairwise comparisons leads to polynomial runtime complexity that prohibits the application to large document collections. In this paper, we propose two alternative grouping techniques based on locality sensitive hashing, approximate nearest neighbor search and a fast clustering algorithm. They exhibit linear and log-linear runtime complexity, making them much more scalable. We report experimental results that confirm the improved runtime behavior while also showing that the quality of the summary concept maps remains comparable.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1103", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1103.flac", "type": "audio", "duration": 783.445}, {"from": "Assistant", "value": "We consider the problem of learning distributed representations for entities and relations of multi-relational data so as to predict missing links therein. Convolutional neural networks have recently shown their superiority for this problem, bringing increased model expressiveness while remaining parameter efficient. Despite the success, previous convolution designs fail to model full interactions between input entities and relations, which potentially limits the performance of link prediction. In this work we introduce ConvR, an adaptive convolutional network designed to maximize entity-relation interactions in a convolutional fashion. ConvR adaptively constructs convolution filters from relation representations, and applies these filters across entity representations to generate convolutional features. As such, ConvR enables rich interactions between entity and relation representations at diverse regions, and all the convolutional features generated will be able to capture such interactions. We evaluate ConvR on multiple benchmark datasets. Experimental results show that: (1) ConvR performs substantially better than competitive baselines in almost all the metrics and on all the datasets; (2) Compared with state-of-the-art convolutional models, ConvR is not only more effective but also more efficient. It offers a 7% increase in MRR and a 6% increase in Hits@10, while saving 12% in parameter storage.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1104", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1104.flac", "type": "audio", "duration": 983.979}, {"from": "Assistant", "value": "Knowledge graphs have been developed rapidly in recent years and shown their usefulness for many artificial intelligence tasks. However, knowledge graphs often have lots of missing facts. To solve this problem, many knowledge graph embedding models to populate knowledge graphs have been developed and have shown outstanding performance these days. However, knowledge graph embedding models are so called-black box. Hence, we actually does not know how information of a knowledge graph is processed and the models are hard to interpret. In this paper, we utilize graph patterns in a knowledge graph to overcome such problems. Our proposed model, graph pattern entity ranking Model (GRank), constructs an entity ranking system for each graph pattern and evaluate them using a measure for a ranking system. By doing so, we can find helpful graph patterns for predicting facts. Then we conduct the link prediction tasks on standard data sets to evaluate GRank. We show our approach outperforms other state-of-the-art approaches such as ComplEx and TorusE on standard metrics such as HITS@n and MRR. Moreover, This model is easily interpretable because output facts are described by graph patterns.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1105", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1105.flac", "type": "audio", "duration": 1022.016}, {"from": "Assistant", "value": "Modern weakly supervised methods for event detection (ED) avoid time-consuming human annotation and achieve promising results by learning from auto-labeled data. However, these methods typically rely on sophisticated pre-defined rules as well as existing instances in knowledge bases for automatic annotation and thus suffer from low coverage, topic bias, and data noise. To address these issues, we build a large event-related candidate set with good coverage and then apply an adversarial training mechanism to iteratively identify those informative instances from the candidate set and filter out those noisy ones. The experiments on two real-world datasets show that our candidate selection and adversarial training can cooperate together to obtain more diverse and accurate training data for ED, and significantly outperform the state-of-the-art methods in various weakly supervised scenarios. The datasets and source code can be obtained fromhttps://github.com/thunlp/Adv-ED.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1108", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1108.flac", "type": "audio", "duration": 1160.213}, {"from": "Assistant", "value": "Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification. Recognising text documents of classes that have never been seen in the learning stage, so-called zero-shot text classification, is therefore difficult and only limited previous works tackled this problem. In this paper, we propose a two-phase framework together with data augmentation and feature augmentation to solve this problem. Four kinds of semantic knowledge (word embeddings, class descriptions, class hierarchy, and a general knowledge graph) are incorporated into the proposed framework to deal with instances of unseen classes effectively. Experimental results show that each and the combination of the two phases achieve the best overall accuracy compared with baselines and recent approaches in classifying real-world texts under the zero-shot scenario.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1109", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1109.flac", "type": "audio", "duration": 1091.499}, {"from": "Assistant", "value": "A standard word embedding algorithm, such as word2vec and glove, makes a strong assumption that words are likely to be semantically related only if they co-occur locally within a window of fixed size. However, this strong assumption may not capture the semantic association between words that co-occur frequently but non-locally within documents. In this paper, we propose a graph-based word embedding method, named ‘word-node2vec’. By relaxing the strong constraint of locality, our method is able to capture both the local and non-local co-occurrences. Word-node2vec constructs a graph where every node represents a word and an edge between two nodes represents a combination of both local (e.g. word2vec) and document-level co-occurrences. Our experiments show that word-node2vec outperforms word2vec and glove on a range of different tasks, such as predicting word-pair similarity, word analogy and concept categorization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1110", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1110.flac", "type": "audio", "duration": 1158.144}, {"from": "Assistant", "value": "In traditional Distributional Semantic Models (DSMs) the multiple senses of a polysemous word are conflated into a single vector space representation. In this work, we propose a DSM that learns multiple distributional representations of a word based on different topics. First, a separate DSM is trained for each topic and then each of the topic-based DSMs is aligned to a common vector space. Our unsupervised mapping approach is motivated by the hypothesis that words preserving their relative distances in different topic semantic sub-spaces constitute robust semantic anchors that define the mappings between them. Aligned cross-topic representations achieve state-of-the-art results for the task of contextual word similarity. Furthermore, evaluation on NLP downstream tasks shows that multiple topic-based embeddings outperform single-prototype models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1112", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1112.flac", "type": "audio", "duration": 1098.368}, {"from": "Assistant", "value": "Contextual word representations derived from large-scale neural language models are successful across a diverse set of NLP tasks, suggesting that they encode useful and transferable features of language. To shed light on the linguistic knowledge they capture, we study the representations produced by several recent pretrained contextualizers (variants of ELMo, the OpenAI transformer language model, and BERT) with a suite of sixteen diverse probing tasks. We find that linear models trained on top of frozen contextual representations are competitive with state-of-the-art task-specific models in many cases, but fail on tasks requiring fine-grained linguistic knowledge (e.g., conjunct identification). To investigate the transferability of contextual word representations, we quantify differences in the transferability of individual layers within contextualizers, especially between recurrent neural networks (RNNs) and transformers. For instance, higher layers of RNNs are more task-specific, while transformer layers do not exhibit the same monotonic trend. In addition, to better understand what makes contextual word representations transferable, we compare language model pretraining with eleven supervised pretraining tasks. For any given task, pretraining on a closely related task yields better performance than language model pretraining (which is better on average) when the pretraining dataset is fixed. However, language model pretraining on more data gives the best results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1113", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1113.flac", "type": "audio", "duration": 1046.827}, {"from": "Assistant", "value": "We address part-of-speech (POS) induction by maximizing the mutual information between the induced label and its context. We focus on two training objectives that are amenable to stochastic gradient descent (SGD): a novel generalization of the classical Brown clustering objective and a recently proposed variational lower bound. While both objectives are subject to noise in gradient updates, we show through analysis and experiments that the variational lower bound is robust whereas the generalized Brown objective is vulnerable. We obtain strong performance on a multitude of datasets and languages with a simple architecture that encodes morphology and context.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1114", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1114.flac", "type": "audio", "duration": 1173.717}, {"from": "Assistant", "value": "Recurrent neural network grammars (RNNG) are generative models of language which jointly model syntax and surface structure by incrementally generating a syntax tree and sentence in a top-down, left-to-right order. Supervised RNNGs achieve strong language modeling and parsing performance, but require an annotated corpus of parse trees. In this work, we experiment with unsupervised learning of RNNGs. Since directly marginalizing over the space of latent trees is intractable, we instead apply amortized variational inference. To maximize the evidence lower bound, we develop an inference network parameterized as a neural CRF constituency parser. On language modeling, unsupervised RNNGs perform as well their supervised counterparts on benchmarks in English and Chinese. On constituency grammar induction, they are competitive with recent neural language models that induce tree structures from words through attention mechanisms.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1115", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1115.flac", "type": "audio", "duration": 827.264}, {"from": "Assistant", "value": "There has been considerable attention devoted to models that learn to jointly infer an expression’s syntactic structure and its semantics. Yet, Nangia and Bowman (2018) has recently shown that the current best systems fail to learn the correct parsing strategy on mathematical expressions generated from a simple context-free grammar. In this work, we present a recursive model inspired by Choi et al. (2018) that reaches near perfect accuracy on this task. Our model is composed of two separated modules for syntax and semantics. They are cooperatively trained with standard continuous and discrete optimisation schemes. Our model does not require any linguistic structure for supervision, and its recursive nature allows for out-of-domain generalisation. Additionally, our approach performs competitively on several natural language tasks, such as Natural Language Inference and Sentiment Analysis.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1118", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1118.flac", "type": "audio", "duration": 1093.995}, {"from": "Assistant", "value": "Syntax has been demonstrated highly effective in neural machine translation (NMT). Previous NMT models integrate syntax by representing 1-best tree outputs from a well-trained parsing system, e.g., the representative Tree-RNN and Tree-Linearization methods, which may suffer from error propagation. In this work, we propose a novel method to integrate source-side syntax implicitly for NMT. The basic idea is to use the intermediate hidden representations of a well-trained end-to-end dependency parser, which are referred to as syntax-aware word representations (SAWRs). Then, we simply concatenate such SAWRs with ordinary word embeddings to enhance basic NMT models. The method can be straightforwardly integrated into the widely-used sequence-to-sequence (Seq2Seq) NMT models. We start with a representative RNN-based Seq2Seq baseline system, and test the effectiveness of our proposed method on two benchmark datasets of the Chinese-English and English-Vietnamese translation tasks, respectively. Experimental results show that the proposed approach is able to bring significant BLEU score improvements on the two datasets compared with the baseline, 1.74 points for Chinese-English translation and 0.80 point for English-Vietnamese translation, respectively. In addition, the approach also outperforms the explicit Tree-RNN and Tree-Linearization methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1119", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1119.flac", "type": "audio", "duration": 1063.616}, {"from": "Assistant", "value": "Current state-of-the-art NMT systems use large neural networks that are not only slow to train, but also often require many heuristics and optimization tricks, such as specialized learning rate schedules and large batch sizes. This is undesirable as it requires extensive hyperparameter tuning. In this paper, we propose a curriculum learning framework for NMT that reduces training time, reduces the need for specialized heuristics or large batch sizes, and results in overall better performance. Our framework consists of a principled way of deciding which training samples are shown to the model at different times during training, based on the estimated difficulty of a sample and the current competence of the model. Filtering training samples in this manner prevents the model from getting stuck in bad local optima, making it converge faster and reach a better solution than the common approach of uniformly sampling training examples. Furthermore, the proposed method can be easily applied to existing NMT models by simply modifying their input data pipelines. We show that our framework can help improve the training time and the performance of both recurrent neural network models and Transformers, achieving up to a 70% decrease in training time, while at the same time obtaining accuracy improvements of up to 2.2 BLEU.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1120", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1120.flac", "type": "audio", "duration": 1032.405}, {"from": "Assistant", "value": "The overreliance on large parallel corpora significantly limits the applicability of machine translation systems to the majority of language pairs. Back-translation has been dominantly used in previous approaches for unsupervised neural machine translation, where pseudo sentence pairs are generated to train the models with a reconstruction loss. However, the pseudo sentences are usually of low quality as translation errors accumulate during training. To avoid this fundamental issue, we propose an alternative but more effective approach, extract-edit, to extract and then edit real sentences from the target monolingual corpora. Furthermore, we introduce a comparative translation loss to evaluate the translated target sentences and thus train the unsupervised translation systems. Experiments show that the proposed approach consistently outperforms the previous state-of-the-art unsupervised machine translation systems across two benchmarks (English-French and English-German) and two low-resource language pairs (English-Romanian and English-Russian) by more than 2 (up to 3.63) BLEU points.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1121", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1121.flac", "type": "audio", "duration": 1092.608}, {"from": "Assistant", "value": "Generalization and reliability of multilingual translation often highly depend on the amount of available parallel data for each language pair of interest. In this paper, we focus on zero-shot generalization—a challenging setup that tests models on translation directions they have not been optimized for at training time. To solve the problem, we (i) reformulate multilingual translation as probabilistic inference, (ii) define the notion of zero-shot consistency and show why standard training often results in models unsuitable for zero-shot tasks, and (iii) introduce a consistent agreement-based training method that encourages the model to produce equivalent translations of parallel sentences in auxiliary languages. We test our multilingual NMT models on multiple public zero-shot translation benchmarks (IWSLT17, UN corpus, Europarl) and show that agreement-based learning often results in 2-3 BLEU zero-shot improvement over strong baselines without any loss in performance on supervised translation directions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1122", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1122.flac", "type": "audio", "duration": 863.381}, {"from": "Assistant", "value": "Recently, the Transformer model that is based solely on attention mechanisms, has advanced the state-of-the-art on various machine translation tasks. However, recent studies reveal that the lack of recurrence modeling hinders its further improvement of translation capacity. In response to this problem, we propose to directly model recurrence for Transformer with an additional recurrence encoder. In addition to the standard recurrent neural network, we introduce a novel attentive recurrent network to leverage the strengths of both attention models and recurrent networks. Experimental results on the widely-used WMT14 English⇒German and WMT17 Chinese⇒English translation tasks demonstrate the effectiveness of the proposed approach. Our studies also reveal that the proposed model benefits from a short-cut that bridges the source and target sequences with a single recurrent layer, which outperforms its deep counterpart.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1158", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1158.flac", "type": "audio", "duration": 679.68}, {"from": "Assistant", "value": "We introduce a theoretical analysis of crosslingual transfer in probabilistic topic models. By formulating posterior inference through Gibbs sampling as a process of language transfer, we propose a new measure that quantifies the loss of knowledge across languages during this process. This measure enables us to derive a PAC-Bayesian bound that elucidates the factors affecting model quality, both during training and in downstream applications. We provide experimental validation of the analysis on a diverse set of five languages, and discuss best practices for data collection and model design based on our analysis.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1159", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1159.flac", "type": "audio", "duration": 1080.619}, {"from": "Assistant", "value": "The need for tree structure modelling on top of sequence modelling is an open issue in neural dependency parsing. We investigate the impact of adding a tree layer on top of a sequential model by recursively composing subtree representations (composition) in a transition-based parser that uses features extracted by a BiLSTM. Composition seems superfluous with such a model, suggesting that BiLSTMs capture information about subtrees. We perform model ablations to tease out the conditions under which composition helps. When ablating the backward LSTM, performance drops and composition does not recover much of the gap. When ablating the forward LSTM, performance drops less dramatically and composition recovers a substantial part of the gap, indicating that a forward LSTM and composition capture similar information. We take the backward LSTM to be related to lookahead features and the forward LSTM to the rich history-based features both crucial for transition-based parsers. To capture history-based information, composition is better than a forward LSTM on its own, but it is even better to have a forward LSTM as part of a BiLSTM. We correlate results with language properties, showing that the improved lookahead of a backward LSTM is especially important for head-final languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1160", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1160.flac", "type": "audio", "duration": 1093.141}, {"from": "Assistant", "value": "Combinatory categorial grammars are linguistically motivated and useful for semantic parsing, but costly to acquire in a supervised way and difficult to acquire in an unsupervised way. We propose an alternative making use of cross-lingual learning: an existing source-language parser is used together with a parallel corpus to induce a grammar and parsing model for a target language. On the PASCAL benchmark, cross-lingual CCG induction outperforms CCG induction from gold-standard POS tags on 3 out of 8 languages, and unsupervised CCG induction on 6 out of 8 languages. We also show that cross-lingually induced CCGs reflect syntactic properties of the target languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1161", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1161.flac", "type": "audio", "duration": 1076.203}, {"from": "Assistant", "value": "Recent approaches to cross-lingual word embedding have generally been based on linear transformations between the sets of embedding vectors in the two languages. In this paper, we propose an approach that instead expresses the two monolingual embedding spaces as probability densities defined by a Gaussian mixture model, and matches the two densities using a method called normalizing flow. The method requires no explicit supervision, and can be learned with only a seed dictionary of words that have identical strings. We argue that this formulation has several intuitively attractive properties, particularly with the respect to improving robustness and generalization to mappings between difficult language pairs or word pairs. On a benchmark data set of bilingual lexicon induction and cross-lingual word similarity, our approach can achieve competitive or superior performance compared to state-of-the-art published results, with particularly strong results being found on etymologically distant and/or morphologically rich languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1162", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1162.flac", "type": "audio", "duration": 1120.875}, {"from": "Assistant", "value": "We introduce a novel method for multilingual transfer that utilizes deep contextual embeddings, pretrained in an unsupervised fashion. While contextual embeddings have been shown to yield richer representations of meaning compared to their static counterparts, aligning them poses a challenge due to their dynamic nature. To this end, we construct context-independent variants of the original monolingual spaces and utilize their mapping to derive an alignment for the context-dependent spaces. This mapping readily supports processing of a target language, improving transfer by context-aware embeddings. Our experimental results demonstrate the effectiveness of this approach for zero-shot and few-shot learning of dependency parsing. Specifically, our method consistently outperforms the previous state-of-the-art on 6 tested languages, yielding an improvement of 6.8 LAS points on average.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1163", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1163.flac", "type": "audio", "duration": 1071.68}, {"from": "Assistant", "value": "Rumours can spread quickly through social media, and malicious ones can bring about significant economical and social impact. Motivated by this, our paper focuses on the task of rumour detection; particularly, we are interested in understanding how early we can detect them. Although there are numerous studies on rumour detection, few are concerned with the timing of the detection. A successfully-detected malicious rumour can still cause significant damage if it isn’t detected in a timely manner, and so timing is crucial. To address this, we present a novel methodology for early rumour detection. Our model treats social media posts (e.g. tweets) as a data stream and integrates reinforcement learning to learn the number minimum number of posts required before we classify an event as a rumour. Experiments on Twitter and Weibo demonstrate that our model identifies rumours earlier than state-of-the-art systems while maintaining a comparable accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1164", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1164.flac", "type": "audio", "duration": 1077.76}, {"from": "Assistant", "value": "Automatic hashtag annotation plays an important role in content understanding for microblog posts. To date, progress made in this field has been restricted to phrase selection from limited candidates, or word-level hashtag discovery using topic models. Different from previous work considering hashtags to be inseparable, our work is the first effort to annotate hashtags with a novel sequence generation framework via viewing the hashtag as a short sequence of words. Moreover, to address the data sparsity issue in processing short microblog posts, we propose to jointly model the target posts and the conversation contexts initiated by them with bidirectional attention. Extensive experimental results on two large-scale datasets, newly collected from English Twitter and Chinese Weibo, show that our model significantly outperforms state-of-the-art models based on classification. Further studies demonstrate our ability to effectively generate rare and even unseen hashtags, which is however not possible for most existing methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1165", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1165.flac", "type": "audio", "duration": 972.651}, {"from": "Assistant", "value": "Visual modifications to text are often used to obfuscate offensive comments in social media (e.g., “!d10t”) or as a writing style (“1337” in “leet speak”), among other scenarios. We consider this as a new type of adversarial attack in NLP, a setting to which humans are very robust, as our experiments with both simple and more difficult visual perturbations demonstrate. We investigate the impact of visual adversarial attacks on current NLP systems on character-, word-, and sentence-level tasks, showing that both neural and non-neural models are, in contrast to humans, extremely sensitive to such attacks, suffering performance decreases of up to 82%. We then explore three shielding methods—visual character embeddings, adversarial training, and rule-based recovery—which substantially improve the robustness of the models. However, the shielding methods still fall behind performances achieved in non-attack scenarios, which demonstrates the difficulty of dealing with visual attacks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1166", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1166.flac", "type": "audio", "duration": 1197.973}, {"from": "Assistant", "value": "Controversial posts are those that split the preferences of a community, receiving both significant positive and significant negative feedback. Our inclusion of the word “community” here is deliberate: what is controversial to some audiences may not be so to others. Using data from several different communities on reddit.com, we predict the ultimate controversiality of posts, leveraging features drawn from both the textual content and the tree structure of the early comments that initiate the discussion. We find that even when only a handful of comments are available, e.g., the first 5 comments made within 15 minutes of the original post, discussion features often add predictive capacity to strong content-and- rate only baselines. Additional experiments on domain transfer suggest that conversation- structure features often generalize to other communities better than conversation-content features do.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1167", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1167.flac", "type": "audio", "duration": 1049.963}, {"from": "Assistant", "value": "Understanding the dynamics of international politics is important yet challenging for civilians. In this work, we explore unsupervised neural models to infer relations between nations from news articles. We extend existing models by incorporating shallow linguistics information and propose a new automatic evaluation metric that aligns relationship dynamics with manually annotated key events. As understanding international relations requires carefully analyzing complex relationships, we conduct in-person human evaluations with three groups of participants. Overall, humans prefer the outputs of our model and give insightful feedback that suggests future directions for human-centered models. Furthermore, our model reveals interesting regional differences in news coverage. For instance, with respect to US-China relations, Singaporean media focus more on “strengthening” and “purchasing”, while US media focus more on “criticizing” and “denouncing”.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1199", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1199.flac", "type": "audio", "duration": 931.285}, {"from": "Assistant", "value": "Machine learning has shown promise for automatic detection of Alzheimer’s disease (AD) through speech; however, efforts are hampered by a scarcity of data, especially in languages other than English. We propose a method to learn a correspondence between independently engineered lexicosyntactic features in two languages, using a large parallel corpus of out-of-domain movie dialogue data. We apply it to dementia detection in Mandarin Chinese, and demonstrate that our method outperforms both unilingual and machine translation-based baselines. This appears to be the first study that transfers feature domains in detecting cognitive decline.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1200", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1200.flac", "type": "audio", "duration": 929.6}, {"from": "Assistant", "value": "Recent work has shown that visual context improves cross-lingual sense disambiguation for nouns. We extend this line of work to the more challenging task of cross-lingual verb sense disambiguation, introducing the MultiSense dataset of 9,504 images annotated with English, German, and Spanish verbs. Each image in MultiSense is annotated with an English verb and its translation in German or Spanish. We show that cross-lingual verb sense disambiguation models benefit from visual context, compared to unimodal baselines. We also show that the verb sense predicted by our best disambiguation model can improve the results of a text-only machine translation system when used for a multimodal translation task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1201", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1201.flac", "type": "audio", "duration": 892.416}, {"from": "Assistant", "value": "Language identification for code-switching (CS), the phenomenon of alternating between two or more languages in conversations, has traditionally been approached under the assumption of a single language per token. However, if at least one language is morphologically rich, a large number of words can be composed of morphemes from more than one language (intra-word CS). In this paper, we extend the language identification task to the subword-level, such that it includes splitting mixed words while tagging each part with a language ID. We further propose a model for this task, which is based on a segmental recurrent neural network. In experiments on a new Spanish–Wixarika dataset and on an adapted German–Turkish dataset, our proposed model performs slightly better than or roughly on par with our best baseline, respectively. Considering only mixed words, however, it strongly outperforms all baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1202", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1202.flac", "type": "audio", "duration": 120.939}, {"from": "Assistant", "value": "Current research on spoken language translation (SLT) has to confront with the scarcity of sizeable and publicly available training corpora. This problem hinders the adoption of neural end-to-end approaches, which represent the state of the art in the two parent tasks of SLT: automatic speech recognition and machine translation. To fill this gap, we created MuST-C, a multilingual speech translation corpus whose size and quality will facilitate the training of end-to-end systems for SLT from English into 8 languages. For each target language, MuST-C comprises at least 385 hours of audio recordings from English TED Talks, which are automatically aligned at the sentence level with their manual transcriptions and translations. Together with a description of the corpus creation methodology (scalable to add new data and cover new languages), we provide an empirical verification of its quality and SLT results computed with a state-of-the-art approach on each language direction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1205", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1205.flac", "type": "audio", "duration": 945.387}, {"from": "Assistant", "value": "The explicit use of syntactic information has been proved useful for neural machine translation (NMT). However, previous methods resort to either tree-structured neural networks or long linearized sequences, both of which are inefficient. Neural syntactic distance (NSD) enables us to represent a constituent tree using a sequence whose length is identical to the number of words in the sentence. NSD has been used for constituent parsing, but not in machine translation. We propose five strategies to improve NMT with NSD. Experiments show that it is not trivial to improve NMT with NSD; however, the proposed strategies are shown to improve translation performance of the baseline model (+2.1 (En–Ja), +1.3 (Ja–En), +1.2 (En–Ch), and +1.0 (Ch–En) BLEU).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1206", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1206.flac", "type": "audio", "duration": 855.488}, {"from": "Assistant", "value": "Incremental domain adaptation, in which a system learns from the correct output for each input immediately after making its prediction for that input, can dramatically improve system performance for interactive machine translation. Users of interactive systems are sensitive to the speed of adaptation and how often a system repeats mistakes, despite being corrected. Adaptation is most commonly assessed using corpus-level BLEU- or TER-derived metrics that do not explicitly take adaptation speed into account. We find that these metrics often do not capture immediate adaptation effects, such as zero-shot and one-shot learning of domain-specific lexical items. To this end, we propose new metrics that directly evaluate immediate adaptation performance for machine translation. We use these metrics to choose the most suitable adaptation method from a range of different adaptation techniques for neural machine translation systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1207", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1207.flac", "type": "audio", "duration": 868.139}, {"from": "Assistant", "value": "Despite some empirical success at correcting exposure bias in machine translation, scheduled sampling algorithms suffer from a major drawback: they incorrectly assume that words in the reference translations and in sampled sequences are aligned at each time step. Our new differentiable sampling algorithm addresses this issue by optimizing the probability that the reference can be aligned with the sampled output, based on a soft alignment predicted by the model itself. As a result, the output distribution at each time step is evaluated with respect to the whole predicted sequence. Experiments on IWSLT translation tasks show that our approach improves BLEU compared to maximum likelihood and scheduled sampling baselines. In addition, our approach is simpler to train with no need for sampling schedule and yields models that achieve larger improvements with smaller beam sizes.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1208", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1208.flac", "type": "audio", "duration": 903.488}, {"from": "Assistant", "value": "We consider the problem of making efficient use of heterogeneous training data in neural machine translation (NMT). Specifically, given a training dataset with a sentence-level feature such as noise, we seek an optimal curriculum, or order for presenting examples to the system during training. Our curriculum framework allows examples to appear an arbitrary number of times, and thus generalizes data weighting, filtering, and fine-tuning schemes. Rather than relying on prior knowledge to design a curriculum, we use reinforcement learning to learn one automatically, jointly with the NMT system, in the course of a single training run. We show that this approach can beat uniform baselines on Paracrawl and WMT English-to-French datasets by +3.4 and +1.3 BLEU respectively. Additionally, we match the performance of strong filtering baselines and hand-designed, state-of-the-art curricula.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1210", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1210.flac", "type": "audio", "duration": 918.165}, {"from": "Assistant", "value": "We present the first exploration of meaning shift over short periods of time in online communities using distributional representations. We create a small annotated dataset and use it to assess the performance of a standard model for meaning shift detection on short-term meaning shift. We find that the model has problems distinguishing meaning shift from referential phenomena, and propose a measure of contextual variability to remedy this.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1211", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1211.flac", "type": "audio", "duration": 850.197}, {"from": "Assistant", "value": "We examine the new task of detecting derogatory compounds (e.g. “curry muncher”). Derogatory compounds are much more difficult to detect than derogatory unigrams (e.g. “idiot”) since they are more sparsely represented in lexical resources previously found effective for this task (e.g. Wiktionary). We propose an unsupervised classification approach that incorporates linguistic properties of compounds. It mostly depends on a simple distributional representation. We compare our approach against previously established methods proposed for extracting derogatory unigrams.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1212", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1212.flac", "type": "audio", "duration": 799.787}, {"from": "Assistant", "value": "Collaborative filtering (CF) is a core technique for recommender systems. Traditional CF approaches exploit user-item relations (e.g., clicks, likes, and views) only and hence they suffer from the data sparsity issue. Items are usually associated with unstructured text such as article abstracts and product reviews. We develop a Personalized Neural Embedding (PNE) framework to exploit both interactions and words seamlessly. We learn such embeddings of users, items, and words jointly, and predict user preferences on items based on these learned representations. PNE estimates the probability that a user will like an item by two terms—behavior factors and semantic factors. On two real-world datasets, PNE shows better performance than four state-of-the-art baselines in terms of three metrics. We also show that PNE learns meaningful word embeddings by visualization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1213", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1213.flac", "type": "audio", "duration": 935.168}, {"from": "Assistant", "value": "A growing number of state-of-the-art transfer learning methods employ language models pretrained on large generic corpora. In this paper we present a conceptually simple and effective transfer learning approach that addresses the problem of catastrophic forgetting. Specifically, we combine the task-specific optimization function with an auxiliary language model objective, which is adjusted during the training process. This preserves language regularities captured by language models, while enabling sufficient adaptation for solving the target task. Our method does not require pretraining or finetuning separate components of the network and we train our models end-to-end in a single step. We present results on a variety of challenging affective and text classification tasks, surpassing well established transfer learning methods with greater level of complexity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1214", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1214.flac", "type": "audio", "duration": 1016.832}, {"from": "Assistant", "value": "Tweets are short messages that often include specialized language such as hashtags and emojis. In this paper, we present a simple strategy to process emojis: replace them with their natural language description and use pretrained word embeddings as normally done with standard words. We show that this strategy is more effective than using pretrained emoji embeddings for tweet classification. Specifically, we obtain new state-of-the-art results in irony detection and sentiment analysis despite our neural network is simpler than previous proposals.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1216", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1216.flac", "type": "audio", "duration": 913.856}, {"from": "Assistant", "value": "In the context of fake news, bias, and propaganda, we study two important but relatively under-explored problems: (i) trustworthiness estimation (on a 3-point scale) and (ii) political ideology detection (left/right bias on a 7-point scale) of entire news outlets, as opposed to evaluating individual articles. In particular, we propose a multi-task ordinal regression framework that models the two problems jointly. This is motivated by the observation that hyper-partisanship is often linked to low trustworthiness, e.g., appealing to emotions rather than sticking to the facts, while center media tend to be generally more impartial and trustworthy. We further use several auxiliary tasks, modeling centrality, hyper-partisanship, as well as left-vs.-right bias on a coarse-grained scale. The evaluation results show sizable performance gains by the joint models over models that target the problems in isolation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1217", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1217.flac", "type": "audio", "duration": 867.072}, {"from": "Assistant", "value": "A pun is a form of wordplay for an intended humorous or rhetorical effect, where a word suggests two or more meanings by exploiting polysemy (homographic pun) or phonological similarity to another word (heterographic pun). This paper presents an approach that addresses pun detection and pun location jointly from a sequence labeling perspective. We employ a new tagging scheme such that the model is capable of performing such a joint task, where useful structural information can be properly captured. We show that our proposed model is effective in handling both homographic and heterographic puns. Empirical results on the benchmark datasets demonstrate that our approach can achieve new state-of-the-art results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1218", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1218.flac", "type": "audio", "duration": 818.432}, {"from": "Assistant", "value": "We explore the challenge of action prediction from textual descriptions of scenes, a testbed to approximate whether text inference can be used to predict upcoming actions. As a case of study, we consider the world of the Harry Potter fantasy novels and inferring what spell will be cast next given a fragment of a story. Spells act as keywords that abstract actions (e.g. ‘Alohomora’ to open a door) and denote a response to the environment. This idea is used to automatically build HPAC, a corpus containing 82,836 samples and 85 actions. We then evaluate different baselines. Among the tested models, an LSTM-based approach obtains the best performance for frequent actions and large scene descriptions, but approaches such as logistic regression behave well on infrequent actions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1219", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1219.flac", "type": "audio", "duration": 860.651}, {"from": "Assistant", "value": "Peer-review plays a critical role in the scientific writing and publication ecosystem. To assess the efficiency and efficacy of the reviewing process, one essential element is to understand and evaluate the reviews themselves. In this work, we study the content and structure of peer reviews under the argument mining framework, through automatically detecting (1) the argumentative propositions put forward by reviewers, and (2) their types (e.g., evaluating the work or making suggestions for improvement). We first collect 14.2K reviews from major machine learning and natural language processing venues. 400 reviews are annotated with 10,386 propositions and corresponding types of Evaluation, Request, Fact, Reference, or Quote. We then train state-of-the-art proposition segmentation and classification models on the data to evaluate their utilities and identify new challenges for this new domain, motivating future directions for argument mining. Further experiments show that proposition usage varies across venues in amount, type, and topic.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1221", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1221.flac", "type": "audio", "duration": 758.187}, {"from": "Assistant", "value": "Abuse on the Internet represents a significant societal problem of our time. Previous research on automated abusive language detection in Twitter has shown that community-based profiling of users is a promising technique for this task. However, existing approaches only capture shallow properties of online communities by modeling follower–following relationships. In contrast, working with graph convolutional networks (GCNs), we present the first approach that captures not only the structure of online communities but also the linguistic behavior of the users within them. We show that such a heterogeneous graph-structured modeling of communities significantly advances the current state of the art in abusive language detection.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1222", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1222.flac", "type": "audio", "duration": 1893.227}, {"from": "Assistant", "value": "Meaning conflation deficiency is one of the main limiting factors of word representations which, given their widespread use at the core of many NLP systems, can lead to inaccurate semantic understanding of the input text and inevitably hamper the performance. Sense representations target this problem. However, their potential impact has rarely been investigated in downstream NLP applications. Through a set of experiments on a state-of-the-art reverse dictionary system based on neural networks, we show that a simple adjustment aimed at addressing the meaning conflation deficiency can lead to substantial improvements.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1223", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1223.flac", "type": "audio", "duration": 851.093}, {"from": "Assistant", "value": "Generating from Abstract Meaning Representation (AMR) is an underspecified problem, as many syntactic decisions are not specified by the semantic graph. To explicitly account for this variation, we break down generating from AMR into two steps: first generate a syntactic structure, and then generate the surface form. We show that decomposing the generation process this way leads to state-of-the-art single model performance generating from AMR without additional unlabelled data. We also demonstrate that we can generate meaning-preserving syntactic paraphrases of the same AMR graph, as judged by humans.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1224", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1224.flac", "type": "audio", "duration": 970.219}, {"from": "Assistant", "value": "We present a resource for the task of FrameNet semantic frame disambiguation of over 5,000 word-sentence pairs from the Wikipedia corpus. The annotations were collected using a novel crowdsourcing approach with multiple workers per sentence to capture inter-annotator disagreement. In contrast to the typical approach of attributing the best single frame to each word, we provide a list of frames with disagreement-based scores that express the confidence with which each frame applies to the word. This is based on the idea that inter-annotator disagreement is at least partly caused by ambiguity that is inherent to the text and frames. We have found many examples where the semantics of individual frames overlap sufficiently to make them acceptable alternatives for interpreting a sentence. We have argued that ignoring this ambiguity creates an overly arbitrary target for training and evaluating natural language processing systems - if humans cannot agree, why would we expect the correct answer from a machine to be any different? To process this data we also utilized an expanded lemma-set provided by the Framester system, which merges FN with WordNet to enhance coverage. Our dataset includes annotations of 1,000 sentence-word pairs whose lemmas are not part of FN. Finally we present metrics for evaluating frame disambiguation systems that account for ambiguity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1225", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1225.flac", "type": "audio", "duration": 952.832}, {"from": "Assistant", "value": "Several datasets have recently been constructed to expose brittleness in models trained on existing benchmarks. While model performance on these challenge datasets is significantly lower compared to the original benchmark, it is unclear what particular weaknesses they reveal. For example, a challenge dataset may be difficult because it targets phenomena that current models cannot capture, or because it simply exploits blind spots in a model’s specific training set. We introduce inoculation by fine-tuning, a new analysis method for studying challenge datasets by exposing models (the metaphorical patient) to a small amount of data from the challenge dataset (a metaphorical pathogen) and assessing how well they can adapt. We apply our method to analyze the NLI “stress tests” (Naik et al., 2018) and the Adversarial SQuAD dataset (Jia and Liang, 2017). We show that after slight exposure, some of these datasets are no longer challenging, while others remain difficult. Our results indicate that failures on challenge datasets may lead to very different conclusions about models, training datasets, and the challenge datasets themselves.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1255", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1255.flac", "type": "audio", "duration": 786.197}, {"from": "Assistant", "value": "Unsupervised document representation learning is an important task providing pre-trained features for NLP applications. Unlike most previous work which learn the embedding based on self-prediction of the surface of text, we explicitly exploit the inter-document information and directly model the relations of documents in embedding space with a discriminative network and a novel objective. Extensive experiments on both small and large public datasets show the competitiveness of the proposed method. In evaluations on standard document classification, our model has errors that are 5 to 13% lower than state-of-the-art unsupervised embedding models. The reduction in error is even more pronounced in scarce label setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1258", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1258.flac", "type": "audio", "duration": 1040.32}, {"from": "Assistant", "value": "Cross-domain sentiment classification aims to predict sentiment polarity on a target domain utilizing a classifier learned from a source domain. Most existing adversarial learning methods focus on aligning the global marginal distribution by fooling a domain discriminator, without taking category-specific decision boundaries into consideration, which can lead to the mismatch of category-level features. In this work, we propose an adversarial category alignment network (ACAN), which attempts to enhance category consistency between the source domain and the target domain. Specifically, we increase the discrepancy of two polarity classifiers to provide diverse views, locating ambiguous features near the decision boundaries. Then the generator learns to create better features away from the category boundaries by minimizing this discrepancy. Experimental results on benchmark datasets show that the proposed method can achieve state-of-the-art performance and produce more discriminative features.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1259", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1259.flac", "type": "audio", "duration": 1110.357}, {"from": "Assistant", "value": "Opinion target extraction and opinion words extraction are two fundamental subtasks in Aspect Based Sentiment Analysis (ABSA). Recently, many methods have made progress on these two tasks. However, few works aim at extracting opinion targets and opinion words as pairs. In this paper, we propose a novel sequence labeling subtask for ABSA named TOWE (Target-oriented Opinion Words Extraction), which aims at extracting the corresponding opinion words for a given opinion target. A target-fused sequence labeling neural network model is designed to perform this task. The opinion target information is well encoded into context by an Inward-Outward LSTM. Then left and right contexts of the opinion target and the global context are combined to find the corresponding opinion words. We build four datasets for TOWE based on several popular ABSA benchmarks from laptop and restaurant reviews. The experimental results show that our proposed model outperforms the other compared methods significantly. We believe that our work may not only be helpful for downstream sentiment analysis task, but can also be used for pair-wise opinion summarization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1269", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1269.flac", "type": "audio", "duration": 1094.507}, {"from": "Assistant", "value": "Recent work in neural generation has attracted significant interest in controlling the form of text, such as style, persona, and politeness. However, there has been less work on controlling neural text generation for content. This paper introduces the notion of Content Transfer for long-form text generation, where the task is to generate a next sentence in a document that both fits its context and is grounded in a content-rich external textual source such as a news story. Our experiments on Wikipedia data show significant improvements against competitive baselines. As another contribution of this paper, we release a benchmark dataset of 640k Wikipedia referenced sentences paired with the source articles to encourage exploration of this new task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1270", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1270.flac", "type": "audio", "duration": 959.061}, {"from": "Assistant", "value": "Reading strategies have been shown to improve comprehension levels, especially for readers lacking adequate prior knowledge. Just as the process of knowledge accumulation is time-consuming for human readers, it is resource-demanding to impart rich general domain knowledge into a deep language model via pre-training. Inspired by reading strategies identified in cognitive science, and given limited computational resources - just a pre-trained model and a fixed number of training instances - we propose three general strategies aimed to improve non-extractive machine reading comprehension (MRC): (i) BACK AND FORTH READING that considers both the original and reverse order of an input sequence, (ii) HIGHLIGHTING, which adds a trainable embedding to the text embedding of tokens that are relevant to the question and candidate answers, and (iii) SELF-ASSESSMENT that generates practice questions and candidate answers directly from the text in an unsupervised manner. By fine-tuning a pre-trained language model (Radford et al., 2018) with our proposed strategies on the largest general domain multiple-choice MRC dataset RACE, we obtain a 5.8% absolute increase in accuracy over the previous best result achieved by the same pre-trained model fine-tuned on RACE without the use of strategies. We further fine-tune the resulting model on a target MRC task, leading to an absolute improvement of 6.2% in average accuracy over previous state-of-the-art approaches on six representative non-extractive MRC datasets from different domains (i.e., ARC, OpenBookQA, MCTest, SemEval-2018 Task 11, ROCStories, and MultiRC). These results demonstrate the effectiveness of our proposed strategies and the versatility and general applicability of our fine-tuned models that incorporate these strategies. Core code is available athttps://github.com/nlpdata/strategy/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1271", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1271.flac", "type": "audio", "duration": 1112.832}, {"from": "Assistant", "value": "We propose a multi-task learning framework to learn a joint Machine Reading Comprehension (MRC) model that can be applied to a wide range of MRC tasks in different domains. Inspired by recent ideas of data selection in machine translation, we develop a novel sample re-weighting scheme to assign sample-specific weights to the loss. Empirical study shows that our approach can be applied to many existing MRC models. Combined with contextual representations from pre-trained language models (such as ELMo), we achieve new state-of-the-art results on a set of MRC benchmark datasets. We release our code athttps://github.com/xycforgithub/MultiTask-MRC.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1306", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1306.flac", "type": "audio", "duration": 953.195}, {"from": "Assistant", "value": "We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate “few-shot” models for classes existing at the tail of the class distribution, for which little data is available. Inspired by the rich semantic correlations between classes at the long tail and those at the head, we take advantage of the knowledge from data-rich classes at the head of the distribution to boost the performance of the data-poor classes at the tail. First, we propose to leverage implicit relational knowledge among class labels from knowledge graph embeddings and learn explicit relational knowledge using graph convolution networks. Second, we integrate that relational knowledge into relation extraction model by coarse-to-fine knowledge-aware attention mechanism. We demonstrate our results for a large-scale benchmark dataset which show that our approach significantly outperforms other baselines, especially for long-tail relations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1307", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1307.flac", "type": "audio", "duration": 934.016}, {"from": "Assistant", "value": "Distant supervision has been widely used in relation extraction tasks without hand-labeled datasets recently. However, the automatically constructed datasets comprise numbers of wrongly labeled negative instances due to the incompleteness of knowledge bases, which is neglected by current distant supervised methods resulting in seriously misleading in both training and testing processes. To address this issue, we propose a novel semi-distant supervision approach for relation extraction by constructing a small accurate dataset and properly leveraging numerous instances without relation labels. In our approach, we construct accurate instances by both knowledge base and entity descriptions determined to avoid wrong negative labeling and further utilize unlabeled instances sufficiently using generative adversarial network (GAN) framework. Experimental results on real-world datasets show that our approach can achieve significant improvements in distant supervised relation extraction over strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1308", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1308.flac", "type": "audio", "duration": 1078.421}, {"from": "Assistant", "value": "We introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs. The graphs are dynamically constructed by selecting the most confident entity spans and linking these nodes with confidence-weighted relation types and coreferences. The dynamic span graph allow coreference and relation type confidences to propagate through the graph to iteratively refine the span representations. This is unlike previous multi-task frameworks for information extraction in which the only interaction between tasks is in the shared first-layer LSTM. Our framework significantly outperforms state-of-the-art on multiple information extraction tasks across multiple datasets reflecting different domains. We further observe that the span enumeration approach is good at detecting nested span entities, with significant F1 score improvement on the ACE dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1309", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1309.flac", "type": "audio", "duration": 1137.28}, {"from": "Assistant", "value": "Open Information Extraction (OpenIE), the problem of harvesting triples from natural language text whose predicate relations are not aligned to any pre-defined ontology, has been a popular subject of research for the last decade. However, this research has largely ignored the vast quantity of facts available in semi-structured webpages. In this paper, we define the problem of OpenIE from semi-structured websites to extract such facts, and present an approach for solving it. We also introduce a labeled evaluation dataset to motivate research in this area. Given a semi-structured website and a set of seed facts for some relations existing on its pages, we employ a semi-supervised label propagation technique to automatically create training data for the relations present on the site. We then use this training data to learn a classifier for relation extraction. Experimental results of this method on our new benchmark dataset obtained a precision of over 70%. A larger scale extraction experiment on 31 websites in the movie vertical resulted in the extraction of over 2 million triples.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1316", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1316.flac", "type": "audio", "duration": 964.779}, {"from": "Assistant", "value": "The uncertainty measurement of classifiers’ predictions is especially important in applications such as medical diagnoses that need to ensure limited human resources can focus on the most uncertain predictions returned by machine learning models. However, few existing uncertainty models attempt to improve overall prediction accuracy where human resources are involved in the text classification task. In this paper, we propose a novel neural-network-based model that applies a new dropout-entropy method for uncertainty measurement. We also design a metric learning method on feature representations, which can boost the performance of dropout-based uncertainty methods with smaller prediction variance in accurate prediction trials. Extensive experiments on real-world data sets demonstrate that our method can achieve a considerable improvement in overall prediction accuracy compared to existing approaches. In particular, our model improved the accuracy from 0.78 to 0.92 when 30% of the most uncertain predictions were handed over to human experts in “20NewsGroup” data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1317", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1317.flac", "type": "audio", "duration": 900.971}, {"from": "Assistant", "value": "Sentence simplification is the task of rewriting texts so they are easier to understand. Recent research has applied sequence-to-sequence (Seq2Seq) models to this task, focusing largely on training-time improvements via reinforcement learning and memory augmentation. One of the main problems with applying generic Seq2Seq models for simplification is that these models tend to copy directly from the original sentence, resulting in outputs that are relatively long and complex. We aim to alleviate this issue through the use of two main techniques. First, we incorporate content word complexities, as predicted with a leveled word complexity model, into our loss function during training. Second, we generate a large set of diverse candidate simplifications at test time, and rerank these to promote fluency, adequacy, and simplicity. Here, we measure simplicity through a novel sentence complexity model. These extensions allow our models to perform competitively with state-of-the-art systems while generating simpler sentences. We report standard automatic and human evaluation metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1318", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1318.flac", "type": "audio", "duration": 1114.859}, {"from": "Assistant", "value": "Users participate in online discussion forums to learn from others and share their knowledge with the community. They often start a thread with a question or by sharing their new findings on a certain topic. We find that, unlike Community Question Answering, where questions are mostly factoid based, the threads in a forum are often open-ended (e.g., asking for recommendations from others) without a single correct answer. In this paper, we address the task of identifying helpful posts in a forum thread to help users comprehend long running discussion threads, which often contain repetitive or irrelevant posts. We propose a recurrent neural network based architecture to model (i) the relevance of a post regarding the original post starting the thread and (ii) the novelty it brings to the discussion, compared to the previous posts in the thread. Experimental results on different types of online forum datasets show that our model significantly outperforms the state-of-the-art neural network models for text classification.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1319", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1319.flac", "type": "audio", "duration": 999.083}, {"from": "Assistant", "value": "Training data for text classification is often limited in practice, especially for applications with many output classes or involving many related classification problems. This means classifiers must generalize from limited evidence, but the manner and extent of generalization is task dependent. Current practice primarily relies on pre-trained word embeddings to map words unseen in training to similar seen ones. Unfortunately, this squishes many components of meaning into highly restricted capacity. Our alternative begins with sparse pre-trained representations derived from unlabeled parsed corpora; based on the available training data, we select features that offers the relevant generalizations. This produces task-specific semantic vectors; here, we show that a feed-forward network over these vectors is especially effective in low-data scenarios, compared to existing state-of-the-art methods. By further pairing this network with a convolutional neural network, we keep this edge in low data scenarios and remain competitive when using full training sets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1320", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1320.flac", "type": "audio", "duration": 1037.888}, {"from": "Assistant", "value": "Text style transfer rephrases a text from a source style (e.g., informal) to a target style (e.g., formal) while keeping its original meaning. Despite the success existing works have achieved using a parallel corpus for the two styles, transferring text style has proven significantly more challenging when there is no parallel training corpus. In this paper, we address this challenge by using a reinforcement-learning-based generator-evaluator architecture. Our generator employs an attention-based encoder-decoder to transfer a sentence from the source style to the target style. Our evaluator is an adversarially trained style discriminator with semantic and syntactic constraints that score the generated sentence for style, meaning preservation, and fluency. Experimental results on two different style transfer tasks–sentiment transfer, and formality transfer–show that our model outperforms state-of-the-art approaches. Furthermore, we perform a manual evaluation that demonstrates the effectiveness of the proposed method using subjective metrics of generated text quality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1348", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1348.flac", "type": "audio", "duration": 1104.256}, {"from": "Assistant", "value": "Sentence fusion is the task of joining several independent sentences into a single coherent text. Current datasets for sentence fusion are small and insufficient for training modern neural models. In this paper, we propose a method for automatically-generating fusion examples from raw text and present DiscoFuse, a large scale dataset for discourse-based sentence fusion. We author a set of rules for identifying a diverse set of discourse phenomena in raw text, and decomposing the text into two independent sentences. We apply our approach on two document collections: Wikipedia and Sports articles, yielding 60 million fusion examples annotated with discourse information required to reconstruct the fused text. We develop a sequence-to-sequence model on DiscoFuse and thoroughly analyze its strengths and weaknesses with respect to the various discourse phenomena, using both automatic as well as human evaluation. Finally, we conduct transfer learning experiments with WebSplit, a recent dataset for text simplification. We show that pretraining on DiscoFuse substantially improves performance on WebSplit when viewed as a sentence fusion task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1349", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1349.flac", "type": "audio", "duration": 1010.155}, {"from": "Assistant", "value": "Sequence-to-sequence models for open-domain dialogue generation tend to favor generic, uninformative responses. Past work has focused on word frequency-based approaches to improving specificity, such as penalizing responses with only common words. In this work, we examine whether specificity is solely a frequency-related notion and find that more linguistically-driven specificity measures are better suited to improving response informativeness. However, we find that forcing a sequence-to-sequence model to be more specific can expose a host of other problems in the responses, including flawed discourse and implausible semantics. We rerank our model’s outputs using externally-trained classifiers targeting each of these identified factors. Experiments show that our final model using linguistically motivated specificity and plausibility reranking improves the informativeness, reasonableness, and grammatically of responses.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1350", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1350.flac", "type": "audio", "duration": 1131.243}, {"from": "Assistant", "value": "When reading a text, it is common to become stuck on unfamiliar words and phrases, such as polysemous words with novel senses, rarely used idioms, internet slang, or emerging entities. If we humans cannot figure out the meaning of those expressions from the immediate local context, we consult dictionaries for definitions or search documents or the web to find other global context to help in interpretation. Can machines help us do this work? Which type of context is more important for machines to solve the problem? To answer these questions, we undertake a task of describing a given phrase in natural language based on its local and global contexts. To solve this task, we propose a neural description model that consists of two context encoders and a description decoder. In contrast to the existing methods for non-standard English explanation [Ni+ 2017] and definition generation [Noraset+ 2017; Gadetsky+ 2018], our model appropriately takes important clues from both local and global contexts. Experimental results on three existing datasets (including WordNet, Oxford and Urban Dictionaries) and a dataset newly created from Wikipedia demonstrate the effectiveness of our method over previous work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1351", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1351.flac", "type": "audio", "duration": 947.968}, {"from": "Assistant", "value": "Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to construct. Very little work adequately exploits unannotated data – such as discourse markers between sentences – mainly because of data sparseness and ineffective extraction methods. In the present work, we propose a method to automatically discover sentence pairs with relevant discourse markers, and apply it to massive amounts of data. Our resulting dataset contains 174 discourse markers with at least 10k examples each, even for rare markers such as “coincidentally” or “amazingly”. We use the resulting data as supervision for learning transferable sentence embeddings. In addition, we show that even though sentence representation learning through prediction of discourse marker yields state of the art results across different transfer tasks, it’s not clear that our models made use of the semantic relation between sentences, thus leaving room for further improvements.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1409", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1409.flac", "type": "audio", "duration": 849.685}, {"from": "Assistant", "value": "Pre-trained language model representations have been successful in a wide range of language understanding tasks. In this paper, we examine different strategies to integrate pre-trained representations into sequence to sequence models and apply it to neural machine translation and abstractive summarization. We find that pre-trained representations are most effective when added to the encoder network which slows inference by only 14%. Our experiments in machine translation show gains of up to 5.3 BLEU in a simulated resource-poor setup. While returns diminish with more labeled data, we still observe improvements when millions of sentence-pairs are available. Finally, on abstractive summarization we achieve a new state of the art on the full text version of CNN/DailyMail.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1410", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1410.flac", "type": "audio", "duration": 1423.232}, {"from": "Assistant", "value": "We improve the informativeness of models for conditional text generation using techniques from computational pragmatics. These techniques formulate language production as a game between speakers and listeners, in which a speaker should generate output text that a listener can use to correctly identify the original input that the text describes. While such approaches are widely used in cognitive science and grounded language learning, they have received less attention for more standard language generation tasks. We consider two pragmatic modeling methods for text generation: one where pragmatics is imposed by information preservation, and another where pragmatics is imposed by explicit modeling of distractors. We find that these methods improve the performance of strong existing systems for abstractive summarization and generation from structured meaning representations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1411", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1411.flac", "type": "audio", "duration": 871.68}, {"from": "Assistant", "value": "The variational autoencoder (VAE) imposes a probabilistic distribution (typically Gaussian) on the latent space and penalizes the Kullback-Leibler (KL) divergence between the posterior and prior. In NLP, VAEs are extremely difficult to train due to the problem of KL collapsing to zero. One has to implement various heuristics such as KL weight annealing and word dropout in a carefully engineered manner to successfully train a VAE for text. In this paper, we propose to use the Wasserstein autoencoder (WAE) for probabilistic sentence generation, where the encoder could be either stochastic or deterministic. We show theoretically and empirically that, in the original WAE, the stochastically encoded Gaussian distribution tends to become a Dirac-delta function, and we propose a variant of WAE that encourages the stochasticity of the encoder. Experimental results show that the latent space learned by WAE exhibits properties of continuity and smoothness as in VAEs, while simultaneously achieving much higher BLEU scores for sentence reconstruction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-1412", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-1412.flac", "type": "audio", "duration": 1148.459}, {"from": "Assistant", "value": "Understanding procedural language requires reasoning about both hierarchical and temporal relations between events. For example, “boiling pasta” is a sub-event of “making a pasta dish”, typically happens before “draining pasta,” and requires the use of omitted tools (e.g. a strainer, sink...). While people are able to choose when and how to use abstract versus concrete instructions, the NLP community lacks corpora and tasks for evaluating if our models can do the same. In this paper, we introduce KidsCook, a parallel script corpus, as well as a cloze task which matches video captions with missing procedural details. Experimental results show that state-of-the-art models struggle at this task, which requires inducing functional commonsense knowledge not explicitly stated in text.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-3002", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-3002.flac", "type": "audio", "duration": 1094.208}, {"from": "Assistant", "value": "Many text corpora exhibit socially problematic biases, which can be propagated or amplified in the models trained on such data. For example, doctor cooccurs more frequently with male pronouns than female pronouns. In this study we (i) propose a metric to measure gender bias; (ii) measure bias in a text corpus and the text generated from a recurrent neural network language model trained on the text corpus; (iii) propose a regularization loss term for the language model that minimizes the projection of encoder-trained embeddings onto an embedding subspace that encodes gender; (iv) finally, evaluate efficacy of our proposed method on reducing gender bias. We find this regularization method to be effective in reducing gender bias up to an optimal weight assigned to the loss term, beyond which the model becomes unstable as the perplexity increases. We replicate this study on three training corpora—Penn Treebank, WikiText-2, and CNN/Daily Mail—resulting in similar conclusions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-3003", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-3003.flac", "type": "audio", "duration": 906.645}, {"from": "Assistant", "value": "It has been established that the performance of speech recognition systems depends on multiple factors including the lexical content, speaker identity and dialect. Here we use three English datasets of acted emotion to demonstrate that emotional content also impacts the performance of commercial systems. On two of the corpora, emotion is a bigger contributor to recognition errors than speaker identity and on two, neutral speech is recognized considerably better than emotional speech. We further evaluate the commercial systems on spontaneous interactions that contain portions of emotional speech. We propose and validate on the acted datasets, a method that allows us to evaluate the overall impact of emotion on recognition even when manual transcripts are not available. Using this method, we show that emotion in natural spontaneous dialogue is a less prominent but still significant factor in recognition accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-3004", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-3004.flac", "type": "audio", "duration": 779.947}, {"from": "Assistant", "value": "When developing topic classifiers for real-world applications, we begin by defining a set of meaningful topic labels. Ideally, an intelligent classifier can understand these labels right away and start classifying documents. Indeed, a human can confidently tell if an article is about science, politics, sports, or none of the above, after knowing just the class labels. We study the problem of training an initial topic classifier using only class labels. We investigate existing techniques for solving this problem and propose a simple but effective approach. Experiments on a variety of topic classification data sets show that learning from class labels can save significant initial labeling effort, essentially providing a ”free” warm start to the topic classifier.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-3012", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-3012.flac", "type": "audio", "duration": 827.968}, {"from": "Assistant", "value": "Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for translating rare words. In NMT, pretrained word embeddings have been shown to improve NMT of low-resource domains, and a search-based approach is proposed to address the rare word problem. In this study, we effectively combine these two approaches in the context of multimodal NMT and explore how we can take full advantage of pretrained word embeddings to better translate rare words. We report overall performance improvements of 1.24 METEOR and 2.49 BLEU and achieve an improvement of 7.67 F-score for rare word translation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-5002", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-5002.flac", "type": "audio", "duration": 11488.811}, {"from": "Assistant", "value": "This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting-edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning models for language understanding and reasoning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-5003", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-5003.flac", "type": "audio", "duration": 10402.027}, {"from": "Assistant", "value": "This tutorial is designed to help researchers answer the following sorts of questions: - Are people happier on the weekend? - What was 1861’s word of the year? - Are Democrats and Republicans more different than ever? - When did “gay” stop meaning “happy”? - Are gender stereotypes getting weaker, stronger, or just different? - Who is a linguistic leader? - How can we get internet users to be more polite and objective? Such questions are fundamental to the social sciences and humanities, and scholars in these disciplines are increasingly turning to computational techniques for answers. Meanwhile, the ACL community is increasingly engaged with data that varies across time, and with the social insights that can be offered by analyzing temporal patterns and trends. The purpose of this tutorial is to facilitate this convergence in two main ways: 1. By synthesizing recent computational techniques for handling and modeling temporal data, such as dynamic word embeddings, the tutorial will provide a starting point for future computational research. It will also identify useful tools for social scientists and digital humanities scholars. 2. The tutorial will provide an overview of techniques and datasets from the quantitative social sciences and the digital humanities, which are not well-known in the computational linguistics community. These techniques include vector autoregressive models, multiple comparisons corrections for hypothesis testing, and causal inference. Datasets include historical newspaper archives and corpora of contemporary political speech.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-5005", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-5005.flac", "type": "audio", "duration": 10548.992}, {"from": "Assistant", "value": "The goal of this tutorial is to bring the fields of computational linguistics and computational cognitive science closer: we will introduce different stages of language acquisition and their parallel problems in NLP. As an example, one of the early challenges children face is mapping the meaning of word labels (such as “cat”) to their referents (the furry animal in the living room). Word learning is similar to the word alignment problem in machine translation. We explain the current computational models of language acquisition, their limitations, and how the insights from these models can be incorporated into NLP applications. Moreover, we discuss how we can take advantage of the cognitive science of language in computational linguistics: for example, by designing cognitively-motivated evaluations task or buildings language-learning inductive biases into our models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "N19-5006", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/N19-5006.flac", "type": "audio", "duration": 11508.949}, {"from": "Assistant", "value": "Rapid growth in adoption of electronic health records (EHRs) has led to an unprecedented expansion in the availability of large longitudinal datasets. Large initiatives such as the Electronic Medical Records and Genomics (eMERGE) Network, the Patient-Centered Outcomes Research Network (PCORNet), and the Observational Health Data Science and Informatics (OHDSI) consortium, have been established and have reported successful applications of secondary use of EHRs in clinical research and practice. In these applications, natural language processing (NLP) technologies have played a crucial role as much of detailed patient information in EHRs is embedded in narrative clinical documents. Meanwhile, a number of clinical NLP systems, such as MedLEE, MetaMap/MetaMap Lite, cTAKES, and MedTagger have been developed and utilized to extract useful information from diverse types of clinical text, such as clinical notes, radiology reports, and pathology reports. Success stories in applying these tools have been reported widely. Despite the demonstrated success of NLP in the clinical domain, methodologies and tools developed for the clinical NLP are still underknown and underutilized by students and experts in the general NLP domain, mainly due to the limited exposure to EHR data. Through this tutorial, we would like to introduce NLP methodologies and tools developed in the clinical domain, and showcase the real-world NLP applications in clinical research and practice at Mayo Clinic (the No. 1 national hospital ranked by the U.S. News & World Report) and the University of Minnesota (the No. 41 best global universities ranked by the U.S. News & World Report). We will review NLP techniques in solving clinical problems and facilitating clinical research, the state-of-the art clinical NLP tools, and share collaboration experience with clinicians, as well as publicly available EHR data and medical resources, and finally conclude the tutorial with vast opportunities and challenges of clinical NLP. The tutorial will provide an overview of clinical backgrounds, and does not presume knowledge in medicine or health care. The goal of this tutorial is to encourage NLP researchers in the general domain (as opposed to the specialized clinical domain) to contribute to this burgeoning area. In this tutorial, we will first present an overview of clinical NLP. We will then dive into two subareas of clinical NLP in clinical research, including big data infrastructure for large-scale clinical NLP and advances of NLP in clinical research, and two subareas in clinical practice, including clinical information extraction and patient cohort retrieval using EHRs. Around 70% of the tutorial will review clinical problems, cutting-edge methodologies, and real-world clinical NLP tools while another 30% introduce use cases at Mayo Clinic and the University of Minnesota. Finally, we will conclude the tutorial with challenges and opportunities in this rapidly developing domain.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_1", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_1.flac", "type": "audio", "duration": 720.051}, {"from": "Assistant", "value": "The design of expressive representations of entities and relations in a knowledge graph is an important endeavor. While many of the existing approaches have primarily focused on learning from relational patterns and structural information, the intrinsic complexity of KG entities has been more or less overlooked. More concretely, we hypothesize KG entities may be more complex than we think, i.e., an entity may wear many hats and relational triplets may form due to more than a single reason. To this end, this paper proposes to learn disentangled representations of KG entities - a new method that disentangles the inner latent properties of KG entities. Our disentangled process operates at the graph level and a neighborhood mechanism is leveraged to disentangle the hidden properties of each entity. This disentangled representation learning approach is model agnostic and compatible with canonical KG embedding approaches. We conduct extensive experiments on several benchmark datasets, equipping a variety of models (DistMult, SimplE, and QuatE) with our proposed disentangling mechanism. Experimental results demonstrate that our proposed approach substantially improves performance on key metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_2", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_2.flac", "type": "audio", "duration": 705.051}, {"from": "Assistant", "value": "We propose a multi-task, probabilistic approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Knowledge Base pairs. To achieve this, we bias the latent space of sentences via a Variational Autoencoder (VAE) that is trained jointly with a relation classifier. The latent code guides the pair representations and influences sentence reconstruction. Experimental results on two datasets created via distant supervision indicate that multi-task learning results in performance benefits. Additional exploration of employing Knowledge Base priors into theVAE reveals that the sentence space can be shifted towards that of the Knowledge Base, offering interpretability and further improving results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_3", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_3.flac", "type": "audio", "duration": 681.771}, {"from": "Assistant", "value": "Existing works on information extraction (IE) have mainly solved the four main tasks separately (entity mention recognition, relation extraction, event trigger detection, and argument extraction), thus failing to benefit from inter-dependencies between tasks. This paper presents a novel deep learning model to simultaneously solve the four tasks of IE in a single model (called FourIE). Compared to few prior work on jointly performing four IE tasks, FourIE features two novel contributions to capture inter-dependencies between tasks. First, at the representation level, we introduce an interaction graph between instances of the four tasks that is used to enrich the prediction representation for one instance with those from related instances of other tasks. Second, at the label level, we propose a dependency graph for the information types in the four IE tasks that captures the connections between the types expressed in an input sentence. A new regularization mechanism is introduced to enforce the consistency between the golden and predicted type dependency graphs to improve representation learning. We show that the proposed model achieves the state-of-the-art performance for joint IE on both monolingual and multilingual learning settings with three different languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_4", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_4.flac", "type": "audio", "duration": 505.057}, {"from": "Assistant", "value": "The tasks of Rich Semantic Parsing, such as Abstract Meaning Representation (AMR), share similar goals with Information Extraction (IE) to convert natural language texts into structured semantic representations. To take advantage of such similarity, we propose a novel AMR-guided framework for joint information extraction to discover entities, relations, and events with the help of a pre-trained AMR parser. Our framework consists of two novel components: 1) an AMR based semantic graph aggregator to let the candidate entity and event trigger nodes collect neighborhood information from AMR graph for passing message among related knowledge elements; 2) an AMR guided graph decoder to extract knowledge elements based on the order decided by the hierarchical structures in AMR. Experiments on multiple datasets have shown that the AMR graph encoder and decoder have provided significant gains and our approach has achieved new state-of-the-art performance on all IE subtasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_5", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_5.flac", "type": "audio", "duration": 696.645}, {"from": "Assistant", "value": "End-to-end relation extraction aims to identify named entities and extract relations between them. Most recent work models these two subtasks jointly, either by casting them in one structured prediction framework, or performing multi-task learning through shared representations. In this work, we present a simple pipelined approach for entity and relation extraction, and establish the new state-of-the-art on standard benchmarks (ACE04, ACE05 and SciERC), obtaining a 1.7%-2.8% absolute improvement in relation F1 over previous joint models with the same pre-trained encoders. Our approach essentially builds on two independent encoders and merely uses the entity model to construct the input for the relation model. Through a series of careful examinations, we validate the importance of learning distinct contextual representations for entities and relations, fusing entity information early in the relation model, and incorporating global context. Finally, we also present an efficient approximation to our approach which requires only one pass of both entity and relation encoders at inference time, achieving an 8-16× speedup with a slight reduction in accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_6", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_6.flac", "type": "audio", "duration": 649.3}, {"from": "Assistant", "value": "Grounding events into a precise timeline is important for natural language understanding but has received limited attention in recent work. This problem is challenging due to the inherent ambiguity of language and the requirement for information propagation over inter-related events. This paper first formulates this problem based on a 4-tuple temporal representation used in entity slot filling, which allows us to represent fuzzy time spans more conveniently. We then propose a graph attention network-based approach to propagate temporal information over document-level event graphs constructed by shared entity arguments and temporal relations. To better evaluate our approach, we present a challenging new benchmark on the ACE2005 corpus, where more than 78% of events do not have time spans mentioned explicitly in their local contexts. The proposed approach yields an absolute gain of 7.0% in match rate over contextualized embedding approaches, and 16.3% higher match rate compared to sentence-level manual event time argument annotation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_7", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_7.flac", "type": "audio", "duration": 354.22}, {"from": "Assistant", "value": "Due to its effectiveness and performance, the Transformer translation model has attracted wide attention, most recently in terms of probing-based approaches. Previous work focuses on using or probing source linguistic features in the encoder. To date, the way word translation evolves in Transformer layers has not yet been investigated. Naively, one might assume that encoder layers capture source information while decoder layers translate. In this work, we show that this is not quite the case: translation already happens progressively in encoder layers and even in the input embeddings. More surprisingly, we find that some of the lower decoder layers do not actually do that much decoding. We show all of this in terms of a probing approach where we project representations of the layer analyzed to the final trained and frozen classifier level of the Transformer decoder to measure word translation accuracy. Our findings motivate and explain a Transformer configuration change: if translation already happens in the encoder layers, perhaps we can increase the number of encoder layers, while decreasing the number of decoder layers, boosting decoding speed, without loss in translation quality? Our experiments show that this is indeed the case: we can increase speed by up to a factor 2.3 with small gains in translation quality, while an 18-4 deep encoder configuration boosts translation quality by +1.42 BLEU (En-De) at a speed-up of 1.4.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_8", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_8.flac", "type": "audio", "duration": 383.06}, {"from": "Assistant", "value": "Probing neural models for the ability to perform downstream tasks using their activation patterns is often used to localize what parts of the network specialize in performing what tasks. However, little work addressed potential mediating factors in such comparisons. As a test-case mediating factor, we consider the prediction’s context length, namely the length of the span whose processing is minimally required to perform the prediction. We show that not controlling for context length may lead to contradictory conclusions as to the localization patterns of the network, depending on the distribution of the probing dataset. Indeed, when probing BERT with seven tasks, we find that it is possible to get 196 different rankings between them when manipulating the distribution of context lengths in the probing dataset. We conclude by presenting best practices for conducting such comparisons in the future.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_9", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_9.flac", "type": "audio", "duration": 411.481}, {"from": "Assistant", "value": "Recent works have shown that supervised models often exploit data artifacts to achieve good test scores while their performance severely degrades on samples outside their training distribution. Contrast sets (Gardneret al., 2020) quantify this phenomenon by perturbing test samples in a minimal way such that the output label is modified. While most contrast sets were created manually, requiring intensive annotation effort, we present a novel method which leverages rich semantic input representation to automatically generate contrast sets for the visual question answering task. Our method computes the answer of perturbed questions, thus vastly reducing annotation cost and enabling thorough evaluation of models’ performance on various semantic aspects (e.g., spatial or relational reasoning). We demonstrate the effectiveness of our approach on the GQA dataset and its semantic scene graph image representation. We find that, despite GQA’s compositionality and carefully balanced label distribution, two high-performing models drop 13-17% in accuracy compared to the original test set. Finally, we show that our automatic perturbation can be applied to the training set to mitigate the degradation in performance, opening the door to more robust models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_10", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_10.flac", "type": "audio", "duration": 721.049}, {"from": "Assistant", "value": "We analyze if large language models are able to predict patterns of human reading behavior. We compare the performance of language-specific and multilingual pretrained transformer models to predict reading time measures reflecting natural human sentence processing on Dutch, English, German, and Russian texts. This results in accurate models of human reading behavior, which indicates that transformer models implicitly encode relative importance in language in a way that is comparable to human processing mechanisms. We find that BERT and XLM models successfully predict a range of eye tracking features. In a series of experiments, we analyze the cross-domain and cross-language abilities of these models and show how they reflect human sentence processing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_11", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_11.flac", "type": "audio", "duration": 378.044}, {"from": "Assistant", "value": "Analysing whether neural language models encode linguistic information has become popular in NLP. One method of doing so, which is frequently cited to support the claim that models like BERT encode syntax, is called probing; probes are small supervised models trained to extract linguistic information from another model’s output. If a probe is able to predict a particular structure, it is argued that the model whose output it is trained on must have implicitly learnt to encode it. However, drawing a generalisation about a model’s linguistic knowledge about a specific phenomena based on what a probe is able to learn may be problematic: in this work, we show that semantic cues in training data means that syntactic probes do not properly isolate syntax. We generate a new corpus of semantically nonsensical but syntactically well-formed Jabberwocky sentences, which we use to evaluate two probes trained on normal data. We train the probes on several popular language models (BERT, GPT-2, and RoBERTa), and find that in all settings they perform worse when evaluated on these data, for one probe by an average of 15.4 UUAS points absolute. Although in most cases they still outperform the baselines, their lead is reduced substantially, e.g. by 53% in the case of BERT for one probe. This begs the question: what empirical scores constitute knowing syntax?", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_12", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_12.flac", "type": "audio", "duration": 468.695}, {"from": "Assistant", "value": "Probes are models devised to investigate the encoding of knowledge—e.g. syntactic structure—in contextual representations. Probes are often designed for simplicity, which has led to restrictions on probe design that may not allow for the full exploitation of the structure of encoded information; one such restriction is linearity. We examine the case of a structural probe (Hewitt and Manning, 2019), which aims to investigate the encoding of syntactic structure in contextual representations through learning only linear transformations. By observing that the structural probe learns a metric, we are able to kernelize it and develop a novel non-linear variant with an identical number of parameters. We test on 6 languages and find that the radial-basis function (RBF) kernel, in conjunction with regularization, achieves a statistically significant improvement over the baseline in all languages—implying that at least part of the syntactic knowledge is encoded non-linearly. We conclude by discussing how the RBF kernel resembles BERT’s self-attention layers and speculate that this resemblance leads to the RBF-based probe’s stronger performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_13", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_13.flac", "type": "audio", "duration": 720.492}, {"from": "Assistant", "value": "Adversarial attacks alter NLP model predictions by perturbing test-time inputs. However, it is much less understood whether, and how, predictions can be manipulated with small, concealed changes to the training data. In this work, we develop a new data poisoning attack that allows an adversary to control model predictions whenever a desired trigger phrase is present in the input. For instance, we insert 50 poison examples into a sentiment model’s training set that causes the model to frequently predict Positive whenever the input contains “James Bond”. Crucially, we craft these poison examples using a gradient-based procedure so that they do not mention the trigger phrase. We also apply our poison attack to language modeling (“Apple iPhone” triggers negative generations) and machine translation (“iced coffee” mistranslated as “hot coffee”). We conclude by proposing three defenses that can mitigate our attack at some cost in prediction accuracy or extra human annotation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_14", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_14.flac", "type": "audio", "duration": 610.22}, {"from": "Assistant", "value": "Translating text into a language unknown to the text’s author, dubbed outbound translation, is a modern need for which the user experience has significant room for improvement, beyond the basic machine translation facility. We demonstrate this by showing three ways in which user confidence in the outbound translation, as well as its overall final quality, can be affected: backward translation, quality estimation (with alignment) and source paraphrasing. In this paper, we describe an experiment on outbound translation from English to Czech and Estonian. We examine the effects of each proposed feedback module and further focus on how the quality of machine translation systems influence these findings and the user perception of success. We show that backward translation feedback has a mixed effect on the whole process: it increases user confidence in the produced translation, but not the objective quality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_15", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_15.flac", "type": "audio", "duration": 695.043}, {"from": "Assistant", "value": "Data filtering for machine translation (MT) describes the task of selecting a subset of a given, possibly noisy corpus with the aim to maximize the performance of an MT system trained on this selected data. Over the years, many different filtering approaches have been proposed. However, varying task definitions and data conditions make it difficult to draw a meaningful comparison. In the present work, we aim for a more systematic approach to the task at hand. First, we analyze the performance of language identification, a tool commonly used for data filtering in the MT community and identify specific weaknesses. Based on our findings, we then propose several novel methods for data filtering, based on cross-lingual word embeddings. We compare our approaches to one of the winning methods from the WMT 2018 shared task on parallel corpus filtering on three real-life, high resource MT tasks. We find that said method, which was performing very strong in the WMT shared task, does not perform well within our more realistic task conditions. While we find that our approaches come out at the top on all three tasks, different variants perform best on different tasks. Further experiments on the WMT 2020 shared task for parallel corpus filtering show that our methods achieve comparable results to the strongest submissions of this campaign.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_16", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_16.flac", "type": "audio", "duration": 396.063}, {"from": "Assistant", "value": "Successful methods for unsupervised neural machine translation (UNMT) employ cross-lingual pretraining via self-supervision, often in the form of a masked language modeling or a sequence generation task, which requires the model to align the lexical- and high-level representations of the two languages. While cross-lingual pretraining works for similar languages with abundant corpora, it performs poorly in low-resource and distant languages. Previous research has shown that this is because the representations are not sufficiently aligned. In this paper, we enhance the bilingual masked language model pretraining with lexical-level information by using type-level cross-lingual subword embeddings. Empirical results demonstrate improved performance both on UNMT (up to 4.5 BLEU) and bilingual lexicon induction using our method compared to a UNMT baseline.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_17", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_17.flac", "type": "audio", "duration": 308.059}, {"from": "Assistant", "value": "Many NLP models operate over sequences of subword tokens produced by hand-crafted tokenization rules and heuristic subword induction algorithms. A simple universal alternative is to represent every computerized text as a sequence of bytes via UTF-8, obviating the need for an embedding layer since there are fewer token types (256) than dimensions. Surprisingly, replacing the ubiquitous embedding layer with one-hot representations of each byte does not hurt performance; experiments on byte-to-byte machine translation from English to 10 different languages show a consistent improvement in BLEU, rivaling character-level and even standard subword-level models. A deeper investigation reveals that the combination of embeddingless models with decoder-input dropout amounts to token dropout, which benefits byte-to-byte models in particular.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_18", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_18.flac", "type": "audio", "duration": 431.613}, {"from": "Assistant", "value": "We propose a data augmentation method for neural machine translation. It works by interpreting language models and phrasal alignment causally. Specifically, it creates augmented parallel translation corpora by generating (path-specific) counterfactual aligned phrases. We generate these by sampling new source phrases from a masked language model, then sampling an aligned counterfactual target phrase by noting that a translation language model can be interpreted as a Gumbel-Max Structural Causal Model (Oberst and Sontag, 2019). Compared to previous work, our method takes both context and alignment into account to maintain the symmetry between source and target sequences. Experiments on IWSLT’15 English → Vietnamese, WMT’17 English → German, WMT’18 English → Turkish, and WMT’19 robust English → French show that the method can improve the performance of translation, backtranslation and translation robustness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_19", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_19.flac", "type": "audio", "duration": 824.053}, {"from": "Assistant", "value": "Neural Machine Translation (NMT) models have been observed to produce poor translations when there are few/no parallel sentences to train the models. In the absence of parallel data, several approaches have turned to the use of images to learn translations. Since images of words, e.g., horse may be unchanged across languages, translations can be identified via images associated with words in different languages that have a high degree of visual similarity. However, translating via images has been shown to improve upon text-only models only marginally. To better understand when images are useful for translation, we study image translatability of words, which we define as the translatability of words via images, by measuring intra- and inter-cluster similarities of image representations of words that are translations of each other. We find that images of words are not always invariant across languages, and that language pairs with shared culture, meaning having either a common language family, ethnicity or religion, have improved image translatability (i.e., have more similar images for similar words) compared to its converse, regardless of their geographic proximity. In addition, in line with previous works that show images help more in translating concrete words, we found that concrete words have improved image translatability compared to abstract ones.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_20", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_20.flac", "type": "audio", "duration": 723.051}, {"from": "Assistant", "value": "We propose a simple method to align multilingual contextual embeddings as a post-pretraining step for improved cross-lingual transferability of the pretrained language models. Using parallel data, our method aligns embeddings on the word level through the recently proposed Translation Language Modeling objective as well as on the sentence level via contrastive learning and random input shuffling. We also perform sentence-level code-switching with English when finetuning on downstream tasks. On XNLI, our best model (initialized from mBERT) improves over mBERT by 4.7% in the zero-shot setting and achieves comparable result to XLM for translate-train while using less than 18% of the same parallel data and 31% fewer model parameters. On MLQA, our model outperforms XLM-R_Base, which has 57% more parameters than ours.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_21", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_21.flac", "type": "audio", "duration": 419.051}, {"from": "Assistant", "value": "In online domain-specific customer service applications, many companies struggle to deploy advanced NLP models successfully, due to the limited availability of and noise in their datasets. While prior research demonstrated the potential of migrating large open-domain pretrained models for domain-specific tasks, the appropriate (pre)training strategies have not yet been rigorously evaluated in such social media customer service settings, especially under multilingual conditions. We address this gap by collecting a multilingual social media corpus containing customer service conversations (865k tweets), comparing various pipelines of pretraining and finetuning approaches, applying them on 5 different end tasks. We show that pretraining a generic multilingual transformer model on our in-domain dataset, before finetuning on specific end tasks, consistently boosts performance, especially in non-English settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_22", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_22.flac", "type": "audio", "duration": 743.015}, {"from": "Assistant", "value": "Interpretability or explainability is an emerging research field in NLP. From a user-centric point of view, the goal is to build models that provide proper justification for their decisions, similar to those of humans, by requiring the models to satisfy additional constraints. To this end, we introduce a new application on legal text where, contrary to mainstream literature targeting word-level rationales, we conceive rationales as selected paragraphs in multi-paragraph structured court cases. We also release a new dataset comprising European Court of Human Rights cases, including annotations for paragraph-level rationales. We use this dataset to study the effect of already proposed rationale constraints, i.e., sparsity, continuity, and comprehensiveness, formulated as regularizers. Our findings indicate that some of these constraints are not beneficial in paragraph-level rationale extraction, while others need re-formulation to better handle the multi-label nature of the task we consider. We also introduce a new constraint, singularity, which further improves the quality of rationales, even compared with noisy rationale supervision. Experimental results indicate that the newly introduced task is very challenging and there is a large scope for further research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_23", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_23.flac", "type": "audio", "duration": 722.048}, {"from": "Assistant", "value": "Predicting the answer to a product-related question is an emerging field of research that recently attracted a lot of attention. Answering subjective and opinion-based questions is most challenging due to the dependency on customer generated content. Previous works mostly focused on review-aware answer prediction; however, these approaches fail for new or unpopular products, having no (or only a few) reviews at hand. In this work, we propose a novel and complementary approach for predicting the answer for such questions, based on the answers for similar questions asked on similar products. We measure the contextual similarity between products based on the answers they provide for the same question. A mixture-of-expert framework is used to predict the answer by aggregating the answers from contextually similar products. Empirical results demonstrate that our model outperforms strong baselines on some segments of questions, namely those that have roughly ten or more similar resolved questions in the corpus. We additionally publish two large-scale datasets used in this work, one is of similar product question pairs, and the second is of product question-answer pairs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_24", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_24.flac", "type": "audio", "duration": 432.007}, {"from": "Assistant", "value": "Siamese Neural Networks have been widely used to perform similarity classification in multi-class settings. Their architecture can be used to group the clinical trials belonging to the same drug-development pathway along the several clinical trial phases. Here we present an approach for the unmet need of drug-development pathway reconstruction, based on an Enhanced hybrid Siamese-Deep Neural Network (EnSidNet). The proposed model demonstrates significant improvement above baselines in a 1-shot evaluation setting and in a classical similarity setting. EnSidNet can be an essential tool in a semi-supervised learning environment: by selecting clinical trials highly likely to belong to the same drug-development pathway it is possible to speed up the labelling process of human experts, allowing the check of a consistent volume of data, further used in the model’s training dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_25", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_25.flac", "type": "audio", "duration": 486.621}, {"from": "Assistant", "value": "Leveraging deep learning models for Anomaly Detection (AD) has seen widespread use in recent years due to superior performances over traditional methods. Recent deep methods for anomalies in images learn better features of normality in an end-to-end self-supervised setting. These methods train a model to discriminate between different transformations applied to visual data and then use the output to compute an anomaly score. We use this approach for AD in text, by introducing a novel pretext task on text sequences. We learn our DATE model end-to-end, enforcing two independent and complementary self-supervision signals, one at the token-level and one at the sequence-level. Under this new task formulation, we show strong quantitative and qualitative results on the 20Newsgroups and AG News datasets. In the semi-supervised setting, we outperform state-of-the-art results by +13.5% and +6.9%, respectively (AUROC). In the unsupervised configuration, DATE surpasses all other methods even when 10% of its training data is contaminated with outliers (compared with 0% for the others).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_26", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_26.flac", "type": "audio", "duration": 418.772}, {"from": "Assistant", "value": "There is an emerging interest in the application of natural language processing models to source code processing tasks. One of the major problems in applying deep learning to software engineering is that source code often contains a lot of rare identifiers, resulting in huge vocabularies. We propose a simple, yet effective method, based on identifier anonymization, to handle out-of-vocabulary (OOV) identifiers. Our method can be treated as a preprocessing step and, therefore, allows for easy implementation. We show that the proposed OOV anonymization method significantly improves the performance of the Transformer in two code processing tasks: code completion and bug fixing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_27", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_27.flac", "type": "audio", "duration": 420.049}, {"from": "Assistant", "value": "We present a fast and scalable architecture called Explicit Modular Decomposition (EMD), in which we incorporate both classification-based and extraction-based methods and design four modules (for clas- sification and sequence labelling) to jointly extract dialogue states. Experimental results based on the MultiWoz 2.0 dataset validates the superiority of our proposed model in terms of both complexity and scalability when compared to the state-of-the-art methods, especially in the scenario of multi-domain dialogues entangled with many turns of utterances.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_28", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_28.flac", "type": "audio", "duration": 719.403}, {"from": "Assistant", "value": "There are two approaches for pairwise sentence scoring: Cross-encoders, which perform full-attention over the input pair, and Bi-encoders, which map each input independently to a dense vector space. While cross-encoders often achieve higher performance, they are too slow for many practical use cases. Bi-encoders, on the other hand, require substantial training data and fine-tuning over the target task to achieve competitive performance. We present a simple yet efficient data augmentation strategy called Augmented SBERT, where we use the cross-encoder to label a larger set of input pairs to augment the training data for the bi-encoder. We show that, in this process, selecting the sentence pairs is non-trivial and crucial for the success of the method. We evaluate our approach on multiple tasks (in-domain) as well as on a domain adaptation task. Augmented SBERT achieves an improvement of up to 6 points for in-domain and of up to 37 points for domain adaptation tasks compared to the original bi-encoder performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_29", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_29.flac", "type": "audio", "duration": 590.623}, {"from": "Assistant", "value": "The de-facto standard decoding method for semantic parsing in recent years has been to autoregressively decode the abstract syntax tree of the target program using a top-down depth-first traversal. In this work, we propose an alternative approach: a Semi-autoregressive Bottom-up Parser (SmBoP) that constructs at decoding step t the top-K sub-trees of height ≤ t. Our parser enjoys several benefits compared to top-down autoregressive parsing. From an efficiency perspective, bottom-up parsing allows to decode all sub-trees of a certain height in parallel, leading to logarithmic runtime complexity rather than linear. From a modeling perspective, a bottom-up parser learns representations for meaningful semantic sub-programs at each step, rather than for semantically-vacuous partial trees. We apply SmBoP on Spider, a challenging zero-shot semantic parsing benchmark, and show that SmBoP leads to a 2.2x speed-up in decoding time and a ~5x speed-up in training time, compared to a semantic parser that uses autoregressive decoding. SmBoP obtains 71.1 denotation accuracy on Spider, establishing a new state-of-the-art, and 69.5 exact match, comparable to the 69.6 exact match of the autoregressive RAT-SQL+GraPPa.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_30", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_30.flac", "type": "audio", "duration": 705.051}, {"from": "Assistant", "value": "Graph-based semantic parsing aims to represent textual meaning through directed graphs. As one of the most promising general-purpose meaning representations, these structures and their parsing have gained a significant interest momentum during recent years, with several diverse formalisms being proposed. Yet, owing to this very heterogeneity, most of the research effort has focused mainly on solutions specific to a given formalism. In this work, instead, we reframe semantic parsing towards multiple formalisms as Multilingual Neural Machine Translation (MNMT), and propose SGL, a many-to-many seq2seq architecture trained with an MNMT objective. Backed by several experiments, we show that this framework is indeed effective once the learning procedure is enhanced with large parallel corpora coming from Machine Translation: we report competitive performances on AMR and UCCA parsing, especially once paired with pre-trained architectures. Furthermore, we find that models trained under this configuration scale remarkably well to tasks such as cross-lingual AMR parsing: SGL outperforms all its competitors by a large margin without even explicitly seeing non-English to AMR examples at training time and, once these examples are included as well, sets an unprecedented state of the art in this task. We release our code and our models for research purposes athttps://github.com/SapienzaNLP/sgl.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_31", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_31.flac", "type": "audio", "duration": 706.048}, {"from": "Assistant", "value": "While cross-lingual techniques are finding increasing success in a wide range of Natural Language Processing tasks, their application to Semantic Role Labeling (SRL) has been strongly limited by the fact that each language adopts its own linguistic formalism, from PropBank for English to AnCora for Spanish and PDT-Vallex for Czech, inter alia. In this work, we address this issue and present a unified model to perform cross-lingual SRL over heterogeneous linguistic resources. Our model implicitly learns a high-quality mapping for different formalisms across diverse languages without resorting to word alignment and/or translation techniques. We find that, not only is our cross-lingual system competitive with the current state of the art but that it is also robust to low-data scenarios. Most interestingly, our unified model is able to annotate a sentence in a single forward pass with all the inventories it was trained with, providing a tool for the analysis and comparison of linguistic theories across different languages. We release our code and model athttps://github.com/SapienzaNLP/unify-srl.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_32", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_32.flac", "type": "audio", "duration": 694.208}, {"from": "Assistant", "value": "We release FoolMeTwice (FM2 for short), a large dataset of challenging entailment pairs collected through a fun multi-player game. Gamification encourages adversarial examples, drastically lowering the number of examples that can be solved using “shortcuts” compared to other popular entailment datasets. Players are presented with two tasks. The first task asks the player to write a plausible claim based on the evidence from a Wikipedia page. The second one shows two plausible claims written by other players, one of which is false, and the goal is to identify it before the time runs out. Players “pay” to see clues retrieved from the evidence pool: the more evidence the player needs, the harder the claim. Game-play between motivated players leads to diverse strategies for crafting claims, such as temporal inference and diverting to unrelated evidence, and results in higher quality data for the entailment and evidence retrieval tasks. We open source the dataset and the game code.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_33", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_33.flac", "type": "audio", "duration": 701.057}, {"from": "Assistant", "value": "The importance of building semantic parsers which can be applied to new domains and generate programs unseen at training has long been acknowledged, and datasets testing out-of-domain performance are becoming increasingly available. However, little or no attention has been devoted to learning algorithms or objectives which promote domain generalization, with virtually all existing approaches relying on standard supervised learning. In this work, we use a meta-learning framework which targets zero-shot domain generalization for semantic parsing. We apply a model-agnostic training algorithm that simulates zero-shot parsing by constructing virtual train and test sets from disjoint domains. The learning objective capitalizes on the intuition that gradient steps that improve source-domain performance should also improve target-domain performance, thus encouraging a parser to generalize to unseen target domains. Experimental results on the (English) Spider and Chinese Spider datasets show that the meta-learning objective significantly boosts the performance of a baseline parser.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_34", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_34.flac", "type": "audio", "duration": 716.521}, {"from": "Assistant", "value": "We rely on arguments in our daily lives to deliver our opinions and base them on evidence, making them more convincing in turn. However, finding and formulating arguments can be challenging. In this work, we present the Arg-CTRL - a language model for argument generation that can be controlled to generate sentence-level arguments for a given topic, stance, and aspect. We define argument aspect detection as a necessary method to allow this fine-granular control and crowdsource a dataset with 5,032 arguments annotated with aspects. Our evaluation shows that the Arg-CTRL is able to generate high-quality, aspect-specific arguments, applicable to automatic counter-argument generation. We publish the model weights and all datasets and code to train the Arg-CTRL.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_35", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_35.flac", "type": "audio", "duration": 704.052}, {"from": "Assistant", "value": "We propose neural models to generate text from formal meaning representations based on Discourse Representation Structures (DRSs). DRSs are document-level representations which encode rich semantic detail pertaining to rhetorical relations, presupposition, and co-reference within and across sentences. We formalize the task of neural DRS-to-text generation and provide modeling solutions for the problems of condition ordering and variable naming which render generation from DRSs non-trivial. Our generator relies on a novel sibling treeLSTM model which is able to accurately represent DRS structures and is more generally suited to trees with wide branches. We achieve competitive performance (59.48 BLEU) on the GMB benchmark against several strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_36", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_36.flac", "type": "audio", "duration": 635.298}, {"from": "Assistant", "value": "Natural language often exhibits inherent hierarchical structure ingrained with complex syntax and semantics. However, most state-of-the-art deep generative models learn embeddings only in Euclidean vector space, without accounting for this structural property of language. In this paper, we investigate text generation in a hyperbolic latent space to learn continuous hierarchical representations. An Adversarial Poincare Variational Autoencoder (APo-VAE) is presented, where both the prior and variational posterior of latent variables are defined over a Poincare ball via wrapped normal distributions. By adopting the primal-dual formulation of Kullback-Leibler divergence, an adversarial learning procedure is introduced to empower robust model training. Extensive experiments in language modeling, unaligned style transfer, and dialog-response generation demonstrate the effectiveness of the proposed APo-VAE model over VAEs in Euclidean latent space, thanks to its superb capabilities in capturing latent language hierarchies in hyperbolic space.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_37", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_37.flac", "type": "audio", "duration": 737.048}, {"from": "Assistant", "value": "We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and spoken dialogue systems by utilizing techniques including tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization. Our data and code can be found athttps://github.com/Yale-LILY/dart.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_38", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_38.flac", "type": "audio", "duration": 932.049}, {"from": "Assistant", "value": "Transfer learning based on pretraining language models on a large amount of raw data has become a new norm to reach state-of-the-art performance in NLP. Still, it remains unclear how this approach should be applied for unseen languages that are not covered by any available large-scale multilingual language model and for which only a small amount of raw data is generally available. In this work, by comparing multilingual and monolingual models, we show that such models behave in multiple ways on unseen languages. Some languages greatly benefit from transfer learning and behave similarly to closely related high resource languages whereas others apparently do not. Focusing on the latter, we show that this failure to transfer is largely related to the impact of the script used to write such languages. We show that transliterating those languages significantly improves the potential of large-scale multilingual language models on downstream tasks. This result provides a promising direction towards making these massively multilingual models useful for a new set of unseen languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_39", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_39.flac", "type": "audio", "duration": 611.056}, {"from": "Assistant", "value": "Generative adversarial networks (GANs) have succeeded in inducing cross-lingual word embeddings - maps of matching words across languages - without supervision. Despite these successes, GANs’ performance for the difficult case of distant languages is still not satisfactory. These limitations have been explained by GANs’ incorrect assumption that source and target embedding spaces are related by a single linear mapping and are approximately isomorphic. We assume instead that, especially across distant languages, the mapping is only piece-wise linear, and propose a multi-adversarial learning method. This novel method induces the seed cross-lingual dictionary through multiple mappings, each induced to fit the mapping for one subspace. Our experiments on unsupervised bilingual lexicon induction and cross-lingual document classification show that this method improves performance over previous single-mapping methods, especially for distant languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_40", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_40.flac", "type": "audio", "duration": 678.046}, {"from": "Assistant", "value": "Multilingual pretrained representations generally rely on subword segmentation algorithms to create a shared multilingual vocabulary. However, standard heuristic algorithms often lead to sub-optimal segmentation, especially for languages with limited amounts of data. In this paper, we take two major steps towards alleviating this problem. First, we demonstrate empirically that applying existing subword regularization methods (Kudo, 2018; Provilkov et al., 2020) during fine-tuning of pre-trained multilingual representations improves the effectiveness of cross-lingual transfer. Second, to take full advantage of different possible input segmentations, we propose Multi-view Subword Regularization (MVR), a method that enforces the consistency of predictors between using inputs tokenized by the standard and probabilistic segmentations. Results on the XTREME multilingual benchmark (Hu et al., 2020) show that MVR brings consistent improvements of up to 2.5 points over using standard segmentation algorithms.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_41", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_41.flac", "type": "audio", "duration": 726.763}, {"from": "Assistant", "value": "The recent “Text-to-Text Transfer Transformer” (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We detail the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks. We also describe a simple technique to prevent “accidental translation” in the zero-shot setting, where a generative model chooses to (partially) translate its prediction into the wrong language. All of the code and model checkpoints used in this work are publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_43", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_43.flac", "type": "audio", "duration": 416.055}, {"from": "Assistant", "value": "Recent advances in open-domain QA have led to strong models based on dense retrieval, but only focused on retrieving textual passages. In this work, we tackle open-domain QA over tables for the first time, and show that retrieval can be improved by a retriever designed to handle tabular context. We present an effective pre-training procedure for our retriever and improve retrieval quality with mined hard negatives. As relevant datasets are missing, we extract a subset of Natural Questions (Kwiatkowski et al., 2019) into a Table QA dataset. We find that our retriever improves retrieval results from 72.0 to 81.1 recall@10 and end-to-end QA results from 33.8 to 37.7 exact match, over a BERT based retriever.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_44", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_44.flac", "type": "audio", "duration": 720.004}, {"from": "Assistant", "value": "We introduce a new dataset for Question Rewriting in Conversational Context (QReCC), which contains 14K conversations with 80K question-answer pairs. The task in QReCC is to find answers to conversational questions within a collection of 10M web pages (split into 54M passages). Answers to questions in the same conversation may be distributed across several web pages. QReCC provides annotations that allow us to train and evaluate individual subtasks of question rewriting, passage retrieval and reading comprehension required for the end-to-end conversational question answering (QA) task. We report the effectiveness of a strong baseline approach that combines the state-of-the-art model for question rewriting, and competitive models for open-domain QA. Our results set the first baseline for the QReCC dataset with F1 of 19.10, compared to the human upper bound of 75.45, indicating the difficulty of the setup and a large room for improvement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_45", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_45.flac", "type": "audio", "duration": 897.103}, {"from": "Assistant", "value": "The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG. Here we propose a new model, QA-GNN, which addresses the above challenges through two key innovations: (i) relevance scoring, where we use LMs to estimate the importance of KG nodes relative to the given QA context, and (ii) joint reasoning, where we connect the QA context and KG to form a joint graph, and mutually update their representations through graph-based message passing. We evaluate QA-GNN on the CommonsenseQA and OpenBookQA datasets, and show its improvement over existing LM and LM+KG models, as well as its capability to perform interpretable and structured reasoning, e.g., correctly handling negation in questions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_46", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_46.flac", "type": "audio", "duration": 724.045}, {"from": "Assistant", "value": "Multilingual question answering tasks typically assume that answers exist in the same language as the question. Yet in practice, many languages face both information scarcity—where languages have few reference articles—and information asymmetry—where questions reference concepts from other cultures. This work extends open-retrieval question answering to a cross-lingual setting enabling questions from one language to be answered via answer content from another language. We construct a large-scale dataset built on 40K information-seeking questions across 7 diverse non-English languages that TyDi QA could not find same-language answers for. Based on this dataset, we introduce a task framework, called Cross-lingual Open-Retrieval Question Answering (XOR QA), that consists of three new tasks involving cross-lingual document retrieval from multilingual and English resources. We establish baselines with state-of-the-art machine translation systems and cross-lingual pretrained models. Experimental results suggest that XOR QA is a challenging task that will facilitate the development of novel techniques for multilingual question answering. Our data and code are available athttps://nlp.cs.washington.edu/xorqa/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_47", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_47.flac", "type": "audio", "duration": 726.251}, {"from": "Assistant", "value": "We introduce SPARTA, a novel neural retrieval method that shows great promise in performance, generalization, and interpretability for open-domain question answering. Unlike many neural ranking methods that use dense vector nearest neighbor search, SPARTA learns a sparse representation that can be efficiently implemented as an Inverted Index. The resulting representation enables scalable neural retrieval that does not require expensive approximate vector search and leads to better performance than its dense counterpart. We validated our approaches on 4 open-domain question answering (OpenQA) tasks and 11 retrieval question answering (ReQA) tasks. SPARTA achieves new state-of-the-art results across a variety of open-domain question answering tasks in both English and Chinese datasets, including open SQuAD, CMRC and etc. Analysis also confirms that the proposed method creates human interpretable representation and allows flexible control over the trade-off between performance and efficiency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_48", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_48.flac", "type": "audio", "duration": 710.016}, {"from": "Assistant", "value": "Abusive language detection is an emerging field in natural language processing which has received a large amount of attention recently. Still the success of automatic detection is limited. Particularly, the detection of implicitly abusive language, i.e. abusive language that is not conveyed by abusive words (e.g. dumbass or scum), is not working well. In this position paper, we explain why existing datasets make learning implicit abuse difficult and what needs to be changed in the design of such datasets. Arguing for a divide-and-conquer strategy, we present a list of subtypes of implicitly abusive language and formulate research tasks and questions for future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_49", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_49.flac", "type": "audio", "duration": 705.051}, {"from": "Assistant", "value": "Natural language processing (NLP) applications are now more powerful and ubiquitous than ever before. With rapidly developing (neural) models and ever-more available data, current NLP models have access to more information than any human speaker during their life. Still, it would be hard to argue that NLP models have reached human-level capacity. In this position paper, we argue that the reason for the current limitations is a focus on information content while ignoring language’s social factors. We show that current NLP systems systematically break down when faced with interpreting the social factors of language. This limits applications to a subset of information-related tasks and prevents NLP from reaching human-level performance. At the same time, systems that incorporate even a minimum of social factors already show remarkable improvements. We formalize a taxonomy of seven social factors based on linguistic theory and exemplify current failures and emerging successes for each of them. We suggest that the NLP community address social factors to get closer to the goal of human-like language understanding.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_50", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_50.flac", "type": "audio", "duration": 432.843}, {"from": "Assistant", "value": "The field of NLP has made substantial progress in building meaning representations. However, an important aspect of linguistic meaning, social meaning, has been largely overlooked. We introduce the concept of social meaning to NLP and discuss how insights from sociolinguistics can inform work on representation learning in NLP. We also identify key challenges for this new line of research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_51", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_51.flac", "type": "audio", "duration": 447.843}, {"from": "Assistant", "value": "Preregistration refers to the practice of specifying what you are going to do, and what you expect to find in your study, before carrying out the study. This practice is increasingly common in medicine and psychology, but is rarely discussed in NLP. This paper discusses preregistration in more detail, explores how NLP researchers could preregister their work, and presents several preregistration questions for different kinds of studies. Finally, we argue in favour of registered reports, which could provide firmer grounds for slow science in NLP research. The goal of this paper is to elicit a discussion in the NLP community, which we hope to synthesise into a general NLP preregistration form in future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_52", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_52.flac", "type": "audio", "duration": 720.043}, {"from": "Assistant", "value": "Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as more information is gathered and revised. In order to adapt, models must be sensitive to subtle differences in supporting evidence. We present VitaminC, a benchmark infused with challenging cases that require fact verification models to discern and adjust to slight factual changes. We collect over 100,000 Wikipedia revisions that modify an underlying fact, and leverage these revisions, together with additional synthetically constructed ones, to create a total of over 400,000 claim-evidence pairs. Unlike previous resources, the examples in VitaminC are contrastive, i.e., they contain evidence pairs that are nearly identical in language and content, with the exception that one supports a given claim while the other does not. We show that training using this design increases robustness—improving accuracy by 10% on adversarial fact verification and 6% on adversarial natural language inference (NLI). Moreover, the structure of VitaminC leads us to define additional tasks for fact-checking resources: tagging relevant words in the evidence for verifying the claim, identifying factual revisions, and providing automatic edits via factually consistent text generation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_53", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_53.flac", "type": "audio", "duration": 719.125}, {"from": "Assistant", "value": "NLP systems rarely give special consideration to numbers found in text. This starkly contrasts with the consensus in neuroscience that, in the brain, numbers are represented differently from words. We arrange recent NLP work on numeracy into a comprehensive taxonomy of tasks and methods. We break down the subjective notion of numeracy into 7 subtasks, arranged along two dimensions: granularity (exact vs approximate) and units (abstract vs grounded). We analyze the myriad representational choices made by over a dozen previously published number encoders and decoders. We synthesize best practices for representing numbers in text and articulate a vision for holistic numeracy in NLP, comprised of design trade-offs and a unified evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_54", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_54.flac", "type": "audio", "duration": 786.019}, {"from": "Assistant", "value": "Allowing users to interact with multi-document summarizers is a promising direction towards improving and customizing summary results. Different ideas for interactive summarization have been proposed in previous work but these solutions are highly divergent and incomparable. In this paper, we develop an end-to-end evaluation framework for interactive summarization, focusing on expansion-based interaction, which considers the accumulating information along a user session. Our framework includes a procedure of collecting real user sessions, as well as evaluation measures relying on summarization standards, but adapted to reflect interaction. All of our solutions and resources are available publicly as a benchmark, allowing comparison of future developments in interactive summarization, and spurring progress in its methodological evaluation. We demonstrate the use of our framework by evaluating and comparing baseline implementations that we developed for this purpose, which will serve as part of our benchmark. Our extensive experimentation and analysis motivate the proposed evaluation framework design and support its viability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_55", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_55.flac", "type": "audio", "duration": 702.055}, {"from": "Assistant", "value": "In recent years online shopping has gained momentum and became an important venue for customers wishing to save time and simplify their shopping process. A key advantage of shopping online is the ability to read what other customers are saying about products of interest. In this work, we aim to maintain this advantage in situations where extreme brevity is needed, for example, when shopping by voice. We suggest a novel task of extracting a single representative helpful sentence from a set of reviews for a given product. The selected sentence should meet two conditions: first, it should be helpful for a purchase decision and second, the opinion it expresses should be supported by multiple reviewers. This task is closely related to the task of Multi Document Summarization in the product reviews domain but differs in its objective and its level of conciseness. We collect a dataset in English of sentence helpfulness scores via crowd-sourcing and demonstrate its reliability despite the inherent subjectivity involved. Next, we describe a complete model that extracts representative helpful sentences with positive and negative sentiment towards the product and demonstrate that it outperforms several baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_56", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_56.flac", "type": "audio", "duration": 519.04}, {"from": "Assistant", "value": "In this paper we apply self-knowledge distillation to text summarization which we argue can alleviate problems with maximum-likelihood training on single reference and noisy datasets. Instead of relying on one-hot annotation labels, our student summarization model is trained with guidance from a teacher which generates smoothed labels to help regularize training. Furthermore, to better model uncertainty during training, we introduce multiple noise signals for both teacher and student models. We demonstrate experimentally on three benchmarks that our framework boosts the performance of both pretrained and non-pretrained summarizers achieving state-of-the-art results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_57", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_57.flac", "type": "audio", "duration": 663.046}, {"from": "Assistant", "value": "Models pretrained with self-supervised objectives on large text corpora achieve state-of-the-art performance on English text summarization tasks. However, these models are typically fine-tuned on hundreds of thousands of data points, an infeasible requirement when applying summarization to new, niche domains. In this work, we introduce a novel and generalizable method, called WikiTransfer, for fine-tuning pretrained models for summarization in an unsupervised, dataset-specific manner. WikiTransfer fine-tunes pretrained models on pseudo-summaries, produced from generic Wikipedia data, which contain characteristics of the target dataset, such as the length and level of abstraction of the desired summaries. WikiTransfer models achieve state-of-the-art, zero-shot abstractive summarization performance on the CNN-DailyMail dataset and demonstrate the effectiveness of our approach on three additional diverse datasets. These models are more robust to noisy data and also achieve better or comparable few-shot performance using 10 and 100 training examples when compared to few-shot transfer from other summarization datasets. To further boost performance, we employ data augmentation via round-trip translation as well as introduce a regularization term for improved few-shot transfer. To understand the role of dataset aspects in transfer performance and the quality of the resulting output summaries, we further study the effect of the components of our unsupervised fine-tuning data and analyze few-shot performance using both automatic and human evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_58", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_58.flac", "type": "audio", "duration": 586.048}, {"from": "Assistant", "value": "Automatic abstractive summaries are found to often distort or fabricate facts in the article. This inconsistency between summary and original text has seriously impacted its applicability. We propose a fact-aware summarization model FASum to extract and integrate factual relations into the summary generation process via graph attention. We then design a factual corrector model FC to automatically correct factual errors from summaries generated by existing systems. Empirical results show that the fact-aware summarization can produce abstractive summaries with higher factual consistency compared with existing systems, and the correction model improves the factual consistency of given summaries via modifying only a few keywords.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_59", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_59.flac", "type": "audio", "duration": 592.016}, {"from": "Assistant", "value": "Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce domain. In this paper, we explore retrieval-based methods for intent classification and slot filling tasks in few-shot settings. Retrieval-based methods make predictions based on labeled examples in the retrieval index that are similar to the input, and thus can adapt to new domains simply by changing the index without having to retrain the model. However, it is non-trivial to apply such methods on tasks with a complex label space like slot filling. To this end, we propose a span-level retrieval method that learns similar contextualized representations for spans with the same label via a novel batch-softmax objective. At inference time, we use the labels of the retrieved spans to construct the final structure with the highest aggregated score. Our method outperforms previous systems in various few-shot settings on the CLINC and SNIPS benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_60", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_60.flac", "type": "audio", "duration": 719.052}, {"from": "Assistant", "value": "Ad hominem attacks are those that target some feature of a person’s character instead of the position the person is maintaining. These attacks are harmful because they propagate implicit biases and diminish a person’s credibility. Since dialogue systems respond directly to user input, it is important to study ad hominems in dialogue responses. To this end, we propose categories of ad hominems, compose an annotated dataset, and build a classifier to analyze human and dialogue system responses to English Twitter posts. We specifically compare responses to Twitter topics about marginalized communities (#BlackLivesMatter, #MeToo) versus other topics (#Vegan, #WFH), because the abusive language of ad hominems could further amplify the skew of power away from marginalized populations. Furthermore, we propose a constrained decoding technique that uses salient n-gram similarity as a soft constraint for top-k sampling to reduce the amount of ad hominems generated. Our results indicate that 1) responses from both humans and DialoGPT contain more ad hominems for discussions around marginalized communities, 2) different quantities of ad hominems in the training data can influence the likelihood of generating ad hominems, and 3) we can use constrained decoding techniques to reduce ad hominems in generated dialogue responses.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_61", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_61.flac", "type": "audio", "duration": 718.016}, {"from": "Assistant", "value": "This work aims to build a dialogue agent that can weave new factual content into conversations as naturally as humans. We draw insights from linguistic principles of conversational analysis and annotate human-human conversations from the Switchboard Dialog Act Corpus to examine humans strategies for acknowledgement, transition, detail selection and presentation. When current chatbots (explicitly provided with new factual content) introduce facts into a conversation, their generated responses do not acknowledge the prior turns. This is because models trained with two contexts - new factual content and conversational history - generate responses that are non-specific w.r.t. one of the contexts, typically the conversational history. We show that specificity w.r.t. conversational history is better captured by pointwise conditional mutual information (pcmi_h) than by the established use of pointwise mutual information (pmi). Our proposed method, Fused-PCMI, trades off pmi for pcmi_h and is preferred by humans for overall quality over the Max-PMI baseline 60% of the time. Human evaluators also judge responses with higher pcmi_h better at acknowledgement 74% of the time. The results demonstrate that systems mimicking human conversational traits (in this case acknowledgement) improve overall quality and more broadly illustrate the utility of linguistic principles in improving dialogue agents.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_62", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_62.flac", "type": "audio", "duration": 723.023}, {"from": "Assistant", "value": "Frame-based state representation is widely used in modern task-oriented dialog systems to model user intentions and slot values. However, a fixed design of domain ontology makes it difficult to extend to new services and APIs. Recent work proposed to use natural language descriptions to define the domain ontology instead of tag names for each intent or slot, thus offering a dynamic set of schema. In this paper, we conduct in-depth comparative studies to understand the use of natural language description for schema in dialog state tracking. Our discussion mainly covers three aspects: encoder architectures, impact of supplementary training, and effective schema description styles. We introduce a set of newly designed bench-marking descriptions and reveal the model robustness on both homogeneous and heterogeneous description styles in training and evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_63", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_63.flac", "type": "audio", "duration": 728.062}, {"from": "Assistant", "value": "Spoken language understanding, usually including intent detection and slot filling, is a core component to build a spoken dialog system. Recent research shows promising results by jointly learning of those two tasks based on the fact that slot filling and intent detection are sharing semantic knowledge. Furthermore, attention mechanism boosts joint learning to achieve state-of-the-art results. However, current joint learning models ignore the following important facts: 1. Long-term slot context is not traced effectively, which is crucial for future slot filling. 2. Slot tagging and intent detection could be mutually rewarding, but bi-directional interaction between slot filling and intent detection remains seldom explored. In this paper, we propose a novel approach to model long-term slot context and to fully utilize the semantic correlation between slots and intents. We adopt a key-value memory network to model slot context dynamically and to track more important slot tags decoded before, which are then fed into our decoder for slot tagging. Furthermore, gated memory information is utilized to perform intent detection, mutually improving both tasks through global optimization. Experiments on benchmark ATIS and Snips datasets show that our model achieves state-of-the-art performance and outperforms other methods, especially for the slot filling task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_64", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_64.flac", "type": "audio", "duration": 935.044}, {"from": "Assistant", "value": "We seek to create agents that both act and communicate with other agents in pursuit of a goal. Towards this end, we extend LIGHT (Urbanek et al. 2019)—a large-scale crowd-sourced fantasy text-game—with a dataset of quests. These contain natural language motivations paired with in-game goals and human demonstrations; completing a quest might require dialogue or actions (or both). We introduce a reinforcement learning system that (1) incorporates large-scale language modeling-based and commonsense reasoning-based pre-training to imbue the agent with relevant priors; and (2) leverages a factorized action space of action commands and dialogue, balancing between the two. We conduct zero-shot evaluations using held-out human expert demonstrations, showing that our agents are able to act consistently and talk naturally with respect to their motivations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_65", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_65.flac", "type": "audio", "duration": 695.02}, {"from": "Assistant", "value": "In entity linking, mentions of named entities in raw text are disambiguated against a knowledge base (KB). This work focuses on linking to unseen KBs that do not have training data and whose schema is unknown during training. Our approach relies on methods to flexibly convert entities with several attribute-value pairs from arbitrary KBs into flat strings, which we use in conjunction with state-of-the-art models for zero-shot linking. We further improve the generalization of our model using two regularization schemes based on shuffling of entity attributes and handling of unseen attributes. Experiments on English datasets where models are trained on the CoNLL dataset, and tested on the TAC-KBP 2010 dataset show that our models are 12% (absolute) more accurate than baseline models that simply flatten entities from the target KB. Unlike prior work, our approach also allows for seamlessly combining multiple training datasets. We test this ability by adding both a completely different dataset (Wikia), as well as increasing amount of training data from the TAC-KBP 2010 training set. Our models are more accurate across the board compared to baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_66", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_66.flac", "type": "audio", "duration": 712.063}, {"from": "Assistant", "value": "State-of-the-art deep neural networks require large-scale labeled training data that is often expensive to obtain or not available for many tasks. Weak supervision in the form of domain-specific rules has been shown to be useful in such settings to automatically generate weakly labeled training data. However, learning with weak rules is challenging due to their inherent heuristic and noisy nature. An additional challenge is rule coverage and overlap, where prior work on weak supervision only considers instances that are covered by weak rules, thus leaving valuable unlabeled data behind. In this work, we develop a weak supervision framework (ASTRA) that leverages all the available data for a given task. To this end, we leverage task-specific unlabeled data through self-training with a model (student) that considers contextualized representations and predicts pseudo-labels for instances that may not be covered by weak rules. We further develop a rule attention network (teacher) that learns how to aggregate student pseudo-labels with weak rule labels, conditioned on their fidelity and the underlying context of an instance. Finally, we construct a semi-supervised learning objective for end-to-end training with unlabeled data, domain-specific rules, and a small amount of labeled data. Extensive experiments on six benchmark datasets for text classification demonstrate the effectiveness of our approach with significant improvements over state-of-the-art baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_67", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_67.flac", "type": "audio", "duration": 694.059}, {"from": "Assistant", "value": "This paper presents the first study on using large-scale pre-trained language models for automated generation of an event-level temporal graph for a document. Despite the huge success of neural pre-training methods in NLP tasks, its potential for temporal reasoning over event graphs has not been sufficiently explored. Part of the reason is the difficulty in obtaining large training corpora with human-annotated events and temporal links. We address this challenge by using existing IE/NLP tools to automatically generate a large quantity (89,000) of system-produced document-graph pairs, and propose a novel formulation of the contextualized graph generation problem as a sequence-to-sequence mapping task. These strategies enable us to leverage and fine-tune pre-trained language models on the system-induced training data for the graph generation task. Our experiments show that our approach is highly effective in generating structurally and semantically valid graphs. Further, evaluation on a challenging hand-labeled, out-of-domain corpus shows that our method outperforms the closest existing method by a large margin on several metrics. We also show a downstream application of our approach by adapting it to answer open-ended temporal questions in a reading comprehension setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_68", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_68.flac", "type": "audio", "duration": 720.004}, {"from": "Assistant", "value": "Knowledge bases often consist of facts which are harvested from a variety of sources, many of which are noisy and some of which conflict, resulting in a level of uncertainty for each triple. Knowledge bases are also often incomplete, prompting the use of embedding methods to generalize from known facts, however, existing embedding methods only model triple-level uncertainty, and reasoning results lack global consistency. To address these shortcomings, we propose BEUrRE, a novel uncertain knowledge graph embedding method with calibrated probabilistic semantics. BEUrRE models each entity as a box (i.e. axis-aligned hyperrectangle) and relations between two entities as affine transforms on the head and tail entity boxes. The geometry of the boxes allows for efficient calculation of intersections and volumes, endowing the model with calibrated probabilistic semantics and facilitating the incorporation of relational constraints. Extensive experiments on two benchmark datasets show that BEUrRE consistently outperforms baselines on confidence prediction and fact ranking due to its probabilistic calibration and ability to capture high-order dependencies among facts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_69", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_69.flac", "type": "audio", "duration": 719.006}, {"from": "Assistant", "value": "Event extraction has long been treated as a sentence-level task in the IE community. We argue that this setting does not match human informative seeking behavior and leads to incomplete and uninformative extraction results. We propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. We also compile a new document-level event extraction benchmark dataset WikiEvents which includes complete event and coreference annotation. On the task of argument extraction, we achieve an absolute gain of 7.6% F1 and 5.7% F1 over the next best model on the RAMS and WikiEvents dataset respectively. On the more challenging task of informative argument extraction, which requires implicit coreference reasoning, we achieve a 9.3% F1 gain over the best baseline. To demonstrate the portability of our model, we also create the first end-to-end zero-shot event extraction framework and achieve 97% of fully supervised model’s trigger extraction performance and 82% of the argument extraction performance given only access to 10 out of the 33 types on ACE.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_70", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_70.flac", "type": "audio", "duration": 398.06}, {"from": "Assistant", "value": "Template filling is generally tackled by a pipeline of two separate supervised systems – one for role-filler extraction and another for template/event recognition. Since pipelines consider events in isolation, they can suffer from error propagation. We introduce a framework based on end-to-end generative transformers for this task (i.e., GTT). It naturally models the dependence between entities both within a single event and across the multiple events described in a document. Experiments demonstrate that this framework substantially outperforms pipeline-based approaches, and other neural end-to-end baselines that do not model between-event dependencies. We further show that our framework specifically improves performance on documents containing multiple events.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_71", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_71.flac", "type": "audio", "duration": 463.726}, {"from": "Assistant", "value": "Recent studies indicate that NLU models are prone to rely on shortcut features for prediction, without achieving true language understanding. As a result, these models fail to generalize to real-world out-of-distribution data. In this work, we show that the words in the NLU training set can be modeled as a long-tailed distribution. There are two findings: 1) NLU models have strong preference for features located at the head of the long-tailed distribution, and 2) Shortcut features are picked up during very early few iterations of the model training. These two observations are further employed to formulate a measurement which can quantify the shortcut degree of each training sample. Based on this shortcut measurement, we propose a shortcut mitigation framework LGTR, to suppress the model from making overconfident predictions for samples with large shortcut degree. Experimental results on three NLU benchmarks demonstrate that our long-tailed distribution explanation accurately reflects the shortcut learning behavior of NLU models. Experimental analysis further indicates that LGTR can improve the generalization accuracy on OOD data, while preserving the accuracy on in-distribution data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_72", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_72.flac", "type": "audio", "duration": 498.048}, {"from": "Assistant", "value": "Multi-layer multi-head self-attention mechanism is widely applied in modern neural language models. Attention redundancy has been observed among attention heads but has not been deeply studied in the literature. Using BERT-base model as an example, this paper provides a comprehensive study on attention redundancy which is helpful for model interpretation and model compression. We analyze the attention redundancy with Five-Ws and How. (What) We define and focus the study on redundancy matrices generated from pre-trained and fine-tuned BERT-base model for GLUE datasets. (How) We use both token-based and sentence-based distance functions to measure the redundancy. (Where) Clear and similar redundancy patterns (cluster structure) are observed among attention heads. (When) Redundancy patterns are similar in both pre-training and fine-tuning phases. (Who) We discover that redundancy patterns are task-agnostic. Similar redundancy patterns even exist for randomly generated token sequences. (“Why”) We also evaluate influences of the pre-training dropout ratios on attention redundancy. Based on the phase-independent and task-agnostic attention redundancy patterns, we propose a simple zero-shot pruning method as a case study. Experiments on fine-tuning GLUE tasks verify its effectiveness. The comprehensive analyses on attention redundancy make model understanding and zero-shot model pruning promising.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_73", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_73.flac", "type": "audio", "duration": 709.764}, {"from": "Assistant", "value": "Large Transformers pretrained over clinical notes from Electronic Health Records (EHR) have afforded substantial gains in performance on predictive clinical tasks. The cost of training such models (and the necessity of data access to do so) coupled with their utility motivates parameter sharing, i.e., the release of pretrained models such as ClinicalBERT. While most efforts have used deidentified EHR, many researchers have access to large sets of sensitive, non-deidentified EHR with which they might train a BERT model (or similar). Would it be safe to release the weights of such a model if they did? In this work, we design a battery of approaches intended to recover Personal Health Information (PHI) from a trained BERT. Specifically, we attempt to recover patient names and conditions with which they are associated. We find that simple probing methods are not able to meaningfully extract sensitive information from BERT trained over the MIMIC-III corpus of EHR. However, more sophisticated “attacks” may succeed in doing so: To facilitate such research, we make our experimental setup and baseline probing models available athttps://github.com/elehman16/exposing_patient_data_release.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_74", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_74.flac", "type": "audio", "duration": 426.411}, {"from": "Assistant", "value": "The dominant approach in probing neural networks for linguistic properties is to train a new shallow multi-layer perceptron (MLP) on top of the model’s internal representations. This approach can detect properties encoded in the model, but at the cost of adding new parameters that may learn the task directly. We instead propose a subtractive pruning-based probe, where we find an existing subnetwork that performs the linguistic task of interest. Compared to an MLP, the subnetwork probe achieves both higher accuracy on pre-trained models and lower accuracy on random models, so it is both better at finding properties of interest and worse at learning on its own. Next, by varying the complexity of each probe, we show that subnetwork probing Pareto-dominates MLP probing in that it achieves higher accuracy given any budget of probe complexity. Finally, we analyze the resulting subnetworks across various tasks to locate where each task is encoded, and we find that lower-level tasks are captured in lower layers, reproducing similar findings in past work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_75", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_75.flac", "type": "audio", "duration": 380.366}, {"from": "Assistant", "value": "Widespread adoption of deep models has motivated a pressing need for approaches to interpret network outputs and to facilitate model debugging. Instance attribution methods constitute one means of accomplishing these goals by retrieving training instances that (may have) led to a particular prediction. Influence functions (IF; Koh and Liang 2017) provide machinery for doing this by quantifying the effect that perturbing individual train instances would have on a specific test prediction. However, even approximating the IF is computationally expensive, to the degree that may be prohibitive in many cases. Might simpler approaches (e.g., retrieving train examples most similar to a given test point) perform comparably? In this work, we evaluate the degree to which different potential instance attribution agree with respect to the importance of training samples. We find that simple retrieval methods yield training instances that differ from those identified via gradient-based methods (such as IFs), but that nonetheless exhibit desirable characteristics similar to more complex attribution methods. Code for all methods and experiments in this paper is available at:https://github.com/successar/instance_attributions_NLP.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_76", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_76.flac", "type": "audio", "duration": 344.683}, {"from": "Assistant", "value": "Understanding and executing natural language instructions in a grounded domain is one of the hallmarks of artificial intelligence. In this paper, we focus on instruction understanding in the blocks world domain and investigate the language understanding abilities of two top-performing systems for the task. We aim to understand if the test performance of these models indicates an understanding of the spatial domain and of the natural language instructions relative to it, or whether they merely over-fit spurious signals in the dataset. We formulate a set of expectations one might have from an instruction following model and concretely characterize the different dimensions of robustness such a model should possess. Despite decent test performance, we find that state-of-the-art models fall short of these expectations and are extremely brittle. We then propose a learning strategy that involves data augmentation and show through extensive experiments that the proposed learning strategy yields models that are competitive on the original test set while satisfying our expectations much better.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_77", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_77.flac", "type": "audio", "duration": 703.007}, {"from": "Assistant", "value": "Multimodal pre-training has propelled great advancement in vision-and-language research. These large-scale pre-trained models, although successful, fatefully suffer from slow inference speed due to enormous computational cost mainly from cross-modal attention in Transformer architecture. When applied to real-life applications, such latency and computation demand severely deter the practical use of pre-trained models. In this paper, we study Image-text retrieval (ITR), the most mature scenario of V+L application, which has been widely studied even prior to the emergence of recent pre-trained models. We propose a simple yet highly effective approach, LightningDOT that accelerates the inference time of ITR by thousands of times, without sacrificing accuracy. LightningDOT removes the time-consuming cross-modal attention by extracting pre-cached feature indexes offline, and employing instant dot-product matching online, which significantly speeds up retrieval process. In fact, our LightningDOT achieves superior performance across mainstream ITR benchmarks such as Flickr30k and COCO datasets, outperforming existing pre-trained models that consume 1000 times magnitude of computational hours using the same features.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_78", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_78.flac", "type": "audio", "duration": 570.005}, {"from": "Assistant", "value": "We generalize the notion of measuring social biases in word embeddings to visually grounded word embeddings. Biases are present in grounded embeddings, and indeed seem to be equally or more significant than for ungrounded embeddings. This is despite the fact that vision and language can suffer from different biases, which one might hope could attenuate the biases in both. Multiple ways exist to generalize metrics measuring bias in word embeddings to this new setting. We introduce the space of generalizations (Grounded-WEAT and Grounded-SEAT) and demonstrate that three generalizations answer different yet important questions about how biases, language, and vision interact. These metrics are used on a new dataset, the first for grounded bias, created by augmenting standard linguistic bias benchmarks with 10,228 images from COCO, Conceptual Captions, and Google Images. Dataset construction is challenging because vision datasets are themselves very biased. The presence of these biases in systems will begin to have real-world consequences as they are deployed, making carefully measuring bias and then mitigating it critical to building a fair society.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_79", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_79.flac", "type": "audio", "duration": 720.051}, {"from": "Assistant", "value": "Human communication is multimodal in nature; it is through multiple modalities such as language, voice, and facial expressions, that opinions and emotions are expressed. Data in this domain exhibits complex multi-relational and temporal interactions. Learning from this data is a fundamentally challenging research problem. In this paper, we propose Modal-Temporal Attention Graph (MTAG). MTAG is an interpretable graph-based neural model that provides a suitable framework for analyzing multimodal sequential data. We first introduce a procedure to convert unaligned multimodal sequence data into a graph with heterogeneous nodes and edges that captures the rich interactions across modalities and through time. Then, a novel graph fusion operation, called MTAG fusion, along with a dynamic pruning and read-out technique, is designed to efficiently process this modal-temporal graph and capture various interactions. By learning to focus only on the important interactions within the graph, MTAG achieves state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks, while utilizing significantly fewer model parameters.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_80", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_80.flac", "type": "audio", "duration": 685.056}, {"from": "Assistant", "value": "Grounding natural language instructions on the web to perform previously unseen tasks enables accessibility and automation. We introduce a task and dataset to train AI agents from open-domain, step-by-step instructions originally written for people. We build RUSS (Rapid Universal Support Service) to tackle this problem. RUSS consists of two models: First, a BERT-LSTM with pointers parses instructions to WebLang, a domain-specific language we design for grounding natural language on the web. Then, a grounding model retrieves the unique IDs of any webpage elements requested in the WebLang. RUSS may interact with the user through a dialogue (e.g. ask for an address) or execute a web operation (e.g. click a button) inside the web runtime. To augment training, we synthesize natural language instructions mapped to WebLang. Our dataset consists of 80 different customer service problems from help websites, with a total of 741 step-by-step instructions and their corresponding actions. RUSS achieves 76.7% end-to-end accuracy predicting agent actions from single instructions. It outperforms state-of-the-art models that directly map instructions to actions without WebLang. Our user study shows that RUSS is preferred by actual users over web navigation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_81", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_81.flac", "type": "audio", "duration": 390.933}, {"from": "Assistant", "value": "Standard architectures used in instruction following often struggle on novel compositions of subgoals (e.g. navigating to landmarks or picking up objects) observed during training. We propose a modular architecture for following natural language instructions that describe sequences of diverse subgoals. In our approach, subgoal modules each carry out natural language instructions for a specific subgoal type. A sequence of modules to execute is chosen by learning to segment the instructions and predicting a subgoal type for each segment. When compared to standard, non-modular sequence-to-sequence approaches on ALFRED, a challenging instruction following benchmark, we find that modularization improves generalization to novel subgoal compositions, as well as to environments unseen in training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_82", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_82.flac", "type": "audio", "duration": 418.048}, {"from": "Assistant", "value": "Vision language navigation is the task that requires an agent to navigate through a 3D environment based on natural language instructions. One key challenge in this task is to ground instructions with the current visual information that the agent perceives. Most of the existing work employs soft attention over individual words to locate the instruction required for the next action. However, different words have different functions in a sentence (e.g., modifiers convey attributes, verbs convey actions). Syntax information like dependencies and phrase structures can aid the agent to locate important parts of the instruction. Hence, in this paper, we propose a navigation agent that utilizes syntax information derived from a dependency tree to enhance alignment between the instruction and the current visual scenes. Empirically, our agent outperforms the baseline model that does not use syntax information on the Room-to-Room dataset, especially in the unseen environment. Besides, our agent achieves the new state-of-the-art on Room-Across-Room dataset, which contains instructions in 3 languages (English, Hindi, and Telugu). We also show that our agent is better at aligning instructions with the current visual information via qualitative visualizations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_83", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_83.flac", "type": "audio", "duration": 650.971}, {"from": "Assistant", "value": "Exploiting label hierarchies has become a promising approach to tackling the zero-shot multi-label text classification (ZS-MTC) problem. Conventional methods aim to learn a matching model between text and labels, using a graph encoder to incorporate label hierarchies to obtain effective label representations (Rios and Kavuluru, 2018). More recently, pretrained models like BERT (Devlin et al., 2018) have been used to convert classification tasks into a textual entailment task (Yin et al., 2019). This approach is naturally suitable for the ZS-MTC task. However, pretrained models are underexplored in the existing work because they do not generate individual vector representations for text or labels, making it unintuitive to combine them with conventional graph encoding methods. In this paper, we explore to improve pretrained models with label hierarchies on the ZS-MTC task. We propose a Reinforced Label Hierarchy Reasoning (RLHR) approach to encourage interdependence among labels in the hierarchies during training. Meanwhile, to overcome the weakness of flat predictions, we design a rollback algorithm that can remove logical errors from predictions during inference. Experimental results on three real-life datasets show that our approach achieves better performance and outperforms previous non-pretrained methods on the ZS-MTC task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_84", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_84.flac", "type": "audio", "duration": 672.32}, {"from": "Assistant", "value": "Fine-tuned pre-trained language models (LMs) have achieved enormous success in many natural language processing (NLP) tasks, but they still require excessive labeled data in the fine-tuning stage. We study the problem of fine-tuning pre-trained LMs using only weak supervision, without any labeled data. This problem is challenging because the high capacity of LMs makes them prone to overfitting the noisy labels generated by weak supervision. To address this problem, we develop a contrastive self-training framework, COSINE, to enable fine-tuning LMs with weak supervision. Underpinned by contrastive regularization and confidence-based reweighting, our framework gradually improves model fitting while effectively suppressing error propagation. Experiments on sequence, token, and sentence pair classification tasks show that our model outperforms the strongest baseline by large margins and achieves competitive performance with fully-supervised fine-tuning methods. Our implementation is available onhttps://github.com/yueyu1030/COSINE.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_85", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_85.flac", "type": "audio", "duration": 729.06}, {"from": "Assistant", "value": "We address the problem of enhancing model robustness through regularization. Specifically, we focus on methods that regularize the model posterior difference between clean and noisy inputs. Theoretically, we provide a connection of two recent methods, Jacobian Regularization and Virtual Adversarial Training, under this framework. Additionally, we generalize the posterior differential regularization to the family of f-divergences and characterize the overall framework in terms of the Jacobian matrix. Empirically, we compare those regularizations and standard BERT training on a diverse set of tasks to provide a comprehensive profile of their effect on model generalization. For both fully supervised and semi-supervised settings, we show that regularizing the posterior difference with f-divergence can result in well-improved model robustness. In particular, with a proper f-divergence, a BERT-base model can achieve comparable generalization as its BERT-large counterpart for in-domain, adversarial and domain shift scenarios, indicating the great potential of the proposed framework for enhancing NLP model robustness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_86", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_86.flac", "type": "audio", "duration": 698.283}, {"from": "Assistant", "value": "The choice of negative examples is important in noise contrastive estimation. Recent works find that hard negatives—highest-scoring incorrect examples under the model—are effective in practice, but they are used without a formal justification. We develop analytical tools to understand the role of hard negatives. Specifically, we view the contrastive loss as a biased estimator of the gradient of the cross-entropy loss, and show both theoretically and empirically that setting the negative distribution to be the model distribution results in bias reduction. We also derive a general form of the score function that unifies various architectures used in text retrieval. By combining hard negatives with appropriate score functions, we obtain strong results on the challenging task of zero-shot entity linking.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_87", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_87.flac", "type": "audio", "duration": 722.001}, {"from": "Assistant", "value": "The robustness and security of natural language processing (NLP) models are significantly important in real-world applications. In the context of text classification tasks, adversarial examples can be designed by substituting words with synonyms under certain semantic and syntactic constraints, such that a well-trained model will give a wrong prediction. Therefore, it is crucial to develop techniques to provide a rigorous and provable robustness guarantee against such attacks. In this paper, we propose WordDP to achieve certified robustness against word substitution at- tacks in text classification via differential privacy (DP). We establish the connection between DP and adversarial robustness for the first time in the text domain and propose a conceptual exponential mechanism-based algorithm to formally achieve the robustness. We further present a practical simulated exponential mechanism that has efficient inference with certified robustness. We not only provide a rigorous analytic derivation of the certified condition but also experimentally compare the utility of WordDP with existing defense algorithms. The results show that WordDP achieves higher accuracy and more than 30X efficiency improvement over the state-of-the-art certified robustness mechanism in typical text classification tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_88", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_88.flac", "type": "audio", "duration": 671.057}, {"from": "Assistant", "value": "Meta-learning promises few-shot learners that can adapt to new distributions by repurposing knowledge acquired from previous training. However, we believe meta-learning has not yet succeeded in NLP due to the lack of a well-defined task distribution, leading to attempts that treat datasets as tasks. Such an ad hoc task distribution causes problems of quantity and quality. Since there’s only a handful of datasets for any NLP problem, meta-learners tend to overfit their adaptation mechanism and, since NLP datasets are highly heterogeneous, many learning episodes have poor transfer between their support and query sets, which discourages the meta-learner from adapting. To alleviate these issues, we propose DReCA (Decomposing datasets into Reasoning Categories), a simple method for discovering and using latent reasoning categories in a dataset, to form additional high quality tasks. DReCA works by splitting examples into label groups, embedding them with a finetuned BERT model and then clustering each group into reasoning categories. Across four few-shot NLI problems, we demonstrate that using DReCA improves the accuracy of meta-learners by 1.5-4%", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_89", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_89.flac", "type": "audio", "duration": 712.922}, {"from": "Assistant", "value": "Unsupervised translation has reached impressive performance on resource-rich language pairs such as English-French and English-German. However, early studies have shown that in more realistic settings involving low-resource, rare languages, unsupervised translation performs poorly, achieving less than 3.0 BLEU. In this work, we show that multilinguality is critical to making unsupervised systems practical for low-resource settings. In particular, we present a single model for 5 low-resource languages (Gujarati, Kazakh, Nepali, Sinhala, and Turkish) to and from English directions, which leverages monolingual and auxiliary parallel data from other high-resource language pairs via a three-stage training scheme. We outperform all current state-of-the-art unsupervised baselines for these languages, achieving gains of up to 14.4 BLEU. Additionally, we outperform strong supervised baselines for various language pairs as well as match the performance of the current state-of-the-art supervised model for Nepali-English. We conduct a series of ablation studies to establish the robustness of our model under different degrees of data quality, as well as to analyze the factors which led to the superior performance of the proposed approach over traditional unsupervised models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_90", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_90.flac", "type": "audio", "duration": 716.331}, {"from": "Assistant", "value": "While traditional corpus-level evaluation metrics for machine translation (MT) correlate well with fluency, they struggle to reflect adequacy. Model-based MT metrics trained on segment-level human judgments have emerged as an attractive replacement due to strong correlation results. These models, however, require potentially expensive re-training for new domains and languages. Furthermore, their decisions are inherently non-transparent and appear to reflect unwelcome biases. We explore the simple type-based classifier metric, MacroF1, and study its applicability to MT evaluation. We find that MacroF1 is competitive on direct assessment, and outperforms others in indicating downstream cross-lingual information retrieval task performance. Further, we show that MacroF1 can be used to effectively compare supervised and unsupervised neural machine translation, and reveal significant qualitative differences in the methods’ outputs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_91", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_91.flac", "type": "audio", "duration": 706.048}, {"from": "Assistant", "value": "Reference-free evaluation has the potential to make machine translation evaluation substantially more scalable, allowing us to pivot easily to new languages or domains. It has been recently shown that the probabilities given by a large, multilingual model can achieve state of the art results when used as a reference-free metric. We experiment with various modifications to this model, and demonstrate that by scaling it up we can match the performance of BLEU. We analyze various potential weaknesses of the approach, and find that it is surprisingly robust and likely to offer reasonable performance across a broad spectrum of domains and different system qualities.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_92", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_92.flac", "type": "audio", "duration": 625.728}, {"from": "Assistant", "value": "In this work, we study hallucinations in Neural Machine Translation (NMT), which lie at an extreme end on the spectrum of NMT pathologies. Firstly, we connect the phenomenon of hallucinations under source perturbation to the Long-Tail theory of Feldman, and present an empirically validated hypothesis that explains hallucinations under source perturbation. Secondly, we consider hallucinations under corpus-level noise (without any source perturbation) and demonstrate that two prominent types of natural hallucinations (detached and oscillatory outputs) could be generated and explained through specific corpus-level noise patterns. Finally, we elucidate the phenomenon of hallucination amplification in popular data-generation processes such as Backtranslation and sequence-level Knowledge Distillation. We have released the datasets and code to replicate our results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_93", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_93.flac", "type": "audio", "duration": 420.212}, {"from": "Assistant", "value": "We propose a straightforward vocabulary adaptation scheme to extend the language capacity of multilingual machine translation models, paving the way towards efficient continual learning for multilingual machine translation. Our approach is suitable for large-scale datasets, applies to distant languages with unseen scripts, incurs only minor degradation on the translation performance for the original language pairs and provides competitive performance even in the case where we only possess monolingual data for the new languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_94", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_94.flac", "type": "audio", "duration": 389.05}, {"from": "Assistant", "value": "One key ingredient of neural machine translation is the use of large datasets from different domains and resources (e.g. Europarl, TED talks). These datasets contain documents translated by professional translators using different but consistent translation styles. Despite that, the model is usually trained in a way that neither explicitly captures the variety of translation styles present in the data nor translates new data in different and controllable styles. In this work, we investigate methods to augment the state of the art Transformer model with translator information that is available in part of the training data. We show that our style-augmented translation models are able to capture the style variations of translators and to generate translations with different styles on new data. Indeed, the generated variations differ significantly, up to +4.5 BLEU score difference. Despite that, human evaluation confirms that the translations are of the same quality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_95", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_95.flac", "type": "audio", "duration": 708.046}, {"from": "Assistant", "value": "Recent work on unsupervised question answering has shown that models can be trained with procedurally generated question-answer pairs and can achieve performance competitive with supervised methods. In this work, we consider the task of unsupervised reading comprehension and present a method that performs “test-time learning” (TTL) on a given context (text passage), without requiring training on large-scale human-authored datasets containingcontext-question-answertriplets. This method operates directly on a single test context, uses self-supervision to train models on synthetically generated question-answer pairs, and then infers answers to unseen human-authored questions for this context. Our method achieves accuracies competitive with fully supervised methods and significantly outperforms current unsupervised methods. TTL methods with a smaller model are also competitive with the current state-of-the-art in unsupervised reading comprehension.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_96", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_96.flac", "type": "audio", "duration": 721.049}, {"from": "Assistant", "value": "Transformer based architectures are recently used for the task of answering questions over tables. In order to improve the accuracy on this task, specialized pre-training techniques have been developed and applied on millions of open-domain web tables. In this paper, we propose two novel approaches demonstrating that one can achieve superior performance on table QA task without even using any of these specialized pre-training techniques. The first model, called RCI interaction, leverages a transformer based architecture that independently classifies rows and columns to identify relevant cells. While this model yields extremely high accuracy at finding cell values on recent benchmarks, a second model we propose, called RCI representation, provides a significant efficiency advantage for online QA systems over tables by materializing embeddings for existing tables. Experiments on recent benchmarks prove that the proposed methods can effectively locate cell values on tables (up to ~98% Hit@1 accuracy on WikiSQL lookup questions). Also, the interaction model outperforms the state-of-the-art transformer based approaches, pre-trained on very large table corpora (TAPAS and TaBERT), achieving ~3.4% and ~18.86% additional precision improvement on the standard WikiSQL benchmark.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_97", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_97.flac", "type": "audio", "duration": 682.368}, {"from": "Assistant", "value": "Many state-of-the-art (SOTA) language models have achieved high accuracy on several multi-hop reasoning problems. However, these approaches tend to not be interpretable because they do not make the intermediate reasoning steps explicit. Moreover, models trained on simpler tasks tend to fail when directly tested on more complex problems. We propose the Explainable multi-hop Verbal Reasoner (EVR) to solve these limitations by (a) decomposing multi-hop reasoning problems into several simple ones, and (b) using natural language to guide the intermediate reasoning hops. We implement EVR by extending the classic reasoning paradigm General Problem Solver (GPS) with a SOTA generative language model to generate subgoals and perform inference in natural language at each reasoning step. Evaluation of EVR on the RuleTaker synthetic question answering (QA) dataset shows that EVR achieves SOTA performance while being able to generate all reasoning steps in natural language. Furthermore, EVR generalizes better than other strong methods when trained on simpler tasks or less training data (up to 35.7% and 7.7% absolute improvement respectively).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_98", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_98.flac", "type": "audio", "duration": 719.04}, {"from": "Assistant", "value": "Current textual question answering (QA) models achieve strong performance on in-domain test sets, but often do so by fitting surface-level patterns, so they fail to generalize to out-of-distribution settings. To make a more robust and understandable QA system, we model question answering as an alignment problem. We decompose both the question and context into smaller units based on off-the-shelf semantic representations (here, semantic roles), and align the question to a subgraph of the context in order to find the answer. We formulate our model as a structured SVM, with alignment scores computed via BERT, and we can train end-to-end despite using beam search for approximate inference. Our use of explicit alignments allows us to explore a set of constraints with which we can prohibit certain types of bad model behavior arising in cross-domain settings. Furthermore, by investigating differences in scores across different potential answers, we can seek to understand what particular aspects of the input lead the model to choose the answer without relying on post-hoc explanation techniques. We train our model on SQuAD v1.1 and test it on several adversarial and out-of-domain datasets. The results show that our model is more robust than the standard BERT QA model, and constraints derived from alignment scores allow us to effectively trade off coverage and accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_99", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_99.flac", "type": "audio", "duration": 704.052}, {"from": "Assistant", "value": "We propose a general framework called Text Modular Networks(TMNs) for building interpretable systems that learn to solve complex tasks by decomposing them into simpler ones solvable by existing models. To ensure solvability of simpler tasks, TMNs learn the textual input-output behavior (i.e.,language) of existing models through their datasets. This differs from prior decomposition-based approaches which, besides being designed specifically for each complex task, produce decompositions independent of existing sub-models. Specifically, we focus on Question Answering (QA) and show how to train a next-question generator to sequentially produce sub-questions targeting appropriate sub-models, without additional human annotation. These sub-questions and answers provide a faithful natural language explanation of the model’s reasoning. We use this framework to build ModularQA, a system that can answer multi-hop reasoning questions by decomposing them into sub-questions answerable by a neural factoid single-span QA model and a symbolic calculator. Our experiments show that ModularQA is more versatile than existing explainable systems for DROP and HotpotQA datasets, is more robust than state-of-the-art blackbox (uninterpretable) systems, and generates more understandable and trustworthy explanations compared to prior work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_100", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_100.flac", "type": "audio", "duration": 386.496}, {"from": "Assistant", "value": "State-of-the-art Machine Reading Comprehension (MRC) models for Open-domain Question Answering (QA) are typically trained for span selection using distantly supervised positive examples and heuristically retrieved negative examples. This training scheme possibly explains empirical observations that these models achieve a high recall amongst their top few predictions, but a low overall accuracy, motivating the need for answer re-ranking. We develop a successful re-ranking approach (RECONSIDER) for span-extraction tasks that improves upon the performance of MRC models, even beyond large-scale pre-training. RECONSIDER is trained on positive and negative examples extracted from high confidence MRC model predictions, and uses in-passage span annotations to perform span-focused re-ranking over a smaller candidate set. As a result, RECONSIDER learns to eliminate close false positives, achieving a new extractive state of the art on four QA tasks, with 45.5% Exact Match accuracy on Natural Questions with real user questions, and 61.7% on TriviaQA. We will release all related data, models, and code.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_101", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_101.flac", "type": "audio", "duration": 410.041}, {"from": "Assistant", "value": "Recent work (Feng et al., 2018) establishes the presence of short, uninterpretable input fragments that yield high confidence and accuracy in neural models. We refer to these as Minimal Prediction Preserving Inputs (MPPIs). In the context of question answering, we investigate competing hypotheses for the existence of MPPIs, including poor posterior calibration of neural models, lack of pretraining, and “dataset bias” (where a model learns to attend to spurious, non-generalizable cues in the training data). We discover a perplexing invariance of MPPIs to random training seed, model architecture, pretraining, and training domain. MPPIs demonstrate remarkable transferability across domains achieving significantly higher performance than comparably short queries. Additionally, penalizing over-confidence on MPPIs fails to improve either generalization or adversarial robustness. These results suggest the interpretability of MPPIs is insufficient to characterize generalization capacity of these models. We hope this focused investigation encourages more systematic analysis of model behavior outside of the human interpretable distribution of examples.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_102", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_102.flac", "type": "audio", "duration": 385.813}, {"from": "Assistant", "value": "Negation is a core construction in natural language. Despite being very successful on many tasks, state-of-the-art pre-trained language models often handle negation incorrectly. To improve language models in this regard, we propose to augment the language modeling objective with an unlikelihood objective that is based on negated generic sentences from a raw text corpus. By training BERT with the resulting combined objective we reduce the mean top 1 error rate to 4% on the negated LAMA dataset. We also see some improvements on the negated NLI benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_103", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_103.flac", "type": "audio", "duration": 379.043}, {"from": "Assistant", "value": "Recent neural text-to-SQL models can effectively translate natural language questions to corresponding SQL queries on unseen databases. Working mostly on the Spider dataset, researchers have proposed increasingly sophisticated solutions to the problem. Contrary to this trend, in this paper we focus on simplifications. We begin by building DuoRAT, a re-implementation of the state-of-the-art RAT-SQL model that unlike RAT-SQL is using only relation-aware or vanilla transformers as the building blocks. We perform several ablation experiments using DuoRAT as the baseline model. Our experiments confirm the usefulness of some techniques and point out the redundancy of others, including structural SQL features and features that link the question with the schema.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_104", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_104.flac", "type": "audio", "duration": 728.725}, {"from": "Assistant", "value": "Natural Language Inference (NLI) has garnered significant attention in recent years; however, the promise of applying NLI breakthroughs to other downstream NLP tasks has remained unfulfilled. In this work, we use the multiple-choice reading comprehension (MCRC) and checking factual correctness of textual summarization (CFCS) tasks to investigate potential reasons for this. Our findings show that: (1) the relatively shorter length of premises in traditional NLI datasets is the primary challenge prohibiting usage in downstream applications (which do better with longer contexts); (2) this challenge can be addressed by automatically converting resource-rich reading comprehension datasets into longer-premise NLI datasets; and (3) models trained on the converted, longer-premise datasets outperform those trained using short-premise traditional NLI datasets on downstream tasks primarily due to the difference in premise lengths.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_105", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_105.flac", "type": "audio", "duration": 649.045}, {"from": "Assistant", "value": "Learning to capture text-table alignment is essential for tasks like text-to-SQL. A model needs to correctly recognize natural language references to columns and values and to ground them in the given database schema. In this paper, we present a novel weakly supervised Structure-Grounded pretraining framework (STRUG) for text-to-SQL that can effectively learn to capture text-table alignment based on a parallel text-table corpus. We identify a set of novel pretraining tasks: column grounding, value grounding and column-value mapping, and leverage them to pretrain a text-table encoder. Additionally, to evaluate different methods under more realistic text-table alignment settings, we create a new evaluation set Spider-Realistic based on Spider dev set with explicit mentions of column names removed, and adopt eight existing text-to-SQL datasets for cross-database evaluation. STRUG brings significant improvement over BERTLARGE in all settings. Compared with existing pretraining methods such as GRAPPA, STRUG achieves similar performance on Spider, and outperforms all baselines on more realistic sets. All the code and data used in this work will be open-sourced to facilitate future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_106", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_106.flac", "type": "audio", "duration": 723.776}, {"from": "Assistant", "value": "Text classification is usually studied by labeling natural language texts with relevant categories from a predefined set. In the real world, new classes might keep challenging the existing system with limited labeled data. The system should be intelligent enough to recognize upcoming new classes with a few examples. In this work, we define a new task in the NLP domain, incremental few-shot text classification, where the system incrementally handles multiple rounds of new classes. For each round, there is a batch of new classes with a few labeled examples per class. Two major challenges exist in this new task: (i) For the learning process, the system should incrementally learn new classes round by round without re-training on the examples of preceding classes; (ii) For the performance, the system should perform well on new classes without much loss on preceding classes. In addition to formulating the new task, we also release two benchmark datasets in the incremental few-shot setting: intent classification and relation classification. Moreover, we propose two entailment approaches, ENTAILMENT and HYBRID, which show promise for solving this novel problem.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_107", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_107.flac", "type": "audio", "duration": 720.422}, {"from": "Assistant", "value": "We propose TRACIE, a novel temporal reasoning dataset that evaluates the degree to which systems understand implicit events—events that are not mentioned explicitly in natural language text but can be inferred from it. This introduces a new challenge in temporal reasoning research, where prior work has focused on explicitly mentioned events. Human readers can infer implicit events via commonsense reasoning, resulting in a more comprehensive understanding of the situation and, consequently, better reasoning about time. We find, however, that state-of-the-art models struggle when predicting temporal relationships between implicit and explicit events. To address this, we propose a neuro-symbolic temporal reasoning model, SymTime, which exploits distant supervision signals from large-scale text and uses temporal rules to combine start times and durations to infer end times. SymTime outperforms strong baseline systems on TRACIE by 5%, and by 11% in a zero prior knowledge training setting. Our approach also generalizes to other temporal reasoning tasks, as evidenced by a gain of 1%-9% on MATRES, an explicit event benchmark.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_108", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_108.flac", "type": "audio", "duration": 375.049}, {"from": "Assistant", "value": "Pre-trained language models have achieved huge success on a wide range of NLP tasks. However, contextual representations from pre-trained models contain entangled semantic and syntactic information, and therefore cannot be directly used to derive useful semantic sentence embeddings for some tasks. Paraphrase pairs offer an effective way of learning the distinction between semantics and syntax, as they naturally share semantics and often vary in syntax. In this work, we present ParaBART, a semantic sentence embedding model that learns to disentangle semantics and syntax in sentence embeddings obtained by pre-trained language models. ParaBART is trained to perform syntax-guided paraphrasing, based on a source sentence that shares semantics with the target paraphrase, and a parse tree that specifies the target syntax. In this way, ParaBART learns disentangled semantic and syntactic representations from their respective inputs with separate encoders. Experiments in English show that ParaBART outperforms state-of-the-art sentence embedding models on unsupervised semantic similarity tasks. Additionally, we show that our approach can effectively remove syntactic information from semantic sentence embeddings, leading to better robustness against syntactic variation on downstream semantic tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_109", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_109.flac", "type": "audio", "duration": 767.048}, {"from": "Assistant", "value": "Abstractive conversation summarization has received much attention recently. However, these generated summaries often suffer from insufficient, redundant, or incorrect content, largely due to the unstructured and complex characteristics of human-human interactions. To this end, we propose to explicitly model the rich structures in conversations for more precise and accurate conversation summarization, by first incorporating discourse relations between utterances and action triples (“who-doing-what”) in utterances through structured graphs to better encode conversations, and then designing a multi-granularity decoder to generate summaries by combining all levels of information. Experiments show that our proposed models outperform state-of-the-art methods and generalize well in other domains in terms of both automatic evaluations and human judgments. We have publicly released our code athttps://github.com/GT-SALT/Structure-Aware-BART.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_110", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_110.flac", "type": "audio", "duration": 761.045}, {"from": "Assistant", "value": "We propose a new approach to generate multiple variants of the target summary with diverse content and varying lengths, then score and select admissible ones according to users’ needs. Abstractive summarizers trained on single reference summaries may struggle to produce outputs that achieve multiple desirable properties, i.e., capturing the most important information, being faithful to the original, grammatical and fluent. In this paper, we propose a two-staged strategy to generate a diverse set of candidate summaries from the source text in stage one, then score and select admissible ones in stage two. Importantly, our generator gives a precise control over the length of the summary, which is especially well-suited when space is limited. Our selectors are designed to predict the optimal summary length and put special emphasis on faithfulness to the original text. Both stages can be effectively trained, optimized and evaluated. Our experiments on benchmark summarization datasets suggest that this paradigm can achieve state-of-the-art performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_111", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_111.flac", "type": "audio", "duration": 899.605}, {"from": "Assistant", "value": "Presentations are critical for communication in all areas of our lives, yet the creation of slide decks is often tedious and time-consuming. There has been limited research aiming to automate the document-to-slides generation process and all face a critical challenge: no publicly available dataset for training and benchmarking. In this work, we first contribute a new dataset, SciDuet, consisting of pairs of papers and their corresponding slides decks from recent years’ NLP and ML conferences (e.g., ACL). Secondly, we present D2S, a novel system that tackles the document-to-slides task with a two-step approach: 1) Use slide titles to retrieve relevant and engaging text, figures, and tables; 2) Summarize the retrieved context into bullet points with long-form question answering. Our evaluation suggests that long-form QA outperforms state-of-the-art summarization baselines on both automated ROUGE metrics and qualitative human evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_112", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_112.flac", "type": "audio", "duration": 702.059}, {"from": "Assistant", "value": "The quadratic computational and memory complexities of large Transformers have limited their scalability for long document summarization. In this paper, we propose Hepos, a novel efficient encoder-decoder attention with head-wise positional strides to effectively pinpoint salient information from the source. We further conduct a systematic study of existing efficient self-attentions. Combined with Hepos, we are able to process ten times more tokens than existing models that use full attentions. For evaluation, we present a new dataset, GovReport, with significantly longer documents and summaries. Results show that our models produce significantly higher ROUGE scores than competitive comparisons, including new state-of-the-art results on PubMed. Human evaluation also shows that our models generate more informative summaries with fewer unfaithful errors.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_113", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_113.flac", "type": "audio", "duration": 719.04}, {"from": "Assistant", "value": "Although some recent works show potential complementarity among different state-of-the-art systems, few works try to investigate this problem in text summarization. Researchers in other areas commonly refer to the techniques of reranking or stacking to approach this problem. In this work, we highlight several limitations of previous methods, which motivates us to present a new framework Refactor that provides a unified view of text summarization and summaries combination. Experimentally, we perform a comprehensive evaluation that involves twenty-two base systems, four datasets, and three different application scenarios. Besides new state-of-the-art results on CNN/DailyMail dataset (46.18 ROUGE-1), we also elaborate on how our proposed method addresses the limitations of the traditional methods and the effectiveness of the Refactor model sheds light on insight for performance improvement. Our system can be directly used by other researchers as an off-the-shelf tool to achieve further performance improvements. We open-source all the code and provide a convenient interface to use it:https://github.com/yixinL7/Refactoring-Summarization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_114", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_114.flac", "type": "audio", "duration": 739.051}, {"from": "Assistant", "value": "Recent pre-trained abstractive summarization systems have started to achieve credible performance, but a major barrier to their use in practice is their propensity to output summaries that are not faithful to the input and that contain factual errors. While a number of annotated datasets and statistical models for assessing factuality have been explored, there is no clear picture of what errors are most important to target or where current techniques are succeeding and failing. We explore both synthetic and human-labeled data sources for training models to identify factual errors in summarization, and study factuality at the word-, dependency-, and sentence-level. Our observations are threefold. First, exhibited factual errors differ significantly across datasets, and commonly-used training sets of simple synthetic errors do not reflect errors made on abstractive datasets like XSum. Second, human-labeled data with fine-grained annotations provides a more effective training signal than sentence-level annotations or synthetic data. Finally, we show that our best factuality detection model enables training of more factual XSum summarization models by allowing us to identify non-factual tokens in the training data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_115", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_115.flac", "type": "audio", "duration": 675.051}, {"from": "Assistant", "value": "The development of neural networks and pretraining techniques has spawned many sentence-level tagging systems that achieved superior performance on typical benchmarks. However, a relatively less discussed topic is what if more context information is introduced into current top-scoring tagging systems. Although several existing works have attempted to shift tagging systems from sentence-level to document-level, there is still no consensus conclusion about when and why it works, which limits the applicability of the larger-context approach in tagging tasks. In this paper, instead of pursuing a state-of-the-art tagging system by architectural exploration, we focus on investigating when and why the larger-context training, as a general strategy, can work. To this end, we conduct a thorough comparative study on four proposed aggregators for context information collecting and present an attribute-aided evaluation method to interpret the improvement brought by larger-context training. Experimentally, we set up a testbed based on four tagging tasks and thirteen datasets. Hopefully, our preliminary observations can deepen the understanding of larger-context training and enlighten more follow-up works on the use of contextual information.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_116", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_116.flac", "type": "audio", "duration": 515.042}, {"from": "Assistant", "value": "Prior methods to text segmentation are mostly at token level. Despite the adequacy, this nature limits their full potential to capture the long-term dependencies among segments. In this work, we propose a novel framework that incrementally segments natural language sentences at segment level. For every step in segmentation, it recognizes the leftmost segment of the remaining sequence. Implementations involve LSTM-minus technique to construct the phrase representations and recurrent neural networks (RNN) to model the iterations of determining the leftmost segments. We have conducted extensive experiments on syntactic chunking and Chinese part-of-speech (POS) tagging across 3 datasets, demonstrating that our methods have significantly outperformed previous all baselines and achieved new state-of-the-art results. Moreover, qualitative analysis and the study on segmenting long-length sentences verify its effectiveness in modeling long-term dependencies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_117", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_117.flac", "type": "audio", "duration": 717.056}, {"from": "Assistant", "value": "Probabilistic context-free grammars (PCFGs) with neural parameterization have been shown to be effective in unsupervised phrase-structure grammar induction. However, due to the cubic computational complexity of PCFG representation and parsing, previous approaches cannot scale up to a relatively large number of (nonterminal and preterminal) symbols. In this work, we present a new parameterization form of PCFGs based on tensor decomposition, which has at most quadratic computational complexity in the symbol number and therefore allows us to use a much larger number of symbols. We further use neural parameterization for the new form to improve unsupervised parsing performance. We evaluate our model across ten languages and empirically demonstrate the effectiveness of using more symbols.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_118", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_118.flac", "type": "audio", "duration": 694.251}, {"from": "Assistant", "value": "Named Entity Recognition (NER) remains difficult in real-world settings; current challenges include short texts (low context), emerging entities, and complex entities (e.g. movie names). Gazetteer features can help, but results have been mixed due to challenges with adding extra features, and a lack of realistic evaluation data. It has been shown that including gazetteer features can cause models to overuse or underuse them, leading to poor generalization. We propose GEMNET, a novel approach for gazetteer knowledge integration, including (1) a flexible Contextual Gazetteer Representation (CGR) encoder that can be fused with any word-level model; and (2) a Mixture-of- Experts gating network that overcomes the feature overuse issue by learning to conditionally combine the context and gazetteer features, instead of assigning them fixed weights. To comprehensively evaluate our approaches, we create 3 large NER datasets (24M tokens) reflecting current challenges. In an uncased setting, our methods show large gains (up to +49% F1) in recognizing difficult entities compared to existing baselines. On standard benchmarks, we achieve a new uncased SOTA on CoNLL03 and WNUT17.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_119", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_119.flac", "type": "audio", "duration": 524.005}, {"from": "Assistant", "value": "We investigate video-aided grammar induction, which learns a constituency parser from both unlabeled text and its corresponding video. Existing methods of multi-modal grammar induction focus on grammar induction from text-image pairs, with promising results showing that the information from static images is useful in induction. However, videos provide even richer information, including not only static objects but also actions and state changes useful for inducing verb phrases. In this paper, we explore rich features (e.g. action, object, scene, audio, face, OCR and speech) from videos, taking the recent Compound PCFG model as the baseline. We further propose a Multi-Modal Compound PCFG model (MMC-PCFG) to effectively aggregate these rich features from different modalities. Our proposed MMC-PCFG is trained end-to-end and outperforms each individual modality and previous state-of-the-art systems on three benchmarks, i.e. DiDeMo, YouCook2 and MSRVTT, confirming the effectiveness of leveraging video information for unsupervised grammar induction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_120", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_120.flac", "type": "audio", "duration": 684.013}, {"from": "Assistant", "value": "Evaluating the quality of responses generated by open-domain conversation systems is a challenging task. This is partly because there can be multiple appropriate responses to a given dialogue history. Reference-based metrics that rely on comparisons to a set of known correct responses often fail to account for this variety, and consequently correlate poorly with human judgment. To address this problem, researchers have investigated the possibility of assessing response quality without using a set of known correct responses. RUBER demonstrated that an automatic response evaluation model could be made using unsupervised learning for the next-utterance prediction (NUP) task. For the unsupervised learning of such model, we propose a method of manipulating a golden response to create a new negative response that is designed to be inappropriate within the context while maintaining high similarity with the original golden response. We find, from our experiments on English datasets, that using the negative samples generated by our method alongside random negative samples can increase the model’s correlation with human evaluations. The process of generating such negative samples is automated and does not rely on human annotation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_121", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_121.flac", "type": "audio", "duration": 659.052}, {"from": "Assistant", "value": "Knowledge is now starting to power neural dialogue agents. At the same time, the risk of misinformation and disinformation from dialogue agents also rises. Verifying the veracity of information from formal sources are widely studied in computational fact checking. In this work, we ask: How robust are fact checking systems on claims in colloquial style? We aim to open up new discussions in the intersection of fact verification and dialogue safety. In order to investigate how fact checking systems behave on colloquial claims, we transfer the styles of claims from FEVER (Thorne et al., 2018) into colloquialism. We find that existing fact checking systems that perform well on claims in formal style significantly degenerate on colloquial claims with the same semantics. Especially, we show that document retrieval is the weakest spot in the system even vulnerable to filler words, such as “yeah” and “you know”. The document recall of WikiAPI retriever (Hanselowski et al., 2018) which is 90.0% on FEVER, drops to 72.2% on the colloquial claims. We compare the characteristics of colloquial claims to those of claims in formal style, and demonstrate the challenging issues in them.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_122", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_122.flac", "type": "audio", "duration": 617.999}, {"from": "Assistant", "value": "Retrieval-based dialogue systems display an outstanding performance when pre-trained language models are used, which includes bidirectional encoder representations from transformers (BERT). During the multi-turn response selection, BERT focuses on training the relationship between the context with multiple utterances and the response. However, this method of training is insufficient when considering the relations between each utterance in the context. This leads to a problem of not completely understanding the context flow that is required to select a response. To address this issue, we propose a new fine-grained post-training method that reflects the characteristics of the multi-turn dialogue. Specifically, the model learns the utterance level interactions by training every short context-response pair in a dialogue session. Furthermore, by using a new training objective, the utterance relevance classification, the model understands the semantic relevance and coherence between the dialogue utterances. Experimental results show that our model achieves new state-of-the-art with significant margins on three benchmark datasets. This suggests that the fine-grained post-training method is highly effective for the response selection task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_123", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_123.flac", "type": "audio", "duration": 607.016}, {"from": "Assistant", "value": "Most chatbot literature that focuses on improving the fluency and coherence of a chatbot, is dedicated to making chatbots more human-like. However, very little work delves into what really separates humans from chatbots – humans intrinsically understand the effect their responses have on the interlocutor and often respond with an intention such as proposing an optimistic view to make the interlocutor feel better. This paper proposes an innovative framework to train chatbots to possess human-like intentions. Our framework includes a guiding chatbot and an interlocutor model that plays the role of humans. The guiding chatbot is assigned an intention and learns to induce the interlocutor to reply with responses matching the intention, for example, long responses, joyful responses, responses with specific words, etc. We examined our framework using three experimental setups and evaluated the guiding chatbot with four different metrics to demonstrate flexibility and performance advantages. Additionally, we performed trials with human interlocutors to substantiate the guiding chatbot’s effectiveness in influencing the responses of humans to a certain extent. Code will be made available to the public.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_124", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_124.flac", "type": "audio", "duration": 658.053}, {"from": "Assistant", "value": "Existing dialogue corpora and models are typically designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e.g., booking hotels), open-domain chatbots aim at making socially engaging conversations. In this work, we propose to integrate both types of systems by Adding Chit-Chat to ENhance Task-ORiented dialogues (ACCENTOR), with the goal of making virtual assistant conversations more engaging and interactive. Specifically, we propose a Human <-> AI collaborative data collection approach for generating diverse chit-chat responses to augment task-oriented dialogues with minimal annotation effort. We then present our new chit-chat-based annotations to 23.8K dialogues from two popular task-oriented datasets (Schema-Guided Dialogue and MultiWOZ 2.1) and demonstrate their advantage over the originals via human evaluation. Lastly, we propose three new models for adding chit-chat to task-oriented dialogues, explicitly trained to predict user goals and to generate contextually relevant chit-chat responses. Automatic and human evaluations show that, compared with the state-of-the-art task-oriented baseline, our models can code-switch between task and chit-chat to be more engaging, interesting, knowledgeable, and humanlike, while maintaining competitive task performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_125", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_125.flac", "type": "audio", "duration": 354.058}, {"from": "Assistant", "value": "External syntactic and semantic information has been largely ignored by existing neural coreference resolution models. In this paper, we present a heterogeneous graph-based model to incorporate syntactic and semantic structures of sentences. The proposed graph contains a syntactic sub-graph where tokens are connected based on a dependency tree, and a semantic sub-graph that contains arguments and predicates as nodes and semantic role labels as edges. By applying a graph attention network, we can obtain syntactically and semantically augmented word representation, which can be integrated using an attentive integration layer and gating mechanism. Experiments on the OntoNotes 5.0 benchmark show the effectiveness of our proposed model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_126", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_126.flac", "type": "audio", "duration": 408.044}, {"from": "Assistant", "value": "Implicit discourse relation recognition (IDRR) aims to identify logical relations between two adjacent sentences in the discourse. Existing models fail to fully utilize the contextual information which plays an important role in interpreting each local sentence. In this paper, we thus propose a novel graph-based Context Tracking Network (CT-Net) to model the discourse context for IDRR. The CT-Net firstly converts the discourse into the paragraph association graph (PAG), where each sentence tracks their closely related context from the intricate discourse through different types of edges. Then, the CT-Net extracts contextual representation from the PAG through a specially designed cross-grained updating mechanism, which can effectively integrate both sentence-level and token-level contextual semantics. Experiments on PDTB 2.0 show that the CT-Net gains better performance than models that roughly model the context.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_127", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_127.flac", "type": "audio", "duration": 713.363}, {"from": "Assistant", "value": "Most of the previous Rhetorical Structure Theory (RST) parsing methods are based on supervised learning such as neural networks, that require an annotated corpus of sufficient size and quality. However, the RST Discourse Treebank (RST-DT), the benchmark corpus for RST parsing in English, is small due to the costly annotation of RST trees. The lack of large annotated training data causes poor performance especially in relation labeling. Therefore, we propose a method for improving neural RST parsing models by exploiting silver data, i.e., automatically annotated data. We create large-scale silver data from an unlabeled corpus by using a state-of-the-art RST parser. To obtain high-quality silver data, we extract agreement subtrees from RST trees for documents built using the RST parsers. We then pre-train a neural RST parser with the obtained silver data and fine-tune it on the RST-DT. Experimental results show that our method achieved the best micro-F1 scores for Nuclearity and Relation at 75.0 and 63.2, respectively. Furthermore, we obtained a remarkable gain in the Relation score, 3.0 points, against the previous state-of-the-art parser.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_128", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_128.flac", "type": "audio", "duration": 555.05}, {"from": "Assistant", "value": "We introduce a novel top-down end-to-end formulation of document level discourse parsing in the Rhetorical Structure Theory (RST) framework. In this formulation, we consider discourse parsing as a sequence of splitting decisions at token boundaries and use a seq2seq network to model the splitting decisions. Our framework facilitates discourse parsing from scratch without requiring discourse segmentation as a prerequisite; rather, it yields segmentation as part of the parsing process. Our unified parsing model adopts a beam search to decode the best tree structure by searching through a space of high scoring trees. With extensive experiments on the standard RST discourse treebank, we demonstrate that our parser outperforms existing methods by a good margin in both end-to-end parsing and parsing with gold segmentation. More importantly, it does so without using any handcrafted features, making it faster and easily adaptable to new languages and domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_129", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_129.flac", "type": "audio", "duration": 720.051}, {"from": "Assistant", "value": "Discourse signals are often implicit, leaving it up to the interpreter to draw the required inferences. At the same time, discourse is embedded in a social context, meaning that interpreters apply their own assumptions and beliefs when resolving these inferences, leading to multiple, valid interpretations. However, current discourse data and frameworks ignore the social aspect, expecting only a single ground truth. We present the first discourse dataset with multiple and subjective interpretations of English conversation in the form of perceived conversation acts and intents. We carefully analyze our dataset and create computational models to (1) confirm our hypothesis that taking into account the bias of the interpreters leads to better predictions of the interpretations, (2) and show disagreements are nuanced and require a deeper understanding of the different contextual factors. We share our dataset and code athttp://github.com/elisaF/subjective_discourse.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_130", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_130.flac", "type": "audio", "duration": 409.043}, {"from": "Assistant", "value": "Recent work on entity coreference resolution (CR) follows current trends in Deep Learning applied to embeddings and relatively simple task-related features. SOTA models do not make use of hierarchical representations of discourse structure. In this work, we leverage automatically constructed discourse parse trees within a neural approach and demonstrate a significant improvement on two benchmark entity coreference-resolution datasets. We explore how the impact varies depending upon the type of mention.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_131", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_131.flac", "type": "audio", "duration": 411.051}, {"from": "Assistant", "value": "While Yu and Poesio (2020) have recently demonstrated the superiority of their neural multi-task learning (MTL) model to rule-based approaches for bridging anaphora resolution, there is little understanding of (1) how it is better than the rule-based approaches (e.g., are the two approaches making similar or complementary mistakes?) and (2) what should be improved. To shed light on these issues, we (1) propose a hybrid rule-based and MTL approach that would enable a better understanding of their comparative strengths and weaknesses; and (2) perform a manual analysis of the errors made by the MTL model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_132", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_132.flac", "type": "audio", "duration": 574.02}, {"from": "Assistant", "value": "Syntax is fundamental to our thinking about language. Failing to capture the structure of input language could lead to generalization problems and over-parametrization. In the present work, we propose a new syntax-aware language model: Syntactic Ordered Memory (SOM). The model explicitly models the structure with an incremental parser and maintains the conditional probability setting of a standard language model (left-to-right). To train the incremental parser and avoid exposure bias, we also propose a novel dynamic oracle, so that SOM is more robust to wrong parsing decisions. Experiments show that SOM can achieve strong results in language modeling, incremental parsing, and syntactic generalization tests while using fewer parameters than other models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_133", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_133.flac", "type": "audio", "duration": 413.056}, {"from": "Assistant", "value": "Policy gradient algorithms have found wide adoption in NLP, but have recently become subject to criticism, doubting their suitability for NMT. Choshen et al. (2020) identify multiple weaknesses and suspect that their success is determined by the shape of output distributions rather than the reward. In this paper, we revisit these claims and study them under a wider range of configurations. Our experiments on in-domain and cross-domain adaptation reveal the importance of exploration and reward scaling, and provide empirical counter-evidence to these claims.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_134", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_134.flac", "type": "audio", "duration": 536.52}, {"from": "Assistant", "value": "Sequential information, a.k.a., orders, is assumed to be essential for processing a sequence with recurrent neural network or convolutional neural network based encoders. However, is it possible to encode natural languages without orders? Given a bag of words from a disordered sentence, humans may still be able to understand what those words mean by reordering or reconstructing them. Inspired by such an intuition, in this paper, we perform a study to investigate how “order” information takes effects in natural language learning. By running comprehensive comparisons, we quantitatively compare the ability of several representative neural models to organize sentences from a bag of words under three typical scenarios, and summarize some empirical findings and challenges, which can shed light on future research on this line of work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_135", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_135.flac", "type": "audio", "duration": 705.045}, {"from": "Assistant", "value": "Transformer is an attention-based neural network, which consists of two sublayers, namely, Self-Attention Network (SAN) and Feed-Forward Network (FFN). Existing research explores to enhance the two sublayers separately to improve the capability of Transformer for text representation. In this paper, we present a novel understanding of SAN and FFN as Mask Attention Networks (MANs) and show that they are two special cases of MANs with static mask matrices. However, their static mask matrices limit the capability for localness modeling in text representation learning. We therefore introduce a new layer named dynamic mask attention network (DMAN) with a learnable mask matrix which is able to model localness adaptively. To incorporate advantages of DMAN, SAN, and FFN, we propose a sequential layered structure to combine the three types of layers. Extensive experiments on various tasks, including neural machine translation and text summarization demonstrate that our model outperforms the original Transformer.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_136", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_136.flac", "type": "audio", "duration": 662.001}, {"from": "Assistant", "value": "Coarse-grained linguistic information, such as named entities or phrases, facilitates adequately representation learning in pre-training. Previous works mainly focus on extending the objective of BERT’s Masked Language Modeling (MLM) from masking individual tokens to contiguous sequences of n tokens. We argue that such contiguously masking method neglects to model the intra-dependencies and inter-relation of coarse-grained linguistic information. As an alternative, we propose ERNIE-Gram, an explicitly n-gram masking method to enhance the integration of coarse-grained information into pre-training. In ERNIE-Gram, n-grams are masked and predicted directly using explicit n-gram identities rather than contiguous sequences of n tokens. Furthermore, ERNIE-Gram employs a generator model to sample plausible n-gram identities as optional n-gram masks and predict them in both coarse-grained and fine-grained manners to enable comprehensive n-gram prediction and relation modeling. We pre-train ERNIE-Gram on English and Chinese text corpora and fine-tune on 19 downstream tasks. Experimental results show that ERNIE-Gram outperforms previous pre-training models like XLNet and RoBERTa by a large margin, and achieves comparable results with state-of-the-art methods. The source codes and pre-trained models have been released athttps://github.com/PaddlePaddle/ERNIE.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_137", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_137.flac", "type": "audio", "duration": 644.005}, {"from": "Assistant", "value": "Chinese pre-trained language models usually process text as a sequence of characters, while ignoring more coarse granularity, e.g., words. In this work, we propose a novel pre-training paradigm for Chinese — Lattice-BERT, which explicitly incorporates word representations along with characters, thus can model a sentence in a multi-granularity manner. Specifically, we construct a lattice graph from the characters and words in a sentence and feed all these text units into transformers. We design a lattice position attention mechanism to exploit the lattice structures in self-attention layers. We further propose a masked segment prediction task to push the model to learn from rich but redundant information inherent in lattices, while avoiding learning unexpected tricks. Experiments on 11 Chinese natural language understanding tasks show that our model can bring an average increase of 1.5% under the 12-layer setting, which achieves new state-of-the-art among base-size models on the CLUE benchmarks. Further analysis shows that Lattice-BERT can harness the lattice structures, and the improvement comes from the exploration of redundant information and multi-granularity representations. Our code will be available athttps://github.com/alibaba/pretrained-language-models/LatticeBERT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_138", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_138.flac", "type": "audio", "duration": 670.058}, {"from": "Assistant", "value": "Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events. While distributional models—most recently pre-trained, Transformer language models—have demonstrated improvements in modeling event plausibility, their performance still falls short of humans’. In this work, we show that Transformer-based plausibility models are markedly inconsistent across the conceptual classes of a lexical hierarchy, inferring that “a person breathing” is plausible while “a dentist breathing” is not, for example. We find this inconsistency persists even when models are softly injected with lexical knowledge, and we present a simple post-hoc method of forcing model consistency that improves correlation with human plausibility judgements.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_139", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_139.flac", "type": "audio", "duration": 673.054}, {"from": "Assistant", "value": "Contextual word embedding models, such as BioBERT and Bio_ClinicalBERT, have achieved state-of-the-art results in biomedical natural language processing tasks by focusing their pre-training process on domain-specific corpora. However, such models do not take into consideration structured expert domain knowledge from a knowledge base. We introduce UmlsBERT, a contextual embedding model that integrates domain knowledge during the pre-training process via a novel knowledge augmentation strategy. More specifically, the augmentation on UmlsBERT with the Unified Medical Language System (UMLS) Metathesaurus is performed in two ways: i) connecting words that have the same underlying ‘concept’ in UMLS and ii) leveraging semantic type knowledge in UMLS to create clinically meaningful input embeddings. By applying these two strategies, UmlsBERT can encode clinical domain knowledge into word embeddings and outperform existing domain-specific models on common named-entity recognition (NER) and clinical natural language inference tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_140", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_140.flac", "type": "audio", "duration": 640.768}, {"from": "Assistant", "value": "Word representations empowered with additional linguistic information have been widely studied and proved to outperform traditional embeddings. Current methods mainly focus on learning embeddings for words while embeddings of linguistic information (referred to as grain embeddings) are discarded after the learning. This work proposes a framework field embedding to jointly learn both word and grain embeddings by incorporating morphological, phonetic, and syntactical linguistic fields. The framework leverages an innovative fine-grained pipeline that integrates multiple linguistic fields and produces high-quality grain sequences for learning supreme word representations. A novel algorithm is also designed to learn embeddings for words and grains by capturing information that is contained within each field and that is shared across them. Experimental results of lexical tasks and downstream natural language processing tasks illustrate that our framework can learn better word embeddings and grain embeddings. Qualitative evaluations show grain embeddings effectively capture the semantic information.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_141", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_141.flac", "type": "audio", "duration": 534.013}, {"from": "Assistant", "value": "Automated metaphor detection is a challenging task to identify the metaphorical expression of words in a sentence. To tackle this problem, we adopt pre-trained contextualized models, e.g., BERT and RoBERTa. To this end, we propose a novel metaphor detection model, namelymetaphor-aware late interaction over BERT (MelBERT). Our model not only leverages contextualized word representation but also benefits from linguistic metaphor identification theories to detect whether the target word is metaphorical. Our empirical results demonstrate that MelBERT outperforms several strong baselines on four benchmark datasets, i.e., VUA-18, VUA-20, MOH-X, and TroFi.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_142", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_142.flac", "type": "audio", "duration": 428.013}, {"from": "Assistant", "value": "Word sense disambiguation (WSD) is a long-standing problem in natural language processing. One significant challenge in supervised all-words WSD is to classify among senses for a majority of words that lie in the long-tail distribution. For instance, 84% of the annotated words have less than 10 examples in the SemCor training data. This issue is more pronounced as the imbalance occurs in both word and sense distributions. In this work, we propose MetricWSD, a non-parametric few-shot learning approach to mitigate this data imbalance issue. By learning to compute distances among the senses of a given word through episodic training, MetricWSD transfers knowledge (a learned metric space) from high-frequency words to infrequent ones. MetricWSD constructs the training episodes tailored to word frequencies and explicitly addresses the problem of the skewed distribution, as opposed to mixing all the words trained with parametric models in previous work. Without resorting to any lexical resources, MetricWSD obtains strong performance against parametric alternatives, achieving a 75.1 F1 score on the unified WSD evaluation benchmark (Raganato et al., 2017b). Our analysis further validates that infrequent words and senses enjoy significant improvement.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_143", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_143.flac", "type": "audio", "duration": 689.045}, {"from": "Assistant", "value": "Machine learning solutions are often criticized for the lack of explanation of their successes and failures. Understanding which instances are misclassified and why is essential to improve the learning process. This work helps to fill this gap by proposing a methodology to characterize, quantify and measure the impact of hard instances in the task of polarity classification of movie reviews. We characterize such instances into two categories: neutrality, where the text does not convey a clear polarity, and discrepancy, where the polarity of the text is the opposite of its true rating. We quantify the number of hard instances in polarity classification of movie reviews and provide empirical evidence about the need to pay attention to such problematic instances, as they are much harder to classify, for both machine and human classifiers. To the best of our knowledge, this is the first systematic analysis of the impact of hard instances in polarity detection from well-formed textual reviews.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_144", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_144.flac", "type": "audio", "duration": 694.044}, {"from": "Assistant", "value": "Fine-grained opinion mining (OM) has achieved increasing attraction in the natural language processing (NLP) community, which aims to find the opinion structures of “Who expressed what opinions towards what” in one sentence. In this work, motivated by its span-based representations of opinion expressions and roles, we propose a unified span-based approach for the end-to-end OM setting. Furthermore, inspired by the unified span-based formalism of OM and constituent parsing, we explore two different methods (multi-task learning and graph convolutional neural network) to integrate syntactic constituents into the proposed model to help OM. We conduct experiments on the commonly used MPQA 2.0 dataset. The experimental results show that our proposed unified span-based approach achieves significant improvements over previous works in the exact F1 score and reduces the number of wrongly-predicted opinion expressions and roles, showing the effectiveness of our method. In addition, incorporating the syntactic constituents achieves promising improvements over the strong baseline enhanced by contextualized word representations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_145", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_145.flac", "type": "audio", "duration": 517.805}, {"from": "Assistant", "value": "Opinion target extraction and opinion term extraction are two fundamental tasks in Aspect Based Sentiment Analysis (ABSA). Many recent works on ABSA focus on Target-oriented Opinion Words (or Terms) Extraction (TOWE), which aims at extracting the corresponding opinion words for a given opinion target. TOWE can be further applied to Aspect-Opinion Pair Extraction (AOPE) which aims at extracting aspects (i.e., opinion targets) and opinion terms in pairs. In this paper, we propose Target-Specified sequence labeling with Multi-head Self-Attention (TSMSA) for TOWE, in which any pre-trained language model with multi-head self-attention can be integrated conveniently. As a case study, we also develop a Multi-Task structure named MT-TSMSA for AOPE by combining our TSMSA with an aspect and opinion term extraction module. Experimental results indicate that TSMSA outperforms the benchmark methods on TOWE significantly; meanwhile, the performance of MT-TSMSA is similar or even better than state-of-the-art AOPE baseline models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_146", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_146.flac", "type": "audio", "duration": 684.013}, {"from": "Assistant", "value": "Aspect-based Sentiment Analysis (ABSA), aiming at predicting the polarities for aspects, is a fine-grained task in the field of sentiment analysis. Previous work showed syntactic information, e.g. dependency trees, can effectively improve the ABSA performance. Recently, pre-trained models (PTMs) also have shown their effectiveness on ABSA. Therefore, the question naturally arises whether PTMs contain sufficient syntactic information for ABSA so that we can obtain a good ABSA model only based on PTMs. In this paper, we firstly compare the induced trees from PTMs and the dependency parsing trees on several popular models for the ABSA task, showing that the induced tree from fine-tuned RoBERTa (FT-RoBERTa) outperforms the parser-provided tree. The further analysis experiments reveal that the FT-RoBERTa Induced Tree is more sentiment-word-oriented and could benefit the ABSA task. The experiments also show that the pure RoBERTa-based model can outperform or approximate to the previous SOTA performances on six datasets across four languages since it implicitly incorporates the task-oriented syntactic information.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_147", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_147.flac", "type": "audio", "duration": 710.6}, {"from": "Assistant", "value": "Domain divergence plays a significant role in estimating the performance of a model in new domains. While there is a significant literature on divergence measures, researchers find it hard to choose an appropriate divergence for a given NLP application. We address this shortcoming by both surveying the literature and through an empirical study. We develop a taxonomy of divergence measures consisting of three classes — Information-theoretic, Geometric, and Higher-order measures and identify the relationships between them. Further, to understand the common use-cases of these measures, we recognise three novel applications – 1) Data Selection, 2) Learning Representation, and 3) Decisions in the Wild – and use it to organise our literature. From this, we identify that Information-theoretic measures are prevalent for 1) and 3), and Higher-order measures are more common for 2). To further help researchers choose appropriate measures to predict drop in performance – an important aspect of Decisions in the Wild, we perform correlation analysis spanning 130 domain adaptation scenarios, 3 varied NLP tasks and 12 divergence measures identified from our survey. To calculate these divergences, we consider the current contextual word representations (CWR) and contrast with the older distributed representations. We find that traditional measures over word distributions still serve as strong baselines, while higher-order measures with CWR are effective.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_148", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_148.flac", "type": "audio", "duration": 686.777}, {"from": "Assistant", "value": "The goal of stance detection is to identify whether the author of a text is in favor of, neutral or against a specific target. Despite substantial progress on this task, one of the remaining challenges is the scarcity of annotations. Data augmentation is commonly used to address annotation scarcity by generating more training samples. However, the augmented sentences that are generated by existing methods are either less diversified or inconsistent with the given target and stance label. In this paper, we formulate the data augmentation of stance detection as a conditional masked language modeling task and augment the dataset by predicting the masked word conditioned on both its context and the auxiliary sentence that contains target and label information. Moreover, we propose another simple yet effective method that generates target-aware sentence by replacing a target mention with the other. Experimental results show that our proposed methods significantly outperforms previous augmentation methods on 11 targets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_149", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_149.flac", "type": "audio", "duration": 713.045}, {"from": "Assistant", "value": "We propose a Transformer-based sequence-to-sequence model for automatic speech recognition (ASR) capable of simultaneously transcribing and annotating audio with linguistic information such as phonemic transcripts or part-of-speech (POS) tags. Since linguistic information is important in natural language processing (NLP), the proposed ASR is especially useful for speech interface applications, including spoken dialogue systems and speech translation, which combine ASR and NLP. To produce linguistic annotations, we train the ASR system using modified training targets: each grapheme or multi-grapheme unit in the target transcript is followed by an aligned phoneme sequence and/or POS tag. Since our method has access to the underlying audio data, we can estimate linguistic annotations more accurately than pipeline approaches in which NLP-based methods are applied to a hypothesized ASR transcript. Experimental results on Japanese and English datasets show that the proposed ASR system is capable of simultaneously producing high-quality transcriptions and linguistic annotations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_150", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_150.flac", "type": "audio", "duration": 432.299}, {"from": "Assistant", "value": "A conventional approach to improving the performance of end-to-end speech translation (E2E-ST) models is to leverage the source transcription via pre-training and joint training with automatic speech recognition (ASR) and neural machine translation (NMT) tasks. However, since the input modalities are different, it is difficult to leverage source language text successfully. In this work, we focus on sequence-level knowledge distillation (SeqKD) from external text-based NMT models. To leverage the full potential of the source language information, we propose backward SeqKD, SeqKD from a target-to-source backward NMT model. To this end, we train a bilingual E2E-ST model to predict paraphrased transcriptions as an auxiliary task with a single decoder. The paraphrases are generated from the translations in bitext via back-translation. We further propose bidirectional SeqKD in which SeqKD from both forward and backward NMT models is combined. Experimental evaluations on both autoregressive and non-autoregressive models show that SeqKD in each direction consistently improves the translation performance, and the effectiveness is complementary regardless of the model capacity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_151", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_151.flac", "type": "audio", "duration": 741.042}, {"from": "Assistant", "value": "End-to-end approaches for sequence tasks are becoming increasingly popular. Yet for complex sequence tasks, like speech translation, systems that cascade several models trained on sub-tasks have shown to be superior, suggesting that the compositionality of cascaded systems simplifies learning and enables sophisticated search capabilities. In this work, we present an end-to-end framework that exploits compositionality to learn searchable hidden representations at intermediate stages of a sequence model using decomposed sub-tasks. These hidden intermediates can be improved using beam search to enhance the overall performance and can also incorporate external models at intermediate stages of the network to re-score or adapt towards out-of-domain data. One instance of the proposed framework is a Multi-Decoder model for speech translation that extracts the searchable hidden intermediates from a speech recognition sub-task. The model demonstrates the aforementioned benefits and outperforms the previous state-of-the-art by around +6 and +3 BLEU on the two test sets of Fisher-CallHome and by around +3 and +4 BLEU on the English-German and English-French test sets of MuST-C.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_152", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_152.flac", "type": "audio", "duration": 448.052}, {"from": "Assistant", "value": "Spoken language understanding (SLU) requires a model to analyze input acoustic signal to understand its linguistic content and make predictions. To boost the models’ performance, various pre-training methods have been proposed to learn rich representations from large-scale unannotated speech and text. However, the inherent disparities between the two modalities necessitate a mutual analysis. In this paper, we propose a novel semi-supervised learning framework, SPLAT, to jointly pre-train the speech and language modules. Besides conducting a self-supervised masked language modeling task on the two individual modules using unpaired speech and text, SPLAT aligns representations from the two modules in a shared latent space using a small amount of paired speech and text. Thus, during fine-tuning, the speech module alone can produce representations carrying both acoustic information and contextual semantic knowledge of an input acoustic signal. Experimental results verify the effectiveness of our approach on various SLU tasks. For example, SPLAT improves the previous state-of-the-art performance on the Spoken SQuAD dataset by more than 10%.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_153", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_153.flac", "type": "audio", "duration": 654.08}, {"from": "Assistant", "value": "Although Question-Answering has long been of research interest, its accessibility to users through a speech interface and its support to multiple languages have not been addressed in prior studies. Towards these ends, we present a new task and a synthetically-generated dataset to do Fact-based Visual Spoken-Question Answering (FVSQA). FVSQA is based on the FVQA dataset, which requires a system to retrieve an entity from Knowledge Graphs (KGs) to answer a question about an image. In FVSQA, the question is spoken rather than typed. Three sub-tasks are proposed: (1) speech-to-text based, (2) end-to-end, without speech-to-text as an intermediate component, and (3) cross-lingual, in which the question is spoken in a language different from that in which the KG is recorded. The end-to-end and cross-lingual tasks are the first to require world knowledge from a multi-relational KG as a differentiable layer in an end-to-end spoken language understanding task, hence the proposed reference implementation is called Worldly-Wise (WoW).WoW is shown to perform end-to-end cross-lingual FVSQA at same levels of accuracy across 3 languages - English, Hindi, and Turkish.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_154", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_154.flac", "type": "audio", "duration": 668.736}, {"from": "Assistant", "value": "Non-autoregressive encoder-decoder models greatly improve decoding speed over autoregressive models, at the expense of generation quality. To mitigate this, iterative decoding models repeatedly infill or refine the proposal of a non-autoregressive model. However, editing at the level of output sequences limits model flexibility. We instead propose *iterative realignment*, which by refining latent alignments allows more flexible edits in fewer steps. Our model, Align-Refine, is an end-to-end Transformer which iteratively realigns connectionist temporal classification (CTC) alignments. On the WSJ dataset, Align-Refine matches an autoregressive baseline with a 14x decoding speedup; on LibriSpeech, we reach an LM-free test-other WER of 9.0% (19% relative improvement on comparable work) in three iterations. We release our code athttps://github.com/amazon-research/align-refine.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_155", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_155.flac", "type": "audio", "duration": 629.005}, {"from": "Assistant", "value": "Causal inference is the process of capturing cause-effect relationship among variables. Most existing works focus on dealing with structured data, while mining causal relationship among factors from unstructured data, like text, has been less examined, but is of great importance, especially in the legal domain. In this paper, we propose a novel Graph-based Causal Inference (GCI) framework, which builds causal graphs from fact descriptions without much human involvement and enables causal inference to facilitate legal practitioners to make proper decisions. We evaluate the framework on a challenging similar charge disambiguation task. Experimental results show that GCI can capture the nuance from fact descriptions among multiple confusing charges and provide explainable discrimination, especially in few-shot settings. We also observe that the causal knowledge contained in GCI can be effectively injected into powerful neural networks for better performance and interpretability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_156", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_156.flac", "type": "audio", "duration": 630.004}, {"from": "Assistant", "value": "Providing a reliable explanation for clinical diagnosis based on the Electronic Medical Record (EMR) is fundamental to the application of Artificial Intelligence in the medical field. Current methods mostly treat the EMR as a text sequence and provide explanations based on a precise medical knowledge base, which is disease-specific and difficult to obtain for experts in reality. Therefore, we propose a counterfactual multi-granularity graph supporting facts extraction (CMGE) method to extract supporting facts from irregular EMR itself without external knowledge bases in this paper. Specifically, we first structure the sequence of EMR into a hierarchical graph network and then obtain the causal relationship between multi-granularity features and diagnosis results through counterfactual intervention on the graph. Features having the strongest causal connection with the results provide interpretive support for the diagnosis. Experimental results on real Chinese EMR of the lymphedema demonstrate that our method can diagnose four types of EMR correctly, and can provide accurate supporting facts for the results. More importantly, the results on different diseases demonstrate the robustness of our approach, which represents the potential application in the medical field.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_157", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_157.flac", "type": "audio", "duration": 632.896}, {"from": "Assistant", "value": "Despite the impressive successes of generation and dialogue systems, how to endow a text generation system with particular personality traits to deliver more personalized responses remains under-investigated. In this work, we look at how to generate personalized responses for questions on Reddit by utilizing personalized user profiles and posting histories. Specifically, we release an open-domainsingle-turndialog dataset made up of 1.5M conversation pairs together with 300k profiles of users and related comments. We then propose a memory network to generate personalized responses in dialogue that utilizes a novel mechanism of splitting memories: one for user profile meta attributes and the other for user-generated information like comment histories. Experimental results show the quantitative and qualitative improvements of our simple split memory network model over the state-of-the-art response generation baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_158", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_158.flac", "type": "audio", "duration": 716.416}, {"from": "Assistant", "value": "Few-shot learning has drawn researchers’ attention to overcome the problem of data scarcity. Recently, large pre-trained language models have shown great performance in few-shot learning for various downstream tasks, such as question answering and machine translation. Nevertheless, little exploration has been made to achieve few-shot learning for the fact-checking task. However, fact-checking is an important problem, especially when the amount of information online is growing exponentially every day. In this paper, we propose a new way of utilizing the powerful transfer learning ability of a language model via a perplexity score. The most notable strength of our methodology lies in its capability in few-shot learning. With only two training samples, our methodology can already outperform the Major Class baseline by more than an absolute 10% on the F1-Macro metric across multiple datasets. Through experiments, we empirically verify the plausibility of the rather surprising usage of the perplexity score in the context of fact-checking and highlight the strength of our few-shot methodology by comparing it to strong fine-tuning-based baseline models. Moreover, we construct and publicly release two new fact-checking datasets related to COVID-19.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_159", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_159.flac", "type": "audio", "duration": 640.383}, {"from": "Assistant", "value": "While deep learning is a powerful tool for natural language processing (NLP) problems, successful solutions to these problems rely heavily on large amounts of annotated samples. However, manually annotating data is expensive and time-consuming. Active Learning (AL) strategies reduce the need for huge volumes of labeled data by iteratively selecting a small number of examples for manual annotation based on their estimated utility in training the given model. In this paper, we argue that since AL strategies choose examples independently, they may potentially select similar examples, all of which may not contribute significantly to the learning process. Our proposed approach, Active2Learning (A2L), actively adapts to the deep learning model being trained to eliminate such redundant examples chosen by an AL strategy. We show that A2L is widely applicable by using it in conjunction with several different AL strategies and NLP tasks. We empirically demonstrate that the proposed approach is further able to reduce the data requirements of state-of-the-art AL strategies by≈3-25%on an absolute scale on multiple NLP tasks while achieving the same performance with virtually no additional computation overhead.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_160", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_160.flac", "type": "audio", "duration": 739.695}, {"from": "Assistant", "value": "Given the diversity of the candidates and complexity of job requirements, and since interviewing is an inherently subjective process, it is an important task to ensure consistent, uniform, efficient and objective interviews that result in high quality recruitment. We propose an interview assistant system to automatically, and in an objective manner, select an optimal set of technical questions (from question banks) personalized for a candidate. This set can help a human interviewer to plan for an upcoming interview of that candidate. We formalize the problem of selecting a set of questions as an integer linear programming problem and use standard solvers to get a solution. We use knowledge graph as background knowledge in this formulation, and derive our objective functions and constraints from it. We use candidate’s resume to personalize the selection of questions. We propose an intrinsic evaluation to compare a set of suggested questions with actually asked questions. We also use expert interviewers to comparatively evaluate our approach with a set of reasonable baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_161", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_161.flac", "type": "audio", "duration": 411.063}, {"from": "Assistant", "value": "Natural language processing (NLP) tasks, ranging from text classification to text generation, have been revolutionised by the pretrained language models, such as BERT. This allows corporations to easily build powerful APIs by encapsulating fine-tuned BERT models for downstream tasks. However, when a fine-tuned BERT model is deployed as a service, it may suffer from different attacks launched by the malicious users. In this work, we first present how an adversary can steal a BERT-based API service (the victim/target model) on multiple benchmark datasets with limited prior knowledge and queries. We further show that the extracted model can lead to highly transferable adversarial attacks against the victim model. Our studies indicate that the potential vulnerabilities of BERT-based API services still hold, even when there is an architectural mismatch between the victim model and the attack model. Finally, we investigate two defence strategies to protect the victim model, and find that unless the performance of the victim model is sacrificed, both model extraction and adversarial transferability can effectively compromise the target models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_162", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_162.flac", "type": "audio", "duration": 721.049}, {"from": "Assistant", "value": "Early exit mechanism aims to accelerate the inference speed of large-scale pre-trained language models. The essential idea is to exit early without passing through all the inference layers at the inference stage. To make accurate predictions for downstream tasks, the hierarchical linguistic information embedded in all layers should be jointly considered. However, much of the research up to now has been limited to use local representations of the exit layer. Such treatment inevitably loses information of the unused past layers as well as the high-level features embedded in future layers, leading to sub-optimal performance. To address this issue, we propose a novel Past-Future method to make comprehensive predictions from a global perspective. We first take into consideration all the linguistic information embedded in the past layers and then take a further step to engage the future information which is originally inaccessible for predictions. Extensive experiments demonstrate that our method outperforms previous early exit methods by a large margin, yielding better and robust performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_163", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_163.flac", "type": "audio", "duration": 738.048}, {"from": "Assistant", "value": "Conditional Random Field (CRF) based neural models are among the most performant methods for solving sequence labeling problems. Despite its great success, CRF has the shortcoming of occasionally generating illegal sequences of tags, e.g. sequences containing an “I-” tag immediately after an “O” tag, which is forbidden by the underlying BIO tagging scheme. In this work, we propose Masked Conditional Random Field (MCRF), an easy to implement variant of CRF that impose restrictions on candidate paths during both training and decoding phases. We show that the proposed method thoroughly resolves this issue and brings significant improvement over existing CRF-based models with near zero additional cost.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_164", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_164.flac", "type": "audio", "duration": 541.002}, {"from": "Assistant", "value": "Prerequisite relations among concepts are crucial for educational applications, such as curriculum planning and intelligent tutoring. In this paper, we propose a novel concept prerequisite relation learning approach, named CPRL, which combines both concept representation learned from a heterogeneous graph and concept pairwise features. Furthermore, we extend CPRL under weakly supervised settings to make our method more practical, including learning prerequisite relations from learning object dependencies and generating training data with data programming. Our experiments on four datasets show that the proposed approach achieves the state-of-the-art results comparing with existing methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_165", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_165.flac", "type": "audio", "duration": 693.013}, {"from": "Assistant", "value": "Recent studies have revealed a security threat to natural language processing (NLP) models, called the Backdoor Attack. Victim models can maintain competitive performance on clean samples while behaving abnormally on samples with a specific trigger word inserted. Previous backdoor attacking methods usually assume that attackers have a certain degree of data knowledge, either the dataset which users would use or proxy datasets for a similar task, for implementing the data poisoning procedure. However, in this paper, we find that it is possible to hack the model in a data-free way by modifying one single word embedding vector, with almost no accuracy sacrificed on clean samples. Experimental results on sentiment analysis and sentence-pair classification tasks show that our method is more efficient and stealthier. We hope this work can raise the awareness of such a critical security risk hidden in the embedding layers of NLP models. Our code is available athttps://github.com/lancopku/Embedding-Poisoning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_166", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_166.flac", "type": "audio", "duration": 717.38}, {"from": "Assistant", "value": "Transformer has achieved great success in the NLP field by composing various advanced models like BERT and GPT. However, Transformer and its existing variants may not be optimal in capturing token distances because the position or distance embeddings used by these methods usually cannot keep the precise information of real distances, which may not be beneficial for modeling the orders and relations of contexts. In this paper, we propose DA-Transformer, which is a distance-aware Transformer that can exploit the real distance. We propose to incorporate the real distances between tokens to re-scale the raw self-attention weights, which are computed by the relevance between attention query and key. Concretely, in different self-attention heads the relative distance between each pair of tokens is weighted by different learnable parameters, which control the different preferences on long- or short-term information of these heads. Since the raw weighted real distances may not be optimal for adjusting self-attention weights, we propose a learnable sigmoid function to map them into re-scaled coefficients that have proper ranges. We first clip the raw self-attention weights via the ReLU function to keep non-negativity and introduce sparsity, and then multiply them with the re-scaled coefficients to encode real distance information into self-attention. Extensive experiments on five benchmark datasets show that DA-Transformer can effectively improve the performance of many tasks and outperform the vanilla Transformer and its several variants.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_167", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_167.flac", "type": "audio", "duration": 698.456}, {"from": "Assistant", "value": "Sentiment analysis has attracted increasing attention in e-commerce. The sentiment polarities underlying user reviews are of great value for business intelligence. Aspect category sentiment analysis (ACSA) and review rating prediction (RP) are two essential tasks to detect the fine-to-coarse sentiment polarities. ACSA and RP are highly correlated and usually employed jointly in real-world e-commerce scenarios. While most public datasets are constructed for ACSA and RP separately, which may limit the further exploitation of both tasks. To address the problem and advance related researches, we present a large-scale Chinese restaurant review dataset ASAP including 46, 730 genuine reviews from a leading online-to-offline (O2O) e-commerce platform in China. Besides a 5-star scale rating, each review is manually annotated according to its sentiment polarities towards 18 pre-defined aspect categories. We hope the release of the dataset could shed some light on the field of sentiment analysis. Moreover, we propose an intuitive yet effective joint model for ACSA and RP. Experimental results demonstrate that the joint model outperforms state-of-the-art baselines on both tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_168", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_168.flac", "type": "audio", "duration": 702.055}, {"from": "Assistant", "value": "The problem of designing NLP solvers for math word problems (MWP) has seen sustained research activity and steady gains in the test accuracy. Since existing solvers achieve high performance on the benchmark datasets for elementary level MWPs containing one-unknown arithmetic word problems, such problems are often considered “solved” with the bulk of research attention moving to more complex MWPs. In this paper, we restrict our attention to English MWPs taught in grades four and lower. We provide strong evidence that the existing MWP solvers rely on shallow heuristics to achieve high performance on the benchmark datasets. To this end, we show that MWP solvers that do not have access to the question asked in the MWP can still solve a large fraction of MWPs. Similarly, models that treat MWPs as bag-of-words can also achieve surprisingly high accuracy. Further, we introduce a challenge dataset, SVAMP, created by applying carefully chosen variations over examples sampled from existing datasets. The best accuracy achieved by state-of-the-art models is substantially lower on SVAMP, thus showing that much remains to be done even for the simplest of the MWPs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_169", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_169.flac", "type": "audio", "duration": 700.523}, {"from": "Assistant", "value": "We annotate 17,000 SNS posts with both the writer’s subjective emotional intensity and the reader’s objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer’s subjective labels than the readers’. The large gap between the subjective and objective emotions imply the complexity of the mapping from a post to the subjective emotion intensities, which also leads to a lower performance with machine learning models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_170", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_170.flac", "type": "audio", "duration": 505.057}, {"from": "Assistant", "value": "In the automatic evaluation of generative question answering (GenQA) systems, it is difficult to assess the correctness of generated answers due to the free-form of the answer. Especially, widely used n-gram similarity metrics often fail to discriminate the incorrect answers since they equally consider all of the tokens. To alleviate this problem, we propose KPQA metric, a new metric for evaluating the correctness of GenQA. Specifically, our new metric assigns different weights to each token via keyphrase prediction, thereby judging whether a generated answer sentence captures the key meaning of the reference answer. To evaluate our metric, we create high-quality human judgments of correctness on two GenQA datasets. Using our human-evaluation datasets, we show that our proposed metric has a significantly higher correlation with human judgments than existing metrics in various datasets. Code for KPQA-metric will be available athttps://github.com/hwanheelee1993/KPQA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_171", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_171.flac", "type": "audio", "duration": 706.048}, {"from": "Assistant", "value": "Text style transfer aims to controllably generate text with targeted stylistic changes while maintaining core meaning from the source sentence constant. Many of the existing style transfer benchmarks primarily focus on individual high-level semantic changes (e.g. positive to negative), which enable controllability at a high level but do not offer fine-grained control involving sentence structure, emphasis, and content of the sentence. In this paper, we introduce a large-scale benchmark, StylePTB, with (1) paired sentences undergoing 21 fine-grained stylistic changes spanning atomic lexical, syntactic, semantic, and thematic transfers of text, as well as (2) compositions of multiple transfers which allow modeling of fine-grained stylistic changes as building blocks for more complex, high-level transfers. By benchmarking existing methods on StylePTB, we find that they struggle to model fine-grained changes and have an even more difficult time composing multiple styles. As a result, StylePTB brings novel challenges that we hope will encourage future research in controllable text style transfer, compositional models, and learning disentangled representations. Solving these challenges would present important steps towards controllable text generation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_172", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_172.flac", "type": "audio", "duration": 370.325}, {"from": "Assistant", "value": "Cant is important for understanding advertising, comedies and dog-whistle politics. However, computational research on cant is hindered by a lack of available datasets. In this paper, we propose a large and diverse Chinese dataset for creating and understanding cant from a computational linguistics perspective. We formulate a task for cant understanding and provide both quantitative and qualitative analysis for tested word embedding similarity and pretrained language models. Experiments suggest that such a task requires deep language understanding, common sense, and world knowledge and thus can be a good testbed for pretrained language models and help models perform better on other tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_173", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_173.flac", "type": "audio", "duration": 397.015}, {"from": "Assistant", "value": "The current COVID-19 pandemic has lead to the creation of many corpora that facilitate NLP research and downstream applications to help fight the pandemic. However, most of these corpora are exclusively for English. As the pandemic is a global problem, it is worth creating COVID-19 related datasets for languages other than English. In this paper, we present the first manually-annotated COVID-19 domain-specific dataset for Vietnamese. Particularly, our dataset is annotated for the named entity recognition (NER) task with newly-defined entity types that can be used in other future epidemics. Our dataset also contains the largest number of entities compared to existing Vietnamese NER datasets. We empirically conduct experiments using strong baselines on our dataset, and find that: automatic Vietnamese word segmentation helps improve the NER results and the highest performances are obtained by fine-tuning pre-trained language models where the monolingual model PhoBERT for Vietnamese (Nguyen and Nguyen, 2020) produces higher results than the multilingual model XLM-R (Conneau et al., 2020). We publicly release our dataset at:https://github.com/VinAIResearch/PhoNER_COVID19", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_174", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_174.flac", "type": "audio", "duration": 693.056}, {"from": "Assistant", "value": "Understanding how news media frame political issues is important due to its impact on public attitudes, yet hard to automate. Computational approaches have largely focused on classifying the frame of a full news article while framing signals are often subtle and local. Furthermore, automatic news analysis is a sensitive domain, and existing classifiers lack transparency in their predictions. This paper addresses both issues with a novel semi-supervised model, which jointly learns to embed local information about the events and related actors in a news article through an auto-encoding framework, and to leverage this signal for document-level frame classification. Our experiments show that: our model outperforms previous models of frame prediction; we can further improve performance with unlabeled training data leveraging the semi-supervised nature of our model; and the learnt event and actor embeddings intuitively corroborate the document-level predictions, providing a nuanced and interpretable article frame representation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_175", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_175.flac", "type": "audio", "duration": 417.365}, {"from": "Assistant", "value": "Neutralisation techniques, e.g. denial of responsibility and denial of victim, are used in the narrative of climate change scepticism to justify lack of action or to promote an alternative view. We first draw on social science to introduce the problem to the community of nlp, present the granularity of the coding schema and then collect manual annotations of neutralised techniques in text relating to climate change, and experiment with supervised and semi- supervised BERT-based models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_176", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_176.flac", "type": "audio", "duration": 703.007}, {"from": "Assistant", "value": "Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. Personally contextualizing the buildup of such ideation is critical for accurate identification of users at risk. In this work, we propose a framework jointly leveraging a user’s emotional history and social information from a user’s neighborhood in a network to contextualize the interpretation of the latest tweet of a user on Twitter. Reflecting upon the scale-free nature of social network relationships, we propose the use of Hyperbolic Graph Convolution Networks, in combination with the Hawkes process to learn the historical emotional spectrum of a user in a time-sensitive manner. Our system significantly outperforms state-of-the-art methods on this task, showing the benefits of both socially and personally contextualized representations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_177", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_177.flac", "type": "audio", "duration": 590.4}, {"from": "Assistant", "value": "This study introduces and analyzes WikiTalkEdit, a dataset of conversations and edit histories from Wikipedia, for research in online cooperation and conversation modeling. The dataset comprises dialog triplets from the Wikipedia Talk pages, and editing actions on the corresponding articles being discussed. We show how the data supports the classic understanding of style matching, where positive emotion and the use of first-person pronouns predict a positive emotional change in a Wikipedia contributor. However, they do not predict editorial behavior. On the other hand, feedback invoking evidentiality and criticism, and references to Wikipedia’s community norms, is more likely to persuade the contributor to perform edits but is less likely to lead to a positive emotion. We developed baseline classifiers trained on pre-trained RoBERTa features that can predict editorial change with an F1 score of .54, as compared to an F1 score of .66 for predicting emotional change. A diagnostic analysis of persisting errors is also provided. We conclude with possible applications and recommendations for future work. The dataset is publicly available for the research community athttps://github.com/kj2013/WikiTalkEdit/.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_178", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_178.flac", "type": "audio", "duration": 685.056}, {"from": "Assistant", "value": "New words are regularly introduced to communities, yet not all of these words persist in a community’s lexicon. Among the many factors contributing to lexical change, we focus on the understudied effect of social networks. We conduct a large-scale analysis of over 80k neologisms in 4420 online communities across a decade. Using Poisson regression and survival analysis, our study demonstrates that the community’s network structure plays a significant role in lexical change. Apart from overall size, properties including dense connections, the lack of local clusters, and more external contacts promote lexical innovation and retention. Unlike offline communities, these topic-based communities do not experience strong lexical leveling despite increased contact but accommodate more niche words. Our work provides support for the sociolinguistic hypothesis that lexical change is partially shaped by the structure of the underlying network but also uncovers findings specific to online communities.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_179", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_179.flac", "type": "audio", "duration": 729.365}, {"from": "Assistant", "value": "The framing of political issues can influence policy and public opinion. Even though the public plays a key role in creating and spreading frames, little is known about how ordinary people on social media frame political issues. By creating a new dataset of immigration-related tweets labeled for multiple framing typologies from political communication theory, we develop supervised models to detect frames. We demonstrate how users’ ideology and region impact framing choices, and how a message’s framing influences audience responses. We find that the more commonly-used issue-generic frames obscure important ideological and regional patterns that are only revealed by immigration-specific frames. Furthermore, frames oriented towards human interests, culture, and politics are associated with higher user engagement. This large-scale analysis of a complex social and linguistic phenomenon contributes to both NLP and social science research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_180", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_180.flac", "type": "audio", "duration": 725.043}, {"from": "Assistant", "value": "The speech act of complaining is used by humans to communicate a negative mismatch between reality and expectations as a reaction to an unfavorable situation. Linguistic theory of pragmatics categorizes complaints into various severity levels based on the face-threat that the complainer is willing to undertake. This is particularly useful for understanding the intent of complainers and how humans develop suitable apology strategies. In this paper, we study the severity level of complaints for the first time in computational linguistics. To facilitate this, we enrich a publicly available data set of complaints with four severity categories and train different transformer-based networks combined with linguistic information achieving 55.7 macro F1. We also jointly model binary complaint classification and complaint severity in a multi-task setting achieving new state-of-the-art results on binary complaint detection reaching up to 88.2 macro F1. Finally, we present a qualitative analysis of the behavior of our models in predicting complaint severity levels.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_181", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_181.flac", "type": "audio", "duration": 705.051}, {"from": "Assistant", "value": "In common law, the outcome of a new case is determined mostly by precedent cases, rather than by existing statutes. However, how exactly does the precedent influence the outcome of a new case? Answering this question is crucial for guaranteeing fair and consistent judicial decision-making. We are the first to approach this question computationally by comparing two longstanding jurisprudential views; Halsbury’s, who believes that the arguments of the precedent are the main determinant of the outcome, and Goodhart’s, who believes that what matters most is the precedent’s facts. We base our study on the corpus of legal cases from the European Court of Human Rights (ECtHR), which allows us to access not only the case itself, but also cases cited in the judges’ arguments (i.e. the precedent cases). Taking an information-theoretic view, and modelling the question as a case out-come classification task, we find that the precedent’s arguments share 0.38 nats of information with the case’s outcome, whereas precedent’s facts only share 0.18 nats of information (i.e.,58% less); suggesting Halsbury’s view may be more accurate in this specific court. We found however in a qualitative analysis that there are specific statues where Goodhart’s view dominates, and present some evidence these are the ones where the legal concept at hand is less straightforward.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_182", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_182.flac", "type": "audio", "duration": 747.056}, {"from": "Assistant", "value": "Online abuse can inflict harm on users and communities, making online spaces unsafe and toxic. Progress in automatically detecting and classifying abusive content is often held back by the lack of high quality and detailed datasets. We introduce a new dataset of primarily English Reddit entries which addresses several limitations of prior work. It (1) contains six conceptually distinct primary categories as well as secondary categories, (2) has labels annotated in the context of the conversation thread, (3) contains rationales and (4) uses an expert-driven group-adjudication process for high quality annotations. We report several baseline models to benchmark the work of future researchers. The annotated dataset, annotation guidelines, models and code are freely available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_183", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_183.flac", "type": "audio", "duration": 646.049}, {"from": "Assistant", "value": "Existing work on automated hate speech classification assumes that the dataset is fixed and the classes are pre-defined. However, the amount of data in social media increases every day, and the hot topics changes rapidly, requiring the classifiers to be able to continuously adapt to new data without forgetting the previously learned knowledge. This ability, referred to as lifelong learning, is crucial for the real-word application of hate speech classifiers in social media. In this work, we propose lifelong learning of hate speech classification on social media. To alleviate catastrophic forgetting, we propose to use Variational Representation Learning (VRL) along with a memory module based on LB-SOINN (Load-Balancing Self-Organizing Incremental Neural Network). Experimentally, we show that combining variational representation learning and the LB-SOINN memory module achieves better performance than the commonly-used lifelong learning techniques.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_184", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_184.flac", "type": "audio", "duration": 678.023}, {"from": "Assistant", "value": "Building NLP systems that serve everyone requires accounting for dialect differences. But dialects are not monolithic entities: rather, distinctions between and within dialects are captured by the presence, absence, and frequency of dozens of dialect features in speech and text, such as the deletion of the copula in “He ∅ running”. In this paper, we introduce the task of dialect feature detection, and present two multitask learning approaches, both based on pretrained transformers. For most dialects, large-scale annotated corpora for these features are unavailable, making it difficult to train recognizers. We train our models on a small number of minimal pairs, building on how linguists typically define dialect features. Evaluation on a test set of 22 dialect features of Indian English demonstrates that these models learn to recognize many features with high accuracy, and that a few minimal pairs can be as effective for training as thousands of labeled examples. We also demonstrate the downstream applicability of dialect feature detection both as a measure of dialect density and as a dialect classifier.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_185", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_185.flac", "type": "audio", "duration": 815.044}, {"from": "Assistant", "value": "When scaled to hundreds of billions of parameters, pretrained language models such as GPT-3 (Brown et al., 2020) achieve remarkable few-shot performance. However, enormous amounts of compute are required for training and applying such big models, resulting in a large carbon footprint and making it difficult for researchers and practitioners to use them. We show that performance similar to GPT-3 can be obtained with language models that are much “greener” in that their parameter count is several orders of magnitude smaller. This is achieved by converting textual inputs into cloze questions that contain a task description, combined with gradient-based optimization; exploiting unlabeled data gives further improvements. We identify key factors required for successful natural language understanding with small language models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_186", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_186.flac", "type": "audio", "duration": 396.063}, {"from": "Assistant", "value": "Recent research investigates factual knowledge stored in large pretrained language models (PLMs). Instead of structural knowledge base (KB) queries, masked sentences such as “Paris is the capital of [MASK]” are used as probes. The good performance on this analysis task has been interpreted as PLMs becoming potential repositories of factual knowledge. In experiments across ten linguistically diverse languages, we study knowledge contained in static embeddings. We show that, when restricting the output space to a candidate set, simple nearest neighbor matching using static embeddings performs better than PLMs. E.g., static embeddings perform 1.6% points better than BERT while just using 0.3% of energy for training. One important factor in their good comparative performance is that static embeddings are standardly learned for a large vocabulary. In contrast, BERT exploits its more sophisticated, but expensive ability to compose meaningful representations from a much smaller subword vocabulary.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_187", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_187.flac", "type": "audio", "duration": 721.003}, {"from": "Assistant", "value": "Knowledge Graph Embeddings (KGEs) have been intensively explored in recent years due to their promise for a wide range of applications. However, existing studies focus on improving the final model performance without acknowledging the computational cost of the proposed approaches, in terms of execution time and environmental impact. This paper proposes a simple yet effective KGE framework which can reduce the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches, while producing competitive performance. We highlight three technical innovations: full batch learning via relational matrices, closed-form Orthogonal Procrustes Analysis for KGEs, and non-negative-sampling training. In addition, as the first KGE method whose entity embeddings also store full relation information, our trained models encode rich semantics and are highly interpretable. Comprehensive experiments and ablation studies involving 13 strong baselines and two standard datasets verify the effectiveness and efficiency of our algorithm.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_188", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_188.flac", "type": "audio", "duration": 419.371}, {"from": "Assistant", "value": "Transformer-based pre-trained language models have significantly improved the performance of various natural language processing (NLP) tasks in the recent years. While effective and prevalent, these models are usually prohibitively large for resource-limited deployment scenarios. A thread of research has thus been working on applying network pruning techniques under the pretrain-then-finetune paradigm widely adopted in NLP. However, the existing pruning results on benchmark transformers, such as BERT, are not as remarkable as the pruning results in the literature of convolutional neural networks (CNNs). In particular, common wisdom in pruning CNN states that sparse pruning technique compresses a model more than that obtained by reducing number of channels and layers, while existing works on sparse pruning of BERT yields inferior results than its small-dense counterparts such as TinyBERT. In this work, we aim to fill this gap by studying how knowledge are transferred and lost during the pre-train, fine-tune, and pruning process, and proposing a knowledge-aware sparse pruning process that achieves significantly superior results than existing literature. We show for the first time that sparse pruning compresses a BERT model significantly more than reducing its number of channels and layers. Experiments on multiple data sets of GLUE benchmark show that our method outperforms the leading competitors with a 20-times weight/FLOPs compression and neglectable loss in prediction accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_189", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_189.flac", "type": "audio", "duration": 403.772}, {"from": "Assistant", "value": "The ease of access to pre-trained transformers has enabled developers to leverage large-scale language models to build exciting applications for their users. While such pre-trained models offer convenient starting points for researchers and developers, there is little consideration for the societal biases captured within these model risking perpetuation of racial, gender, and other harmful biases when these models are deployed at scale. In this paper, we investigate gender and racial bias across ubiquitous pre-trained language models, including GPT-2, XLNet, BERT, RoBERTa, ALBERT and DistilBERT. We evaluate bias within pre-trained transformers using three metrics: WEAT, sequence likelihood, and pronoun ranking. We conclude with an experiment demonstrating the ineffectiveness of word-embedding techniques, such as WEAT, signaling the need for more robust bias testing in transformers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_190", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_190.flac", "type": "audio", "duration": 431.68}, {"from": "Assistant", "value": "Language models (LMs) must be both safe and equitable to be responsibly deployed in practice. With safety in mind, numerous detoxification techniques (e.g., Dathathri et al. 2020; Krause et al. 2020) have been proposed to mitigate toxic LM generations. In this work, we show that these detoxification techniques hurt equity: they decrease the utility of LMs on language used by marginalized groups (e.g., African-American English and minority identity mentions). In particular, we perform automatic and human evaluations of text generation quality when LMs are conditioned on inputs with different dialects and group identifiers. We find that detoxification makes LMs more brittle to distribution shift, especially on language used by marginalized groups. We identify that these failures stem from detoxification methods exploiting spurious correlations in toxicity datasets. Overall, our results highlight the tension between the controllability and distributional robustness of LMs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_191", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_191.flac", "type": "audio", "duration": 306.062}, {"from": "Assistant", "value": "Language models have revolutionized the field of NLP. However, language models capture and proliferate hurtful stereotypes, especially in text generation. Our results show that 4.3% of the time, language models complete a sentence with a hurtful word. These cases are not random, but follow language and gender-specific patterns. We propose a score to measure hurtful sentence completions in language models (HONEST). It uses a systematic template- and lexicon-based bias evaluation methodology for six languages. Our findings suggest that these models replicate and amplify deep-seated societal stereotypes about gender roles. Sentence completions refer to sexual promiscuity when the target is female in 9% of the time, and in 4% to homosexuality when the target is male. The results raise questions about the use of these models in production settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_192", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_192.flac", "type": "audio", "duration": 365.568}, {"from": "Assistant", "value": "We propose EASE, a simple diagnostic tool for Visual Question Answering (VQA) which quantifies the difficulty of an image, question sample. EASE is based on the pattern of answers provided by multiple annotators to a given question. In particular, it considers two aspects of the answers: (i) their Entropy; (ii) their Semantic content. First, we prove the validity of our diagnostic to identify samples that are easy/hard for state-of-art VQA models. Second, we show that EASE can be successfully used to select the most-informative samples for training/fine-tuning. Crucially, only information that is readily available in any VQA dataset is used to compute its scores.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_193", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_193.flac", "type": "audio", "duration": 411.008}, {"from": "Assistant", "value": "Leveraging large-scale unlabeled web videos such as instructional videos for pre-training followed by task-specific finetuning has become the de facto approach for many video-and-language tasks. However, these instructional videos are very noisy, the accompanying ASR narrations are often incomplete, and can be irrelevant to or temporally misaligned with the visual content, limiting the performance of the models trained on such data. To address these issues, we propose an improved video-and-language pre-training method that first adds automatically-extracted dense region captions from the video frames as auxiliary text input, to provide informative visual cues for learning better video and language associations. Second, to alleviate the temporal misalignment issue, our method incorporates an entropy minimization-based constrained attention loss, to encourage the model to automatically focus on the correct caption from a pool of candidate ASR captions. Our overall approach is named DeCEMBERT (Dense Captions and Entropy Minimization). Comprehensive experiments on three video-and-language tasks (text-to-video retrieval, video captioning, and video question answering) across five datasets demonstrate that our approach outperforms previous state-of-the-art methods. Ablation studies on pre-training and downstream tasks show that adding dense captions and constrained attention loss help improve the model performance. Lastly, we also provide attention visualization to show the effect of applying the proposed constrained attention loss.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_194", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_194.flac", "type": "audio", "duration": 755.043}, {"from": "Assistant", "value": "Story visualization is an underexplored task that falls at the intersection of many important research directions in both computer vision and natural language processing. In this task, given a series of natural language captions which compose a story, an agent must generate a sequence of images that correspond to the captions. Prior work has introduced recurrent generative models which outperform text-to-image synthesis models on this task. However, there is room for improvement of generated images in terms of visual quality, coherence and relevance. We present a number of improvements to prior modeling approaches, including (1) the addition of a dual learning framework that utilizes video captioning to reinforce the semantic alignment between the story and generated images, (2) a copy-transform mechanism for sequentially-consistent story visualization, and (3) MART-based transformers to model complex interactions between frames. We present ablation studies to demonstrate the effect of each of these techniques on the generative power of the model for both individual images as well as the entire narrative. Furthermore, due to the complexity and generative nature of the task, standard evaluation metrics do not accurately reflect performance. Therefore, we also provide an exploration of evaluation metrics for the model, focused on aspects of the generated frames such as the presence/quality of generated characters, the relevance to captions, and the diversity of the generated images. We also present correlation experiments of our proposed automated metrics with human evaluations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_195", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_195.flac", "type": "audio", "duration": 743.062}, {"from": "Assistant", "value": "This paper studies zero-shot cross-lingual transfer of vision-language models. Specifically, we focus on multilingual text-to-video search and propose a Transformer-based model that learns contextual multilingual multimodal embeddings. Under a zero-shot setting, we empirically demonstrate that performance degrades significantly when we query the multilingual text-video model with non-English sentences. To address this problem, we introduce a multilingual multimodal pre-training strategy, and collect a new multilingual instructional video dataset (Multi-HowTo100M) for pre-training. Experiments on VTT show that our method significantly improves video search in non-English languages without additional annotations. Furthermore, when multilingual annotations are available, our method outperforms recent baselines by a large margin in multilingual text-to-video search on VTT and VATEX; as well as in multilingual text-to-image search on Multi30K. Our model and Multi-HowTo100M is available athttp://github.com/berniebear/Multi-HT100M.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_196", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_196.flac", "type": "audio", "duration": 672.055}, {"from": "Assistant", "value": "Video Question Answering (VidQA) evaluation metrics have been limited to a single-word answer or selecting a phrase from a fixed set of phrases. These metrics limit the VidQA models’ application scenario. In this work, we leverage semantic roles derived from video descriptions to mask out certain phrases, to introduce VidQAP which poses VidQA as a fill-in-the-phrase task. To enable evaluation of answer phrases, we compute the relative improvement of the predicted answer compared to an empty string. To reduce the influence of language bias in VidQA datasets, we retrieve a video having a different answer for the same question. To facilitate research, we construct ActivityNet-SRL-QA and Charades-SRL-QA and benchmark them by extending three vision-language models. We perform extensive analysis and ablative studies to guide future work. Code and data are public.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_197", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_197.flac", "type": "audio", "duration": 599.573}, {"from": "Assistant", "value": "The lack of publicly available evaluation data for low-resource languages limits progress in Spoken Language Understanding (SLU). As key tasks like intent classification and slot filling require abundant training data, it is desirable to reuse existing data in high-resource languages to develop models for low-resource scenarios. We introduce xSID, a new benchmark for cross-lingual (x) Slot and Intent Detection in 13 languages from 6 language families, including a very low-resource dialect. To tackle the challenge, we propose a joint learning approach, with English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer. We study two setups which differ by type and language coverage of the pre-trained embeddings. Our results show that jointly learning the main tasks with masked language modeling is effective for slots, while machine translation transfer works best for intent classification.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_198", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_198.flac", "type": "audio", "duration": 722.001}, {"from": "Assistant", "value": "Cross-document event coreference resolution is a foundational task for NLP applications involving multi-text processing. However, existing corpora for this task are scarce and relatively small, while annotating only modest-size clusters of documents belonging to the same topic. To complement these resources and enhance future research, we present Wikipedia Event Coreference (WEC), an efficient methodology for gathering a large-scale dataset for cross-document event coreference from Wikipedia, where coreference links are not restricted within predefined topics. We apply this methodology to the English Wikipedia and extract our large-scale WEC-Eng dataset. Notably, our dataset creation method is generic and can be applied with relatively little effort to other Wikipedia languages. To set baseline results, we develop an algorithm that adapts components of state-of-the-art models for within-document coreference resolution to the cross-document setting. Our model is suitably efficient and outperforms previously published state-of-the-art results for the task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_199", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_199.flac", "type": "audio", "duration": 713.259}, {"from": "Assistant", "value": "Computational linguistic research on language change through distributional semantic (DS) models has inspired researchers from fields such as philosophy and literary studies, who use these methods for the exploration and comparison of comparatively small datasets traditionally analyzed by close reading. Research on methods for small data is still in early stages and it is not clear which methods achieve the best results. We investigate the possibilities and limitations of using distributional semantic models for analyzing philosophical data by means of a realistic use-case. We provide a ground truth for evaluation created by philosophy experts and a blueprint for using DS models in a sound methodological setup. We compare three methods for creating specialized models from small datasets. Though the models do not perform well enough to directly support philosophers yet, we find that models designed for small data yield promising directions for future work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_200", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_200.flac", "type": "audio", "duration": 732.056}, {"from": "Assistant", "value": "Challenging problems such as open-domain question answering, fact checking, slot filling and entity linking require access to large, external knowledge sources. While some models do well on individual tasks, developing general models is difficult as each task might require computationally expensive indexing of custom knowledge sources, in addition to dedicated infrastructure. To catalyze research on models that condition on specific information in large textual resources, we present a benchmark for knowledge-intensive language tasks (KILT). All tasks in KILT are grounded in the same snapshot of Wikipedia, reducing engineering turnaround through the re-use of components, as well as accelerating research into task-agnostic memory architectures. We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance. We find that a shared dense vector index coupled with a seq2seq model is a strong baseline, outperforming more tailor-made approaches for fact checking, open-domain question answering and dialogue, and yielding competitive results on entity linking and slot filling, by generating disambiguated text. KILT data and code are available athttps://github.com/facebookresearch/KILT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_201", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_201.flac", "type": "audio", "duration": 704.842}, {"from": "Assistant", "value": "Deep neural networks and huge language models are becoming omnipresent in natural language applications. As they are known for requiring large amounts of training data, there is a growing body of work to improve the performance in low-resource settings. Motivated by the recent fundamental changes towards neural models and the popular pre-train and fine-tune paradigm, we survey promising approaches for low-resource natural language processing. After a discussion about the different dimensions of data availability, we give a structured overview of methods that enable learning when training data is sparse. This includes mechanisms to create additional labeled data like data augmentation and distant supervision as well as transfer learning settings that reduce the need for target supervision. A goal of our survey is to explain how these methods differ in their requirements as understanding them is essential for choosing a technique suited for a specific low-resource setting. Further key aspects of this work are to highlight open issues and to outline promising directions for future research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_202", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_202.flac", "type": "audio", "duration": 667.69}, {"from": "Assistant", "value": "Representation learning approaches for knowledge graphs have been mostly designed for static data. However, many knowledge graphs involve evolving data, e.g., the fact (The President of the United States is Barack Obama) is valid only from 2009 to 2017. This introduces important challenges for knowledge representation learning since the knowledge graphs change over time. In this paper, we present a novel time-aware knowledge graph embebdding approach, TeLM, which performs 4th-order tensor factorization of a Temporal knowledge graph using a Linear temporal regularizer and Multivector embeddings. Moreover, we investigate the effect of the temporal dataset’s time granularity on temporal knowledge graph completion. Experimental results demonstrate that our proposed models trained with the linear temporal regularizer achieve the state-of-the-art performances on link prediction over four well-established temporal knowledge graph completion benchmarks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_203", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_203.flac", "type": "audio", "duration": 690.004}, {"from": "Assistant", "value": "In this work we explore Unsupervised Domain Adaptation (UDA) of pretrained language models for downstream tasks. We introduce UDALM, a fine-tuning procedure, using a mixed classification and Masked Language Model loss, that can adapt to the target domain distribution in a robust and sample efficient manner. Our experiments show that performance of models trained with the mixed loss scales with the amount of available target data and the mixed loss can be effectively used as a stopping criterion during UDA training. Furthermore, we discuss the relationship between A-distance and the target error and explore some limitations of the Domain Adversarial Training approach. Our method is evaluated on twelve domain pairs of the Amazon Reviews Sentiment dataset, yielding 91.74% accuracy, which is an 1.11% absolute improvement over the state-of-the-art.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_204", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_204.flac", "type": "audio", "duration": 395.051}, {"from": "Assistant", "value": "Supervised learning assumes that a ground truth label exists. However, the reliability of this ground truth depends on human annotators, who often disagree. Prior work has shown that this disagreement can be helpful in training models. We propose a novel method to incorporate this disagreement as information: in addition to the standard error computation, we use soft-labels (i.e., probability distributions over the annotator labels) as an auxiliary task in a multi-task neural network. We measure the divergence between the predictions and the target soft-labels with several loss-functions and evaluate the models on various NLP tasks. We find that the soft-label prediction auxiliary task reduces the penalty for errors on ambiguous entities, and thereby mitigates overfitting. It significantly improves performance across tasks, beyond the standard approach and prior work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_205", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_205.flac", "type": "audio", "duration": 525.056}, {"from": "Assistant", "value": "Due to large number of entities in biomedical knowledge bases, only a small fraction of entities have corresponding labelled training data. This necessitates entity linking models which are able to link mentions of unseen entities using learned representations of entities. Previous approaches link each mention independently, ignoring the relationships within and across documents between the entity mentions. These relations can be very useful for linking mentions in biomedical text where linking decisions are often difficult due mentions having a generic or a highly specialized form. In this paper, we introduce a model in which linking decisions can be made not merely by linking to a knowledge base entity but also by grouping multiple mentions together via clustering and jointly making linking predictions. In experiments on the largest publicly available biomedical dataset, we improve the best independent prediction for entity linking by 3.0 points of accuracy, and our clustering-based inference model further improves entity linking by 2.3 points.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_206", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_206.flac", "type": "audio", "duration": 434.051}, {"from": "Assistant", "value": "First-order meta-learning algorithms have been widely used in practice to learn initial model parameters that can be quickly adapted to new tasks due to their efficiency and effectiveness. However, existing studies find that meta-learner can overfit to some specific adaptation when we have heterogeneous tasks, leading to significantly degraded performance. In Natural Language Processing (NLP) applications, datasets are often diverse and each task has its unique characteristics. Therefore, to address the overfitting issue when applying first-order meta-learning to NLP applications, we propose to reduce the variance of the gradient estimator used in task adaptation. To this end, we develop a variance-reduced first-order meta-learning algorithm. The core of our algorithm is to introduce a novel variance reduction term to the gradient estimation when performing the task adaptation. Experiments on two NLP applications: few-shot text classification and multi-domain dialog state tracking demonstrate the superior performance of our proposed method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_207", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_207.flac", "type": "audio", "duration": 420.165}, {"from": "Assistant", "value": "While the predictive performance of modern statistical dependency parsers relies heavily on the availability of expensive expert-annotated treebank data, not all annotations contribute equally to the training of the parsers. In this paper, we attempt to reduce the number of labeled examples needed to train a strong dependency parser using batch active learning (AL). In particular, we investigate whether enforcing diversity in the sampled batches, using determinantal point processes (DPPs), can improve over their diversity-agnostic counterparts. Simulation experiments on an English newswire corpus show that selecting diverse batches with DPPs is superior to strong selection strategies that do not enforce batch diversity, especially during the initial stages of the learning process. Additionally, our diversity-aware strategy is robust under a corpus duplication setting, where diversity-agnostic sampling strategies exhibit significant degradation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_208", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_208.flac", "type": "audio", "duration": 419.051}, {"from": "Assistant", "value": "When fine-tuning pretrained models for classification, researchers either use a generic model head or a task-specific prompt for prediction. Proponents of prompting have argued that prompts provide a method for injecting task-specific guidance, which is beneficial in low-data regimes. We aim to quantify this benefit through rigorous testing of prompts in a fair setting: comparing prompted and head-based fine-tuning in equal conditions across many tasks and data sizes. By controlling for many sources of advantage, we find that prompting does indeed provide a benefit, and that this benefit can be quantified per task. Results show that prompting is often worth 100s of data points on average across classification tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_209", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_209.flac", "type": "audio", "duration": 483.045}, {"from": "Assistant", "value": "Latent alignment objectives such as CTC and AXE significantly improve non-autoregressive machine translation models. Can they improve autoregressive models as well? We explore the possibility of training autoregressive machine translation models with latent alignment objectives, and observe that, in practice, this approach results in degenerate models. We provide a theoretical explanation for these empirical results, and prove that latent alignment objectives are incompatible with teacher forcing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_210", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_210.flac", "type": "audio", "duration": 683.178}, {"from": "Assistant", "value": "Current sequence-to-sequence models are trained to minimize cross-entropy and use softmax to compute the locally normalized probabilities over target sequences. While this setup has led to strong results in a variety of tasks, one unsatisfying aspect is its length bias: models give high scores to short, inadequate hypotheses and often make the empty string the argmax—the so-called cat got your tongue problem. Recently proposed entmax-based sparse sequence-to-sequence models present a possible solution, since they can shrink the search space by assigning zero probability to bad hypotheses, but their ability to handle word-level tasks with transformers has never been tested. In this work, we show that entmax-based models effectively solve the cat got your tongue problem, removing a major source of model error for neural machine translation. In addition, we generalize label smoothing, a critical regularization technique, to the broader family of Fenchel-Young losses, which includes both cross-entropy and the entmax losses. Our resulting label-smoothed entmax loss models set a new state of the art on multilingual grapheme-to-phoneme conversion and deliver improvements and better calibration properties on cross-lingual morphological inflection and machine translation for 7 language pairs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_211", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_211.flac", "type": "audio", "duration": 680.576}, {"from": "Assistant", "value": "Code summarization and generation empower conversion between programming language (PL) and natural language (NL), while code translation avails the migration of legacy code from one PL to another. This paper introduces PLBART, a sequence-to-sequence model capable of performing a broad spectrum of program and language understanding and generation tasks. PLBART is pre-trained on an extensive collection of Java and Python functions and associated NL text via denoising autoencoding. Experiments on code summarization in the English language, code generation, and code translation in seven programming languages show that PLBART outperforms or rivals state-of-the-art models. Moreover, experiments on discriminative tasks, e.g., program repair, clone detection, and vulnerable code detection, demonstrate PLBART’s effectiveness in program understanding. Furthermore, analysis reveals that PLBART learns program syntax, style (e.g., identifier naming convention), logical flow (e.g., “if“ block inside an “else“ block is equivalent to “else if“ block) that are crucial to program semantics and thus excels even with limited annotations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_212", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_212.flac", "type": "audio", "duration": 711.041}, {"from": "Assistant", "value": "Domain classification is the fundamental task in natural language understanding (NLU), which often requires fast accommodation to new emerging domains. This constraint makes it impossible to retrain all previous domains, even if they are accessible to the new model. Most existing continual learning approaches suffer from low accuracy and performance fluctuation, especially when the distributions of old and new data are significantly different. In fact, the key real-world problem is not the absence of old data, but the inefficiency to retrain the model with the whole old dataset. Is it potential to utilize some old data to yield high accuracy and maintain stable performance, while at the same time, without introducing extra hyperparameters? In this paper, we proposed a hyperparameter-free continual learning model for text data that can stably produce high performance under various environments. Specifically, we utilize Fisher information to select exemplars that can “record” key information of the original model. Also, a novel scheme called dynamical weight consolidation is proposed to enable hyperparameter-free learning during the retrain process. Extensive experiments demonstrate baselines provide fluctuated performance which makes them useless in practice. On the contrary, our proposed model significantly and consistently outperforms the best state-of-the-art method by up to 20% in average accuracy, and each of its component contributes effectively to overall performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_213", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_213.flac", "type": "audio", "duration": 710.206}, {"from": "Assistant", "value": "Source code processing heavily relies on the methods widely used in natural language processing (NLP), but involves specifics that need to be taken into account to achieve higher quality. An example of this specificity is that the semantics of a variable is defined not only by its name but also by the contexts in which the variable occurs. In this work, we develop dynamic embeddings, a recurrent mechanism that adjusts the learned semantics of the variable when it obtains more information about the variable’s role in the program. We show that using the proposed dynamic embeddings significantly improves the performance of the recurrent neural network, in code completion and bug fixing tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_214", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_214.flac", "type": "audio", "duration": 661.056}, {"from": "Assistant", "value": "Cross-Lingual Word Embeddings (CLWEs) encode words from two or more languages in a shared high-dimensional space in which vectors representing words with similar meaning (regardless of language) are closely located. Existing methods for building high-quality CLWEs learn mappings that minimise the ℓ2 norm loss function. However, this optimisation objective has been demonstrated to be sensitive to outliers. Based on the more robust Manhattan norm (aka. ℓ1 norm) goodness-of-fit criterion, this paper proposes a simple post-processing step to improve CLWEs. An advantage of this approach is that it is fully agnostic to the training process of the original CLWEs and can therefore be applied widely. Extensive experiments are performed involving ten diverse languages and embeddings trained on different corpora. Evaluation results based on bilingual lexicon induction and cross-lingual transfer for natural language inference tasks show that the ℓ1 refinement substantially outperforms four state-of-the-art baselines in both supervised and unsupervised settings. It is therefore recommended that this strategy be adopted as a standard for CLWE methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_215", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_215.flac", "type": "audio", "duration": 715.058}, {"from": "Assistant", "value": "This paper introduces Semantic Frame Forecast, a task that predicts the semantic frames that will occur in the next 10, 100, or even 1,000 sentences in a running story. Prior work focused on predicting the immediate future of a story, such as one to a few sentences ahead. However, when novelists write long stories, generating a few sentences is not enough to help them gain high-level insight to develop the follow-up story. In this paper, we formulate a long story as a sequence of “story blocks,” where each block contains a fixed number of sentences (e.g., 10, 100, or 200). This formulation allows us to predict the follow-up story arc beyond the scope of a few sentences. We represent a story block using the term frequencies (TF) of semantic frames in it, normalized by each frame’s inverse document frequency (IDF). We conduct semantic frame forecast experiments on 4,794 books from the Bookcorpus and 7,962 scientific abstracts from CODA-19, with block sizes ranging from 5 to 1,000 sentences. The results show that automated models can forecast the follow-up story blocks better than the random, prior, and replay baselines, indicating the feasibility of the task. We also learn that the models using the frame representation as features outperform all the existing approaches when the block size is over 150 sentences. The human evaluation also shows that the proposed frame representation, when visualized as word clouds, is comprehensible, representative, and specific to humans.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_216", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_216.flac", "type": "audio", "duration": 726.042}, {"from": "Assistant", "value": "The capability to automatically detect human stress can benefit artificial intelligent agents involved in affective computing and human-computer interaction. Stress and emotion are both human affective states, and stress has proven to have important implications on the regulation and expression of emotion. Although a series of methods have been established for multimodal stress detection, limited steps have been taken to explore the underlying inter-dependence between stress and emotion. In this work, we investigate the value of emotion recognition as an auxiliary task to improve stress detection. We propose MUSER – a transformer-based model architecture and a novel multi-task learning algorithm with speed-based dynamic sampling strategy. Evaluation on the Multimodal Stressed Emotion (MuSE) dataset shows that our model is effective for stress detection with both internal and external auxiliary tasks, and achieves state-of-the-art results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_217", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_217.flac", "type": "audio", "duration": 820.965}, {"from": "Assistant", "value": "People rely on digital task management tools, such as email or to-do apps, to manage their tasks. Some of these tasks are large and complex, leading to action paralysis and feelings of being overwhelmed on the part of the user. The micro-productivity literature has shown that such tasks could benefit from being decomposed and organized, in order to reduce user cognitive load. Thus in this paper, we propose a novel end-to-end pipeline that consumes a complex task and induces a dependency graph from unstructured text to represent sub-tasks and their relationships. Our solution first finds nodes for sub-tasks from multiple ‘how-to’ articles on the web by injecting a neural text generator with three key desiderata – relevance, abstraction, and consensus. Then we resolve and infer edges between these subtask nodes by learning task dependency relations. We collect a new dataset of complex tasks with their sub-task graph to develop and evaluate our solutions. Both components of our graph induction solution are evaluated in experiments, demonstrating that our models outperform a state-of-the-art text generator significantly. Our generalizable and scalable end-to-end solution has important implications for boosting user productivity and assisting with digital task management.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_218", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_218.flac", "type": "audio", "duration": 721.003}, {"from": "Assistant", "value": "Continual learning has become increasingly important as it enables NLP models to constantly learn and gain knowledge over time. Previous continual learning methods are mainly designed to preserve knowledge from previous tasks, without much emphasis on how to well generalize models to new tasks. In this work, we propose an information disentanglement based regularization method for continual learning on text classification. Our proposed method first disentangles text hidden spaces into representations that are generic to all tasks and representations specific to each individual task, and further regularizes these representations differently to better constrain the knowledge required to generalize. We also introduce two simple auxiliary tasks: next sentence prediction and task-id prediction, for learning better generic and specific representation spaces. Experiments conducted on large-scale benchmarks demonstrate the effectiveness of our method in continual text classification tasks with various sequences and lengths over state-of-the-art baselines. We have publicly released our code athttps://github.com/GT-SALT/IDBR.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_219", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_219.flac", "type": "audio", "duration": 806.058}, {"from": "Assistant", "value": "Semantic parsing aims at translating natural language (NL) utterances onto machine-interpretable programs, which can be executed against a real-world environment. The expensive annotation of utterance-program pairs has long been acknowledged as a major bottleneck for the deployment of contemporary neural models to real-life applications. In this work, we focus on the task of semi-supervised learning where a limited amount of annotated data is available together with many unlabeled NL utterances. Based on the observation that programs which correspond to NL utterances should always be executable, we propose to encourage a parser to generate executable programs for unlabeled utterances. Due to the large search space of executable programs, conventional methods that use beam-search for approximation, such as self-training and top-k marginal likelihood training, do not perform as well. Instead, we propose a set of new training objectives that are derived by approaching the problem of learning from executions from the posterior regularization perspective. Our new objectives outperform conventional methods on Overnight and GeoQuery, bridging the gap between semi-supervised and supervised learning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_220", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_220.flac", "type": "audio", "duration": 467.046}, {"from": "Assistant", "value": "Synthesizing data for semantic parsing has gained increasing attention recently. However, most methods require handcrafted (high-precision) rules in their generative process, hindering the exploration of diverse unseen data. In this work, we propose a generative model which features a (non-neural) PCFG that models the composition of programs (e.g., SQL), and a BART-based translation model that maps a program to an utterance. Due to the simplicity of PCFG and pre-trained BART, our generative model can be efficiently learned from existing data at hand. Moreover, explicitly modeling compositions using PCFG leads to better exploration of unseen programs, thus generate more diverse data. We evaluate our method in both in-domain and out-of-domain settings of text-to-SQL parsing on the standard benchmarks of GeoQuery and Spider, respectively. Our empirical results show that the synthesized data generated from our model can substantially help a semantic parser achieve better compositional and domain generalization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_221", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_221.flac", "type": "audio", "duration": 929.045}, {"from": "Assistant", "value": "Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. While there is an abundance of textual information throughout the web and many existing knowledge bases, aligning information across these diverse data sources remains a challenge in the literature. Previous work has partially addressed this issue by enriching knowledge graph entities based on “hard” co-occurrence of words present in the entities of the knowledge graphs and external text, while we achieve “soft” augmentation by proposing a knowledge graph enrichment and embedding framework named Edge. Given an original knowledge graph, we first generate a rich but noisy augmented graph using external texts in semantic and structural level. To distill the relevant knowledge and suppress the introduced noise, we design a graph alignment term in a shared embedding space between the original graph and augmented graph. To enhance the embedding learning on the augmented graph, we further regularize the locality relationship of target entity based on negative sampling. Experimental results on four benchmark datasets demonstrate the robustness and effectiveness of Edge in link prediction and node classification.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_222", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_222.flac", "type": "audio", "duration": 717.38}, {"from": "Assistant", "value": "AI assistants can now carry out tasks for users by directly interacting with website UIs. Current semantic parsing and slot-filling techniques cannot flexibly adapt to many different websites without being constantly re-trained. We propose FLIN, a natural language interface for web navigation that maps user commands to concept-level actions (rather than low-level UI actions), thus being able to flexibly adapt to different websites and handle their transient nature. We frame this as a ranking problem: given a user command and a webpage, FLIN learns to score the most relevant navigation instruction (involving action and parameter values). To train and evaluate FLIN, we collect a dataset using nine popular websites from three domains. Our results show that FLIN was able to adapt to new websites in a given domain.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_223", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_223.flac", "type": "audio", "duration": 652.249}, {"from": "Assistant", "value": "The input vocabulary and the representations learned are crucial to the performance of neural NLP models. Using the full vocabulary results in less explainable and more memory intensive models, with the embedding layer often constituting the majority of model parameters. It is thus common to use a smaller vocabulary to lower memory requirements and construct more interpertable models. We propose a vocabulary selection method that views words as members of a team trying to maximize the model’s performance. We apply power indices from cooperative game theory, including the Shapley value and Banzhaf index, that measure the relative importance of individual team members in accomplishing a joint task. We approximately compute these indices to identify the most influential words. Our empirical evaluation examines multiple NLP tasks, including sentence and document classification, question answering and textual entailment. We compare to baselines that select words based on frequency, TF-IDF and regression coefficients under L1 regularization, and show that this game-theoretic vocabulary selection outperforms all baseline on a range of different tasks and datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_224", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_224.flac", "type": "audio", "duration": 522.054}, {"from": "Assistant", "value": "Reasoning about tabular information presents unique challenges to modern NLP approaches which largely rely on pre-trained contextualized embeddings of text. In this paper, we study these challenges through the problem of tabular natural language inference. We propose easy and effective modifications to how information is presented to a model for this task. We show via systematic experiments that these strategies substantially improve tabular inference performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_225", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_225.flac", "type": "audio", "duration": 436.096}, {"from": "Assistant", "value": "We describe a span-level supervised attention loss that improves compositional generalization in semantic parsers. Our approach builds on existing losses that encourage attention maps in neural sequence-to-sequence models to imitate the output of classical word alignment algorithms. Where past work has used word-level alignments, we focus on spans; borrowing ideas from phrase-based machine translation, we align subtrees in semantic parses to spans of input sentences, and encourage neural attention mechanisms to mimic these alignments. This method improves the performance of transformers, RNNs, and structured decoders on three benchmarks of compositional generalization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_226", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_226.flac", "type": "audio", "duration": 659.052}, {"from": "Assistant", "value": "Finetuning deep pre-trained language models has shown state-of-the-art performances on a wide range of Natural Language Processing (NLP) applications. Nevertheless, their generalization performance drops under domain shift. In the case of Arabic language, diglossia makes building and annotating corpora for each dialect and/or domain a more challenging task. Unsupervised Domain Adaptation tackles this issue by transferring the learned knowledge from labeled source domain data to unlabeled target domain data. In this paper, we propose a new unsupervised domain adaptation method for Arabic cross-domain and cross-dialect sentiment analysis from Contextualized Word Embedding. Several experiments are performed adopting the coarse-grained and the fine-grained taxonomies of Arabic dialects. The obtained results show that our method yields very promising results and outperforms several domain adaptation methods for most of the evaluated datasets. On average, our method increases the performance by an improvement rate of 20.8% over the zero-shot transfer learning from BERT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_227", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_227.flac", "type": "audio", "duration": 712.063}, {"from": "Assistant", "value": "The majority of work in targeted sentiment analysis has concentrated on finding better methods to improve the overall results. Within this paper we show that these models are not robust to linguistic phenomena, specifically negation and speculation. In this paper, we propose a multi-task learning method to incorporate information from syntactic and semantic auxiliary tasks, including negation and speculation scope detection, to create English-language models that are more robust to these phenomena. Further we create two challenge datasets to evaluate model performance on negated and speculative samples. We find that multi-task models and transfer learning via language modelling can improve performance on these challenge datasets, but the overall performances indicate that there is still much room for improvement. We release both the datasets and the source code athttps://github.com/jerbarnes/multitask_negation_for_targeted_sentiment.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_228", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_228.flac", "type": "audio", "duration": 702.775}, {"from": "Assistant", "value": "The flexibility of the inference process in Variational Autoencoders (VAEs) has recently led to revising traditional probabilistic topic models giving rise to Neural Topic Models (NTM). Although these approaches have achieved significant results, surprisingly very little work has been done on how to disentangle the latent topics. Existing topic models when applied to reviews may extract topics associated with writers’ subjective opinions mixed with those related to factual descriptions such as plot summaries in movie and book reviews. It is thus desirable to automatically separate opinion topics from plot/neutral ones enabling a better interpretability. In this paper, we propose a neural topic model combined with adversarial training to disentangle opinion topics from plot and neutral ones. We conduct an extensive experimental assessment introducing a new collection of movie and book reviews paired with their plots, namely MOBO dataset, showing an improved coherence and variety of topics, a consistent disentanglement rate, and sentiment classification performance superior to other supervised topic models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_229", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_229.flac", "type": "audio", "duration": 743.015}, {"from": "Assistant", "value": "Recent work on aspect-level sentiment classification has demonstrated the efficacy of incorporating syntactic structures such as dependency trees with graph neural networks (GNN), but these approaches are usually vulnerable to parsing errors. To better leverage syntactic information in the face of unavoidable errors, we propose a simple yet effective graph ensemble technique, GraphMerge, to make use of the predictions from different parsers. Instead of assigning one set of model parameters to each dependency tree, we first combine the dependency relations from different parses before applying GNNs over the resulting graph. This allows GNN models to be robust to parse errors at no additional computational cost, and helps avoid overparameterization and overfitting from GNN layer stacking by introducing more connectivity into the ensemble graph. Our experiments on the SemEval 2014 Task 4 and ACL 14 Twitter datasets show that our GraphMerge model not only outperforms models with single dependency tree, but also beats other ensemble models without adding model parameters.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_230", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_230.flac", "type": "audio", "duration": 713.045}, {"from": "Assistant", "value": "The problem of detecting psychological stress in online posts, and more broadly, of detecting people in distress or in need of help, is a sensitive application for which the ability to interpret models is vital. Here, we present work exploring the use of a semantically related task, emotion detection, for equally competent but more explainable and human-like psychological stress detection as compared to a black-box model. In particular, we explore the use of multi-task learning as well as emotion-based language model fine-tuning. With our emotion-infused models, we see comparable results to state-of-the-art BERT. Our analysis of the words used for prediction show that our emotion-infused models mirror psychological components of stress.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_231", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_231.flac", "type": "audio", "duration": 713.728}, {"from": "Assistant", "value": "It is popular that neural graph-based models are applied in existing aspect-based sentiment analysis (ABSA) studies for utilizing word relations through dependency parses to facilitate the task with better semantic guidance for analyzing context and aspect words. However, most of these studies only leverage dependency relations without considering their dependency types, and are limited in lacking efficient mechanisms to distinguish the important relations as well as learn from different layers of graph based models. To address such limitations, in this paper, we propose an approach to explicitly utilize dependency types for ABSA with type-aware graph convolutional networks (T-GCN), where attention is used in T-GCN to distinguish different edges (relations) in the graph and attentive layer ensemble is proposed to comprehensively learn from different layers of T-GCN. The validity and effectiveness of our approach are demonstrated in the experimental results, where state-of-the-art performance is achieved on six English benchmark datasets. Further experiments are conducted to analyze the contributions of each component in our approach and illustrate how different layers in T-GCN help ABSA with quantitative and qualitative analysis.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_232", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_232.flac", "type": "audio", "duration": 718.4}, {"from": "Assistant", "value": "We present the first supertagging-based parser for linear context-free rewriting systems (LCFRS). It utilizes neural classifiers and outperforms previous LCFRS-based parsers in both accuracy and parsing speed by a wide margin. Our results keep up with the best (general) discontinuous parsers, particularly the scores for discontinuous constituents establish a new state of the art. The heart of our approach is an efficient lexicalization procedure which induces a lexical LCFRS from any discontinuous treebank. We describe a modification to usual chart-based LCFRS parsing that accounts for supertagging and introduce a procedure that transforms lexical LCFRS derivations into equivalent parse trees of the original treebank. Our approach is evaluated on the English Discontinuous Penn Treebank and the German treebanks Negra and Tiger.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_233", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_233.flac", "type": "audio", "duration": 411.063}, {"from": "Assistant", "value": "We show that a general algorithm for efficient computation of outside values under the minimum of superior functions framework proposed by Knuth (1977) would yield a sub-exponential time algorithm for SAT, violating the Strong Exponential Time Hypothesis (SETH).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_234", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_234.flac", "type": "audio", "duration": 410.459}, {"from": "Assistant", "value": "Naturally-occurring bracketings, such as answer fragments to natural language questions and hyperlinks on webpages, can reflect human syntactic intuition regarding phrasal boundaries. Their availability and approximate correspondence to syntax make them appealing as distant information sources to incorporate into unsupervised constituency parsing. But they are noisy and incomplete; to address this challenge, we develop a partial-brackets-aware structured ramp loss in learning. Experiments demonstrate that our distantly-supervised models trained on naturally-occurring bracketing data are more accurate in inducing syntactic structures than competing unsupervised systems. On the English WSJ corpus, our models achieve an unlabeled F1 score of 68.9 for constituency parsing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_235", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_235.flac", "type": "audio", "duration": 788.062}, {"from": "Assistant", "value": "Conversational agents trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior. We introduce a new human-and-model-in-the-loop framework for evaluating the toxicity of such models, and compare a variety of existing methods in both the cases of non-adversarial and adversarial users that expose their weaknesses. We then go on to propose two novel methods for safe conversational agents, by either training on data from our new human-and-model-in-the-loop framework in a two-stage system, or ”baking-in” safety to the generative model itself. We find our new techniques are (i) safer than existing models; while (ii) maintaining usability metrics such as engagingness relative to state-of-the-art chatbots. In contrast, we expose serious safety issues in existing standard systems like GPT2, DialoGPT, and BlenderBot.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_236", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_236.flac", "type": "audio", "duration": 707.582}, {"from": "Assistant", "value": "Semantic parsing using sequence-to-sequence models allows parsing of deeper representations compared to traditional word tagging based models. In spite of these advantages, widespread adoption of these models for real-time conversational use cases has been stymied by higher compute requirements and thus higher latency. In this work, we propose a non-autoregressive approach to predict semantic parse trees with an efficient seq2seq model architecture. By combining non-autoregressive prediction with convolutional neural networks, we achieve significant latency gains and parameter size reduction compared to traditional RNN models. Our novel architecture achieves up to an 81% reduction in latency on TOP dataset and retains competitive performance to non-pretrained models on three different semantic parsing datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_237", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_237.flac", "type": "audio", "duration": 711.204}, {"from": "Assistant", "value": "A key challenge of dialog systems research is to effectively and efficiently adapt to new domains. A scalable paradigm for adaptation necessitates the development of generalizable models that perform well in few-shot settings. In this paper, we focus on the intent classification problem which aims to identify user intents given utterances addressed to the dialog system. We propose two approaches for improving the generalizability of utterance classification models: (1) observers and (2) example-driven training. Prior work has shown that BERT-like models tend to attribute a significant amount of attention to the [CLS] token, which we hypothesize results in diluted representations. Observers are tokens that are not attended to, and are an alternative to the [CLS] token as a semantic representation of utterances. Example-driven training learns to classify utterances by comparing to examples, thereby using the underlying encoder as a sentence similarity model. These methods are complementary; improving the representation through observers allows the example-driven model to better measure sentence similarities. When combined, the proposed methods attain state-of-the-art results on three intent prediction datasets (banking77, clinc150, hwu64) in both the full data and few-shot (10 examples per intent) settings. Furthermore, we demonstrate that the proposed approach can transfer to new intents and across datasets without any additional training.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_238", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_238.flac", "type": "audio", "duration": 715.5}, {"from": "Assistant", "value": "For task-oriented dialog systems, training a Reinforcement Learning (RL) based Dialog Management module suffers from low sample efficiency and slow convergence speed due to the sparse rewards in RL. To solve this problem, many strategies have been proposed to give proper rewards when training RL, but their rewards lack interpretability and cannot accurately estimate the distribution of state-action pairs in real dialogs. In this paper, we propose a multi-level reward modeling approach that factorizes a reward into a three-level hierarchy: domain, act, and slot. Based on inverse adversarial reinforcement learning, our designed reward model can provide more accurate and explainable reward signals for state-action pairs. Extensive evaluations show that our approach can be applied to a wide range of reinforcement learning-based dialog systems and significantly improves both the performance and the speed of convergence.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_239", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_239.flac", "type": "audio", "duration": 739.254}, {"from": "Assistant", "value": "Existing goal-oriented dialogue datasets focus mainly on identifying slots and values. However, customer support interactions in reality often involve agents following multi-step procedures derived from explicitly-defined company policies as well. To study customer service dialogue systems in more realistic settings, we introduce the Action-Based Conversations Dataset (ABCD), a fully-labeled dataset with over 10K human-to-human dialogues containing 55 distinct user intents requiring unique sequences of actions constrained by policies to achieve task success. We propose two additional dialog tasks, Action State Tracking and Cascading Dialogue Success, and establish a series of baselines involving large-scale, pre-trained language models on this dataset. Empirical results demonstrate that while more sophisticated networks outperform simpler models, a considerable gap (50.8% absolute accuracy) still exists to reach human-level performance on ABCD.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_240", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_240.flac", "type": "audio", "duration": 557.0}, {"from": "Assistant", "value": "Dialogue systems pretrained with large language models generate locally coherent responses, but lack fine-grained control over responses necessary to achieve specific goals. A promising method to control response generation is exemplar-based generation, in which models edit exemplar responses that are retrieved from training data, or hand-written to strategically address discourse-level goals, to fit new dialogue contexts. We present an Exemplar-based Dialogue Generation model, EDGE, that uses the semantic frames present in exemplar responses to guide response generation. We show that controlling dialogue generation based on the semantic frames of exemplars improves the coherence of generated responses, while preserving semantic meaning and conversation goals present in exemplar responses.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_241", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_241.flac", "type": "audio", "duration": 734.016}, {"from": "Assistant", "value": "Classical information retrieval systems such as BM25 rely on exact lexical match and can carry out search efficiently with inverted list index. Recent neural IR models shifts towards soft matching all query document terms, but they lose the computation efficiency of exact match systems. This paper presents COIL, a contextualized exact match retrieval architecture, where scoring is based on overlapping query document tokens’ contextualized representations. The new architecture stores contextualized token representations in inverted lists, bringing together the efficiency of exact match and the representation power of deep language models. Our experimental results show COIL outperforms classical lexical retrievers and state-of-the-art deep LM retrievers with similar or smaller latency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_242", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_242.flac", "type": "audio", "duration": 725.925}, {"from": "Assistant", "value": "In this paper, we explore text classification with extremely weak supervision, i.e., only relying on the surface text of class names. This is a more challenging setting than the seed-driven weak supervision, which allows a few seed words per class. We opt to attack this problem from a representation learning perspective—ideal document representations should lead to nearly the same results between clustering and the desired classification. In particular, one can classify the same corpus differently (e.g., based on topics and locations), so document representations should be adaptive to the given class names. We propose a novel framework X-Class to realize the adaptive representations. Specifically, we first estimate class representations by incrementally adding the most similar word to each class until inconsistency arises. Following a tailored mixture of class attention mechanisms, we obtain the document representation via a weighted average of contextualized word representations. With the prior of each document assigned to its nearest class, we then cluster and align the documents to classes. Finally, we pick the most confident documents from each cluster to train a text classifier. Extensive experiments demonstrate that X-Class can rival and even outperform seed-driven weakly supervised methods on 7 benchmark datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_243", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_243.flac", "type": "audio", "duration": 770.02}, {"from": "Assistant", "value": "Neural topic models can augment or replace bag-of-words inputs with the learned representations of deep pre-trained transformer-based word prediction models. One added benefit when using representations from multilingual models is that they facilitate zero-shot polylingual topic modeling. However, while it has been widely observed that pre-trained embeddings should be fine-tuned to a given task, it is not immediately clear what supervision should look like for an unsupervised task such as topic modeling. Thus, we propose several methods for fine-tuning encoders to improve both monolingual and zero-shot polylingual neural topic modeling. We consider fine-tuning on auxiliary tasks, constructing a new topic classification task, integrating the topic classification objective directly into topic model training, and continued pre-training. We find that fine-tuning encoder representations on topic classification and integrating the topic classification task directly into topic modeling improves topic quality, and that fine-tuning encoder representations on any task is the most important factor for facilitating cross-lingual transfer.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_244", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_244.flac", "type": "audio", "duration": 392.043}, {"from": "Assistant", "value": "Text classification is a significant branch of natural language processing, and has many applications including document classification and sentiment analysis. Unsurprisingly, those who do text classification are concerned with the run-time of their algorithms, many of which depend on the size of the corpus’ vocabulary due to their bag-of-words representation. Although many studies have examined the effect of preprocessing techniques on vocabulary size and accuracy, none have examined how these methods affect a model’s run-time. To fill this gap, we provide a comprehensive study that examines how preprocessing techniques affect the vocabulary size, model performance, and model run-time, evaluating ten techniques over four models and two datasets. We show that some individual methods can reduce run-time with no loss of accuracy, while some combinations of methods can trade 2-5% of the accuracy for up to a 65% reduction of run-time. Furthermore, some combinations of preprocessing techniques can even provide a 15% reduction in run-time while simultaneously improving model accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_245", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_245.flac", "type": "audio", "duration": 420.049}, {"from": "Assistant", "value": "Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation process. However, prior research rarely considers the faithfulness of the derived explanations to justify the decision-making process. To the best of our knowledge, this is the first work that models and evaluates faithfully explainable recommendation under the framework of KG reasoning. Specifically, we propose neural logic reasoning for explainable recommendation (LOGER) by drawing on interpretable logical rules to guide the path-reasoning process for explanation generation. We experiment on three large-scale datasets in the e-commerce domain, demonstrating the effectiveness of our method in delivering high-quality recommendations as well as ascertaining the faithfulness of the derived explanation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_246", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_246.flac", "type": "audio", "duration": 356.055}, {"from": "Assistant", "value": "The increasing popularity of voice-based personal assistants provides new opportunities for conversational recommendation. One particularly interesting area is movie recommendation, which can benefit from an open-ended interaction with the user, through a natural conversation. We explore one promising direction for conversational recommendation: mapping a conversational user, for whom there is limited or no data available, to most similar external reviewers, whose preferences are known, by representing the conversation as a user’s interest vector, and adapting collaborative filtering techniques to estimate the current user’s preferences for new movies. We call our proposed method ConvExtr (Conversational Collaborative Filtering using External Data), which 1) infers a user’s sentiment towards an entity from the conversation context, and 2) transforms the ratings of “similar” external reviewers to predict the current user’s preferences. We implement these steps by adapting contextual sentiment prediction techniques, and domain adaptation, respectively. To evaluate our method, we develop and make available a finely annotated dataset of movie recommendation conversations, which we call MovieSent. Our results demonstrate that ConvExtr can improve the accuracy of predicting users’ ratings for new movies by exploiting conversation content and external data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_247", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_247.flac", "type": "audio", "duration": 422.699}, {"from": "Assistant", "value": "Text-based games simulate worlds and interact with players using natural language. Recent work has used them as a testbed for autonomous language-understanding agents, with the motivation being that understanding the meanings of words or semantics is a key component of how humans understand, reason, and act in these worlds. However, it remains unclear to what extent artificial agents utilize semantic understanding of the text. To this end, we perform experiments to systematically reduce the amount of semantic information available to a learning agent. Surprisingly, we find that an agent is capable of achieving high scores even in the complete absence of language semantics, indicating that the currently popular experimental setup and models may be poorly designed to understand and leverage game texts. To remedy this deficiency, we propose an inverse dynamics decoder to regularize the representation space and encourage exploration, which shows improved performance on several games including Zork I. We discuss the implications of our findings for designing future agents with stronger semantic understanding.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_248", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_248.flac", "type": "audio", "duration": 384.058}, {"from": "Assistant", "value": "Recent research in Visual Question Answering (VQA) has revealed state-of-the-art models to be inconsistent in their understanding of the world - they answer seemingly difficult questions requiring reasoning correctly but get simpler associated sub-questions wrong. These sub-questions pertain to lower level visual concepts in the image that models ideally should understand to be able to answer the reasoning question correctly. To address this, we first present a gradient-based interpretability approach to determine the questions most strongly correlated with the reasoning question on an image, and use this to evaluate VQA models on their ability to identify the relevant sub-questions needed to answer a reasoning question. Next, we propose a contrastive gradient learning based approach called Sub-question Oriented Tuning (SOrT) which encourages models to rank relevant sub-questions higher than irrelevant questions for an <image, reasoning-question> pair. We show that SOrT improves model consistency by up to 6.5% points over existing approaches, while also improving visual grounding and robustness to rephrasings of questions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_249", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_249.flac", "type": "audio", "duration": 401.566}, {"from": "Assistant", "value": "Using natural language as a hint can supply an additional reward for playing sparse-reward games. Achieving a goal should involve several different hints, while the given hints are usually incomplete. Those unmentioned latent hints still rely on the sparse reward signal, and make the learning process difficult. In this paper, we propose semi-supervised initialization (SSI) that allows the agent to learn from various possible hints before training under different tasks. Experiments show that SSI not only helps to learn faster (1.2x) but also has a higher success rate (11%relative improvement) of the final policy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_250", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_250.flac", "type": "audio", "duration": 418.006}, {"from": "Assistant", "value": "Zero-shot learning aims to recognize unseen objects using their semantic representations. Most existing works use visual attributes labeled by humans, not suitable for large-scale applications. In this paper, we revisit the use of documents as semantic representations. We argue that documents like Wikipedia pages contain rich visual information, which however can easily be buried by the vast amount of non-visual sentences. To address this issue, we propose a semi-automatic mechanism for visual sentence extraction that leverages the document section headers and the clustering structure of visual sentences. The extracted visual sentences, after a novel weighting scheme to distinguish similar classes, essentially form semantic representations like visual attributes but need much less human effort. On the ImageNet dataset with over 10,000 unseen classes, our representations lead to a 64% relative improvement against the commonly used ones.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_251", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_251.flac", "type": "audio", "duration": 693.056}, {"from": "Assistant", "value": "Automatic personalized corrective feedback can help language learners from different backgrounds better acquire a new language. This paper introduces a learner English dataset in which learner errors are accompanied by information about possible error sources. This dataset contains manually annotated error causes for learner writing errors. These causes tie learner mistakes to structures from their first languages, when the rules in English and in the first language diverge. This new dataset will enable second language acquisition researchers to computationally analyze a large quantity of learner errors that are related to language transfer from the learners’ first language. The dataset can also be applied in personalizing grammatical error correction systems according to the learners’ first language and in providing feedback that is informed by the cause of an error.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_14", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_14.flac", "type": "audio", "duration": 716.057}, {"from": "Assistant", "value": "Weakly-supervised text classification aims to induce text classifiers from only a few user-provided seed words. The vast majority of previous work assumes high-quality seed words are given. However, the expert-annotated seed words are sometimes non-trivial to come up with. Furthermore, in the weakly-supervised learning setting, we do not have any labeled document to measure the seed words’ efficacy, making the seed word selection process “a walk in the dark”. In this work, we remove the need for expert-curated seed words by first mining (noisy) candidate seed words associated with the category names. We then train interim models with individual candidate seed words. Lastly, we estimate the interim models’ error rate in an unsupervised manner. The seed words that yield the lowest estimated error rates are added to the final seed word set. A comprehensive evaluation of six binary classification tasks on four popular datasets demonstrates that the proposed method outperforms a baseline using only category name seed words and obtained comparable performance as a counterpart using expert-annotated seed words.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_15", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_15.flac", "type": "audio", "duration": 661.002}, {"from": "Assistant", "value": "For a computer to naturally interact with a human, it needs to be human-like. In this paper, we propose a neural response generation model with multi-task learning of generation and classification, focusing on emotion. Our model based on BART (Lewis et al., 2020), a pre-trained transformer encoder-decoder model, is trained to generate responses and recognize emotions simultaneously. Furthermore, we weight the losses for the tasks to control the update of parameters. Automatic evaluations and crowdsourced manual evaluations show that the proposed model makes generated responses more emotionally aware.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_16", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_16.flac", "type": "audio", "duration": 383.06}, {"from": "Assistant", "value": "Grammatical error correction (GEC) suffers from a lack of sufficient parallel data. Studies on GEC have proposed several methods to generate pseudo data, which comprise pairs of grammatical and artificially produced ungrammatical sentences. Currently, a mainstream approach to generate pseudo data is back-translation (BT). Most previous studies using BT have employed the same architecture for both the GEC and BT models. However, GEC models have different correction tendencies depending on the architecture of their models. Thus, in this study, we compare the correction tendencies of GEC models trained on pseudo data generated by three BT models with different architectures, namely, Transformer, CNN, and LSTM. The results confirm that the correction tendencies for each error type are different for every BT model. In addition, we investigate the correction tendencies when using a combination of pseudo data generated by different BT models. As a result, we find that the combination of different BT models improves or interpolates the performance of each error type compared with using a single BT model with different seeds.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_17", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_17.flac", "type": "audio", "duration": 584.284}, {"from": "Assistant", "value": "The quality and quantity of parallel sentences are known as very important training data for constructing neural machine translation (NMT) systems. However, these resources are not available for many low-resource language pairs. Many existing methods need strong supervision are not suitable. Although several attempts at developing unsupervised models, they ignore the language-invariant between languages. In this paper, we propose an approach based on transfer learning to mine parallel sentences in the unsupervised setting. With the help of bilingual corpora of rich-resource language pairs, we can mine parallel sentences without bilingual supervision of low-resource language pairs. Experiments show that our approach improves the performance of mined parallel sentences compared with previous methods. In particular, we achieve excellent results at two real-world low-resource language pairs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_18", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_18.flac", "type": "audio", "duration": 376.047}, {"from": "Assistant", "value": "Recently, neural machine translation is widely used for its high translation accuracy, but it is also known to show poor performance at long sentence translation. Besides, this tendency appears prominently for low resource languages. We assume that these problems are caused by long sentences being few in the train data. Therefore, we propose a data augmentation method for handling long sentences. Our method is simple; we only use given parallel corpora as train data and generate long sentences by concatenating two sentences. Based on our experiments, we confirm improvements in long sentence translation by proposed data augmentation despite the simplicity. Moreover, the proposed method improves translation quality more when combined with back-translation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_19", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_19.flac", "type": "audio", "duration": 585.212}, {"from": "Assistant", "value": "Although research on emotion classification has significantly progressed in high-resource languages, it is still infancy for resource-constrained languages like Bengali. However, unavailability of necessary language processing tools and deficiency of benchmark corpora makes the emotion classification task in Bengali more challenging and complicated. This work proposes a transformer-based technique to classify the Bengali text into one of the six basic emotions: anger, fear, disgust, sadness, joy, and surprise. A Bengali emotion corpus consists of 6243 texts is developed for the classification task. Experimentation carried out using various machine learning (LR, RF, MNB, SVM), deep neural networks (CNN, BiLSTM, CNN+BiLSTM) and transformer (Bangla-BERT, m-BERT, XLM-R) based approaches. Experimental outcomes indicate that XLM-R outdoes all other techniques by achieving the highest weighted f_1-score of 69.73% on the test data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_20", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_20.flac", "type": "audio", "duration": 658.053}, {"from": "Assistant", "value": "This paper proposes a new abstractive document summarization model, hierarchical BART (Hie-BART), which captures hierarchical structures of a document (i.e., sentence-word structures) in the BART model. Although the existing BART model has achieved a state-of-the-art performance on document summarization tasks, the model does not have the interactions between sentence-level information and word-level information. In machine translation tasks, the performance of neural machine translation models has been improved by incorporating multi-granularity self-attention (MG-SA), which captures the relationships between words and phrases. Inspired by the previous work, the proposed Hie-BART model incorporates MG-SA into the encoder of the BART model for capturing sentence-word structures. Evaluations on the CNN/Daily Mail dataset show that the proposed Hie-BART model outperforms some strong baselines and improves the performance of a non-hierarchical BART model (+0.23 ROUGE-L).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_21", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_21.flac", "type": "audio", "duration": 566.219}, {"from": "Assistant", "value": "In primary school, children’s books, as well as in modern language learning apps, multi-modal learning strategies like illustrations of terms and phrases are used to support reading comprehension. Also, several studies in educational psychology suggest that integrating cross-modal information will improve reading comprehension. We claim that state-of- he-art multi-modal transformers, which could be used in a language learner context to improve human reading, will perform poorly because of the short and relatively simple textual data those models are trained with. To prove our hypotheses, we collected a new multi-modal image-retrieval dataset based on data from Wikipedia. In an in-depth data analysis, we highlight the differences between our dataset and other popular datasets. Additionally, we evaluate several state-of-the-art multi-modal transformers on text-image retrieval on our dataset and analyze their meager results, which verify our claims.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-tutorials_1", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-tutorials_1.flac", "type": "audio", "duration": 13030.04}, {"from": "Assistant", "value": "The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query for a particular task. Although the most common formulation of text ranking is search, instances of the task can also be found in many text processing applications. This tutorial provides an overview of text ranking with neural network architectures known as transformers, of which BERT (Bidirectional Encoder Representations from Transformers) is the best-known example. These models produce high quality results across many domains, tasks, and settings. This tutorial, which is based on the preprint of a forthcoming book to be published by Morgan and & Claypool under the Synthesis Lectures on Human Language Technologies series, provides an overview of existing work as a single point of entry for practitioners who wish to deploy transformers for text ranking in real-world applications and researchers who wish to pursue work in this area. We cover a wide range of techniques, grouped into two categories: transformer models that perform reranking in multi-stage ranking architectures and learned dense representations that perform ranking directly.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-tutorials_2", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-tutorials_2.flac", "type": "audio", "duration": 10556.418}, {"from": "Assistant", "value": "Deep neural networks have constantly pushed the state-of-the-art performance in natural language processing and are considered as the de-facto modeling approach in solving complex NLP tasks such as machine translation, summarization and question-answering. Despite the proven efficacy of deep neural networks at-large, their opaqueness is a major cause of concern. In this tutorial, we will present research work on interpreting fine-grained components of a neural network model from two perspectives, i) fine-grained interpretation, and ii) causation analysis. The former is a class of methods to analyze neurons with respect to a desired language concept or a task. The latter studies the role of neurons and input features in explaining the decisions made by the model. We will also discuss how interpretation methods and causation analysis can connect towards better interpretability of model prediction. Finally, we will walk you through various toolkits that facilitate fine-grained interpretation and causation analysis of neural models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-tutorials_3", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-tutorials_3.flac", "type": "audio", "duration": 14383.36}, {"from": "Assistant", "value": "Due to its great power in modeling non-Euclidean data like graphs or manifolds, deep learning on graph techniques (i.e., Graph Neural Networks (GNNs)) have opened a new door to solving challenging graph-related NLP problems. There has seen a surge of interests in applying deep learning on graph techniques to NLP, and has achieved considerable success in many NLP tasks, ranging from classification tasks like sentence classification, semantic role labeling and relation extraction, to generation tasks like machine translation, question generation and summarization. Despite these successes, deep learning on graphs for NLP still face many challenges, including automatically transforming original text sequence data into highly graph-structured data, and effectively modeling complex data that involves mapping between graph-based inputs and other highly structured output data such as sequences, trees, and graph data with multi-types in both nodes and edges. This tutorial will cover relevant and interesting topics on applying deep learning on graph techniques to NLP, including automatic graph construction for NLP, graph representation learning for NLP, advanced GNN based models (e.g., graph2seq, graph2tree, and graph2graph) for NLP, and the applications of GNNs in various NLP tasks (e.g., machine translation, natural language generation, information extraction and semantic parsing). In addition, hands-on demonstration sessions will be included to help the audience gain practical experience on applying GNNs to solve challenging NLP problems using our recently developed open source library – Graph4NLP, the first library for researchers and practitioners for easy use of GNNs for various NLP tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-tutorials_4", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-tutorials_4.flac", "type": "audio", "duration": 9785.051}, {"from": "Assistant", "value": "The advent of Deep Learning and the availability of large scale datasets has accelerated research on Natural Language Generation with a focus on newer tasks and better models. With such rapid progress, it is vital to assess the extent of scientific progress made and identify the areas/components that need improvement. To accomplish this in an automatic and reliable manner, the NLP community has actively pursued the development of automatic evaluation metrics. Especially in the last few years, there has been an increasing focus on evaluation metrics, with several criticisms of existing metrics and proposals for several new metrics. This tutorial presents the evolution of automatic evaluation metrics to their current state along with the emerging trends in this field by specifically addressing the following questions: (i) What makes NLG evaluation challenging? (ii) Why do we need automatic evaluation metrics? (iii) What are the existing automatic evaluation metrics and how can they be organised in a coherent taxonomy? (iv) What are the criticisms and shortcomings of existing metrics? (v) What are the possible future directions of research?", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-tutorials_6", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-tutorials_6.flac", "type": "audio", "duration": 12205.244}, {"from": "Assistant", "value": "In this tutorial, we present a portion of unique industry experience in efficient natural language data annotation via crowdsourcing shared by both leading researchers and engineers from Yandex. We will make an introduction to data labeling via public crowdsourcing marketplaces and will present the key components of efficient label collection. This will be followed by a practical session, where participants address a real-world language resource production task, experiment with selecting settings for the labeling process, and launch their label collection project on one of the largest crowdsourcing marketplaces. The projects will be run on real crowds within the tutorial session and we will present useful quality control techniques and provide the attendees with an opportunity to discuss their own annotation ideas.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_1", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_1.flac", "type": "audio", "duration": 679.207}, {"from": "Assistant", "value": "Email and chat communication tools are increasingly important for completing daily tasks. Accurate real-time phrase completion can save time and bolster productivity. Modern text prediction algorithms are based on large language models which typically rely on the prior words in a message to predict a completion. We examine how additional contextual signals (from previous messages, time, and subject) affect the performance of a commercial text prediction model. We compare contextual text prediction in chat and email messages from two of the largest commercial platforms Microsoft Teams and Outlook, finding that contextual signals contribute to performance differently between these scenarios. On emails, time context is most beneficial with small relative gains of 2% over baseline. Whereas, in chat scenarios, using a tailored set of previous messages as context yields relative improvements over the baseline between 9.3% and 18.6% across various critical service-oriented text prediction metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_3", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_3.flac", "type": "audio", "duration": 723.859}, {"from": "Assistant", "value": "In dialog systems, the Natural Language Understanding (NLU) component typically makes the interpretation decision (including domain, intent and slots) for an utterance before the mentioned entities are resolved. This may result in intent classification and slot tagging errors. In this work, we propose to leverage Entity Resolution (ER) features in NLU reranking and introduce a novel loss term based on ER signals to better learn model weights in the reranking framework. In addition, for a multi-domain dialog scenario, we propose a score distribution matching method to ensure scores generated by the NLU reranking models for different domains are properly calibrated. In offline experiments, we demonstrate our proposed approach significantly outperforms the baseline model on both single-domain and cross-domain evaluations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_4", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_4.flac", "type": "audio", "duration": 754.496}, {"from": "Assistant", "value": "In recent years, incorporating external knowledge for response generation in open-domain conversation systems has attracted great interest. To improve the relevancy of retrieved knowledge, we propose a neural entity linking (NEL) approach. Different from formal documents, such as news, conversational utterances are informal and multi-turn, which makes it more challenging to disambiguate the entities. Therefore, we present a context-aware named entity recognition model (NER) and entity resolution (ER) model to utilize dialogue context information. We conduct NEL experiments on three open-domain conversation datasets and validate that incorporating context information improves the performance of NER and ER models. The end-to-end NEL approach outperforms the baseline by 62.8% relatively in F1 metric. Furthermore, we verify that using external knowledge based on NEL benefits the neural response generation model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_5", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_5.flac", "type": "audio", "duration": 723.051}, {"from": "Assistant", "value": "One main challenge in building task-oriented dialogue systems is the limited amount of supervised training data available. In this work, we present a method for training retrieval-based dialogue systems using a small amount of high-quality, annotated data and a larger, unlabeled dataset. We show that pretraining using unlabeled data can bring better model performance with a 31% boost in Recall@1 compared with no pretraining. The proposed finetuning technique based on a small amount of high-quality, annotated data resulted in 26% offline and 33% online performance improvement in Recall@1 over the pretrained model. The model is deployed in an agent-support application and evaluated on live customer service contacts, providing additional insights into the real-world implications compared with most other publications in the domain often using asynchronous transcripts (e.g. Reddit data). The high performance of 74% Recall@1 shown in the customer service example demonstrates the effectiveness of this pretrain-finetune approach in dealing with the limited supervised data challenge.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_6", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_6.flac", "type": "audio", "duration": 692.048}, {"from": "Assistant", "value": "In commercial dialogue systems, the Spoken Language Understanding (SLU) component tends to have numerous domains thus context is needed to help resolve ambiguities. Previous works that incorporate context for SLU have mostly focused on domains where context is limited to a few minutes. However, there are domains that have related context that could span up to hours and days. In this paper, we propose temporal representations that combine wall-clock second difference and turn order offset information to utilize both recent and distant context in a novel large-scale setup. Experiments on the Contextual Domain Classification (CDC) task with various encoder architectures show that temporal representations combining both information outperforms only one of the two. We further demonstrate that our contextual Transformer is able to reduce 13.04% of classification errors compared to a non-contextual baseline. We also conduct empirical analyses to study recent versus distant context and opportunities to lower deployment costs.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_7", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_7.flac", "type": "audio", "duration": 674.052}, {"from": "Assistant", "value": "One of the first building blocks to create a voice assistant relates to the task of tagging entities or attributes in user queries. This can be particularly challenging when entities are in the tenth of millions, as is the case of e.g. music catalogs. Training slot tagging models at an industrial scale requires large quantities of accurately labeled user queries, which are often hard and costly to gather. On the other hand, voice assistants typically collect plenty of unlabeled queries that often remain unexploited. This paper presents a weakly-supervised methodology to label large amounts of voice query logs, enhanced with a manual filtering step. Our experimental evaluations show that slot tagging models trained on weakly-supervised data outperform models trained on hand-annotated or synthetic data, at a lower cost. Further, manual filtering of weakly-supervised data leads to a very significant reduction in Sentence Error Rate, while allowing us to drastically reduce human curation efforts from weeks to hours, with respect to hand-annotation of queries. The method is applied to successfully bootstrap a slot tagging system for a major music streaming service that currently serves several tens of thousands of daily voice queries.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_8", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_8.flac", "type": "audio", "duration": 723.285}, {"from": "Assistant", "value": "Scaling conversational personal assistants to a multitude of languages puts high demands on collecting and labelling data, a setting in which cross-lingual learning techniques can help to reconcile the need for well-performing Natural Language Understanding (NLU) with a desideratum to support many languages without incurring unacceptable cost. In this work, we show that automatically annotating unlabeled utterances using Machine Translation in an offline fashion and adding them to the training data can improve performance for existing NLU features for low-resource languages, where a straightforward translate-test approach as considered in existing literature would fail the latency requirements of a live environment. We demonstrate the effectiveness of our method with intrinsic and extrinsic evaluation using a real-world commercial dialog system in German. Beyond an intrinsic evaluation, where 56% of the resulting automatically labeled utterances had a perfect match with ground-truth labels, we see significant performance improvements in an extrinsic evaluation settings when manual labeled data is available in small quantities.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_9", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_9.flac", "type": "audio", "duration": 741.056}, {"from": "Assistant", "value": "Spoken language understanding (SLU) extracts the intended mean- ing from a user utterance and is a critical component of conversational virtual agents. In enterprise virtual agents (EVAs), language understanding is substantially challenging. First, the users are infrequent callers who are unfamiliar with the expectations of a pre-designed conversation flow. Second, the users are paying customers of an enterprise who demand a reliable, consistent and efficient user experience when resolving their issues. In this work, we describe a general and robust framework for intent and entity extraction utilizing a hybrid of statistical and rule-based approaches. Our framework includes confidence modeling that incorporates information from all components in the SLU pipeline, a critical addition for EVAs to en- sure accuracy. Our focus is on creating accurate and scalable SLU that can be deployed rapidly for a large class of EVA applications with little need for human intervention.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_10", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_10.flac", "type": "audio", "duration": 666.965}, {"from": "Assistant", "value": "Developing Text Normalization (TN) systems for Text-to-Speech (TTS) on new languages is hard. We propose a novel architecture to facilitate it for multiple languages while using data less than 3% of the size of the data used by the state of the art results on English. We treat TN as a sequence classification problem and propose a granular tokenization mechanism that enables the system to learn majority of the classes and their normalizations from the training data itself. This is further combined with minimal precoded linguistic knowledge for other classes. We publish the first results on TN for TTS in Spanish and Tamil and also demonstrate that the performance of the approach is comparable with the previous work done on English. All annotated datasets used for experimentation will be released.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_11", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_11.flac", "type": "audio", "duration": 495.04}, {"from": "Assistant", "value": "Neural Machine Translation (NMT) has achieved significant breakthrough in performance but is known to suffer vulnerability to input perturbations. As real input noise is difficult to predict during training, robustness is a big issue for system deployment. In this paper, we improve the robustness of NMT models by reducing the effect of noisy words through a Context-Enhanced Reconstruction (CER) approach. CER trains the model to resist noise in two steps: (1) perturbation step that breaks the naturalness of input sequence with made-up words; (2) reconstruction step that defends the noise propagation by generating better and more robust contextual representation. Experimental results on Chinese-English (ZH-EN) and French-English (FR-EN) translation tasks demonstrate robustness improvement on both news and social media text. Further fine-tuning experiments on social media text show our approach can converge at a higher position and provide a better adaptation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_12", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_12.flac", "type": "audio", "duration": 639.04}, {"from": "Assistant", "value": "We propose to improve unsupervised neural machine translation with cross-lingual supervision (), which utilizes supervision signals from high resource language pairs to improve the translation of zero-source languages. Specifically, for training En-Ro system without parallel corpus, we can leverage the corpus from En-Fr and En-De to collectively train the translation from one language into many languages under one model. % is based on multilingual models which require no changes to the standard unsupervised NMT. Simple and effective, significantly improves the translation quality with a big margin in the benchmark unsupervised translation tasks, and even achieves comparable performance to supervised NMT. In particular, on WMT’14 -tasks achieves 37.6 and 35.18 BLEU score, which is very close to the large scale supervised setting and on WMT’16 -tasks achieves 35.09 BLEU score which is even better than the supervised Transformer baseline.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_13", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_13.flac", "type": "audio", "duration": 717.056}, {"from": "Assistant", "value": "Most of the recent Natural Language Processing(NLP) studies are based on the Pretrain-Finetuning Approach (PFA), but in small and medium-sized enterprises or companies with insufficient hardware there are many limitations to servicing NLP application software using such technology due to slow speed and insufficient memory. The latest PFA technologies require large amounts of data, especially for low-resource languages, making them much more difficult to work with. We propose a new tokenization method, ONE-Piece, to address this limitation that combines the morphology-considered subword tokenization method and the vocabulary method used after probing for an existing method that has not been carefully considered before. Our proposed method can also be used without modifying the model structure. We experiment by applying ONE-Piece to Korean, a morphologically-rich and low-resource language. We derive an optimal subword tokenization result for Korean-English machine translation by conducting a case study that combines the subword tokenization method, morphological segmentation, and vocabulary method. Through comparative experiments with all the tokenization methods currently used in NLP research, ONE-Piece achieves performance comparable to the current Korean-English machine translation state-of-the-art model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_14", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_14.flac", "type": "audio", "duration": 623.04}, {"from": "Assistant", "value": "Automatic translation of dialogue texts is a much needed demand in many real life scenarios. However, the currently existing neural machine translation delivers unsatisfying results. In this paper, we conduct a deep analysis of a dialogue corpus and summarize three major issues on dialogue translation, including pronoun dropping (), punctuation dropping (), and typos (). In response to these challenges, we propose a joint learning method to identify omission and typo, and utilize context to translate dialogue utterances. To properly evaluate the performance, we propose a manually annotated dataset with 1,931 Chinese-English parallel utterances from 300 dialogues as a benchmark testbed for dialogue translation. Our experiments show that the proposed method improves translation quality by 3.2 BLEU over the baselines. It also elevates the recovery rate of omitted pronouns from 26.09% to 47.16%. We will publish the code and dataset publicly at https://xxx.xx.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_15", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_15.flac", "type": "audio", "duration": 668.139}, {"from": "Assistant", "value": "Transformer and its variants have achieved great success in natural language processing. Since Transformer models are huge in size, serving these models is a challenge for real industrial applications. In this paper, we propose , a highly efficient inference library for models in the Transformer family. includes a series of GPU optimization techniques to both streamline the computation of Transformer layers and reduce memory footprint. supports models trained using PyTorch and Tensorflow. Experimental results on standard machine translation benchmarks show that achieves up to 14x speedup compared with TensorFlow and 1.4x speedup compared with , a concurrent CUDA implementation. The code will be released publicly after the review.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_16", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_16.flac", "type": "audio", "duration": 629.056}, {"from": "Assistant", "value": "Transformer-based methods are appealing for multilingual text classification, but common research benchmarks like XNLI (Conneau et al., 2018) do not reflect the data availability and task variety of industry applications. We present an empirical comparison of transformer-based text classification models in a variety of practical monolingual and multilingual pretraining and fine-tuning settings. We evaluate these methods on two distinct tasks in five different languages. Departing from prior work, our results show that multilingual language models can outperform monolingual ones in some downstream tasks and target languages. We additionally show that practical modifications such as task- and domain-adaptive pretraining and data augmentation can improve classification performance without the need for additional labeled data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_17", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_17.flac", "type": "audio", "duration": 348.416}, {"from": "Assistant", "value": "E-commerce has grown substantially over the last several years, and chatbots for intelligent customer service are concurrently drawing attention. We presented AliMe Assist, a Chinese intelligent assistant designed for creating an innovative online shopping experience in E-commerce. Based on question answering (QA), AliMe Assist offers assistance service, customer service, and chatting service. According to the survey of user studies and the real online testing, emotional comfort of customers’ negative emotions, which make up more than 5% of whole number of customer visits on AliMe, is a key point for providing considerate service. In this paper, we propose a framework to obtain proper answer to customers’ emotional questions. The framework takes emotion classification model as a core, and final answer selection is based on topic classification and text matching. Our experiments on real online systems show that the framework is very promising.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_18", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_18.flac", "type": "audio", "duration": 778.923}, {"from": "Assistant", "value": "We consider the problem of scaling automated suggested replies for a commercial email application to multiple languages. Faced with increased compute requirements and low language resources for language expansion, we build a single universal model for improving the quality and reducing run-time costs of our production system. However, restricted data movement across regional centers prevents joint training across languages. To this end, we propose a multi-lingual multi-task continual learning framework, with auxiliary tasks and language adapters to train universal language representation across regions. The experimental results show positive cross-lingual transfer across languages while reducing catastrophic forgetting across regions. Our online results on real user traffic show significant CTR and Char-saved gain as well as 65% training cost reduction compared with per-language models. As a consequence, we have scaled the feature in multiple languages including low-resource markets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_19", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_19.flac", "type": "audio", "duration": 719.979}, {"from": "Assistant", "value": "Nowadays, with many e-commerce platforms conducting global business, e-commerce search systems are required to handle product retrieval under multilingual scenarios. Moreover, comparing with maintaining per-country specific e-commerce search systems, having an universal system across countries can further reduce the operational and computational costs, and facilitate business expansion to new countries. In this paper, we introduce an universal end-to-end multilingual retrieval system, and discuss our learnings and technical details when training and deploying the system to serve billion-scale product retrieval for e-commerce search. In particular, we propose a multilingual graph attention based retrieval network by leveraging recent advances in transformer-based multilingual language models and graph neural network architectures to capture the interactions between search queries and items in e-commerce search. Offline experiments on five countries data show that our algorithm outperforms the state-of-the-art baselines by 35% recall and 25% mAP on average. Moreover, the proposed model shows significant increase of conversion/revenue in online A/B experiments and has been deployed in production for multiple countries.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_20", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_20.flac", "type": "audio", "duration": 698.526}, {"from": "Assistant", "value": "We present Query2Prod2Vec, a model that grounds lexical representations for product search in product embeddings: in our model, meaning is a mapping between words and a latent space of products in a digital shop. We leverage shopping sessions to learn the underlying space and use merchandising annotations to build lexical analogies for evaluation: our experiments show that our model is more accurate than known techniques from the NLP and IR literature. Finally, we stress the importance of data efficiency for product search outside of retail giants, and highlight how Query2Prod2Vec fits with practical constraints faced by most practitioners.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_21", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_21.flac", "type": "audio", "duration": 745.088}, {"from": "Assistant", "value": "This work demonstrates the development process of a machine learning architecture for inference that can scale to a large volume of requests. We used a BERT model that was fine-tuned for emotion analysis, returning a probability distribution of emotions given a paragraph. The model was deployed as a gRPC service on Kubernetes. Apache Spark was used to perform inference in batches by calling the service. We encountered some performance and concurrency challenges and created solutions to achieve faster running time. Starting with 200 successful inference requests per minute, we were able to achieve as high as 18 thousand successful requests per minute with the same batch job resource allocation. As a result, we successfully stored emotion probabilities for 95 million paragraphs within 96 hours.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_22", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_22.flac", "type": "audio", "duration": 731.285}, {"from": "Assistant", "value": "Although deep neural networks have been widely employed and proven effective in sentiment analysis tasks, it remains challenging for model developers to assess their models for erroneous predictions that might exist prior to deployment. Once deployed, emergent errors can be hard to identify in prediction run-time and impossible to trace back to their sources. To address such gaps, in this paper we propose an error detection framework for sentiment analysis based on explainable features. We perform global-level feature validation with human-in-the-loop assessment, followed by an integration of global and local-level feature contribution analysis. Experimental results show that, given limited human-in-the-loop intervention, our method is able to identify erroneous model predictions on unseen data with high precision.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_23", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_23.flac", "type": "audio", "duration": 627.055}, {"from": "Assistant", "value": "Building automatic technical support system is an important yet challenge task. Conceptually, to answer a user question on a technical forum, a human expert has to first retrieve relevant documents, and then read them carefully to identify the answer snippet. Despite huge success the researchers have achieved in coping with general domain question answering (QA), much less attentions have been paid for investigating technical QA. Specifically, existing methods suffer from several unique challenges (i) the question and answer rarely overlaps substantially and (ii) very limited data size. In this paper, we propose a novel framework of deep transfer learning to effectively address technical QA across tasks and domains. To this end, we present an adjustable joint learning approach for document retrieval and reading comprehension tasks. Our experiments on the TechQA demonstrates superior performance compared with state-of-the-art methods.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_24", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_24.flac", "type": "audio", "duration": 725.056}, {"from": "Assistant", "value": "In this study, we demonstrate the viability of deploying BERT-style models to AWS Lambda in a production environment. Since the freely available pre-trained models are too large to be deployed in this environment, we utilize knowledge distillation and fine-tune the models on proprietary datasets for two real-world tasks: sentiment analysis and semantic textual similarity. As a result, we obtain models that are tuned for a specific domain and deployable in the serverless environment. The subsequent performance analysis shows that this solution does not only report latency levels acceptable for production use but that it is also a cost-effective alternative to small-to-medium size deployments of BERT models, all without any infrastructure overhead.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_25", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_25.flac", "type": "audio", "duration": 729.045}, {"from": "Assistant", "value": "Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries. In this paper, we propose a novel architecture that jointly solves the NER and EL tasks by combining them in a joint reranking module. We show that our proposed framework improves NER accuracy by up to 3.13% and EL accuracy by up to 3.6% in F1 score. The features used also lead to better accuracies in other natural language understanding tasks, such as domain classification and semantic parsing.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_26", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_26.flac", "type": "audio", "duration": 721.259}, {"from": "Assistant", "value": "Named entity linking (NEL) or mapping “strings” to “things” in a knowledge base is a fundamental preprocessing step in systems that require knowledge of entities such as information extraction and question answering. In this work, we lay out and investigate two challenges faced by individuals or organizations building NEL systems. Can they directly use an off-the-shelf system? If not, how easily can such a system be repurposed for their use case? First, we conduct a study of off-the-shelf commercial and academic NEL systems. We find that most systems struggle to link rare entities, with commercial solutions lagging their academic counterparts by 10%+. Second, for a use case where the NEL model is used in a sports question-answering (QA) system, we investigate how to close the loop in our analysis by repurposing the best off-the-shelf model (Bootleg) to correct sport-related errors. We show how tailoring a simple technique for patching models using weak labeling can provide a 25% absolute improvement in accuracy of sport-related errors.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_27", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_27.flac", "type": "audio", "duration": 670.059}, {"from": "Assistant", "value": "Complex natural language understanding modules in dialog systems have a richer understanding of user utterances, and thus are critical in providing a better user experience. However, these models are often created from scratch, for specific clients and use cases and require the annotation of large datasets. This encourages the sharing of annotated data across multiple clients. To facilitate this we introduce the idea ofintent features: domain and topic agnostic properties of intents that can be learnt from the syntactic cues only, and hence can be shared. We introduce a new neural network architecture, the Global-Local model, that shows significant improvement over strong baselines for identifying these features in a deployed, multi-intent natural language understanding module, and more generally in a classification setting where a part of an utterance has to be classified utilizing the whole context.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_28", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_28.flac", "type": "audio", "duration": 775.04}, {"from": "Assistant", "value": "Contracts are arguably the most important type of business documents. Despite their significance in business, legal contract review largely remains an arduous, expensive and manual process. In this paper, we describe TECUS: a commercial system designed and deployed for contract understanding and used by a wide range of enterprise users for the past few years. We reflect on the challenges and design decisions when building TECUS. We also summarize the data science life cycle of TECUS and share lessons learned.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_29", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_29.flac", "type": "audio", "duration": 712.128}, {"from": "Assistant", "value": "In developing an online question-answering system for the medical domains, natural language inference (NLI) models play a central role in question matching and intention detection. However, which models are best for our datasets? Manually selecting or tuning a model is time-consuming. Thus we experiment with automatically optimizing the model architectures on the task at hand via neural architecture search (NAS). First, we formulate a novel architecture search space based on the previous NAS literature, supporting cross-sentence attention (cross-attn) modeling. Second, we propose to modify the ENAS method to accelerate and stabilize the search results. We conduct extensive experiments on our two medical NLI tasks. Results show that our system can easily outperform the classical baseline models. We compare different NAS methods and demonstrate our approach provides the best results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_30", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_30.flac", "type": "audio", "duration": 508.053}, {"from": "Assistant", "value": "Detecting an Out-of-Domain (OOD) utterance is crucial for a robust dialog system. Most dialog systems are trained on a pool of annotated OOD data to achieve this goal. However, collecting the annotated OOD data for a given domain is an expensive process. To mitigate this issue, previous works have proposed generative adversarial networks (GAN) based models to generate OOD data for a given domain automatically. However, these proposed models do not work directly with the text. They work with the text’s latent space instead, enforcing these models to include components responsible for encoding text into latent space and decoding it back, such as auto-encoder. These components increase the model complexity, making it difficult to train. We propose OodGAN, a sequential generative adversarial network (SeqGAN) based model for OOD data generation. Our proposed model works directly on the text and hence eliminates the need to include an auto-encoder. OOD data generated using OodGAN model outperforms state-of-the-art in OOD detection metrics for ROSTD (67% relative improvement in FPR 0.95) and OSQ datasets (28% relative improvement in FPR 0.95)", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_31", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_31.flac", "type": "audio", "duration": 710.043}, {"from": "Assistant", "value": "Neural models for text generation are often designed in an end-to-end fashion, typically with zero control over intermediate computations, limiting their practical usability in downstream applications. In this work, we incorporate explicit means into neural models to ensure topical continuity, informativeness and content diversity of generated radiology reports. For the purpose we propose a method to compute image representations specific to each sentential context and eliminate redundant content by exploiting diverse sentence states. We conduct experiments to generate radiology reports from medical images of chest x-rays using MIMIC-CXR. Our model outperforms baselines by up to 18% and 29% respective in the evaluation for informativeness and content ordering respectively, relative on objective metrics and 16% on human evaluation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_32", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_32.flac", "type": "audio", "duration": 711.04}, {"from": "Assistant", "value": "Although there are many studies on neural language generation (NLG), few trials are put into the real world, especially in the advertising domain. Generating ads with NLG models can help copywriters in their creation. However, few studies have adequately evaluated the effect of generated ads with actual serving included because it requires a large amount of training data and a particular environment. In this paper, we demonstrate a practical use case of generating ad-text with an NLG model. Specially, we show how to improve the ads’ impact, deploy models to a product, and evaluate the generated ads.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_34", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_34.flac", "type": "audio", "duration": 698.015}, {"from": "Assistant", "value": "In this paper, we present LATEX-Numeric - a high-precision fully-automated scalable framework for extracting E-commerce numeric attributes from unstructured product text like product description. Most of the past work on attribute extraction is not scalable as they rely on manually curated training data, either with or without use of active learning. We rely on distant supervision for training data generation, removing dependency on manual labels. One issue with distant supervision is that it leads to incomplete training annotation due to missing attribute values while matching. We propose a multi-task learning architecture to deal with missing labels in the training data, leading to F1 improvement of 9.2% for numeric attributes over state-of-the-art single-task architecture. While multi-task architecture benefits both numeric and non-numeric attributes, we present automated techniques to further improve the numeric attributes extraction models. Numeric attributes require a list of units (or aliases) for better matching with distant supervision. We propose an automated algorithm for alias creation using unstructured text and attribute values, leading to a 20.2% F1 improvement. Extensive experiments on real world datasets for 20 numeric attributes across 5 product categories and 3 English marketplaces show that LATEX-numeric achieves a high F1-score, without any manual intervention, making it suitable for practical applications. Finally we show that the improvements are language-agnostic and LATEX-Numeric achieves 13.9% F1 improvement for 3 non-English languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_36", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_36.flac", "type": "audio", "duration": 658.048}, {"from": "Assistant", "value": "Recent advances in transfer learning have improved the performance of virtual assistants considerably. Nevertheless, creating sophisticated voice-enabled applications for new domains remains a challenge, and meager training data is often a key bottleneck. Accordingly, unsupervised learning and SSL (semi-supervised learning) techniques continue to be of vital importance. While a number of such methods have been explored previously in isolation, in this paper we investigate the synergistic use of a number of weakly supervised techniques with a view to improving NLU (Natural Language Understanding) accuracy in low-resource settings. We explore three different approaches incorporating anonymized, unlabeled and automatically transcribed user utterances into the training process, two focused on data augmentation via SSL and another one focused on unsupervised and transfer learning. We show promising results, obtaining gains that range from 4.73% to 7.65% relative improvements on semantic error rate for each individual approach. Moreover, the combination of all three methods together yields a relative improvement of 11.77% over our current baseline model. Our methods are applicable to any new domain with minimal training data, and can be deployed over time into a cycle of continual learning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_37", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_37.flac", "type": "audio", "duration": 705.004}, {"from": "Assistant", "value": "Item categorization is an important application of text classification in e-commerce due to its impact on the online shopping experience of users. One class of text classification techniques that has gained attention recently is using the semantic information of the labels to guide the classification task. We have conducted a systematic investigation of the potential benefits of these methods on a real data set from a major e-commerce company in Japan. Furthermore, using a hyperbolic space to embed product labels that are organized in a hierarchical structure led to better performance compared to using a conventional Euclidean space embedding. These findings demonstrate how label-guided learning can improve item categorization systems in the e-commerce domain.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_38", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_38.flac", "type": "audio", "duration": 656.043}, {"from": "Assistant", "value": "Intent detection is a key component of modern goal-oriented dialog systems that accomplish a user task by predicting the intent of users’ text input. There are three primary challenges in designing robust and accurate intent detection models. First, typical intent detection models require a large amount of labeled data to achieve high accuracy. Unfortunately, in practical scenarios it is more common to find small, unbalanced, and noisy datasets. Secondly, even with large training data, the intent detection models can see a different distribution of test data when being deployed in the real world, leading to poor accuracy. Finally, a practical intent detection model must be computationally efficient in both training and single query inference so that it can be used continuously and re-trained frequently. We benchmark intent detection methods on a variety of datasets. Our results show that Watson Assistant’s intent detection model outperforms other commercial solutions and is comparable to large pretrained language models while requiring only a fraction of computational resources and training data. Watson Assistant demonstrates a higher degree of robustness when the training and test distributions differ.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-industry_39", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-industry_39.flac", "type": "audio", "duration": 690.051}, {"from": "Assistant", "value": "This paper presents a production Semi-Supervised Learning (SSL) pipeline based on the student-teacher framework, which leverages millions of unlabeled examples to improve Natural Language Understanding (NLU) tasks. We investigate two questions related to the use of unlabeled data in production SSL context: 1) how to select samples from a huge unlabeled data pool that are beneficial for SSL training, and 2) how does the selected data affect the performance of different state-of-the-art SSL techniques. We compare four widely used SSL techniques, Pseudo-label (PL), Knowledge Distillation (KD), Virtual Adversarial Training (VAT) and Cross-View Training (CVT) in conjunction with two data selection methods including committee-based selection and submodular optimization based selection. We further examine the benefits and drawbacks of these techniques when applied to intent classification (IC) and named entity recognition (NER) tasks, and provide guidelines specifying when each of these methods might be beneficial to improve large scale NLU systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_calcs-1_19", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_calcs-1_19.flac", "type": "audio", "duration": 493.726}, {"from": "Assistant", "value": "Multilingual models have demonstrated impressive cross-lingual transfer performance. However, test sets like XNLI are monolingual at the example level. In multilingual communities, it is common for polyglots to code-mix when conversing with each other. Inspired by this phenomenon, we present two strong black-box adversarial attacks (one word-level, one phrase-level) for multilingual models that push their ability to handle code-mixed sentences to the limit. The former (PolyGloss) uses bilingual dictionaries to propose perturbations and translations of the clean example for sense disambiguation. The latter (Bumblebee) directly aligns the clean example with its translations before extracting phrases as perturbations. Bumblebee has a success rate of 89.75% against XLM-R-large, bringing its average accuracy of 79.85 down to 8.18 on XNLI. Finally, we propose an efficient adversarial training scheme, Code-mixed Adversarial Training (CAT), that trains in the same number of steps as the original model. Even after controlling for the extra training data introduced, CAT improves model accuracy when the model is prevented from relying on lexical overlaps (+3.45), with a negligible drop (-0.15 points) in performance on the original XNLI test set. t-SNE visualizations reveal that CAT improves a model’s language agnosticity. This paper will be published in the proceedings of NAACL-HLT 2021.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_nlpmc-1_3", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_nlpmc-1_3.flac", "type": "audio", "duration": 698.572}, {"from": "Assistant", "value": "We focus on dialog models in the context of clinical studies where the goal is to help gather, in addition to the close information collected based on a questionnaire, serendipitous information that is medically relevant. To promote user engagement and address this dual goal (collecting both a predefined set of data points and more informal information about the state of the patients), we introduce an ensemble model made of three bots: a task-based, a follow-up and a social bot. We introduce a generic method for developing follow-up bots. We compare different ensemble configurations and we show that the combination of the three bots (i) provides a better basis for collecting information than just the information seeking bot and (ii) collects information in a more user-friendly, more efficient manner that an ensemble model combining the information seeking and the social bot.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_nlpmc-1_5", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_nlpmc-1_5.flac", "type": "audio", "duration": 427.688}, {"from": "Assistant", "value": "In order to provide high-quality care, health professionals must efficiently identify the presence, possibility, or absence of symptoms, treatments and other relevant entities in free-text clinical notes. Such is the task of assertion detection - to identify the assertion class (present, possible, absent) of an entity based on textual cues in unstructured text. We evaluate state-of-the-art medical language models on the task and show that they outperform the baselines in all three classes. As transferability is especially important in the medical domain we further study how the best performing model behaves on unseen data from two other medical datasets. For this purpose we introduce a newly annotated set of 5,000 assertions for the publicly available MIMIC-III dataset. We conclude with an error analysis that reveals situations in which the models still go wrong and points towards future research directions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_nlpmc-1_9", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_nlpmc-1_9.flac", "type": "audio", "duration": 627.333}, {"from": "Assistant", "value": "In medical dialogue summarization, summaries must be coherent and must capture all the medically relevant information in the dialogue. However, learning effective models for summarization require large amounts of labeled data which is especially hard to obtain. We present an algorithm to create synthetic training data with an explicit focus on capturing medically relevant information. We utilize GPT-3 as the backbone of our algorithm and scale 210 human labeled examples to yield results comparable to using 6400 human labeled examples (~30x) leveraging low-shot learning and an ensemble method. In detailed experiments, we show that this approach produces high quality training data that can further be combined with human labeled data to get summaries that are strongly preferable to those produced by models trained on human data alone both in terms of medical accuracy and coherency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_privatenlp-1_3", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_privatenlp-1_3.flac", "type": "audio", "duration": 787.785}, {"from": "Assistant", "value": "Contextual language models have led to significantly better results on a plethora of language understanding tasks, especially when pre-trained on the same data as the downstream task. While this additional pre-training usually improves performance, it can lead to information leakage and therefore risks the privacy of individuals mentioned in the training data. One method to guarantee the privacy of such individuals is to train a differentially-private model, but this usually comes at the expense of model performance. Moreover, it is hard to tell given a privacy parameter𝜖what was the effect on the trained representation. In this work we aim to guide future practitioners and researchers on how to improve privacy while maintaining good model performance. We demonstrate how to train a differentially-private pre-trained language model (i.e., BERT) with a privacy guarantee of𝜖=1and with only a small degradation in performance. We experiment on a dataset of clinical notes with a model trained on a target entity extraction task, and compare it to a similar model trained without differential privacy. Finally, we present experiments showing how to interpret the differentially-private representation and understand the information lost and maintained in this process.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_252", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_252.flac", "type": "audio", "duration": 660.097}, {"from": "Assistant", "value": "Machine translation (MT) is currently evaluated in one of two ways: in a monolingual fashion, by comparison with the system output to one or more human reference translations, or in a trained crosslingual fashion, by building a supervised model to predict quality scores from human-labeled data. In this paper, we propose a more cost-effective, yet well performing unsupervised alternative SentSim: relying on strong pretrained multilingual word and sentence representations, we directly compare the source with the machine translated sentence, thus avoiding the need for both reference translations and labelled training data. The metric builds on state-of-the-art embedding-based approaches – namely BERTScore and Word Mover’s Distance – by incorporating a notion of sentence semantic similarity. By doing so, it achieves better correlation with human scores on different datasets. We show that it outperforms these and other metrics in the standard monolingual setting (MT-reference translation), a well as in the source-MT bilingual setting, where it performs on par with glass-box approaches to quality estimation that rely on MT model information.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_253", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_253.flac", "type": "audio", "duration": 737.048}, {"from": "Assistant", "value": "Automatic image captioning has improved significantly over the last few years, but the problem is far from being solved, with state of the art models still often producing low quality captions when used in the wild. In this paper, we focus on the task of Quality Estimation (QE) for image captions, which attempts to model the caption quality from a human perspective and *without* access to ground-truth references, so that it can be applied at prediction time to detect low-quality captions produced on *previously unseen images*. For this task, we develop a human evaluation process that collects coarse-grained caption annotations from crowdsourced users, which is then used to collect a large scale dataset spanning more than 600k caption quality ratings. We then carefully validate the quality of the collected ratings and establish baseline models for this new QE task. Finally, we further collect fine-grained caption quality annotations from trained raters, and use them to demonstrate that QE models trained over the coarse ratings can effectively detect and filter out low-quality image captions, thereby improving the user experience from captioning systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_254", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_254.flac", "type": "audio", "duration": 707.535}, {"from": "Assistant", "value": "Automated systems that negotiate with humans have broad applications in pedagogy and conversational AI. To advance the development of practical negotiation systems, we present CaSiNo: a novel corpus of over a thousand negotiation dialogues in English. Participants take the role of campsite neighbors and negotiate for food, water, and firewood packages for their upcoming trip. Our design results in diverse and linguistically rich negotiations while maintaining a tractable, closed-domain environment. Inspired by the literature in human-human negotiations, we annotate persuasion strategies and perform correlation analysis to understand how the dialogue behaviors are associated with the negotiation performance. We further propose and evaluate a multi-task framework to recognize these strategies in a given utterance. We find that multi-task learning substantially improves the performance for all strategy labels, especially for the ones that are the most skewed. We release the dataset, annotations, and the code to propel future work in human-machine negotiations:https://github.com/kushalchawla/CaSiNo", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_255", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_255.flac", "type": "audio", "duration": 706.142}, {"from": "Assistant", "value": "Recent progress in Natural Language Understanding (NLU) has seen the latest models outperform human performance on many standard tasks. These impressive results have led the community to introspect on dataset limitations, and iterate on more nuanced challenges. In this paper, we introduce the task of HeadLine Grouping (HLG) and a corresponding dataset (HLGD) consisting of 20,056 pairs of news headlines, each labeled with a binary judgement as to whether the pair belongs within the same group. On HLGD, human annotators achieve high performance of around 0.9 F-1, while current state-of-the art Transformer models only reach 0.75 F-1, opening the path for further improvements. We further propose a novel unsupervised Headline Generator Swap model for the task of HeadLine Grouping that achieves within 3 F-1 of the best supervised model. Finally, we analyze high-performing models with consistency tests, and find that models are not consistent in their predictions, revealing modeling limits of current architectures.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_256", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_256.flac", "type": "audio", "duration": 674.052}, {"from": "Assistant", "value": "We take the first step towards multilingual style transfer by creating and releasing XFORMAL, a benchmark of multiple formal reformulations of informal text in Brazilian Portuguese, French, and Italian. Results on XFORMAL suggest that state-of-the-art style transfer approaches perform close to simple baselines, indicating that style transfer is even more challenging when moving multilingual.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_257", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_257.flac", "type": "audio", "duration": 789.061}, {"from": "Assistant", "value": "Deep Learning-based NLP systems can be sensitive to unseen tokens and hard to learn with high-dimensional inputs, which critically hinder learning generalization. We introduce an approach by grouping input words based on their semantic diversity to simplify input language representation with low ambiguity. Since the semantically diverse words reside in different contexts, we are able to substitute words with their groups and still distinguish word meanings relying on their contexts. We design several algorithms that compute diverse groupings based on random sampling, geometric distances, and entropy maximization, and we prove formal guarantees for the entropy-based algorithms. Experimental results show that our methods generalize NLP models and demonstrate enhanced accuracy on POS tagging and LM tasks and significant improvements on medium-scale machine translation tasks, up to +6.5 BLEU points. Our source code is available athttps://github.com/abdulrafae/dg.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_258", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_258.flac", "type": "audio", "duration": 376.001}, {"from": "Assistant", "value": "Fine-tuning pre-trained language models suchas BERT has become a common practice dom-inating leaderboards across various NLP tasks. Despite its recent success and wide adoption,this process is unstable when there are onlya small number of training samples available. The brittleness of this process is often reflectedby the sensitivity to random seeds. In this pa-per, we propose to tackle this problem basedon the noise stability property of deep nets,which is investigated in recent literature (Aroraet al., 2018; Sanyal et al., 2020). Specifically,we introduce a novel and effective regulariza-tion method to improve fine-tuning on NLPtasks, referred to asLayer-wiseNoiseStabilityRegularization (LNSR). We extend the theo-ries about adding noise to the input and provethat our method gives a stabler regularizationeffect. We provide supportive evidence by ex-perimentally confirming that well-performingmodels show a low sensitivity to noise andfine-tuning with LNSR exhibits clearly bet-ter generalizability and stability. Furthermore,our method also demonstrates advantages overother state-of-the-art algorithms including L2-SP (Li et al., 2018), Mixout (Lee et al., 2020)and SMART (Jiang et al., 20)", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_259", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_259.flac", "type": "audio", "duration": 586.002}, {"from": "Assistant", "value": "Variational autoencoders (VAEs) are widely used for latent variable modeling of text. We focus on variations that learn expressive prior distributions over the latent variable. We find that existing training strategies are not effective for learning rich priors, so we propose adding the importance-sampled log marginal likelihood as a second term to the standard VAE objective to help when learning the prior. Doing so improves results for all priors evaluated, including a novel choice for sentence VAEs based on normalizing flows (NF). Priors parameterized with NF are no longer constrained to a specific distribution family, allowing a more flexible way to encode the data distribution. Our model, which we call FlowPrior, shows a substantial improvement in language modeling tasks compared to strong baselines. We demonstrate that FlowPrior learns an expressive prior with analysis and several forms of evaluation involving generation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_260", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_260.flac", "type": "audio", "duration": 440.459}, {"from": "Assistant", "value": "The current state-of-the-art model HiAGM for hierarchical text classification has two limitations. First, it correlates each text sample with all labels in the dataset which contains irrelevant information. Second, it does not consider any statistical constraint on the label representations learned by the structure encoder, while constraints for representation learning are proved to be helpful in previous work. In this paper, we propose HTCInfoMax to address these issues by introducing information maximization which includes two modules: text-label mutual information maximization and label prior matching. The first module can model the interaction between each text sample and its ground truth labels explicitly which filters out irrelevant information. The second one encourages the structure encoder to learn better representations with desired characteristics for all labels which can better handle label imbalance in hierarchical text classification. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed HTCInfoMax.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_261", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_261.flac", "type": "audio", "duration": 402.053}, {"from": "Assistant", "value": "Humans can distinguish new categories very efficiently with few examples, largely due to the fact that human beings can leverage knowledge obtained from relevant tasks. However, deep learning based text classification model tends to struggle to achieve satisfactory performance when labeled data are scarce. Inspired by human intelligence, we propose to introduce external knowledge into few-shot learning to imitate human knowledge. A novel parameter generator network is investigated to this end, which is able to use the external knowledge to generate different metrics for different tasks. Armed with this network, similar tasks can use similar metrics while different tasks use different metrics. Through experiments, we demonstrate that our method outperforms the SoTA few-shot text classification models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_262", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_262.flac", "type": "audio", "duration": 417.003}, {"from": "Assistant", "value": "Assessing an AI agent that can converse in human language and understand visual content is challenging. Generation metrics, such as BLEU scores favor correct syntax over semantics. Hence a discriminative approach is often used, where an agent ranks a set of candidate options. The mean reciprocal rank (MRR) metric evaluates the model performance by taking into account the rank of a single human-derived answer. This approach, however, raises a new challenge: the ambiguity and synonymy of answers, for instance, semantic equivalence (e.g., ‘yeah’ and ‘yes’). To address this, the normalized discounted cumulative gain (NDCG) metric has been used to capture the relevance of all the correct answers via dense annotations. However, the NDCG metric favors the usually applicable uncertain answers such as ‘I don’t know.’ Crafting a model that excels on both MRR and NDCG metrics is challenging. Ideally, an AI agent should answer a human-like reply and validate the correctness of any answer. To address this issue, we describe a two-step non-parametric ranking approach that can merge strong MRR and NDCG models. Using our approach, we manage to keep most MRR state-of-the-art performance (70.41% vs. 71.24%) and the NDCG state-of-the-art performance (72.16% vs. 75.35%). Moreover, our approach won the recent Visual Dialog 2020 challenge. Source code is available athttps://github.com/idansc/mrr-ndcg.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_263", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_263.flac", "type": "audio", "duration": 865.454}, {"from": "Assistant", "value": "Previous pre-neural work on structured prediction has produced very effective supervised clustering algorithms using linear classifiers, e.g., structured SVM or perceptron. However, these cannot exploit the representation learning ability of neural networks, which would make supervised clustering even more powerful, i.e., general clustering patterns can be learned automatically. In this paper, we design neural networks based on latent structured prediction loss and Transformer models to approach supervised clustering. We tested our methods on the task of automatically recreating categories of intents from publicly available question intent corpora. The results show that our approach delivers 95.65% of F1, outperforming the state of the art by 17.24%.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_264", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_264.flac", "type": "audio", "duration": 674.818}, {"from": "Assistant", "value": "We propose ConVEx (Conversational Value Extractor), an efficient pretraining and fine-tuning neural approach for slot-labeling dialog tasks. Instead of relying on more general pretraining objectives from prior work (e.g., language modeling, response selection), ConVEx’s pretraining objective, a novel pairwise cloze task using Reddit data, is well aligned with its intended usage on sequence labeling tasks. This enables learning domain-specific slot labelers by simply fine-tuning decoding layers of the pretrained general-purpose sequence labeling model, while the majority of the pretrained model’s parameters are kept frozen. We report state-of-the-art performance of ConVEx across a range of diverse domains and data sets for dialog slot-labeling, with the largest gains in the most challenging, few-shot setups. We believe that ConVEx’s reduced pretraining times (i.e., only 18 hours on 12 GPUs) and cost, along with its efficient fine-tuning and strong performance, promise wider portability and scalability for data-efficient sequence-labeling tasks in general.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_265", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_265.flac", "type": "audio", "duration": 692.048}, {"from": "Assistant", "value": "Anaphora and ellipses are two common phenomena in dialogues. Without resolving referring expressions and information omission, dialogue systems may fail to generate consistent and coherent responses. Traditionally, anaphora is resolved by coreference resolution and ellipses by query rewrite. In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding. Given an ongoing dialogue between a user and a dialogue assistant, for the user query, our joint learning model first predicts coreference links between the query and the dialogue context, and then generates a self-contained rewritten user query. To evaluate our model, we annotate a dialogue based coreference resolution dataset, MuDoCo, with rewritten queries. Results show that the performance of query rewrite can be substantially boosted (+2.3% F1) with the aid of coreference modeling. Furthermore, our joint model outperforms the state-of-the-art coreference resolution model (+2% F1) on this dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_266", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_266.flac", "type": "audio", "duration": 719.723}, {"from": "Assistant", "value": "In goal-oriented dialogue systems, users provide information through slot values to achieve specific goals. Practically, some combinations of slot values can be invalid according to external knowledge. For example, a combination of “cheese pizza” (a menu item) and “oreo cookies” (a topping) from an input utterance “Can I order a cheese pizza with oreo cookies on top?” exemplifies such invalid combinations according to the menu of a restaurant business. Traditional dialogue systems allow execution of validation rules as a post-processing step after slots have been filled which can lead to error accumulation. In this paper, we formalize knowledge-driven slot constraints and present a new task of constraint violation detection accompanied with benchmarking data. Then, we propose methods to integrate the external knowledge into the system and model constraint violation detection as an end-to-end classification task and compare it to the traditional rule-based pipeline approach. Experiments on two domains of the MultiDoGO dataset reveal challenges of constraint violation detection and sets the stage for future work and improvements.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_267", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_267.flac", "type": "audio", "duration": 736.049}, {"from": "Assistant", "value": "Training dialogue agents requires a large number of interactions with users: agents have no idea about which responses are bad among a lengthy dialogue. In this paper, we propose loop-clipping policy optimisation (LCPO) to eliminate useless responses. LCPO consists of two stages: loop clipping and advantage clipping. In loop clipping, we clip off useless responses (called loops) from dialogue history (called trajectories). The clipped trajectories are more succinct than the original ones, and the estimation of state-value is more accurate. Second, in advantage clipping, we estimate and clip the advantages of useless responses and normal ones separately. The clipped advantage distinguish useless actions from others and reduce the probabilities of useless actions efficiently. In experiments on Cambridge Restaurant Dialogue System, LCPO uses only 260 training dialogues to achieve 80% success rate, while PPO baseline requires 2160 dialogues. Besides, LCPO receives 3.7/5 scores in human evaluation where the agent interactively collects 100 real-user dialogues in training phase.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_268", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_268.flac", "type": "audio", "duration": 407.046}, {"from": "Assistant", "value": "Relation prediction informed from a combination of text corpora and curated knowledge bases, combining knowledge graph completion with relation extraction, is a relatively little studied task. A system that can perform this task has the ability to extend an arbitrary set of relational database tables with information extracted from a document corpus. OpenKi[1] addresses this task through extraction of named entities and predicates via OpenIE tools then learning relation embeddings from the resulting entity-relation graph for relation prediction, outperforming previous approaches. We present an extension of OpenKi that incorporates embeddings of text-based representations of the entities and the relations. We demonstrate that this results in a substantial performance increase over a system without this information.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_269", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_269.flac", "type": "audio", "duration": 419.051}, {"from": "Assistant", "value": "Recent studies in deep learning have shown significant progress in named entity recognition (NER). However, most existing works assume clean data annotation, while real-world scenarios typically involve a large amount of noises from a variety of sources (e.g., pseudo, weak, or distant annotations). This work studies NER under a noisy labeled setting with calibrated confidence estimation. Based on empirical observations of different training dynamics of noisy and clean labels, we propose strategies for estimating confidence scores based on local and global independence assumptions. We partially marginalize out labels of low confidence with a CRF model. We further propose a calibration method for confidence scores based on the structure of entity labels. We integrate our approach into a self-training framework for boosting performance. Experiments in general noisy settings with four languages and distantly labeled settings demonstrate the effectiveness of our method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_270", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_270.flac", "type": "audio", "duration": 622.891}, {"from": "Assistant", "value": "Existing work on tabular representation-learning jointly models tables and associated text using self-supervised objective functions derived from pretrained language models such as BERT. While this joint pretraining improves tasks involving paired tables and text (e.g., answering questions about tables), we show that it underperforms on tasks that operate over tables without any associated text (e.g., populating missing cells). We devise a simple pretraining objective (corrupt cell detection) that learns exclusively from tabular data and reaches the state-of-the-art on a suite of table-based prediction tasks. Unlike competing approaches, our model (TABBIE) provides embeddings of all table substructures (cells, rows, and columns), and it also requires far less compute to train. A qualitative analysis of our model’s learned cell, column, and row representations shows that it understands complex table semantics and numerical trends.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_271", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_271.flac", "type": "audio", "duration": 878.72}, {"from": "Assistant", "value": "It has been shown that named entity recognition (NER) could benefit from incorporating the long-distance structured information captured by dependency trees. We believe this is because both types of features - the contextual information captured by the linear sequences and the structured information captured by the dependency trees may complement each other. However, existing approaches largely focused on stacking the LSTM and graph neural networks such as graph convolutional networks (GCNs) for building improved NER models, where the exact interaction mechanism between the two types of features is not very clear, and the performance gain does not appear to be significant. In this work, we propose a simple and robust solution to incorporate both types of features with our Synergized-LSTM (Syn-LSTM), which clearly captures how the two types of features interact. We conduct extensive experiments on several standard datasets across four languages. The results demonstrate that the proposed model achieves better performance than previous approaches while requiring fewer parameters. Our further analysis demonstrates that our model can capture longer dependencies compared with strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_272", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_272.flac", "type": "audio", "duration": 702.496}, {"from": "Assistant", "value": "While relation extraction is an essential task in knowledge acquisition and representation, and new-generated relations are common in the real world, less effort is made to predict unseen relations that cannot be observed at the training stage. In this paper, we formulate the zero-shot relation extraction problem by incorporating the text description of seen and unseen relations. We propose a novel multi-task learning model, Zero-Shot BERT (ZS-BERT), to directly predict unseen relations without hand-crafted attribute labeling and multiple pairwise classifications. Given training instances consisting of input sentences and the descriptions of their seen relations, ZS-BERT learns two functions that project sentences and relations into an embedding space by jointly minimizing the distances between them and classifying seen relations. By generating the embeddings of unseen relations and new-coming sentences based on such two functions, we use nearest neighbor search to obtain the prediction of unseen relations. Experiments conducted on two well-known datasets exhibit that ZS-BERT can outperform existing methods by at least 13.54% improvement on F1 score.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_273", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_273.flac", "type": "audio", "duration": 784.022}, {"from": "Assistant", "value": "We study the problem of Event Causality Identification (ECI) to detect causal relation between event mention pairs in text. Although deep learning models have recently shown state-of-the-art performance for ECI, they are limited to the intra-sentence setting where event mention pairs are presented in the same sentences. This work addresses this issue by developing a novel deep learning model for document-level ECI (DECI) to accept inter-sentence event mention pairs. As such, we propose a graph-based model that constructs interaction graphs to capture relevant connections between important objects for DECI in input documents. Such interaction graphs are then consumed by graph convolutional networks to learn document context-augmented representations for causality prediction between events. Various information sources are introduced to enrich the interaction graphs for DECI, featuring discourse, syntax, and semantic information. Our extensive experiments show that the proposed model achieves state-of-the-art performance on two benchmark datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_274", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_274.flac", "type": "audio", "duration": 419.724}, {"from": "Assistant", "value": "Event coreference resolution is an important research problem with many applications. Despite the recent remarkable success of pre-trained language models, we argue that it is still highly beneficial to utilize symbolic features for the task. However, as the input for coreference resolution typically comes from upstream components in the information extraction pipeline, the automatically extracted symbolic features can be noisy and contain errors. Also, depending on the specific context, some features can be more informative than others. Motivated by these observations, we propose a novel context-dependent gated module to adaptively control the information flows from the input symbolic features. Combined with a simple noisy training method, our best models achieve state-of-the-art results on two datasets: ACE 2005 and KBP 2016.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_275", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_275.flac", "type": "audio", "duration": 698.062}, {"from": "Assistant", "value": "Style transfer has been widely explored in natural language generation with non-parallel corpus by directly or indirectly extracting a notion of style from source and target domain corpus. A common shortcoming of existing approaches is the prerequisite of joint annotations across all the stylistic dimensions under consideration. Availability of such dataset across a combination of styles limits the extension of these setups to multiple style dimensions. While cascading single-dimensional models across multiple styles is a possibility, it suffers from content loss, especially when the style dimensions are not completely independent of each other. In our work, we relax this requirement of jointly annotated data across multiple styles by using independently acquired data across different style dimensions without any additional annotations. We initialize an encoder-decoder setup with transformer-based language model pre-trained on a generic corpus and enhance its re-writing capability to multiple target style dimensions by employing multiple style-aware language models as discriminators. Through quantitative and qualitative evaluation, we show the ability of our model to control styles across multiple style dimensions while preserving content of the input text. We compare it against baselines involving cascaded state-of-the-art uni-dimensional style transfer models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_276", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_276.flac", "type": "audio", "duration": 681.041}, {"from": "Assistant", "value": "We propose Future Discriminators for Generation (FUDGE), a flexible and modular method for controlled text generation. Given a pre-existing model G for generating text from a distribution of interest, FUDGE enables conditioning on a desired attribute a (for example, formality) while requiring access only to G’s output logits. FUDGE learns an attribute predictor operating on a partial sequence, and uses this predictor’s outputs to adjust G’s original probabilities. We show that FUDGE models terms corresponding to a Bayesian decomposition of the conditional distribution of G given attribute a. Moreover, FUDGE can easily compose predictors for multiple desired attributes. We evaluate FUDGE on three tasks — couplet completion in poetry, topic control in language generation, and formality change in machine translation — and observe gains in all three tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_277", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_277.flac", "type": "audio", "duration": 692.048}, {"from": "Assistant", "value": "Text Simplification improves the readability of sentences through several rewriting transformations, such as lexical paraphrasing, deletion, and splitting. Current simplification systems are predominantly sequence-to-sequence models that are trained end-to-end to perform all these operations simultaneously. However, such systems limit themselves to mostly deleting words and cannot easily adapt to the requirements of different target audiences. In this paper, we propose a novel hybrid approach that leverages linguistically-motivated rules for splitting and deletion, and couples them with a neural paraphrasing model to produce varied rewriting styles. We introduce a new data augmentation method to improve the paraphrasing capability of our model. Through automatic and manual evaluations, we show that our proposed model establishes a new state-of-the-art for the task, paraphrasing more often than the existing systems, and can control the degree of each simplification operation applied to the input texts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_278", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_278.flac", "type": "audio", "duration": 618.046}, {"from": "Assistant", "value": "Prior work on Data-To-Text Generation, the task of converting knowledge graph (KG) triples into natural text, focused on domain-specific benchmark datasets. In this paper, however, we verbalize the entire English Wikidata KG, and discuss the unique challenges associated with a broad, open-domain, large-scale verbalization. We further show that verbalizing a comprehensive, encyclopedic KG like Wikidata can be used to integrate structured KGs and natural language corpora. In contrast to the many architectures that have been developed to integrate these two sources, our approach converts the KG into natural text, allowing it to be seamlessly integrated into existing language models. It carries the further advantages of improved factual accuracy and reduced toxicity in the resulting language model. We evaluate this approach by augmenting the retrieval corpus in a retrieval language model and showing significant improvements on the knowledge intensive tasks of open domain QA and the LAMA knowledge probe.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_279", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_279.flac", "type": "audio", "duration": 365.041}, {"from": "Assistant", "value": "Story generation is an open-ended and subjective task, which poses a challenge for evaluating story generation models. We present Choose Your Own Adventure, a collaborative writing setup for pairwise model evaluation. Two models generate suggestions to people as they write a short story; we ask writers to choose one of the two suggestions, and we observe which model’s suggestions they prefer. The setup also allows further analysis based on the revisions people make to the suggestions. We show that these measures, combined with automatic metrics, provide an informative picture of the models’ performance, both in cases where the differences in generation methods are small (nucleus vs. top-k sampling) and large (GPT2 vs. Fusion models).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_280", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_280.flac", "type": "audio", "duration": 634.741}, {"from": "Assistant", "value": "In this work, we present an information-theoretic framework that formulates cross-lingual language model pre-training as maximizing mutual information between multilingual-multi-granularity texts. The unified view helps us to better understand the existing methods for learning cross-lingual representations. More importantly, inspired by the framework, we propose a new pre-training task based on contrastive learning. Specifically, we regard a bilingual sentence pair as two views of the same meaning and encourage their encoded representations to be more similar than the negative examples. By leveraging both monolingual and parallel corpora, we jointly train the pretext tasks to improve the cross-lingual transferability of pre-trained models. Experimental results on several benchmarks show that our approach achieves considerably better performance. The code and pre-trained models are available athttps://aka.ms/infoxlm.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_282", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_282.flac", "type": "audio", "duration": 493.726}, {"from": "Assistant", "value": "Multilingual models have demonstrated impressive cross-lingual transfer performance. However, test sets like XNLI are monolingual at the example level. In multilingual communities, it is common for polyglots to code-mix when conversing with each other. Inspired by this phenomenon, we present two strong black-box adversarial attacks (one word-level, one phrase-level) for multilingual models that push their ability to handle code-mixed sentences to the limit. The former uses bilingual dictionaries to propose perturbations and translations of the clean example for sense disambiguation. The latter directly aligns the clean example with its translations before extracting phrases as perturbations. Our phrase-level attack has a success rate of 89.75% against XLM-R-large, bringing its average accuracy of 79.85 down to 8.18 on XNLI. Finally, we propose an efficient adversarial training scheme that trains in the same number of steps as the original model and show that it creates more language-invariant representations, improving clean and robust accuracy in the absence of lexical overlap without degrading performance on the original examples.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_283", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_283.flac", "type": "audio", "duration": 712.017}, {"from": "Assistant", "value": "Multilingual models, such as M-BERT and XLM-R, have gained increasing popularity, due to their zero-shot cross-lingual transfer learning capabilities. However, their generalization ability is still inconsistent for typologically diverse languages and across different benchmarks. Recently, meta-learning has garnered attention as a promising technique for enhancing transfer learning under low-resource scenarios: particularly for cross-lingual transfer in Natural Language Understanding (NLU). In this work, we propose X-METRA-ADA, a cross-lingual MEta-TRAnsfer learning ADAptation approach for NLU. Our approach adapts MAML, an optimization-based meta-learning approach, to learn to adapt to new languages. We extensively evaluate our framework on two challenging cross-lingual NLU tasks: multilingual task-oriented dialog and typologically diverse question answering. We show that our approach outperforms naive fine-tuning, reaching competitive performance on both tasks for most languages. Our analysis reveals that X-METRA-ADA can leverage limited data for faster adaptation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_284", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_284.flac", "type": "audio", "duration": 431.009}, {"from": "Assistant", "value": "Pre-trained cross-lingual encoders such as mBERT (Devlin et al., 2019) and XLM-R (Conneau et al., 2020) have proven impressively effective at enabling transfer-learning of NLP systems from high-resource languages to low-resource languages. This success comes despite the fact that there is no explicit objective to align the contextual embeddings of words/sentences with similar meanings across languages together in the same space. In this paper, we present a new method for learning multilingual encoders, AMBER (Aligned Multilingual Bidirectional EncodeR). AMBER is trained on additional parallel data using two explicit alignment objectives that align the multilingual representations at different granularities. We conduct experiments on zero-shot cross-lingual transfer learning for different tasks including sequence tagging, sentence retrieval and sentence classification. Experimental results on the tasks in the XTREME benchmark (Hu et al., 2020) show that AMBER obtains gains of up to 1.1 average F1 score on sequence tagging and up to 27.3 average accuracy on retrieval over the XLM-R-large model which has 3.2x the parameters of AMBER. Our code and models are available athttp://github.com/junjiehu/amber.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_285", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_285.flac", "type": "audio", "duration": 422.059}, {"from": "Assistant", "value": "Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English. Their success, however, heavily depends on the availability of many annotated image-caption datasets for pretraining, where the texts are not necessarily in English. Although we can utilize machine translation (MT) tools to translate non-English text to English, the performance still largely relies on MT’s quality and may suffer from high latency problems in real-world applications. This paper proposes a new approach to learn cross-lingual cross-modal representations for matching images and their relevant captions in multiple languages. We seamlessly combine cross-lingual pretraining objectives and cross-modal pretraining objectives in a unified framework to learn image and text in a joint embedding space from available English image-caption data, monolingual and parallel corpus. We show that our approach achieves SOTA performance in retrieval tasks on two multimodal multilingual image caption benchmarks: Multi30k with German captions and MSCOCO with Japanese captions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_286", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_286.flac", "type": "audio", "duration": 423.253}, {"from": "Assistant", "value": "Masked language models have quickly become the de facto standard when processing text. Recently, several approaches have been proposed to further enrich word representations with external knowledge sources such as knowledge graphs. However, these models are devised and evaluated in a monolingual setting only. In this work, we propose a language-independent entity prediction task as an intermediate training procedure to ground word representations on entity semantics and bridge the gap across different languages by means of a shared vocabulary of entities. We show that our approach effectively injects new lexical-semantic knowledge into neural models, improving their performance on different semantic tasks in the zero-shot crosslingual setting. As an additional advantage, our intermediate training does not require any supplementary input, allowing our models to be applied to new datasets right away. In our experiments, we use Wikipedia articles in up to 100 languages and already observe consistent gains compared to strong baselines when predicting entities using only the English Wikipedia. Further adding extra languages lead to improvements in most tasks up to a certain point, but overall we found it non-trivial to scale improvements in model transferability by training on ever increasing amounts of Wikipedia languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_287", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_287.flac", "type": "audio", "duration": 727.04}, {"from": "Assistant", "value": "We focus on a type of linguistic formal reasoning where the goal is to reason over explicit knowledge in the form of natural language facts and rules (Clark et al., 2020). A recent work, named PRover (Saha et al., 2020), performs such reasoning by answering a question and also generating a proof graph that explains the answer. However, compositional reasoning is not always unique and there may be multiple ways of reaching the correct answer. Thus, in our work, we address a new and challenging problem of generating multiple proof graphs for reasoning over natural language rule-bases. Each proof provides a different rationale for the answer, thereby improving the interpretability of such reasoning systems. In order to jointly learn from all proof graphs and exploit the correlations between multiple proofs for a question, we pose this task as a set generation problem over structured output spaces where each proof is represented as a directed graph. We propose two variants of a proof-set generation model, multiPRover. Our first model, Multilabel-multiPRover, generates a set of proofs via multi-label classification and implicit conditioning between the proofs; while the second model, Iterative-multiPRover, generates proofs iteratively by explicitly conditioning on the previously generated proofs. Experiments on multiple synthetic, zero-shot, and human-paraphrased datasets reveal that both multiPRover models significantly outperform PRover on datasets containing multiple gold proofs. Iterative-multiPRover obtains state-of-the-art proof F1 in zero-shot scenarios where all examples have single correct proofs. It also generalizes better to questions requiring higher depths of reasoning where multiple proofs are more frequent.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_288", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_288.flac", "type": "audio", "duration": 996.043}, {"from": "Assistant", "value": "Past research has demonstrated that large neural language models (LMs) encode surprising amounts of factual information: however, augmenting or modifying this information requires modifying a corpus and retraining, which is computationally expensive. To address this problem, we develop a neural LM that includes an interpretable neuro-symbolic KB in the form of a “fact memory”. Each element of the fact memory is formed from a triple of vectors, where each vector corresponds to a KB entity or relation. Our LM improves performance on knowledge-intensive question-answering tasks, sometimes dramatically, including a 27 point increase in one setting of WebQuestionsSP over a state-of-the-art open-book model, despite using 5% of the parameters. Most interestingly, we demonstrate that the model can be modified, withoutanyre-training, by updating the fact memory.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_290", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_290.flac", "type": "audio", "duration": 416.043}, {"from": "Assistant", "value": "Targeted syntactic evaluation of subject-verb number agreement in English (TSE) evaluates language models’ syntactic knowledge using hand-crafted minimal pairs of sentences that differ only in the main verb’s conjugation. The method evaluates whether language models rate each grammatical sentence as more likely than its ungrammatical counterpart. We identify two distinct goals for TSE. First, evaluating the systematicity of a language model’s syntactic knowledge: given a sentence, can it conjugate arbitrary verbs correctly? Second, evaluating a model’s likely behavior: given a sentence, does the model concentrate its probability mass on correctly conjugated verbs, even if only on a subset of the possible verbs? We argue that current implementations of TSE do not directly capture either of these goals, and propose new metrics to capture each goal separately. Under our metrics, we find that TSE overestimates systematicity of language models, but that models score up to 40% better on verbs that they predict are likely in context.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_291", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_291.flac", "type": "audio", "duration": 371.496}, {"from": "Assistant", "value": "Recent work has demonstrated the vulnerability of modern text classifiers to universal adversarial attacks, which are input-agnostic sequences of words added to text processed by classifiers. Despite being successful, the word sequences produced in such attacks are often ungrammatical and can be easily distinguished from natural text. We develop adversarial attacks that appear closer to natural English phrases and yet confuse classification systems when added to benign inputs. We leverage an adversarially regularized autoencoder (ARAE) to generate triggers and propose a gradient-based search that aims to maximize the downstream classifier’s prediction loss. Our attacks effectively reduce model accuracy on classification tasks while being less identifiable than prior models as per automatic detection metrics and human-subject studies. Our aim is to demonstrate that adversarial attacks can be made harder to detect than previously thought and to enable the development of appropriate defenses.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_292", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_292.flac", "type": "audio", "duration": 360.619}, {"from": "Assistant", "value": "The embedding-based large-scale query-document retrieval problem is a hot topic in the information retrieval (IR) field. Considering that pre-trained language models like BERT have achieved great success in a wide variety of NLP tasks, we present a QuadrupletBERT model for effective and efficient retrieval in this paper. Unlike most existing BERT-style retrieval models, which only focus on the ranking phase in retrieval systems, our model makes considerable improvements to the retrieval phase and leverages the distances between simple negative and hard negative instances to obtaining better embeddings. Experimental results demonstrate that our QuadrupletBERT achieves state-of-the-art results in embedding-based large-scale retrieval tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_293", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_293.flac", "type": "audio", "duration": 742.04}, {"from": "Assistant", "value": "Representation learning is widely used in NLP for a vast range of tasks. However, representations derived from text corpora often reflect social biases. This phenomenon is pervasive and consistent across different neural models, causing serious concern. Previous methods mostly rely on a pre-specified, user-provided direction or suffer from unstable training. In this paper, we propose an adversarial disentangled debiasing model to dynamically decouple social bias attributes from the intermediate representations trained on the main task. We aim to denoise bias information while training on the downstream task, rather than completely remove social bias and pursue static unbiased representations. Experiments show the effectiveness of our method, both on the effect of debiasing and the main task performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_294", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_294.flac", "type": "audio", "duration": 383.893}, {"from": "Assistant", "value": "Volatility prediction is complex due to the stock market’s stochastic nature. Existing research focuses on the textual elements of financial disclosures like earnings calls transcripts to forecast stock volatility and risk, but ignores the rich acoustic features in the company executives’ speech. Recently, new multimodal approaches that leverage the verbal and vocal cues of speakers in financial disclosures significantly outperform previous state-of-the-art approaches demonstrating the benefits of multimodality and speech. However, the financial realm is still plagued with a severe underrepresentation of various communities spanning diverse demographics, gender, and native speech. While multimodal models are better risk forecasters, it is imperative to also investigate the potential bias that these models may learn from the speech signals of company executives. In this work, we present the first study to discover the gender bias in multimodal volatility prediction due to gender-sensitive audio features and fewer female executives in earnings calls of one of the world’s biggest stock indexes, the S&P 500 index. We quantitatively analyze bias as error disparity and investigate the sources of this bias. Our results suggest that multimodal neural financial models accentuate gender-based stereotypes.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_295", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_295.flac", "type": "audio", "duration": 659.818}, {"from": "Assistant", "value": "The use of crowdworkers in NLP research is growing rapidly, in tandem with the exponential increase in research production in machine learning and AI. Ethical discussion regarding the use of crowdworkers within the NLP research community is typically confined in scope to issues related to labor conditions such as fair pay. We draw attention to the lack of ethical considerations related to the various tasks performed by workers, including labeling, evaluation, and production. We find that the Final Rule, the common ethical framework used by researchers, did not anticipate the use of online crowdsourcing platforms for data collection, resulting in gaps between the spirit and practice of human-subjects ethics in NLP research. We enumerate common scenarios where crowdworkers performing NLP tasks are at risk of harm. We thus recommend that researchers evaluate these risks by considering the three ethical principles set up by the Belmont Report. We also clarify some common misconceptions regarding the Institutional Review Board (IRB) application. We hope this paper will serve to reopen the discussion within our community regarding the ethical use of crowdworkers.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_296", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_296.flac", "type": "audio", "duration": 697.063}, {"from": "Assistant", "value": "Fine-tuned language models have been shown to exhibit biases against protected groups in a host of modeling tasks such as text classification and coreference resolution. Previous works focus on detecting these biases, reducing bias in data representations, and using auxiliary training objectives to mitigate bias during fine-tuning. Although these techniques achieve bias reduction for the task and domain at hand, the effects of bias mitigation may not directly transfer to new tasks, requiring additional data collection and customized annotation of sensitive attributes, and re-evaluation of appropriate fairness metrics. We explore the feasibility and benefits of upstream bias mitigation (UBM) for reducing bias on downstream tasks, by first applying bias mitigation to an upstream model through fine-tuning and subsequently using it for downstream fine-tuning. We find, in extensive experiments across hate speech detection, toxicity detection and coreference resolution tasks over various bias factors, that the effects of UBM are indeed transferable to new downstream tasks or domains via fine-tuning, creating less biased downstream models than directly fine-tuning on the downstream task or transferring from a vanilla upstream model. Though challenges remain, we show that UBM promises more efficient and accessible bias mitigation in LM fine-tuning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_297", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_297.flac", "type": "audio", "duration": 717.055}, {"from": "Assistant", "value": "Recent work in natural language processing (NLP) has focused on ethical challenges such as understanding and mitigating bias in data and algorithms; identifying objectionable content like hate speech, stereotypes and offensive language; and building frameworks for better system design and data handling practices. However, there has been little discussion about the ethical foundations that underlie these efforts. In this work, we study one ethical theory, namely deontological ethics, from the perspective of NLP. In particular, we focus on the generalization principle and the respect for autonomy through informed consent. We provide four case studies to demonstrate how these principles can be used with NLP systems. We also recommend directions to avoid the ethical issues in these systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_298", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_298.flac", "type": "audio", "duration": 421.001}, {"from": "Assistant", "value": "Neural language models are known to have a high capacity for memorization of training samples. This may have serious privacy im- plications when training models on user content such as email correspondence. Differential privacy (DP), a popular choice to train models with privacy guarantees, comes with significant costs in terms of utility degradation and disparate impact on subgroups of users. In this work, we introduce two privacy-preserving regularization methods for training language models that enable joint optimization of utility and privacy through (1) the use of a discriminator and (2) the inclusion of a novel triplet-loss term. We compare our methods with DP through extensive evaluation. We show the advantages of our regularizers with favorable utility-privacy trade-off, faster training with the ability to tap into existing optimization approaches, and ensuring uniform treatment of under-represented subgroups.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_299", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_299.flac", "type": "audio", "duration": 421.047}, {"from": "Assistant", "value": "Recent work has shown that fine-tuning large networks is surprisingly sensitive to changes in random seed(s). We explore the implications of this phenomenon for model fairness across demographic groups in clinical prediction tasks over electronic health records (EHR) in MIMIC-III —— the standard dataset in clinical NLP research. Apparent subgroup performance varies substantially for seeds that yield similar overall performance, although there is no evidence of a trade-off between overall and subgroup performance. However, we also find that the small sample sizes inherent to looking at intersections of minority groups and somewhat rare conditions limit our ability to accurately estimate disparities. Further, we find that jointly optimizing for high overall performance and low disparities does not yield statistically significant improvements. Our results suggest that fairness work using MIMIC-III should carefully account for variations in apparent differences that may arise from stochasticity and small sample sizes.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_300", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_300.flac", "type": "audio", "duration": 711.296}, {"from": "Assistant", "value": "When developing topic models, a critical question that should be asked is: How well will this model work in an applied setting? Because standard performance evaluation of topic interpretability uses automated measures modeled on human evaluation tests that are dissimilar to applied usage, these models’ generalizability remains in question. In this paper, we probe the issue of validity in topic model evaluation and assess how informative coherence measures are for specialized collections used in an applied setting. Informed by the literature, we propose four understandings of interpretability. We evaluate these using a novel experimental framework reflective of varied applied settings, including human evaluations using open labeling, typical of applied research. These evaluations show that for some specialized collections, standard coherence measures may not inform the most appropriate topic model or the optimal number of topics, and current interpretability performance validation methods are challenged as a means to confirm model quality in the absence of ground truth data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_301", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_301.flac", "type": "audio", "duration": 387.605}, {"from": "Assistant", "value": "Existing work on probing of pretrained language models (LMs) has predominantly focused on sentence-level syntactic tasks. In this paper, we introduce document-level discourse probing to evaluate the ability of pretrained LMs to capture document-level relations. We experiment with 7 pretrained LMs, 4 languages, and 7 discourse probing tasks, and find BART to be overall the best model at capturing discourse — but only in its encoder, with BERT performing surprisingly well as the baseline model. Across the different models, there are substantial differences in which layers best capture discourse information, and large disparities between models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_302", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_302.flac", "type": "audio", "duration": 603.045}, {"from": "Assistant", "value": "Transformer architecture achieves great success in abundant natural language processing tasks. The over-parameterization of the Transformer model has motivated plenty of works to alleviate its overfitting for superior performances. With some explorations, we find simple techniques such as dropout, can greatly boost model performance with a careful design. Therefore, in this paper, we integrate different dropout techniques into the training of Transformer models. Specifically, we propose an approach named UniDrop to unites three different dropout techniques from fine-grain to coarse-grain, i.e., feature dropout, structure dropout, and data dropout. Theoretically, we demonstrate that these three dropouts play different roles from regularization perspectives. Empirically, we conduct experiments on both neural machine translation and text classification benchmark datasets. Extensive results indicate that Transformer with UniDrop can achieve around 1.5 BLEU improvement on IWSLT14 translation tasks, and better accuracy for the classification even using strong pre-trained RoBERTa as backbone.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_303", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_303.flac", "type": "audio", "duration": 422.046}, {"from": "Assistant", "value": "The stance detection task aims at detecting the stance of a tweet or a text for a target. These targets can be named entities or free-form sentences (claims). Though the task involves reasoning of the tweet with respect to a target, we find that it is possible to achieve high accuracy on several publicly available Twitter stance detection datasets without looking at the target sentence. Specifically, a simple tweet classification model achieved human-level performance on the WT–WT dataset and more than two-third accuracy on various other datasets. We investigate the existence of biases in such datasets to find the potential spurious correlations of sentiment-stance relations and lexical choice associated with the stance category. Furthermore, we propose a new large dataset free of such biases and demonstrate its aptness on the existing stance detection systems. Our empirical findings show much scope for research on the stance detection task and proposes several considerations for creating future stance detection datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_304", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_304.flac", "type": "audio", "duration": 439.125}, {"from": "Assistant", "value": "Improving model generalization on held-out data is one of the core objectives in common- sense reasoning. Recent work has shown that models trained on the dataset with superficial cues tend to perform well on the easy test set with superficial cues but perform poorly on the hard test set without superficial cues. Previous approaches have resorted to manual methods of encouraging models not to overfit to superficial cues. While some of the methods have improved performance on hard instances, they also lead to degraded performance on easy in- stances. Here, we propose to explicitly learn a model that does well on both the easy test set with superficial cues and the hard test set without superficial cues. Using a meta-learning objective, we learn such a model that improves performance on both the easy test set and the hard test set. By evaluating our models on Choice of Plausible Alternatives (COPA) and Commonsense Explanation, we show that our proposed method leads to improved performance on both the easy test set and the hard test set upon which we observe up to 16.5 percentage points improvement over the baseline.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_305", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_305.flac", "type": "audio", "duration": 675.167}, {"from": "Assistant", "value": "Robustness and counterfactual bias are usually evaluated on a test dataset. However, are these evaluations robust? If the test dataset is perturbed slightly, will the evaluation results keep the same? In this paper, we propose a “double perturbation” framework to uncover model weaknesses beyond the test dataset. The framework first perturbs the test dataset to construct abundant natural sentences similar to the test data, and then diagnoses the prediction change regarding a single-word substitution. We apply this framework to study two perturbation-based approaches that are used to analyze models’ robustness and counterfactual bias in English. (1) For robustness, we focus on synonym substitutions and identify vulnerable examples where prediction can be altered. Our proposed attack attains high success rates (96.0%-99.8%) in finding vulnerable examples on both original and robustly trained CNNs and Transformers. (2) For counterfactual bias, we focus on substituting demographic tokens (e.g., gender, race) and measure the shift of the expected prediction among constructed sentences. Our method is able to reveal the hidden model biases not directly shown in the test dataset. Our code is available athttps://github.com/chong-z/nlp-second-order-attack.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_306", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_306.flac", "type": "audio", "duration": 698.048}, {"from": "Assistant", "value": "Explaining neural network models is important for increasing their trustworthiness in real-world applications. Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features. However, for models with text pairs as inputs (e.g., paraphrase identification), existing methods are not sufficient to capture feature interactions between two texts and their simple extension of computing all word-pair interactions between two texts is computationally inefficient. In this work, we propose the Group Mask (GMASK) method to implicitly detect word correlations by grouping correlated words from the input text pair together and measure their contribution to the corresponding NLP tasks as a whole. The proposed method is evaluated with two different model architectures (decomposable attention model and BERT) across four datasets, including natural language inference and paraphrase identification tasks. Experiments show the effectiveness of GMASK in providing faithful explanations to these models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_307", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_307.flac", "type": "audio", "duration": 665.045}, {"from": "Assistant", "value": "Translation quality can be improved by global information from the required target sentence because the decoder can understand both past and future information. However, the model needs additional cost to produce and consider such global information. In this work, to inject global information but also save cost, we present an efficient method to sample and consider a semantic draft as global information from semantic space for decoding with almost free of cost. Unlike other successful adaptations, we do not have to perform an EM-like process that repeatedly samples a possible semantic from the semantic space. Empirical experiments show that the presented method can achieve competitive performance in common language pairs with a clear advantage in inference efficiency. We will open all our source code on GitHub.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_308", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_308.flac", "type": "audio", "duration": 668.061}, {"from": "Assistant", "value": "Domain Adaptation is widely used in practical applications of neural machine translation, which aims to achieve good performance on both general domain and in-domain data. However, the existing methods for domain adaptation usually suffer from catastrophic forgetting, large domain divergence, and model explosion. To address these three problems, we propose a method of “divide and conquer” which is based on the importance of neurons or parameters for the translation model. In this method, we first prune the model and only keep the important neurons or parameters, making them responsible for both general-domain and in-domain translation. Then we further train the pruned model supervised by the original whole model with knowledge distillation. Last we expand the model to the original size and fine-tune the added parameters for the in-domain translation. We conducted experiments on different language pairs and domains and the results show that our method can achieve significant improvements compared with several strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_309", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_309.flac", "type": "audio", "duration": 605.845}, {"from": "Assistant", "value": "Document-level neural machine translation (NMT) has proven to be of profound value for its effectiveness on capturing contextual information. Nevertheless, existing approaches 1) simply introduce the representations of context sentences without explicitly characterizing the inter-sentence reasoning process; and 2) feed ground-truth target contexts as extra inputs at the training time, thus facing the problem of exposure bias. We approach these problems with an inspiration from human behavior – human translators ordinarily emerge a translation draft in their mind and progressively revise it according to the reasoning in discourse. To this end, we propose a novel Multi-Hop Transformer (MHT) which offers NMT abilities to explicitly model the human-like draft-editing and reasoning process. Specifically, our model serves the sentence-level translation as a draft and properly refines its representations by attending to multiple antecedent sentences iteratively. Experiments on four widely used document translation tasks demonstrate that our method can significantly improve document-level translation performance and can tackle discourse phenomena, such as coreference error and the problem of polysemy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_310", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_310.flac", "type": "audio", "duration": 552.043}, {"from": "Assistant", "value": "Neural machine translation (NMT) models are data-driven and require large-scale training corpus. In practical applications, NMT models are usually trained on a general domain corpus and then fine-tuned by continuing training on the in-domain corpus. However, this bears the risk of catastrophic forgetting that the performance on the general domain is decreased drastically. In this work, we propose a new continual learning framework for NMT models. We consider a scenario where the training is comprised of multiple stages and propose a dynamic knowledge distillation technique to alleviate the problem of catastrophic forgetting systematically. We also find that the bias exists in the output linear projection when fine-tuning on the in-domain corpus, and propose a bias-correction module to eliminate the bias. We conduct experiments on three representative settings of NMT application. Experimental results show that the proposed method achieves superior performance compared to baseline models in all settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_311", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_311.flac", "type": "audio", "duration": 384.058}, {"from": "Assistant", "value": "Unsupervised neural machine translation (UNMT) that relies solely on massive monolingual corpora has achieved remarkable results in several translation tasks. However, in real-world scenarios, massive monolingual corpora do not exist for some extremely low-resource languages such as Estonian, and UNMT systems usually perform poorly when there is not adequate training corpus for one language. In this paper, we first define and analyze the unbalanced training data scenario for UNMT. Based on this scenario, we propose UNMT self-training mechanisms to train a robust UNMT system and improve its performance in this case. Experimental results on several language pairs show that the proposed methods substantially outperform conventional UNMT systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_312", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_312.flac", "type": "audio", "duration": 412.061}, {"from": "Assistant", "value": "Most current neural machine translation models adopt a monotonic decoding order of either left-to-right or right-to-left. In this work, we propose a novel method that breaks up the limitation of these decoding orders, called Smart-Start decoding. More specifically, our method first predicts a median word. It starts to decode the words on the right side of the median word and then generates words on the left. We evaluate the proposed Smart-Start decoding method on three datasets. Experimental results show that the proposed method can significantly outperform strong baseline models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_313", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_313.flac", "type": "audio", "duration": 125.063}, {"from": "Assistant", "value": "Non-Autoregressive machine Translation (NAT) models have demonstrated significant inference speedup but suffer from inferior translation accuracy. The common practice to tackle the problem is transferring the Autoregressive machine Translation (AT) knowledge to NAT models, e.g., with knowledge distillation. In this work, we hypothesize and empirically verify that AT and NAT encoders capture different linguistic properties of source sentences. Therefore, we propose to adopt multi-task learning to transfer the AT knowledge to NAT models through encoder sharing. Specifically, we take the AT model as an auxiliary task to enhance NAT model performance. Experimental results on WMT14 En-De and WMT16 En-Ro datasets show that the proposed Multi-Task NAT achieves significant improvements over the baseline NAT models. Furthermore, the performance on large-scale WMT19 and WMT20 En-De datasets confirm the consistency of our proposed method. In addition, experimental results demonstrate that our Multi-Task NAT is complementary to knowledge distillation, the standard knowledge transfer method for NAT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_314", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_314.flac", "type": "audio", "duration": 719.04}, {"from": "Assistant", "value": "Most of privacy protection studies for textual data focus on removing explicit sensitive identifiers. However, personal writing style, as a strong indicator of the authorship, is often neglected. Recent studies, such as SynTF, have shown promising results on privacy-preserving text mining. However, their anonymization algorithm can only output numeric term vectors which are difficult for the recipients to interpret. We propose a novel text generation model with a two-set exponential mechanism for authorship anonymization. By augmenting the semantic information through a REINFORCE training reward function, the model can generate differentially private text that has a close semantic and similar grammatical structure to the original text while removing personal traits of the writing style. It does not assume any conditioned labels or paralleled text data for training. We evaluate the performance of the proposed model on the real-life peer reviews dataset and the Yelp review dataset. The result suggests that our model outperforms the state-of-the-art on semantic preservation, authorship obfuscation, and stylometric transformation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_315", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_315.flac", "type": "audio", "duration": 728.015}, {"from": "Assistant", "value": "We propose a practical instant question answering (QA) system on product pages of e-commerce services, where for each user query, relevant community question answer (CQA) pairs are retrieved. User queries and CQA pairs differ significantly in language characteristics making relevance learning difficult. Our proposed transformer-based model learns a robust relevance function by jointly learning unified syntactic and semantic representations without the need for human labeled data. This is achieved by distantly supervising our model by distilling from predictions of a syntactic matching system on user queries and simultaneously training with CQA pairs. Training with CQA pairs helps our model learning semantic QA relevance and distant supervision enables learning of syntactic features as well as the nuances of user querying language. Additionally, our model encodes queries and candidate responses independently allowing offline candidate embedding generation thereby minimizing the need for real-time transformer model execution. Consequently, our framework is able to scale to large e-commerce QA traffic. Extensive evaluation on user queries shows that our framework significantly outperforms both syntactic and semantic baselines in offline as well as large scale online A/B setups of a popular e-commerce service.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_316", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_316.flac", "type": "audio", "duration": 630.976}, {"from": "Assistant", "value": "It is challenging to design profitable and practical trading strategies, as stock price movements are highly stochastic, and the market is heavily influenced by chaotic data across sources like news and social media. Existing NLP approaches largely treat stock prediction as a classification or regression problem and are not optimized to make profitable investment decisions. Further, they do not model the temporal dynamics of large volumes of diversely influential text to which the market responds quickly. Building on these shortcomings, we propose a deep reinforcement learning approach that makes time-aware decisions to trade stocks while optimizing profit using textual data. Our method outperforms state-of-the-art in terms of risk-adjusted returns in trading simulations on two benchmarks: Tweets (English) and financial news (Chinese) pertaining to two major indexes and four global stock markets. Through extensive experiments and studies, we build the case for our method as a tool for quantitative trading.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_317", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_317.flac", "type": "audio", "duration": 580.011}, {"from": "Assistant", "value": "Understanding voluminous historical records provides clues on the past in various aspects, such as social and political issues and even natural science facts. However, it is generally difficult to fully utilize the historical records, since most of the documents are not written in a modern language and part of the contents are damaged over time. As a result, restoring the damaged or unrecognizable parts as well as translating the records into modern languages are crucial tasks. In response, we present a multi-task learning approach to restore and translate historical documents based on a self-attention mechanism, specifically utilizing two Korean historical records, ones of the most voluminous historical records in the world. Experimental results show that our approach significantly improves the accuracy of the translation task than baselines without multi-task learning. In addition, we present an in-depth exploratory analysis on our translated results via topic modeling, uncovering several significant historical events.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_318", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_318.flac", "type": "audio", "duration": 591.041}, {"from": "Assistant", "value": "Given the clinical notes written in electronic health records (EHRs), it is challenging to predict the diagnostic codes which is formulated as a multi-label classification task. The large set of labels, the hierarchical dependency, and the imbalanced data make this prediction task extremely hard. Most existing work built a binary prediction for each label independently, ignoring the dependencies between labels. To address this problem, we propose a two-stage framework to improve automatic ICD coding by capturing the label correlation. Specifically, we train a label set distribution estimator to rescore the probability of each label set candidate generated by a base predictor. This paper is the first attempt at learning the label set distribution as a reranking module for ICD coding. In the experiments, our proposed framework is able to improve upon best-performing predictors for medical code prediction on the benchmark MIMIC datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_319", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_319.flac", "type": "audio", "duration": 653.061}, {"from": "Assistant", "value": "Turn-level user satisfaction is one of the most important performance metrics for conversational agents. It can be used to monitor the agent’s performance and provide insights about defective user experiences. While end-to-end deep learning has shown promising results, having access to a large number of reliable annotated samples required by these methods remains challenging. In a large-scale conversational system, there is a growing number of newly developed skills, making the traditional data collection, annotation, and modeling process impractical due to the required annotation costs and the turnaround times. In this paper, we suggest a self-supervised contrastive learning approach that leverages the pool of unlabeled data to learn user-agent interactions. We show that the pre-trained models using the self-supervised objective are transferable to the user satisfaction prediction. In addition, we propose a novel few-shot transfer learning approach that ensures better transferability for very small sample sizes. The suggested few-shot method does not require any inner loop optimization process and is scalable to very large datasets and complex models. Based on our experiments using real data from a large-scale commercial system, the suggested approach is able to significantly reduce the required number of annotations, while improving the generalization on unseen skills.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_320", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_320.flac", "type": "audio", "duration": 721.049}, {"from": "Assistant", "value": "In order to interpret the communicative intents of an utterance, it needs to be grounded in something that is outside of language; that is, grounded in world modalities. In this paper, we argue that dialogue clarification mechanisms make explicit the process of interpreting the communicative intents of the speaker’s utterances by grounding them in the various modalities in which the dialogue is situated. This paper frames dialogue clarification mechanisms as an understudied research problem and a key missing piece in the giant jigsaw puzzle of natural language understanding. We discuss both the theoretical background and practical challenges posed by this problem and propose a recipe for obtaining grounding annotations. We conclude by highlighting ethical issues that need to be addressed in future work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_321", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_321.flac", "type": "audio", "duration": 869.053}, {"from": "Assistant", "value": "We introduce a grey-box adversarial attack and defence framework for sentiment classification. We address the issues of differentiability, label preservation and input reconstruction for adversarial attack and defence in one unified framework. Our results show that once trained, the attacking model is capable of generating high-quality adversarial examples substantially faster (one order of magnitude less in time) than state-of-the-art attacking methods. These examples also preserve the original sentiment according to human evaluation. Additionally, our framework produces an improved classifier that is robust in defending against multiple adversarial attacking methods. Code is available at:https://github.com/ibm-aur-nlp/adv-def-text-dist.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_322", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_322.flac", "type": "audio", "duration": 410.041}, {"from": "Assistant", "value": "Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Most prior work on ML based lemmatization has focused on high resource languages, where data sets (word forms) are readily available. For languages which have no linguistic work available, especially on morphology or in languages where the computational realization of linguistic rules is complex and cumbersome, machine learning based lemmatizers are the way togo. In this paper, we devote our attention to lemmatisation for low resource, morphologically rich scheduled Indian languages using neural methods. Here, low resource means only a small number of word forms are available. We perform tests to analyse the variance in monolingual models’ performance on varying the corpus size and contextual morphological tag data for training. We show that monolingual approaches with data augmentation can give competitive accuracy even in the low resource setting, which augurs well for NLP in low resource setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_323", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_323.flac", "type": "audio", "duration": 732.334}, {"from": "Assistant", "value": "We consider the problem of using observational data to estimate the causal effects of linguistic properties. For example, does writing a complaint politely lead to a faster response time? How much will a positive product review increase sales? This paper addresses two technical challenges related to the problem before developing a practical method. First, we formalize the causal quantity of interest as the effect of a writer’s intent, and establish the assumptions necessary to identify this from observational data. Second, in practice, we only have access to noisy proxies for the linguistic properties of interest—e.g., predictions from classifiers and lexicons. We propose an estimator for this setting and prove that its bias is bounded when we perform an adjustment for the text. Based on these results, we introduce TextCause, an algorithm for estimating causal effects of linguistic properties. The method leverages (1) distant supervision to improve the quality of noisy proxies, and (2) a pre-trained language model (BERT) to adjust for the text. We show that the proposed method outperforms related approaches when estimating the effect of Amazon review sentiment on semi-simulated sales figures. Finally, we present an applied case study investigating the effects of complaint politeness on bureaucratic response times.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_324", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_324.flac", "type": "audio", "duration": 721.816}, {"from": "Assistant", "value": "We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the-loop dataset creation: annotators seek to create examples that a target model will misclassify, but that another person will not. In this paper, we argue that Dynabench addresses a critical need in our community: contemporary models quickly achieve outstanding performance on benchmark tasks but nonetheless fail on simple challenge examples and falter in real-world scenarios. With Dynabench, dataset creation, model development, and model assessment can directly inform each other, leading to more robust and informative benchmarks. We report on four initial NLP tasks, illustrating these concepts and highlighting the promise of the platform, and address potential objections to dynamic benchmarking as a new standard for the field.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_325", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_325.flac", "type": "audio", "duration": 645.05}, {"from": "Assistant", "value": "Natural language processing (NLP) research combines the study of universal principles, through basic science, with applied science targeting specific use cases and settings. However, the process of exchange between basic NLP and applications is often assumed to emerge naturally, resulting in many innovations going unapplied and many important questions left unstudied. We describe a new paradigm of Translational NLP, which aims to structure and facilitate the processes by which basic and applied NLP research inform one another. Translational NLP thus presents a third research paradigm, focused on understanding the challenges posed by application needs and how these challenges can drive innovation in basic science and technology design. We show that many significant advances in NLP research have emerged from the intersection of basic principles with application needs, and present a conceptual framework outlining the stakeholders and key questions in translational research. Our framework provides a roadmap for developing Translational NLP as a dedicated research area, and identifies general translational principles to facilitate exchange between basic and applied research.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_326", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_326.flac", "type": "audio", "duration": 689.963}, {"from": "Assistant", "value": "Previous work indicates that discourse information benefits summarization. In this paper, we explore whether this synergy between discourse and summarization is bidirectional, by inferring document-level discourse trees from pre-trained neural summarizers. In particular, we generate unlabeled RST-style discourse trees from the self-attention matrices of the transformer model. Experiments across models and datasets reveal that the summarizer learns both, dependency- and constituency-style discourse information, which is typically encoded in a single head, covering long- and short-distance discourse dependencies. Overall, the experimental results suggest that the learned discourse information is general and transferable inter-domain.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_327", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_327.flac", "type": "audio", "duration": 879.363}, {"from": "Assistant", "value": "We probe pre-trained transformer language models for bridging inference. We first investigate individual attention heads in BERT and observe that attention heads at higher layers prominently focus on bridging relations in-comparison with the lower and middle layers, also, few specific attention heads concentrate consistently on bridging. More importantly, we consider language models as a whole in our second approach where bridging anaphora resolution is formulated as a masked token prediction task (Of-Cloze test). Our formulation produces optimistic results without any fine-tuning, which indicates that pre-trained language models substantially capture bridging inference. Our further investigation shows that the distance between anaphor-antecedent and the context provided to language models play an important role in the inference.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_328", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_328.flac", "type": "audio", "duration": 771.042}, {"from": "Assistant", "value": "Coherent discourse is distinguished from a mere collection of utterances by the satisfaction of a diverse set of constraints, for example choice of expression, logical relation between denoted events, and implicit compatibility with world-knowledge. Do neural language models encode such constraints? We design an extendable set of test suites addressing different aspects of discourse and dialogue coherence. Unlike most previous coherence evaluation studies, we address specific linguistic devices beyond sentence order perturbations, which allow for a more fine-grained analysis of what constitutes coherence and what neural models trained on a language modelling objective are capable of encoding. Extending the targeted evaluation paradigm for neural language models (Marvin and Linzen, 2018) to phenomena beyond syntax, we show that this paradigm is equally suited to evaluate linguistic qualities that contribute to the notion of coherence.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_329", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_329.flac", "type": "audio", "duration": 726.042}, {"from": "Assistant", "value": "The state-of-the-art on basic, single-antecedent anaphora has greatly improved in recent years. Researchers have therefore started to pay more attention to more complex cases of anaphora such as split-antecedent anaphora, as in “Time-Warner is considering a legal challenge to Telecommunications Inc’s plan to buy half of Showtime Networks Inc–a move that could lead to all-out war between the two powerful companies”. Split-antecedent anaphora is rarer and more complex to resolve than single-antecedent anaphora; as a result, it is not annotated in many datasets designed to test coreference, and previous work on resolving this type of anaphora was carried out in unrealistic conditions that assume gold mentions and/or gold split-antecedent anaphors are available. These systems also focus on split-antecedent anaphors only. In this work, we introduce a system that resolves both single and split-antecedent anaphors, and evaluate it in a more realistic setting that uses predicted mentions. We also start addressing the question of how to evaluate single and split-antecedent anaphors together using standard coreference evaluation metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_330", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_330.flac", "type": "audio", "duration": 404.886}, {"from": "Assistant", "value": "Neural keyphrase generation models have recently attracted much interest due to their ability to output absent keyphrases, that is, keyphrases that do not appear in the source text. In this paper, we discuss the usefulness of absent keyphrases from an Information Retrieval (IR) perspective, and show that the commonly drawn distinction between present and absent keyphrases is not made explicit enough. We introduce a finer-grained categorization scheme that sheds more light on the impact of absent keyphrases on scientific document retrieval. Under this scheme, we find that only a fraction (around 20%) of the words that make up keyphrases actually serves as document expansion, but that this small fraction of words is behind much of the gains observed in retrieval effectiveness. We also discuss how the proposed scheme can offer a new angle to evaluate the output of neural keyphrase generation models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_331", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_331.flac", "type": "audio", "duration": 614.052}, {"from": "Assistant", "value": "Many recent approaches towards neural information retrieval mitigate their computational costs by using a multi-stage ranking pipeline. In the first stage, a number of potentially relevant candidates are retrieved using an efficient retrieval model such as BM25. Although BM25 has proven decent performance as a first-stage ranker, it tends to miss relevant passages. In this context we propose CoRT, a simple neural first-stage ranking model that leverages contextual representations from pretrained language models such as BERT to complement term-based ranking functions while causing no significant delay at query time. Using the MS MARCO dataset, we show that CoRT significantly increases the candidate recall by complementing BM25 with missing candidates. Consequently, we find subsequent re-rankers achieve superior results with less candidates. We further demonstrate that passage retrieval using CoRT can be realized with surprisingly low latencies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_332", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_332.flac", "type": "audio", "duration": 684.06}, {"from": "Assistant", "value": "Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity in short-text or small collection of documents. This work presents a novel neural topic modeling framework using multi-view embed ding spaces: (1) pretrained topic-embeddings, and (2) pretrained word-embeddings (context-insensitive from Glove and context-sensitive from BERT models) jointly from one or many sources to improve topic quality and better deal with polysemy. In doing so, we first build respective pools of pretrained topic (i.e., TopicPool) and word embeddings (i.e., WordPool). We then identify one or more relevant source domain(s) and transfer knowledge to guide meaningful learning in the sparse target domain. Within neural topic modeling, we quantify the quality of topics and document representations via generalization (perplexity), interpretability (topic coherence) and information retrieval (IR) using short-text, long-text, small and large document collections from news and medical domains. Introducing the multi-source multi-view embedding spaces, we have shown state-of-the-art neural topic modeling using 6 source (high-resource) and 5 target (low-resource) corpora.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_333", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_333.flac", "type": "audio", "duration": 678.023}, {"from": "Assistant", "value": "Graph convolutional networks (GCNs) have been applied recently to text classification and produced an excellent performance. However, existing GCN-based methods do not assume an explicit latent semantic structure of documents, making learned representations less effective and difficult to interpret. They are also transductive in nature, thus cannot handle out-of-graph documents. To address these issues, we propose a novel model named inductive Topic Variational Graph Auto-Encoder (T-VGAE), which incorporates a topic model into variational graph-auto-encoder (VGAE) to capture the hidden semantic information between documents and words. T-VGAE inherits the interpretability of the topic model and the efficient information propagation mechanism of VGAE. It learns probabilistic representations of words and documents by jointly encoding and reconstructing the global word-level graph and bipartite graphs of documents, where each document is considered individually and decoupled from the global correlation graph so as to enable inductive learning. Our experiments on several benchmark datasets show that our method outperforms the existing competitive models on supervised and semi-supervised text classification, as well as unsupervised text representation learning. In addition, it has higher interpretability and is able to deal with unseen documents.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_334", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_334.flac", "type": "audio", "duration": 455.018}, {"from": "Assistant", "value": "Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge. This is of paramount importance for entity-level tasks such as entity linking where the ability to model entity relations (especially synonymy) is pivotal. To address this challenge, we propose SapBERT, a pretraining scheme that self-aligns the representation space of biomedical entities. We design a scalable metric learning framework that can leverage UMLS, a massive collection of biomedical ontologies with 4M+ concepts. In contrast with previous pipeline-based hybrid systems, SapBERT offers an elegant one-model-for-all solution to the problem of medical entity linking (MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking datasets. In the scientific domain, we achieve SOTA even without task-specific supervision. With substantial improvement over various domain-specific pretrained MLMs such as BioBERT, SciBERTand and PubMedBERT, our pretraining scheme proves to be both effective and robust.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_335", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_335.flac", "type": "audio", "duration": 614.052}, {"from": "Assistant", "value": "Hierarchical multi-label text classification (HMTC) aims to tag each document with a set of classes from a taxonomic class hierarchy. Most existing HMTC methods train classifiers using massive human-labeled documents, which are often too costly to obtain in real-world applications. In this paper, we explore to conduct HMTC based on only class surface names as supervision signals. We observe that to perform HMTC, human experts typically first pinpoint a few most essential classes for the document as its “core classes”, and then check core classes’ ancestor classes to ensure the coverage. To mimic human experts, we propose a novel HMTC framework, named TaxoClass. Specifically, TaxoClass (1) calculates document-class similarities using a textual entailment model, (2) identifies a document’s core classes and utilizes confident core classes to train a taxonomy-enhanced classifier, and (3) generalizes the classifier via multi-label self-training. Our experiments on two challenging datasets show TaxoClass can achieve around 0.71 Example-F1 using only class names, outperforming the best previous method by 25%.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_336", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_336.flac", "type": "audio", "duration": 645.483}, {"from": "Assistant", "value": "Generating metaphors is a challenging task as it requires a proper understanding of abstract concepts, making connections between unrelated concepts, and deviating from the literal meaning. In this paper, we aim to generate a metaphoric sentence given a literal expression by replacing relevant verbs. Based on a theoretically-grounded connection between metaphors and symbols, we propose a method to automatically construct a parallel corpus by transforming a large number of metaphorical sentences from the Gutenberg Poetry corpus (CITATION) to their literal counterpart using recent advances in masked language modeling coupled with commonsense inference. For the generation task, we incorporate a metaphor discriminator to guide the decoding of a sequence to sequence model fine-tuned on our parallel data to generate high-quality metaphors. Human evaluation on an independent test set of literal statements shows that our best model generates metaphors better than three well-crafted baselines 66% of the time on average. A task-based evaluation shows that human-written poems enhanced with metaphors proposed by our model are preferred 68% of the time compared to poems without metaphors.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_337", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_337.flac", "type": "audio", "duration": 701.056}, {"from": "Assistant", "value": "In most cases, the lack of parallel corpora makes it impossible to directly train supervised models for the text style transfer task. In this paper, we explore training algorithms that instead optimize reward functions that explicitly consider different aspects of the style-transferred outputs. In particular, we leverage semantic similarity metrics originally used for fine-tuning neural machine translation models to explicitly assess the preservation of content between system outputs and input texts. We also investigate the potential weaknesses of the existing automatic metrics and propose efficient strategies of using these metrics for training. The experimental results show that our model provides significant gains in both automatic and human evaluation over strong baselines, indicating the effectiveness of our proposed methods and training strategies.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_338", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_338.flac", "type": "audio", "duration": 693.046}, {"from": "Assistant", "value": "Document grounded generation is the task of using the information provided in a document to improve text generation. This work focuses on two different document grounded generation tasks: Wikipedia Update Generation task and Dialogue response generation. Our work introduces two novel adaptations of large scale pre-trained encoder-decoder models focusing on building context driven representation of the document and enabling specific attention to the information in the document. Additionally, we provide a stronger BART baseline for these tasks. Our proposed techniques outperform existing methods on both automated (at least 48% increase in BLEU-4 points) and human evaluation for closeness to reference and relevance to the document. Furthermore, we perform comprehensive manual inspection of the generated output and categorize errors to provide insights into future directions in modeling these tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_339", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_339.flac", "type": "audio", "duration": 616.002}, {"from": "Assistant", "value": "Conditional text generation often requires lexical constraints, i.e., which words should or shouldn’t be included in the output text. While the dominant recipe for conditional text generation has been large-scale pretrained language models that are finetuned on the task-specific training data, such models do not learn to follow the underlying constraints reliably, even when supervised with large amounts of task-specific examples. We propose NeuroLogic Decoding, a simple yet effective algorithm that enables neural language models – supervised or not – to generate fluent text while satisfying complex lexical constraints. Our approach is powerful yet efficient. It handles any set of lexical constraints that is expressible under predicate logic, while its asymptotic runtime is equivalent to conventional beam search. Empirical results on four benchmarks show that NeuroLogic Decoding outperforms previous approaches, including algorithms that handle a subset of our constraints. Moreover, we find that unsupervised models with NeuroLogic Decoding often outperform supervised models with conventional decoding, even when the latter is based on considerably larger networks. Our results suggest the limit of large-scale neural networks for fine-grained controllable generation and the promise of inference-time algorithms.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_340", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_340.flac", "type": "audio", "duration": 699.014}, {"from": "Assistant", "value": "The ability to generate clarification questions i.e., questions that identify useful missing information in a given context, is important in reducing ambiguity. Humans use previous experience with similar contexts to form a global view and compare it to the given context to ascertain what is missing and what is useful in the context. Inspired by this, we propose a model for clarification question generation where we first identify what is missing by taking a difference between the global and the local view and then train a model to identify what is useful and generate a question about it. Our model outperforms several baselines as judged by both automatic metrics and humans.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_341", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_341.flac", "type": "audio", "duration": 631.049}, {"from": "Assistant", "value": "Large-scale language models (LMs) pretrained on massive corpora of text, such as GPT-2, are powerful open-domain text generators. However, as our systematic examination reveals, it is still challenging for such models to generate coherent long passages of text (e.g., 1000 tokens), especially when the models are fine-tuned to the target domain on a small corpus. Previous planning-then-generation methods also fall short of producing such long text in various domains. To overcome the limitations, we propose a simple but effective method of generating text in a progressive manner, inspired by generating images from low to high resolution. Our method first produces domain-specific content keywords and then progressively refines them into complete passages in multiple stages. The simple design allows our approach to take advantage of pretrained LMs at each stage and effectively adapt to any target domain given only a small set of examples. We conduct a comprehensive empirical study with a broad set of evaluation metrics, and show that our approach significantly improves upon the fine-tuned large LMs and various planning-then-generation methods in terms of quality and sample efficiency. Human evaluation also validates that our model generations are more coherent.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_342", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_342.flac", "type": "audio", "duration": 537.6}, {"from": "Assistant", "value": "In the pursuit of natural language understanding, there has been a long standing interest in tracking state changes throughout narratives. Impressive progress has been made in modeling the state of transaction-centric dialogues and procedural texts. However, this problem has been less intensively studied in the realm of general discourse where ground truth descriptions of states may be loosely defined and state changes are less densely distributed over utterances. This paper proposes to turn to simplified, fully observable systems that show some of these properties: Sports events. We curated 2,263 soccer matches including time-stamped natural language commentary accompanied by discrete events such as a team scoring goals, switching players or being penalized with cards. We propose a new task formulation where, given paragraphs of commentary of a game at different timestamps, the system is asked to recognize the occurrence of in-game events. This domain allows for rich descriptions of state while avoiding the complexities of many other real-world settings. As an initial point of performance measurement, we include two baseline methods from the perspectives of sentence classification with temporal dependence and current state-of-the-art generative model, respectively, and demonstrate that even sophisticated existing methods struggle on the state tracking task when the definition of state broadens or non-event chatter becomes prevalent.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_343", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_343.flac", "type": "audio", "duration": 726.016}, {"from": "Assistant", "value": "With the recent advances of open-domain story generation, the lack of reliable automatic evaluation metrics becomes an increasingly imperative issue that hinders the fast development of story generation. According to conducted researches in this regard, learnable evaluation metrics have promised more accurate assessments by having higher correlations with human judgments. A critical bottleneck of obtaining a reliable learnable evaluation metric is the lack of high-quality training data for classifiers to efficiently distinguish plausible and implausible machine-generated stories. Previous works relied onheuristically manipulatedplausible examples to mimic possible system drawbacks such as repetition, contradiction, or irrelevant content in the text level, which can beunnaturalandoversimplifythe characteristics of implausible machine-generated stories. We propose to tackle these issues by generating a more comprehensive set of implausible stories usingplots, which are structured representations of controllable factors used to generate stories. Since these plots are compact and structured, it is easier to manipulate them to generate text with targeted undesirable properties, while at the same time maintain the grammatical correctness and naturalness of the generated sentences. To improve the quality of generated implausible stories, we further apply the adversarial filtering procedure presented by (CITATION) to select a more nuanced set of implausible texts. Experiments show that the evaluation metrics trained on our generated data result in more reliable automatic assessments that correlate remarkably better with human judgments compared to the baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_344", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_344.flac", "type": "audio", "duration": 743.015}, {"from": "Assistant", "value": "We propose MultiOpEd, an open-domain news editorial corpus that supports various tasks pertaining to the argumentation structure in news editorials, focusing on automatic perspective discovery. News editorial is a genre of persuasive text, where the argumentation structure is usually implicit. However, the arguments presented in an editorial typically center around a concise, focused thesis, which we refer to as their perspective. MultiOpEd aims at supporting the study of multiple tasks relevant to automatic perspective discovery, where a system is expected to produce a single-sentence thesis statement summarizing the arguments presented. We argue that identifying and abstracting such natural language perspectives from editorials is a crucial step toward studying the implicit argumentation structure in news editorials. We first discuss the challenges and define a few conceptual tasks towards our goal. To demonstrate the utility of MultiOpEd and the induced tasks, we study the problem of perspective summarization in a multi-task learning setting, as a case study. We show that, with the induced tasks as auxiliary tasks, we can improve the quality of the perspective summary generated. We hope that MultiOpEd will be a useful resource for future studies on argumentation in the news editorial domain.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_345", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_345.flac", "type": "audio", "duration": 735.33}, {"from": "Assistant", "value": "We release a new benchmark for lexical substitution, the task of finding appropriate substitutes for a target word in a context. For writing, lexical substitution systems can assist humans by suggesting words that humans cannot easily think of. However, existing benchmarks depend on human recall as the only source of data, and therefore lack coverage of the substitutes that would be most helpful to humans. Furthermore, annotators often provide substitutes of low quality, which are not actually appropriate in the given context. We collect higher-coverage and higher-quality data by framing lexical substitution as a classification problem, guided by the intuition that it is easier for humans to judge the appropriateness of candidate substitutes than conjure them from memory. To this end, we use a context-free thesaurus to produce candidates and rely on human judgement to determine contextual appropriateness. Compared to the previous largest benchmark, our Swords benchmark has 3x as many substitutes per target word for the same level of quality, and its substitutes are 1.4x more appropriate (based on human judgement) for the same number of substitutes.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_346", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_346.flac", "type": "audio", "duration": 729.045}, {"from": "Assistant", "value": "Natural language inference requires reasoning about contradictions, negations, and their commonsense implications. Given a simple premise (e.g., “I’m mad at you”), humans can reason about the varying shades of contradictory statements ranging from straightforward negations (“I’m not mad at you”) to commonsense contradictions (“I’m happy”). Moreover, these negated or contradictory statements shift the commonsense implications of the original premise in interesting and nontrivial ways. For example, while “I’m mad” implies “I’m unhappy about something,” negating the premise does not necessarily negate the corresponding commonsense implications. In this paper, we present the first comprehensive study focusing on commonsense implications of negated statements and contradictions. We introduce ANION, a new commonsense knowledge graph with 624K if-then rules focusing on negated and contradictory events. We then present joint generative and discriminative inference models for this new resource, providing novel empirical insights on how logical negations and commonsense contradictions reshape the commonsense implications of their original premises.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_347", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_347.flac", "type": "audio", "duration": 711.04}, {"from": "Assistant", "value": "Self-disclosure in online health conversations may offer a host of benefits, including earlier detection and treatment of medical issues that may have otherwise gone unaddressed. However, research analyzing medical self-disclosure in online communities is limited. We address this shortcoming by introducing a new dataset of health-related posts collected from online social platforms, categorized into three groups (No Self-Disclosure, Possible Self-Disclosure, and Clear Self-Disclosure) with high inter-annotator agreement (_k_=0.88). We make this data available to the research community. We also release a predictive model trained on this dataset that achieves an accuracy of 81.02%, establishing a strong performance benchmark for this task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_348", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_348.flac", "type": "audio", "duration": 447.007}, {"from": "Assistant", "value": "We investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engines; in particular, we explore the emergence of semantic generalization from unsupervised dense representations outside of synthetic environments. A grounding domain, a denotation function and a composition function are learned from user data only. We show how the resulting semantics for noun phrases exhibits compositional properties while being fully learnable without any explicit labelling. We benchmark our grounded semantics on compositionality and zero-shot inference tasks, and we show that it provides better results and better generalizations than SOTA non-grounded models, such as word2vec and BERT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_349", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_349.flac", "type": "audio", "duration": 757.696}, {"from": "Assistant", "value": "This work presents an information-theoretic operationalisation of cross-linguistic non-arbitrariness. It is not a new idea that there are small, cross-linguistic associations between the forms and meanings of words. For instance, it has been claimed (Blasi et al., 2016) that the word for “tongue” is more likely than chance to contain the phone [l]. By controlling for the influence of language family and geographic proximity within a very large concept-aligned, cross-lingual lexicon, we extend methods previously used to detect within language non-arbitrariness (Pimentel et al., 2019) to measure cross-linguistic associations. We find that there is a significant effect of non-arbitrariness, but it is unsurprisingly small (less than 0.5% on average according to our information-theoretic estimate). We also provide a concept-level analysis which shows that a quarter of the concepts considered in our work exhibit a significant level of cross-linguistic non-arbitrariness. In sum, the paper provides new methods to detect cross-linguistic associations at scale, and confirms their effects are minor.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_350", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_350.flac", "type": "audio", "duration": 708.696}, {"from": "Assistant", "value": "The mapping of lexical meanings to wordforms is a major feature of natural languages. While usage pressures might assign short words to frequent meanings (Zipf’s law of abbreviation), the need for a productive and open-ended vocabulary, local constraints on sequences of symbols, and various other factors all shape the lexicons of the world’s languages. Despite their importance in shaping lexical structure, the relative contributions of these factors have not been fully quantified. Taking a coding-theoretic view of the lexicon and making use of a novel generative statistical model, we define upper bounds for the compressibility of the lexicon under various constraints. Examining corpora from 7 typologically diverse languages, we use those upper bounds to quantify the lexicon’s optimality and to explore the relative costs of major constraints on natural codes. We find that (compositional) morphology and graphotactics can sufficiently account for most of the complexity of natural codes—as measured by code length.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_351", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_351.flac", "type": "audio", "duration": 653.061}, {"from": "Assistant", "value": "Lexical complexity is a highly subjective notion, yet this factor is often neglected in lexical simplification and readability systems which use a ”one-size-fits-all” approach. In this paper, we investigate which aspects contribute to the notion of lexical complexity in various groups of readers, focusing on native and non-native speakers of English, and how the notion of complexity changes depending on the proficiency level of a non-native reader. To facilitate reproducibility of our approach and foster further research into these aspects, we release a dataset of complex words annotated by readers with different backgrounds.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_352", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_352.flac", "type": "audio", "duration": 426.063}, {"from": "Assistant", "value": "The complexity loss paradox, which posits that individuals suffering from disease exhibit surprisingly predictable behavioral dynamics, has been observed in a variety of both human and animal physiological systems. The recent advent of online text-based therapy presents a new opportunity to analyze the complexity loss paradox in a novel operationalization: linguistic complexity loss in text-based therapy conversations. In this paper, we analyze linguistic complexity correlates of mental health in the online therapy messages sent between therapists and 7,170 clients who provided 30,437 corresponding survey responses on their anxiety. We found that when clients reported more anxiety, they showed reduced lexical diversity as estimated by the moving average type-token ratio. Therapists, on the other hand, used language of higher reading difficulty, syntactic complexity, and age of acquisition when clients were more anxious. Finally, we found that clients, and to an even greater extent, therapists, exhibited consistent levels of many linguistic complexity measures. These results demonstrate how linguistic analysis of text-based communication can be leveraged as a marker for anxiety, an exciting prospect in a time of both increased online communication and increased mental health issues.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_353", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_353.flac", "type": "audio", "duration": 788.459}, {"from": "Assistant", "value": "Historical linguists have identified regularities in the process of historic sound change. The comparative method utilizes those regularities to reconstruct proto-words based on observed forms in daughter languages. Can this process be efficiently automated? We address the task of proto-word reconstruction, in which the model is exposed to cognates in contemporary daughter languages, and has to predict the proto word in the ancestor language. We provide a novel dataset for this task, encompassing over 8,000 comparative entries, and show that neural sequence models outperform conventional methods applied to this task so far. Error analysis reveals a variability in the ability of neural model to capture different phonological changes, correlating with the complexity of the changes. Analysis of learned embeddings reveals the models learn phonologically meaningful generalizations, corresponding to well-attested phonological shifts documented by historical linguistics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_354", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_354.flac", "type": "audio", "duration": 715.051}, {"from": "Assistant", "value": "Many sequence-to-sequence tasks in natural language processing are roughly monotonic in the alignment between source and target sequence, and previous work has facilitated or enforced learning of monotonic attention behavior via specialized attention functions or pretraining. In this work, we introduce a monotonicity loss function that is compatible with standard attention mechanisms and test it on several sequence-to-sequence tasks: grapheme-to-phoneme conversion, morphological inflection, transliteration, and dialect normalization. Experiments show that we can achieve largely monotonic behavior. Performance is mixed, with larger gains on top of RNN baselines. General monotonicity does not benefit transformer multihead attention, however, we see isolated improvements when only a subset of heads is biased towards monotonic behavior.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_355", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_355.flac", "type": "audio", "duration": 710.379}, {"from": "Assistant", "value": "The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We pursue the construction of a knowledge base (KB) of mechanisms—a fundamental concept across the sciences, which encompasses activities, functions and causal relations, ranging from cellular processes to economic impacts. We extract this information from the natural language of scientific papers by developing a broad, unified schema that strikes a balance between relevance and breadth. We annotate a dataset of mechanisms with our schema and train a model to extract mechanism relations from papers. Our experiments demonstrate the utility of our KB in supporting interdisciplinary scientific search over COVID-19 literature, outperforming the prominent PubMed search in a study with clinical experts. Our search engine, dataset and code are publicly available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_356", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_356.flac", "type": "audio", "duration": 712.043}, {"from": "Assistant", "value": "We propose a neural event coreference model in which event coreference is jointly trained with five tasks: trigger detection, entity coreference, anaphoricity determination, realis detection, and argument extraction. To guide the learning of this complex model, we incorporate cross-task consistency constraints into the learning process as soft constraints via designing penalty functions. In addition, we propose the novel idea of viewing entity coreference and event coreference as a single coreference task, which we believe is a step towards a unified model of coreference resolution. The resulting model achieves state-of-the-art results on the KBP 2017 event coreference dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_357", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_357.flac", "type": "audio", "duration": 676.049}, {"from": "Assistant", "value": "In human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of each layer within modern transformer-based language models, limiting the ability to effectively leverage transformers. Here, we provide a systematic study on the role of dimension reduction methods (principal components analysis, factorization techniques, or multi-layer auto-encoders) as well as the dimensionality of embedding vectors and sample sizes as a function of predictive performance. We first find that fine-tuning large models with a limited amount of data pose a significant difficulty which can be overcome with a pre-trained dimension reduction regime. RoBERTa consistently achieves top performance in human-level tasks, with PCA giving benefit over other reduction methods in better handling users that write longer texts. Finally, we observe that a majority of the tasks achieve results comparable to the best performance with just 1/12 of the embedding dimensions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_358", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_358.flac", "type": "audio", "duration": 396.249}, {"from": "Assistant", "value": "Utilizing clinical texts in survival analysis is difficult because they are largely unstructured. Current automatic extraction models fail to capture textual information comprehensively since their labels are limited in scope. Furthermore, they typically require a large amount of data and high-quality expert annotations for training. In this work, we present a novel method of using BERT-based hidden layer representations of clinical texts as covariates for proportional hazards models to predict patient survival outcomes. We show that hidden layers yield notably more accurate predictions than predefined features, outperforming the previous baseline model by 5.7% on average across C-index and time-dependent AUC. We make our work publicly available athttps://github.com/bionlplab/heart_failure_mortality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_359", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_359.flac", "type": "audio", "duration": 419.004}, {"from": "Assistant", "value": "In this paper, we study the importance of context in predicting the citation worthiness of sentences in scholarly articles. We formulate this problem as a sequence labeling task solved using a hierarchical BiLSTM model. We contribute a new benchmark dataset containing over two million sentences and their corresponding labels. We preserve the sentence order in this dataset and perform document-level train/test splits, which importantly allows incorporating contextual information in the modeling process. We evaluate the proposed approach on three benchmark datasets. Our results quantify the benefits of using context and contextual embeddings for citation worthiness. Lastly, through error analysis, we provide insights into cases where context plays an essential role in predicting citation worthiness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_360", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_360.flac", "type": "audio", "duration": 392.043}, {"from": "Assistant", "value": "While neural networks produce state-of-the-art performance in several NLP tasks, they generally depend heavily on lexicalized information, which transfer poorly between domains. We present a combination of two strategies to mitigate this dependence on lexicalized information in fact verification tasks. We present a data distillation technique for delexicalization, which we then combine with a model distillation method to prevent aggressive data distillation. We show that by using our solution, not only does the performance of an existing state-of-the-art model remain at par with that of the model trained on a fully lexicalized data, but it also performs better than it when tested out of domain. We show that the technique we present encourages models to extract transferable facts from a given fact verification dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_361", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_361.flac", "type": "audio", "duration": 421.056}, {"from": "Assistant", "value": "Coreference resolution is an important compo-nent in analyzing narrative text from admin-istrative data (e.g., clinical or police sources).However, existing coreference models trainedon general language corpora suffer from poortransferability due to domain gaps, especiallywhen they are applied to gender-inclusive datawith lesbian, gay, bisexual, and transgender(LGBT) individuals. In this paper, we an-alyzed the challenges of coreference resolu-tion in an exemplary form of administrativetext written in English: violent death nar-ratives from the USA’s Centers for DiseaseControl’s (CDC) National Violent Death Re-porting System. We developed a set of dataaugmentation rules to improve model perfor-mance using a probabilistic data programmingframework. Experiments on narratives froman administrative database, as well as existinggender-inclusive coreference datasets, demon-strate the effectiveness of data augmentationin training coreference models that can betterhandle text data about LGBT individuals.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_362", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_362.flac", "type": "audio", "duration": 726.059}, {"from": "Assistant", "value": "Tracking entities throughout a procedure described in a text is challenging due to the dynamic nature of the world described in the process. Firstly, we propose to formulate this task as a question answering problem. This enables us to use pre-trained transformer-based language models on other QA benchmarks by adapting those to the procedural text understanding. Secondly, since the transformer-based language models cannot encode the flow of events by themselves, we propose a Time-Stamped Language Model (TSLM) to encode event information in LMs architecture by introducing the timestamp encoding. Our model evaluated on the Propara dataset shows improvements on the published state-of-the-art results with a 3.1% increase in F1 score. Moreover, our model yields better results on the location prediction task on the NPN-Cooking dataset. This result indicates that our approach is effective for procedural text understanding in general.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_363", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_363.flac", "type": "audio", "duration": 725.043}, {"from": "Assistant", "value": "Multi-hop reasoning requires aggregation and inference from multiple facts. To retrieve such facts, we propose a simple approach that retrieves and reranks set of evidence facts jointly. Our approach first generates unsupervised clusters of sentences as candidate evidence by accounting links between sentences and coverage with the given query. Then, a RoBERTa-based reranker is trained to bring the most representative evidence cluster to the top. We specifically emphasize on the importance of retrieving evidence jointly by showing several comparative analyses to other methods that retrieve and rerank evidence sentences individually. First, we introduce several attention- and embedding-based analyses, which indicate that jointly retrieving and reranking approaches can learn compositional knowledge required for multi-hop reasoning. Second, our experiments show that jointly retrieving candidate evidence leads to substantially higher evidence retrieval performance when fed to the same supervised reranker. In particular, our joint retrieval and then reranking approach achieves new state-of-the-art evidence retrieval performance on two multi-hop question answering (QA) datasets: 30.5 Recall@2 on QASC, and 67.6% F1 on MultiRC. When the evidence text from our joint retrieval approach is fed to a RoBERTa-based answer selection classifier, we achieve new state-of-the-art QA performance on MultiRC and second best result on QASC.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_364", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_364.flac", "type": "audio", "duration": 690.051}, {"from": "Assistant", "value": "This paper proposes a question-answering (QA) benchmark for spatial reasoning on natural language text which contains more realistic spatial phenomena not covered by prior work and is challenging for state-of-the-art language models (LM). We propose a distant supervision method to improve on this task. Specifically, we design grammar and reasoning rules to automatically generate a spatial description of visual scenes and corresponding QA pairs. Experiments show that further pretraining LMs on these automatically generated data significantly improves LMs’ capability on spatial understanding, which in turn helps to better solve two external datasets, bAbI, and boolQ. We hope that this work can foster investigations into more sophisticated models for spatial reasoning over text.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_365", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_365.flac", "type": "audio", "duration": 667.063}, {"from": "Assistant", "value": "Readers of academic research papers often read with the goal of answering specific questions. Question Answering systems that can answer those questions can make consumption of the content much more efficient. However, building such tools requires data that reflect the difficulty of the task arising from complex reasoning about claims made in multiple parts of a paper. In contrast, existing information-seeking question answering datasets usually contain questions about generic factoid-type information. We therefore present Qasper, a dataset of 5049 questions over 1585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. The questions are then answered by a separate set of NLP practitioners who also provide supporting evidence to answers. We find that existing models that do well on other QA tasks do not perform well on answering these questions, underperforming humans by at least 27 F1 points when answering them from entire papers, motivating further research in document-grounded, information-seeking QA, which our dataset is designed to facilitate.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_366", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_366.flac", "type": "audio", "duration": 718.216}, {"from": "Assistant", "value": "Current commonsense reasoning research focuses on developing models that use commonsense knowledge to answer multiple-choice questions. However, systems designed to answer multiple-choice questions may not be useful in applications that do not provide a small list of candidate answers to choose from. As a step towards making commonsense reasoning research more realistic, we propose to study open-ended commonsense reasoning (OpenCSR) — the task of answering a commonsense question without any pre-defined choices — using as a resource only a corpus of commonsense facts written in natural language. OpenCSR is challenging due to a large decision space, and because many questions require implicit multi-hop reasoning. As an approach to OpenCSR, we propose DrFact, an efficient Differentiable model for multi-hop Reasoning over knowledge Facts. To evaluate OpenCSR methods, we adapt several popular commonsense reasoning benchmarks, and collect multiple new answers for each test question via crowd-sourcing. Experiments show that DrFact outperforms strong baseline methods by a large margin.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_367", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_367.flac", "type": "audio", "duration": 392.046}, {"from": "Assistant", "value": "Machine reading comprehension is a challenging task especially for querying documents with deep and interconnected contexts. Transformer-based methods have shown advanced performances on this task; however, most of them still treat documents as a flat sequence of tokens. This work proposes a new Transformer-based method that reads a document as tree slices. It contains two modules for identifying more relevant text passage and the best answer span respectively, which are not only jointly trained but also jointly consulted at inference time. Our evaluation results show that our proposed method outperforms several competitive baseline approaches on two datasets from varied domains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_368", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_368.flac", "type": "audio", "duration": 397.061}, {"from": "Assistant", "value": "Complex question answering often requires finding a reasoning chain that consists of multiple evidence pieces. Current approaches incorporate the strengths of structured knowledge and unstructured text, assuming text corpora is semi-structured. Building on dense retrieval methods, we propose a new multi-step retrieval approach (BeamDR) that iteratively forms an evidence chain through beam search in dense representations. When evaluated on multi-hop question answering, BeamDR is competitive to state-of-the-art systems, without using any semi-structured information. Through query composition in dense space, BeamDR captures the implicit relationships between evidence in the reasoning chain. The code is available athttps://github.com/henryzhao5852/BeamDR.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_369", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_369.flac", "type": "audio", "duration": 673.054}, {"from": "Assistant", "value": "Several cluster-based methods for semantic change detection with contextual embeddings emerged recently. They allow a fine-grained analysis of word use change by aggregating embeddings into clusters that reflect the different usages of the word. However, these methods are unscalable in terms of memory consumption and computation time. Therefore, they require a limited set of target words to be picked in advance. This drastically limits the usability of these methods in open exploratory tasks, where each word from the vocabulary can be considered as a potential target. We propose a novel scalable method for word usage-change detection that offers large gains in processing time and significant memory savings while offering the same interpretability and better performance than unscalable methods. We demonstrate the applicability of the proposed method by analysing a large corpus of news articles about COVID-19.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_370", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_370.flac", "type": "audio", "duration": 398.336}, {"from": "Assistant", "value": "The intensity relationship that holds between scalar adjectives (e.g., nice < great < wonderful) is highly relevant for natural language inference and common-sense reasoning. Previous research on scalar adjective ranking has focused on English, mainly due to the availability of datasets for evaluation. We introduce a new multilingual dataset in order to promote research on scalar adjectives in new languages. We perform a series of experiments and set performance baselines on this dataset, using monolingual and multilingual contextual language models. Additionally, we introduce a new binary classification task for English scalar adjective identification which examines the models’ ability to distinguish scalar from relational adjectives. We probe contextualised representations and report baseline results for future comparison on this task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_371", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_371.flac", "type": "audio", "duration": 598.007}, {"from": "Assistant", "value": "Word Sense Disambiguation (WSD) is a historical NLP task aimed at linking words in contexts to discrete sense inventories and it is usually cast as a multi-label classification task. Recently, several neural approaches have employed sense definitions to better represent word meanings. Yet, these approaches do not observe the input sentence and the sense definition candidates all at once, thus potentially reducing the model performance and generalization power. We cope with this issue by reframing WSD as a span extraction problem — which we called Extractive Sense Comprehension (ESC) — and propose ESCHER, a transformer-based neural architecture for this new formulation. By means of an extensive array of experiments, we show that ESC unleashes the full potential of our model, leading it to outdo all of its competitors and to set a new state of the art on the English WSD task. In the few-shot scenario, ESCHER proves to exploit training data efficiently, attaining the same performance as its closest competitor while relying on almost three times fewer annotations. Furthermore, ESCHER can nimbly combine data annotated with senses from different lexical resources, achieving performances that were previously out of everyone’s reach. The model along with data is available athttps://github.com/SapienzaNLP/esc.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_373", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_373.flac", "type": "audio", "duration": 561.528}, {"from": "Assistant", "value": "We present a method for constructing taxonomic trees (e.g., WordNet) using pretrained language models. Our approach is composed of two modules, one that predicts parenthood relations and another that reconciles those pairwise predictions into trees. The parenthood prediction module produces likelihood scores for each potential parent-child pair, creating a graph of parent-child relation scores. The tree reconciliation module treats the task as a graph optimization problem and outputs the maximum spanning tree of this graph. We train our model on subtrees sampled from WordNet, and test on nonoverlapping WordNet subtrees. We show that incorporating web-retrieved glosses can further improve performance. On the task of constructing subtrees of English WordNet, the model achieves 66.7 ancestor F1, a 20.0% relative increase over the previous best published result on this task. In addition, we convert the original English dataset into nine other languages using Open Multilingual WordNet and extend our results across these languages.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_374", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_374.flac", "type": "audio", "duration": 712.063}, {"from": "Assistant", "value": "Within the context of event modeling and understanding, we propose a new method for neural sequence modeling that takes partially-observed sequences of discrete, external knowledge into account. We construct a sequential neural variational autoencoder, which uses Gumbel-Softmax reparametrization within a carefully defined encoder, to allow for successful backpropagation during training. The core idea is to allow semi-supervised external discrete knowledge to guide, but not restrict, the variational latent parameters during training. Our experiments indicate that our approach not only outperforms multiple baselines and the state-of-the-art in narrative script induction, but also converges more quickly.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_375", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_375.flac", "type": "audio", "duration": 423.021}, {"from": "Assistant", "value": "Multi-label emotion classification is an important task in NLP and is essential to many applications. In this work, we propose a sequence-to-emotion (Seq2Emo) approach, which implicitly models emotion correlations in a bi-directional decoder. Experiments on SemEval’18 and GoEmotions datasets show that our approach outperforms state-of-the-art methods (without using external data). In particular, Seq2Emo outperforms the binary relevance (BR) and classifier chain (CC) approaches in a fair setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_376", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_376.flac", "type": "audio", "duration": 702.443}, {"from": "Assistant", "value": "Detecting stance on Twitter is especially challenging because of the short length of each tweet, the continuous coinage of new terminology and hashtags, and the deviation of sentence structure from standard prose. Fine-tuned language models using large-scale in-domain data have been shown to be the new state-of-the-art for many NLP tasks, including stance detection. In this paper, we propose a novel BERT-based fine-tuning method that enhances the masked language model for stance detection. Instead of random token masking, we propose using a weighted log-odds-ratio to identify words with high stance distinguishability and then model an attention mechanism that focuses on these words. We show that our proposed approach outperforms the state of the art for stance detection on Twitter data about the 2020 US Presidential election.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_377", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_377.flac", "type": "audio", "duration": 375.769}, {"from": "Assistant", "value": "Paralinguistics, the non-lexical components of speech, play a crucial role in human-human interaction. Models designed to recognize paralinguistic information, particularly speech emotion and style, are difficult to train because of the limited labeled datasets available. In this work, we present a new framework that enables a neural network to learn to extract paralinguistic attributes from speech using data that are not annotated for emotion. We assess the utility of the learned embeddings on the downstream tasks of emotion recognition and speaking style detection, demonstrating significant improvements over surface acoustic features as well as over embeddings extracted from other unsupervised approaches. Our work enables future systems to leverage the learned embedding extractor as a separate component capable of highlighting the paralinguistic components of speech.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_378", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_378.flac", "type": "audio", "duration": 720.004}, {"from": "Assistant", "value": "This paper studies continual learning (CL) of a sequence of aspect sentiment classification (ASC) tasks. Although some CL techniques have been proposed for document sentiment classification, we are not aware of any CL work on ASC. A CL system that incrementally learns a sequence of ASC tasks should address the following two issues: (1) transfer knowledge learned from previous tasks to the new task to help it learn a better model, and (2) maintain the performance of the models for previous tasks so that they are not forgotten. This paper proposes a novel capsule network based model called B-CL to address these issues. B-CL markedly improves the ASC performance on both the new task and the old tasks via forward and backward knowledge transfer. The effectiveness of B-CL is demonstrated through extensive experiments.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_379", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_379.flac", "type": "audio", "duration": 402.048}, {"from": "Assistant", "value": "Stance detection on social media can help to identify and understand slanted news or commentary in everyday life. In this work, we propose a new model for zero-shot stance detection on Twitter that uses adversarial learning to generalize across topics. Our model achieves state-of-the-art performance on a number of unseen test topics with minimal computational costs. In addition, we extend zero-shot stance detection to topics not previously considered, highlighting future directions for zero-shot transfer.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_380", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_380.flac", "type": "audio", "duration": 701.057}, {"from": "Assistant", "value": "This paper presents an efficient graph-enhanced approach to multi-document summarization (MDS) with an encoder-decoder Transformer model. This model is based on recent advances in pre-training both encoder and decoder on very large text data (Lewis et al., 2019), and it incorporates an efficient encoding mechanism (Beltagy et al., 2020) that avoids the quadratic memory growth typical for traditional Transformers. We show that this powerful combination not only scales to large input documents commonly found when summarizing news clusters; it also enables us to process additional input in the form of auxiliary graph representations, which we derive from the multi-document clusters. We present a mechanism to incorporate such graph information into the encoder-decoder model that was pre-trained on text only. Our approach leads to significant improvements on the Multi-News dataset, overall leading to an average 1.8 ROUGE score improvement over previous work (Li et al., 2020). We also show improvements in a transfer-only setup on the DUC-2004 dataset. The graph encodings lead to summaries that are more abstractive. Human evaluation shows that they are also more informative and factually more consistent with their input documents.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_381", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_381.flac", "type": "audio", "duration": 695.04}, {"from": "Assistant", "value": "Abstractive summarization, the task of generating a concise summary of input documents, requires: (1) reasoning over the source document to determine the salient pieces of information scattered across the long document, and (2) composing a cohesive text by reconstructing these salient facts into a shorter summary that faithfully reflects the complex relations connecting these facts. In this paper, we adapt TP-Transformer (Schlag et al., 2019), an architecture that enriches the original Transformer (Vaswani et al., 2017) with the explicitly compositional Tensor Product Representation (TPR), for the task of abstractive summarization. The key feature of our model is a structural bias that we introduce by encoding two separate representations for each token to represent the syntactic structure (with role vectors) and semantic content (with filler vectors) separately. The model then binds the role and filler vectors into the TPR as the layer output. We argue that the structured intermediate representations enable the model to take better control of the contents (salient facts) and structures (the syntax that connects the facts) when generating the summary. Empirically, we show that our TP-Transformer outperforms the Transformer and the original TP-Transformer significantly on several abstractive summarization datasets based on both automatic and human evaluations. On several syntactic and semantic probing tasks, we demonstrate the emergent structural information in the role vectors and the performance gain by information specificity of the role vectors and improved syntactic interpretability in the TPR layer outputs.(Code and models are available athttps://github.com/jiangycTarheel/TPT-Summ)", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_382", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_382.flac", "type": "audio", "duration": 761.057}, {"from": "Assistant", "value": "Summarization of clinical narratives is a long-standing research problem. Here, we introduce the task of hospital-course summarization. Given the documentation authored throughout a patient’s hospitalization, generate a paragraph that tells the story of the patient admission. We construct an English, text-to-text dataset of 109,000 hospitalizations (2M source notes) and their corresponding summary proxy: the clinician-authored “Brief Hospital Course” paragraph written as part of a discharge note. Exploratory analyses reveal that the BHC paragraphs are highly abstractive with some long extracted fragments; are concise yet comprehensive; differ in style and content organization from the source notes; exhibit minimal lexical cohesion; and represent silver-standard references. Our analysis identifies multiple implications for modeling this complex, multi-document summarization task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_383", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_383.flac", "type": "audio", "duration": 715.058}, {"from": "Assistant", "value": "Modern summarization models generate highly fluent but often factually unreliable outputs. This motivated a surge of metrics attempting to measure the factuality of automatically generated summaries. Due to the lack of common benchmarks, these metrics cannot be compared. Moreover, all these methods treat factuality as a binary concept and fail to provide deeper insights on the kinds of inconsistencies made by different systems. To address these limitations, we devise a typology of factual errors and use it to collect human annotations of generated summaries from state-of-the-art summarization systems for the CNN/DM and XSum datasets. Through these annotations we identify the proportion of different categories of factual errors and benchmark factuality metrics, showing their correlation with human judgement as well as their specific strengths and weaknesses.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_384", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_384.flac", "type": "audio", "duration": 673.054}, {"from": "Assistant", "value": "Neural abstractive summarization models are flexible and can produce coherent summaries, but they are sometimes unfaithful and can be difficult to control. While previous studies attempt to provide different types of guidance to control the output and increase faithfulness, it is not clear how these strategies compare and contrast to each other. In this paper, we propose a general and extensible guided summarization framework (GSum) that can effectively take different kinds of external guidance as input, and we perform experiments across several different varieties. Experiments demonstrate that this model is effective, achieving state-of-the-art performance according to ROUGE on 4 popular summarization datasets when using highlighted sentences as guidance. In addition, we show that our guided model can generate more faithful summaries and demonstrate how different types of guidance generate qualitatively different summaries, lending a degree of controllability to the learned models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_385", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_385.flac", "type": "audio", "duration": 790.291}, {"from": "Assistant", "value": "Evaluation for many natural language understanding (NLU) tasks is broken: Unreliable and biased systems score so highly on standard benchmarks that there is little room for researchers who develop better systems to demonstrate their improvements. The recent trend to abandon IID benchmarks in favor of adversarially-constructed, out-of-distribution test sets ensures that current models will perform poorly, but ultimately only obscures the abilities that we want our benchmarks to measure. In this position paper, we lay out four criteria that we argue NLU benchmarks should meet. We argue most current benchmarks fail at these criteria, and that adversarial data collection does not meaningfully address the causes of these failures. Instead, restoring a healthy evaluation ecosystem will require significant progress in the design of benchmark datasets, the reliability with which they are annotated, their size, and the ways they handle social bias.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_386", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_386.flac", "type": "audio", "duration": 698.735}, {"from": "Assistant", "value": "We propose TuringAdvice, a new challenge task and dataset for language understanding models. Given a written situation that a real person is currently facing, a model must generate helpful advice in natural language. Our evaluation framework tests a fundamental aspect of human language understanding: our ability to use language to resolve open-ended situations by communicating with each other. Empirical results show that today’s models struggle at TuringAdvice, even multibillion parameter models finetuned on 600k in-domain training examples. The best model, T5, writes advice that is at least as helpful as human-written advice in only 14% of cases; a much larger non-finetunable GPT3 model does even worse at 4%. This low performance reveals language understanding errors that are hard to spot outside of a generative setting, showing much room for progress.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_387", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_387.flac", "type": "audio", "duration": 701.01}, {"from": "Assistant", "value": "The #MeToo movement on social media platforms initiated discussions over several facets of sexual harassment in our society. Prior work by the NLP community for automated identification of the narratives related to sexual abuse disclosures barely explored this social phenomenon as an independent task. However, emotional attributes associated with textual conversations related to the #MeToo social movement are complexly intertwined with such narratives. We formulate the task of identifying narratives related to the sexual abuse disclosures in online posts as a joint modeling task that leverages their emotional attributes through multitask learning. Our results demonstrate that positive knowledge transfer via context-specific shared representations of a flexible cross-stitched parameter sharing model helps establish the inherent benefit of jointly modeling tasks related to sexual abuse disclosures with emotion classification from the text in homogeneous and heterogeneous settings. We show how for more domain-specific tasks related to sexual abuse disclosures such as sarcasm identification and dialogue act (refutation, justification, allegation) classification, homogeneous multitask learning is helpful, whereas for more general tasks such as stance and hate speech detection, heterogeneous multitask learning with emotion classification works better.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_388", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_388.flac", "type": "audio", "duration": 418.4}, {"from": "Assistant", "value": "Prior studies have found that women self-promote less than men due to gender stereotypes. In this study we built a BERT-based NLP model to predict whether a Congressional tweet shows self-promotion or not and then used this model to examine whether a gender gap in self-promotion exists among Congressional tweets. After analyzing 2 million Congressional tweets from July 2017 to March 2021, controlling for a number of factors that include political party, chamber, age, number of terms in Congress, number of daily tweets, and number of followers, we found that women in Congress actually perform more self-promotion on Twitter, indicating a reversal of traditional gender norms where women self-promote less than men.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_389", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_389.flac", "type": "audio", "duration": 419.499}, {"from": "Assistant", "value": "Identifying intertextual relationships between authors is of central importance to the study of literature. We report an empirical analysis of intertextuality in classical Latin literature using word embedding models. To enable quantitative evaluation of intertextual search methods, we curate a new dataset of 945 known parallels drawn from traditional scholarship on Latin epic poetry. We train an optimized word2vec model on a large corpus of lemmatized Latin, which achieves state-of-the-art performance for synonym detection and outperforms a widely used lexical method for intertextual search. We then demonstrate that training embeddings on very small corpora can capture salient aspects of literary style and apply this approach to replicate a previous intertextual study of the Roman historian Livy, which relied on hand-crafted stylometric features. Our results advance the development of core computational resources for a major premodern language and highlight a productive avenue for cross-disciplinary collaboration between the study of literature and NLP.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_390", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_390.flac", "type": "audio", "duration": 421.001}, {"from": "Assistant", "value": "Natural language inference (NLI) is the task of determining whether a piece of text is entailed, contradicted by or unrelated to another piece of text. In this paper, we investigate how to tease systematic inferences (i.e., items for which people agree on the NLI label) apart from disagreement items (i.e., items which lead to different annotations), which most prior work has overlooked. To distinguish systematic inferences from disagreement items, we propose Artificial Annotators (AAs) to simulate the uncertainty in the annotation process by capturing the modes in annotations. Results on the CommitmentBank, a corpus of naturally occurring discourses in English, confirm that our approach performs statistically significantly better than all baselines. We further show that AAs learn linguistic patterns and context-dependent reasoning.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_391", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_391.flac", "type": "audio", "duration": 1409.01}, {"from": "Assistant", "value": "Understanding narrative text requires capturing characters’ motivations, goals, and mental states. This paper proposes an Entity-based Narrative Graph (ENG) to model the internal- states of characters in a story. We explicitly model entities, their interactions and the context in which they appear, and learn rich representations for them. We experiment with different task-adaptive pre-training objectives, in-domain training, and symbolic inference to capture dependencies between different decisions in the output space. We evaluate our model on two narrative understanding tasks: predicting character mental states, and desire fulfillment, and conduct a qualitative analysis.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_392", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_392.flac", "type": "audio", "duration": 718.054}, {"from": "Assistant", "value": "Conditioned dialogue generation suffers from the scarcity of labeled responses. In this work, we exploit labeled non-dialogue text data related to the condition, which are much easier to collect. We propose a multi-task learning approach to leverage both labeled dialogue and text data. The 3 tasks jointly optimize the same pre-trained Transformer – conditioned dialogue generation task on the labeled dialogue data, conditioned language encoding task and conditioned language generation task on the labeled text data. Experimental results show that our approach outperforms the state-of-the-art models by leveraging the labeled texts, and it also obtains larger improvement in performance comparing to the previous methods to leverage text data.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_393", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_393.flac", "type": "audio", "duration": 685.056}, {"from": "Assistant", "value": "The task of long-form question answering (LFQA) involves retrieving documents relevant to a given question and using them to generate a paragraph-length answer. While many models have recently been proposed for LFQA, we show in this paper that the task formulation raises fundamental challenges regarding evaluation and dataset creation that currently preclude meaningful modeling progress. To demonstrate these challenges, we first design a new system that relies on sparse attention and contrastive retriever learning to achieve state-of-the-art performance on the ELI5 LFQA dataset. While our system tops the public leaderboard, a detailed analysis reveals several troubling trends: (1) our system’s generated answers are not actually grounded in the documents that it retrieves; (2) ELI5 contains significant train / validation overlap, as at least 81% of ELI5 validation questions occur in paraphrased form in the training set; (3) ROUGE-L is not an informative metric of generated answer quality and can be easily gamed; and (4) human evaluations used for other text generation tasks are unreliable for LFQA. We offer suggestions to mitigate each of these issues, which we hope will lead to more rigorous LFQA research and meaningful progress in the future.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_394", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_394.flac", "type": "audio", "duration": 632.363}, {"from": "Assistant", "value": "Framing involves the positive or negative presentation of an argument or issue depending on the audience and goal of the speaker. Differences in lexical framing, the focus of our work, can have large effects on peoples’ opinions and beliefs. To make progress towards reframing arguments for positive effects, we create a dataset and method for this task. We use a lexical resource for “connotations” to create a parallel corpus and propose a method for argument reframing that combines controllable text generation (positive connotation) with a post-decoding entailment component (same denotation). Our results show that our method is effective compared to strong baselines along the dimensions of fluency, meaning, and trustworthiness/reduction of fear.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_395", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_395.flac", "type": "audio", "duration": 711.019}, {"from": "Assistant", "value": "We consider the problem of learning to simplify medical texts. This is important because most reliable, up-to-date information in biomedicine is dense with jargon and thus practically inaccessible to the lay audience. Furthermore, manual simplification does not scale to the rapidly growing body of biomedical literature, motivating the need for automated approaches. Unfortunately, there are no large-scale resources available for this task. In this work we introduce a new corpus of parallel texts in English comprising technical and lay summaries of all published evidence pertaining to different clinical topics. We then propose a new metric based on likelihood scores from a masked language model pretrained on scientific texts. We show that this automated measure better differentiates between technical and lay summaries than existing heuristics. We introduce and evaluate baseline encoder-decoder Transformer models for simplification and propose a novel augmentation to these in which we explicitly penalize the decoder for producing “jargon” terms; we find that this yields improvements over baselines in terms of readability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_396", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_396.flac", "type": "audio", "duration": 712.683}, {"from": "Assistant", "value": "Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them. Model performance on KPG tasks has increased significantly with evolving deep learning research. However, there lacks a comprehensive comparison among different model designs, and a thorough investigation on related factors that may affect a KPG system’s generalization performance. In this empirical study, we aim to fill this gap by providing extensive experimental results and analyzing the most crucial factors impacting the generalizability of KPG models. We hope this study can help clarify some of the uncertainties surrounding the KPG task and facilitate future research on this topic.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_397", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_397.flac", "type": "audio", "duration": 381.063}, {"from": "Assistant", "value": "How can we effectively inform content selection in Transformer-based abstractive summarization models? In this work, we present a simple-yet-effective attention head masking technique, which is applied on encoder-decoder attentions to pinpoint salient content at inference time. Using attention head masking, we are able to reveal the relation between encoder-decoder attentions and content selection behaviors of summarization models. We then demonstrate its effectiveness on three document summarization datasets based on both in-domain and cross-domain settings. Importantly, our models outperform prior state-of-the-art models on CNN/Daily Mail and New York Times datasets. Moreover, our inference-time masking technique is also data-efficient, requiring only 20% of the training samples to outperform BART fine-tuned on the full CNN/DailyMail dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_398", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_398.flac", "type": "audio", "duration": 724.245}, {"from": "Assistant", "value": "Petroni et al. (2019) demonstrated that it is possible to retrieve world facts from a pre-trained language model by expressing them as cloze-style prompts and interpret the model’s prediction accuracy as a lower bound on the amount of factual information it encodes. Subsequent work has attempted to tighten the estimate by searching for better prompts, using a disjoint set of facts as training data. In this work, we make two complementary contributions to better understand these factual probing techniques. First, we propose OptiPrompt, a novel and efficient method which directly optimizes in continuous embedding space. We find this simple method is able to predict an additional 6.4% of facts in the LAMA benchmark. Second, we raise a more important question: Can we really interpret these probing results as a lower bound? Is it possible that these prompt-search methods learn from the training data too? We find, somewhat surprisingly, that the training data used by these methods contains certain regularities of the underlying fact distribution, and all the existing prompt methods, including ours, are able to exploit them for better fact prediction. We conduct a set of control experiments to disentangle “learning” from “learning to recall”, providing a more detailed picture of what different prompts can reveal about pre-trained language models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_399", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_399.flac", "type": "audio", "duration": 696.043}, {"from": "Assistant", "value": "Saliency methods are widely used to interpret neural network predictions, but different variants of saliency methods often disagree even on the interpretations of the same prediction made by the same model. In these cases, how do we identify when are these interpretations trustworthy enough to be used in analyses? To address this question, we conduct a comprehensive and quantitative evaluation of saliency methods on a fundamental category of NLP models: neural language models. We evaluate the quality of prediction interpretations from two perspectives that each represents a desirable property of these interpretations: plausibility and faithfulness. Our evaluation is conducted on four different datasets constructed from the existing human annotation of syntactic and semantic agreements, on both sentence-level and document-level. Through our evaluation, we identified various ways saliency methods could yield interpretations of low quality. We recommend that future work deploying such methods to neural language models should carefully validate their interpretations before drawing insights.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_400", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_400.flac", "type": "audio", "duration": 686.571}, {"from": "Assistant", "value": "Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness. Existing techniques of generating such examples are typically driven by local heuristic rules that are agnostic to the context, often resulting in unnatural and ungrammatical outputs. This paper presents CLARE, a ContextuaLized AdversaRial Example generation model that produces fluent and grammatical outputs through a mask-then-infill procedure. CLARE builds on a pre-trained masked language model and modifies the inputs in a context-aware manner. We propose three contextualized perturbations, Replace, Insert and Merge, that allow for generating outputs of varied lengths. CLARE can flexibly combine these perturbations and apply them at any position in the inputs, and is thus able to attack the victim model more effectively with fewer edits. Extensive experiments and human evaluation demonstrate that CLARE outperforms the baselines in terms of attack success rate, textual similarity, fluency and grammaticality.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_401", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_401.flac", "type": "audio", "duration": 709.931}, {"from": "Assistant", "value": "Understanding how linguistic structure is encoded in contextualized embedding could help explain their impressive performance across NLP. Existing approaches for probing them usually call for training classifiers and use the accuracy, mutual information, or complexity as a proxy for the representation’s goodness. In this work, we argue that doing so can be unreliable because different representations may need different classifiers. We develop a heuristic, DirectProbe, that directly studies the geometry of a representation by building upon the notion of a version space for a task. Experiments with several linguistic tasks and contextualized embeddings show that, even without training classifiers, DirectProbe can shine lights on how an embedding space represents labels and also anticipate the classifier performance for the representation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_402", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_402.flac", "type": "audio", "duration": 754.048}, {"from": "Assistant", "value": "Transfer learning that adapts a model trained on data-rich sources to low-resource targets has been widely applied in natural language processing (NLP). However, when training a transfer model over multiple sources, not every source is equally useful for the target. To better transfer a model, it is essential to understand the values of the sources. In this paper, we develop , an efficient source valuation framework for quantifying the usefulness of the sources (e.g., ) in transfer learning based on the Shapley value method. Experiments and comprehensive analyses on both cross-domain and cross-lingual transfers demonstrate that our framework is not only effective in choosing useful transfer sources but also the source values match the intuitive source-target similarity.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_403", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_403.flac", "type": "audio", "duration": 680.043}, {"from": "Assistant", "value": "The success of language models based on the Transformer architecture appears to be inconsistent with observed anisotropic properties of representations learned by such models. We resolve this by showing, contrary to previous studies, that the representations do not occupy a narrow cone, but rather drift in common directions. At any training step, all of the embeddings except for the ground-truth target embedding are updated with gradient in the same direction. Compounded over the training set, the embeddings drift and share common components, manifested in their shape in all the models we have empirically tested. Our experiments show that isotropy can be restored using a simple transformation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_404", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_404.flac", "type": "audio", "duration": 715.36}, {"from": "Assistant", "value": "We study how masking and predicting tokens in an unsupervised fashion can give rise to linguistic structures and downstream performance gains. Recent theories have suggested that pretrained language models acquire useful inductive biases through masks that implicitly act as cloze reductions for downstream tasks. While appealing, we show that the success of the random masking strategy used in practice cannot be explained by such cloze-like masks alone. We construct cloze-like masks using task-specific lexicons for three different classification datasets and show that the majority of pretrained performance gains come from generic masks that are not associated with the lexicon. To explain the empirical success of these generic masks, we demonstrate a correspondence between the Masked Language Model (MLM) objective and existing methods for learning statistical dependencies in graphical models. Using this, we derive a method for extracting these learned statistical dependencies in MLMs and show that these dependencies encode useful inductive biases in the form of syntactic structures. In an unsupervised parsing evaluation, simply forming a minimum spanning tree on the implied statistical dependence structure outperforms a classic method for unsupervised parsing (58.74 vs. 55.91 UUAS).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_405", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_405.flac", "type": "audio", "duration": 768.65}, {"from": "Assistant", "value": "Standard autoregressive language models perform only polynomial-time computation to compute the probability of the next symbol. While this is attractive, it means they cannot model distributions whose next-symbol probability ishardto compute. Indeed, they cannot even model them well enough to solve associatedeasydecision problems for which an engineer might want to consult a language model. These limitations apply no matter how much computation and data are used to train the model, unless the model is given access to oracle parameters that growsuperpolynomiallyin sequence length. Thus, simply training larger autoregressive language models is not a panacea for NLP. Alternatives include energy-based models (which give up efficient sampling) and latent-variable autoregressive models (which give up efficient scoring of a given string). Both are powerful enough to escape the above limitations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_406", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_406.flac", "type": "audio", "duration": 381.202}, {"from": "Assistant", "value": "As the excessive pre-training cost arouses the need to improve efficiency, considerable efforts have been made to train BERT progressively–start from an inferior but low-cost model and gradually increase the computational complexity. Our objective is to help advance the understanding of such Transformer growth and discover principles that guide progressive training. First, we find that similar to network architecture selection, Transformer growth also favors compound scaling. Specifically, while existing methods only conduct network growth in a single dimension, we observe that it is beneficial to use compound growth operators and balance multiple dimensions (e.g., depth, width, and input length of the model). Moreover, we explore alternative growth operators in each dimension via controlled comparison to give practical guidance for operator selection. In light of our analyses, the proposed method CompoundGrow speeds up BERT pre-training by 73.6% and 82.2% for the base and large models respectively while achieving comparable performances.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_407", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_407.flac", "type": "audio", "duration": 400.057}, {"from": "Assistant", "value": "Recent progress in language modeling has been driven not only by advances in neural architectures, but also through hardware and optimization improvements. In this paper, we revisit the neural probabilistic language model (NPLM) of Bengio et al. (2003), which simply concatenates word embeddings within a fixed window and passes the result through a feed-forward network to predict the next word. When scaled up to modern hardware, this model (despite its many limitations) performs much better than expected on word-level language model benchmarks. Our analysis reveals that the NPLM achieves lower perplexity than a baseline Transformer with short input contexts but struggles to handle long-term dependencies. Inspired by this result, we modify the Transformer by replacing its first self-attention layer with the NPLM’s local concatenation layer, which results in small but consistent perplexity decreases across three word-level language modeling datasets.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_408", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_408.flac", "type": "audio", "duration": 343.04}, {"from": "Assistant", "value": "Knowledge-intensive tasks such as question answering often require assimilating information from different sections of large inputs such as books or article collections. We propose ReadTwice, a simple and effective technique that combines several strengths of prior approaches to model long-range dependencies with Transformers. The main idea is to read text in small segments, in parallel, summarizing each segment into a memory table to be used in a second read of the text. We show that the method outperforms models of comparable size on several question answering (QA) datasets and sets a new state of the art on the challenging NarrativeQA task, with questions about entire books.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_409", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_409.flac", "type": "audio", "duration": 156.523}, {"from": "Assistant", "value": "We introduce Self-CRItic Pretraining Transformers (SCRIPT) for representation learning of text. The popular masked language modeling (MLM) pretraining methods like BERT replace some tokens with [MASK] and an encoder is trained to recover them, while ELECTRA trains a discriminator to detect replaced tokens proposed by a generator. In contrast, we train a language model as in MLM and further derive a discriminator or critic on top of the encoder without using any additional parameters. That is, the model itself is a critic. SCRIPT combines MLM training and discriminative training for learning rich representations and compute- and sample-efficiency. We demonstrate improved sample-efficiency in pretraining and enhanced representations evidenced by improved downstream task performance on GLUE and SQuAD over strong baselines. Also, the self-critic scores can be directly used as pseudo-log-likelihood for efficient scoring.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_410", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_410.flac", "type": "audio", "duration": 409.019}, {"from": "Assistant", "value": "Natural-language prompts have recently been used to coax pretrained language models into performing other AI tasks, using a fill-in-the-blank paradigm (Petroni et al., 2019) or a few-shot extrapolation paradigm (Brown et al., 2020). For example, language models retain factual knowledge from their training corpora that can be extracted by asking them to “fill in the blank” in a sentential prompt. However, where does this prompt come from? We explore the idea of learning prompts by gradient descent—either fine-tuning prompts taken from previous work, or starting from random initialization. Our prompts consist of “soft words,” i.e., continuous vectors that are not necessarily word type embeddings from the language model. Furthermore, for each task, we optimize a mixture of prompts, learning which prompts are most effective and how to ensemble them. Across multiple English LMs and tasks, our approach hugely outperforms previous methods, showing that the implicit factual knowledge in language models was previously underestimated. Moreover, this knowledge is cheap to elicit: random initialization is nearly as good as informed initialization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_411", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_411.flac", "type": "audio", "duration": 731.051}, {"from": "Assistant", "value": "We present a method for generating comparative summaries that highlight similarities and contradictions in input documents. The key challenge in creating such summaries is the lack of large parallel training data required for training typical summarization systems. To this end, we introduce a hybrid generation approach inspired by traditional concept-to-text systems. To enable accurate comparison between different sources, the model first learns to extract pertinent relations from input documents. The content planning component uses deterministic operators to aggregate these relations after identifying a subset for inclusion into a summary. The surface realization component lexicalizes this information using a text-infilling language model. By separately modeling content selection and realization, we can effectively train them with limited annotations. We implemented and tested the model in the domain of nutrition and health – rife with inconsistencies. Compared to conventional methods, our framework leads to more faithful, relevant and aggregation-sensitive summarization – while being equally fluent.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_412", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_412.flac", "type": "audio", "duration": 629.655}, {"from": "Assistant", "value": "We introduce AVA, an automatic evaluation approach for Question Answering, which given a set of questions associated with Gold Standard answers (references), can estimate system Accuracy. AVA uses Transformer-based language models to encode question, answer, and reference texts. This allows for effectively assessing answer correctness using similarity between the reference and an automatic answer, biased towards the question semantics. To design, train, and test AVA, we built multiple large training, development, and test sets on public and industrial benchmarks. Our innovative solutions achieve up to 74.7% F1 score in predicting human judgment for single answers. Additionally, AVA can be used to evaluate the overall system Accuracy with an error lower than 7% at 95% of confidence when measured on several QA systems.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_413", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_413.flac", "type": "audio", "duration": 720.051}, {"from": "Assistant", "value": "In many natural language processing applications, identifying predictive text can be as important as the predictions themselves. When predicting medical diagnoses, for example, identifying predictive content in clinical notes not only enhances interpretability, but also allows unknown, descriptive (i.e., text-based) risk factors to be identified. We here formalize this problem as predictive extraction and address it using a simple mechanism based on linear attention. Our method preserves differentiability, allowing scalable inference via stochastic gradient descent. Further, the model decomposes predictions into a sum of contributions of distinct text spans. Importantly, we require only document labels, not ground-truth spans. Results show that our model identifies semantically-cohesive spans and assigns them scores that agree with human ratings, while preserving classification performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_414", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_414.flac", "type": "audio", "duration": 719.052}, {"from": "Assistant", "value": "A prevailing paradigm in neural text generation is one-shot generation, where text is produced in a single step. The one-shot setting is inadequate, however, when the constraints the user wishes to impose on the generated text are dynamic, especially when authoring longer documents. We address this limitation with an interactive text generation setting in which the user interacts with the system by issuing commands to edit existing text. To this end, we propose a novel text editing task, and introduce WikiDocEdits, a dataset of single-sentence edits crawled from Wikipedia. We show that our Interactive Editor, a transformer-based model trained on this dataset, outperforms baselines and obtains positive results in both automatic and human evaluations. We present empirical and qualitative analyses of this model’s performance.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_415", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_415.flac", "type": "audio", "duration": 697.017}, {"from": "Assistant", "value": "We consider the task of linking social media accounts that belong to the same author in an automated fashion on the basis of the content and meta-data of the corresponding document streams. We focus on learning an embedding that maps variable-sized samples of user activity–ranging from single posts to entire months of activity–to a vector space, where samples by the same author map to nearby points. Our approach does not require human-annotated data for training purposes, which allows us to leverage large amounts of social media content. The proposed model outperforms several competitive baselines under a novel evaluation framework modeled after established recognition benchmarks in other domains. Our method achieves high linking accuracy, even with small samples from accounts not seen at training time, a prerequisite for practical applications of the proposed linking framework.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_416", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_416.flac", "type": "audio", "duration": 643.007}, {"from": "Assistant", "value": "Neural image-to-text radiology report generation systems offer the potential to improve radiology reporting by reducing the repetitive process of report drafting and identifying possible medical errors. However, existing report generation systems, despite achieving high performances on natural language generation metrics such as CIDEr or BLEU, still suffer from incomplete and inconsistent generations. Here we introduce two new simple rewards to encourage the generation of factually complete and consistent radiology reports: one that encourages the system to generate radiology domain entities consistent with the reference, and one that uses natural language inference to encourage these entities to be described in inferentially consistent ways. We combine these with the novel use of an existing semantic equivalence metric (BERTScore). We further propose a report generation system that optimizes these rewards via reinforcement learning. On two open radiology report datasets, our system substantially improved the F1 score of a clinical information extraction performance by +22.1 (Delta +63.9%). We further show via a human evaluation and a qualitative analysis that our system leads to generations that are more factually complete and consistent compared to the baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_417", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_417.flac", "type": "audio", "duration": 712.043}, {"from": "Assistant", "value": "Existing works in multimodal affective computing tasks, such as emotion recognition and personality recognition, generally adopt a two-phase pipeline by first extracting feature representations for each single modality with hand crafted algorithms, and then performing end-to-end learning with extracted features. However, the extracted features are fixed and cannot be further fine-tuned on different target tasks, and manually finding feature extracting algorithms does not generalize or scale well to different tasks, which can lead to sub-optimal performance. In this paper, we develop a fully end-to-end model that connects the two phases and optimizes them jointly. In addition, we restructure the current datasets to enable the fully end-to-end training. Furthermore, to reduce the computational overhead brought by the end-to-end model, we introduce a sparse cross-modal attention mechanism for the feature extraction. Experimental results show that our fully end-to-end model significantly surpasses the current state-of-the-art models based on the two-phase pipeline. Moreover, by adding the sparse cross-modal attention, our model can maintain the performance with around half less computation in the feature extraction part of the model.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_418", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_418.flac", "type": "audio", "duration": 703.083}, {"from": "Assistant", "value": "Multimodal research has picked up significantly in the space of question answering with the task being extended to visual question answering, charts question answering as well as multimodal input question answering. However, all these explorations produce a unimodal textual output as the answer. In this paper, we propose a novel task - MIMOQA - Multimodal Input Multimodal Output Question Answering in which the output is also multimodal. Through human experiments, we empirically show that such multimodal outputs provide better cognitive understanding of the answers. We also propose a novel multimodal question-answering framework, MExBERT, that incorporates a joint textual and visual attention towards producing such a multimodal output. Our method relies on a novel multimodal dataset curated for this problem from publicly available unimodal datasets. We show the superior performance of MExBERT against strong baselines on both the automatic as well as human metrics.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_419", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_419.flac", "type": "audio", "duration": 399.36}, {"from": "Assistant", "value": "To effectively apply robots in working environments and assist humans, it is essential to develop and evaluate how visual grounding (VG) can affect machine performance on occluded objects. However, current VG works are limited in working environments, such as offices and warehouses, where objects are usually occluded due to space utilization issues. In our work, we propose a novel OCID-Ref dataset featuring a referring expression segmentation task with referring expressions of occluded objects. OCID-Ref consists of 305,694 referring expressions from 2,300 scenes with providing RGB image and point cloud inputs. To resolve challenging occlusion issues, we argue that it’s crucial to take advantage of both 2D and 3D signals to resolve challenging occlusion issues. Our experimental results demonstrate the effectiveness of aggregating 2D and 3D signals but referring to occluded objects still remains challenging for the modern visual grounding systems. OCID-Ref is publicly available athttps://github.com/lluma/OCID-Ref", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_420", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_420.flac", "type": "audio", "duration": 599.098}, {"from": "Assistant", "value": "Pre-trained contextual vision-and-language (V&L) models have achieved impressive performance on various benchmarks. However, existing models require a large amount of parallel image-caption data for pre-training. Such data are costly to collect and require cumbersome curation. Inspired by unsupervised machine translation, we investigate if a strong V&L representation model can be learned through unsupervised pre-training without image-caption corpora. In particular, we propose to conduct “mask-and-predict” pre-training on text-only and image-only corpora and introduce the object tags detected by an object recognition model as anchor points to bridge two modalities. We find that such a simple approach achieves performance close to a model pre-trained with aligned data, on four English V&L benchmarks. Our work challenges the widely held notion that aligned data is necessary for V&L pre-training, while significantly reducing the amount of supervision needed for V&L models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_421", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_421.flac", "type": "audio", "duration": 705.004}, {"from": "Assistant", "value": "When intelligent agents communicate to accomplish shared goals, how do these goals shape the agents’ language? We study the dynamics of learning in latent language policies (LLPs), in which instructor agents generate natural-language subgoal descriptions and executor agents map these descriptions to low-level actions. LLPs can solve challenging long-horizon reinforcement learning problems and provide a rich model for studying task-oriented language use. But previous work has found that LLP training is prone to semantic drift (use of messages in ways inconsistent with their original natural language meanings). Here, we demonstrate theoretically and empirically that multitask training is an effective counter to this problem: we prove that multitask training eliminates semantic drift in a well-studied family of signaling games, and show that multitask training of neural LLPs in a complex strategy game reduces drift and while improving sample efficiency.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_422", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_422.flac", "type": "audio", "duration": 687.055}, {"from": "Assistant", "value": "The success of large-scale contextual language models has attracted great interest in probing what is encoded in their representations. In this work, we consider a new question: to what extent contextual representations of concrete nouns are aligned with corresponding visual representations? We design a probing model that evaluates how effective are text-only representations in distinguishing between matching and non-matching visual representations. Our findings show that language representations alone provide a strong signal for retrieving image patches from the correct object categories. Moreover, they are effective in retrieving specific instances of image patches; textual context plays an important role in this process. Visually grounded language models slightly outperform text-only language models in instance retrieval, but greatly under-perform humans. We hope our analyses inspire future research in understanding and improving the visual capabilities of language models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_423", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_423.flac", "type": "audio", "duration": 340.053}, {"from": "Assistant", "value": "Healthcare predictive analytics aids medical decision-making, diagnosis prediction and drug review analysis. Therefore, prediction accuracy is an important criteria which also necessitates robust predictive language models. However, the models using deep learning have been proven vulnerable towards insignificantly perturbed input instances which are less likely to be misclassified by humans. Recent efforts of generating adversaries using rule-based synonyms and BERT-MLMs have been witnessed in general domain, but the ever-increasing biomedical literature poses unique challenges. We propose BBAEG (Biomedical BERT-based Adversarial Example Generation), a black-box attack algorithm for biomedical text classification, leveraging the strengths of both domain-specific synonym replacement for biomedical named entities and BERT-MLM predictions, spelling variation and number replacement. Through automatic and human evaluation on two datasets, we demonstrate that BBAEG performs stronger attack with better language fluency, semantic coherence as compared to prior work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_424", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_424.flac", "type": "audio", "duration": 394.043}, {"from": "Assistant", "value": "We present a simple yet effective Targeted Adversarial Training (TAT) algorithm to improve adversarial training for natural language understanding. The key idea is to introspect current mistakes and prioritize adversarial training steps to where the model errs the most. Experiments show that TAT can significantly improve accuracy over standard adversarial training on GLUE and attain new state-of-the-art zero-shot results on XNLI. Our code will be released upon acceptance of the paper.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_425", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_425.flac", "type": "audio", "duration": 677.739}, {"from": "Assistant", "value": "The existence of multiple datasets for sarcasm detection prompts us to apply transfer learning to exploit their commonality. The adversarial neural transfer (ANT) framework utilizes multiple loss terms that encourage the source-domain and the target-domain feature distributions to be similar while optimizing for domain-specific performance. However, these objectives may be in conflict, which can lead to optimization difficulties and sometimes diminished transfer. We propose a generalized latent optimization strategy that allows different losses to accommodate each other and improves training dynamics. The proposed method outperforms transfer learning and meta-learning baselines. In particular, we achieve 10.02% absolute performance gain over the previous state of the art on the iSarcasm dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_427", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_427.flac", "type": "audio", "duration": 720.043}, {"from": "Assistant", "value": "Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space. However, different categories often overlap with each other in the representation space at the beginning of the learning process, which poses a significant challenge for distance-based clustering in achieving good separation between different categories. To this end, we propose Supporting Clustering with Contrastive Learning (SCCL) – a novel framework to leverage contrastive learning to promote better separation. We assess the performance of SCCL on short text clustering and show that SCCL significantly advances the state-of-the-art results on most benchmark datasets with 3%-11% improvement on Accuracy and 4%-15% improvement on Normalized Mutual Information. Furthermore, our quantitative analysis demonstrates the effectiveness of SCCL in leveraging the strengths of both bottom-up instance discrimination and top-down clustering to achieve better intra-cluster and inter-cluster distances when evaluated with the ground truth cluster labels.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_428", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_428.flac", "type": "audio", "duration": 730.453}, {"from": "Assistant", "value": "In this paper, we focus on the problem of keyword and document matching by considering different relevance levels. In our recommendation system, different people follow different hot keywords with interest. We need to attach documents to each keyword and then distribute the documents to people who follow these keywords. The ideal documents should have the same topic with the keyword, which we call topic-aware relevance. In other words, topic-aware relevance documents are better than partially-relevance ones in this application. However, previous tasks never define topic-aware relevance clearly. To tackle this problem, we define a three-level relevance in keyword-document matching task: topic-aware relevance, partially-relevance and irrelevance. To capture the relevance between the short keyword and the document at above-mentioned three levels, we should not only combine the latent topic of the document with its deep neural representation, but also model complex interactions between the keyword and the document. To this end, we propose a Two-stage Interaction and Topic-Aware text matching model (TITA). In terms of “topic-aware”, we introduce neural topic model to analyze the topic of the document and then use it to further encode the document. In terms of “two-stage interaction”, we propose two successive stages to model complex interactions between the keyword and the document. Extensive experiments reveal that TITA outperforms other well-designed baselines and shows excellent performance in our recommendation system.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_429", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_429.flac", "type": "audio", "duration": 799.045}, {"from": "Assistant", "value": "Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills. However, existing GEC models tend to produce spurious corrections or fail to detect lots of errors. The quality estimation model is necessary to ensure learners get accurate GEC results and avoid misleading from poorly corrected sentences. Well-trained GEC models can generate several high-quality hypotheses through decoding, such as beam search, which provide valuable GEC evidence and can be used to evaluate GEC quality. However, existing models neglect the possible GEC evidence from different hypotheses. This paper presents the Neural Verification Network (VERNet) for GEC quality estimation with multiple hypotheses. VERNet establishes interactions among hypotheses with a reasoning graph and conducts two kinds of attention mechanisms to propagate GEC evidence to verify the quality of generated hypotheses. Our experiments on four GEC datasets show that VERNet achieves state-of-the-art grammatical error detection performance, achieves the best quality estimation results, and significantly improves GEC performance by reranking hypotheses. All data and source codes are available athttps://github.com/thunlp/VERNet.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_430", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_430.flac", "type": "audio", "duration": 695.04}, {"from": "Assistant", "value": "Side effects during neural network tuning are typically measured by overall accuracy changes. However, we find that even with similar overall accuracy, existing tuning methods result in non-negligible instance-wise side effects. Motivated by neuroscientific evidence and theoretical results, we demonstrate that side effects can be controlled by the number of changed parameters and thus, we propose to conductneural network surgeryby only modifying a limited number of parameters. Neural network surgery can be realized using diverse techniques and we investigate three lines of methods. Experimental results on representative tuning problems validate the effectiveness of the surgery approach. The dynamic selecting method achieves the best overall performance that not only satisfies the tuning goal but also induces fewer instance-wise side effects by changing only10-5of the parameters.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_431", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_431.flac", "type": "audio", "duration": 759.765}, {"from": "Assistant", "value": "In this paper, we focus on identifying interactive argument pairs from two posts with opposite stances to a certain topic. Considering opinions are exchanged from different perspectives of the discussing topic, we study the discrete representations for arguments to capture varying aspects in argumentation languages (e.g., the debate focus and the participant behavior). Moreover, we utilize hierarchical structure to model post-wise information incorporating contextual knowledge. Experimental results on the large-scale dataset collected from CMV show that our proposed framework can significantly outperform the competitive baselines. Further analyses reveal why our model yields superior performance and prove the usefulness of our learned representations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_432", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_432.flac", "type": "audio", "duration": 412.48}, {"from": "Assistant", "value": "In this paper, we introduce UnifiedM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup. The model is trained to handle four tasks: detecting news bias, clickbait, fake news, and verifying rumors. By grouping these tasks together, UnifiedM2 learns a richer representation of misinformation, which leads to state-of-the-art or comparable performance across all tasks. Furthermore, we demonstrate that UnifiedM2’s learned representation is helpful for few-shot learning of unseen misinformation tasks/datasets and the model’s generalizability to unseen events.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_433", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_433.flac", "type": "audio", "duration": 393.044}, {"from": "Assistant", "value": "With advances in neural language models, the focus of linguistic steganography has shifted from edit-based approaches to generation-based ones. While the latter’s payload capacity is impressive, generating genuine-looking texts remains challenging. In this paper, we revisit edit-based linguistic steganography, with the idea that a masked language model offers an off-the-shelf solution. The proposed method eliminates painstaking rule construction and has a high payload capacity for an edit-based model. It is also shown to be more secure against automatic detection than a generation-based method while offering better control of the security/payload capacity trade-off.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_434", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_434.flac", "type": "audio", "duration": 422.046}, {"from": "Assistant", "value": "Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category. This paper explores data augmentation—a technique particularly suitable for training with limited data—for this few-shot, highly-multiclass text classification setting. On four diverse text classification tasks, we find that common data augmentation techniques can improve the performance of triplet networks by up to 3.0% on average. To further boost performance, we present a simple training strategy called curriculum data augmentation, which leverages curriculum learning by first training on only original examples and then introducing augmented data as training progresses. We explore a two-stage and a gradual schedule, and find that, compared with standard single-stage training, curriculum data augmentation trains faster, improves performance, and remains robust to high amounts of noising from augmentation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_435", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_435.flac", "type": "audio", "duration": 718.054}, {"from": "Assistant", "value": "Sequence-to-sequence models have delivered impressive results in word formation tasks such as morphological inflection, often learning to model subtle morphophonological details with limited training data. Despite the performance, the opacity of neural models makes it difficult to determine whether complex generalizations are learned, or whether a kind of separate rote memorization of each morphophonological process takes place. To investigate whether complex alternations are simply memorized or whether there is some level of generalization across related sound changes in a sequence-to-sequence model, we perform several experiments on Finnish consonant gradation—a complex set of sound changes triggered in some words by certain suffixes. We find that our models often—though not always—encode 17 different consonant gradation processes in a handful of dimensions in the RNN. We also show that by scaling the activations in these dimensions we can control whether consonant gradation occurs and the direction of the gradation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_436", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_436.flac", "type": "audio", "duration": 672.853}, {"from": "Assistant", "value": "Recent researches show that pre-trained models (PTMs) are beneficial to Chinese Word Segmentation (CWS). However, PTMs used in previous works usually adopt language modeling as pre-training tasks, lacking task-specific prior segmentation knowledge and ignoring the discrepancy between pre-training tasks and downstream CWS tasks. In this paper, we propose a CWS-specific pre-trained model MetaSeg, which employs a unified architecture and incorporates meta learning algorithm into a multi-criteria pre-training task. Empirical results show that MetaSeg could utilize common prior segmentation knowledge from different existing criteria and alleviate the discrepancy between pre-trained models and downstream CWS tasks. Besides, MetaSeg can achieve new state-of-the-art performance on twelve widely-used CWS datasets and significantly improve model performance in low-resource settings.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_437", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_437.flac", "type": "audio", "duration": 407.046}, {"from": "Assistant", "value": "In this paper, we tackle the task of Definition Generation (DG) in Chinese, which aims at automatically generating a definition for a word. Most existing methods take the source word as an indecomposable semantic unit. However, in parataxis languages like Chinese, word meanings can be composed using the word formation process, where a word (“桃花”, peach-blossom) is formed by formation components (“桃”, peach; “花”, flower) using a formation rule (Modifier-Head). Inspired by this process, we propose to enhance DG with word formation features. We build a formation-informed dataset, and propose a model DeFT, which Decomposes words into formation features, dynamically Fuses different features through a gating mechanism, and generaTes word definitions. Experimental results show that our method is both effective and robust.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_438", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_438.flac", "type": "audio", "duration": 684.06}, {"from": "Assistant", "value": "Morphological analysis (MA) and lexical normalization (LN) are both important tasks for Japanese user-generated text (UGT). To evaluate and compare different MA/LN systems, we have constructed a publicly available Japanese UGT corpus. Our corpus comprises 929 sentences annotated with morphological and normalization information, along with category information we classified for frequent UGT-specific phenomena. Experiments on the corpus demonstrated the low performance of existing MA/LN methods for non-general words and non-standard forms, indicating that the corpus would be a challenging benchmark for further research on UGT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_439", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_439.flac", "type": "audio", "duration": 388.971}, {"from": "Assistant", "value": "Human innovation in language, such as inventing new words, is a challenge for pretrained language models. We assess the ability of one large model, GPT-3, to process new words and decide on their meaning. We create a set of nonce words and prompt GPT-3 to generate their dictionary definitions. We find GPT-3 produces plausible definitions that align with human judgments. Moreover, GPT-3’s definitions are sometimes preferred to those invented by humans, signaling its intriguing ability not just to adapt, but to add to the evolving vocabulary of the English language.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_440", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_440.flac", "type": "audio", "duration": 637.099}, {"from": "Assistant", "value": "Universal Semantic Tagging aims to provide lightweight unified analysis for all languages at the word level. Though the proposed annotation scheme is conceptually promising, the feasibility is only examined in four Indo–European languages. This paper is concerned with extending the annotation scheme to handle Mandarin Chinese and empirically study the plausibility of unifying meaning representations for multiple languages. We discuss a set of language-specific semantic phenomena, propose new annotation specifications and build a richly annotated corpus. The corpus consists of 1100 English–Chinese parallel sentences, where compositional semantic analysis is available for English, and another 1000 Chinese sentences which has enriched syntactic analysis. By means of the new annotations, we also evaluate a series of neural tagging models to gauge how successful semantic tagging can be: accuracies of 92.7% and 94.6% are obtained for Chinese and English respectively. The English tagging performance is remarkably better than the state-of-the-art by 7.7%.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_441", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_441.flac", "type": "audio", "duration": 722.048}, {"from": "Assistant", "value": "Given a database schema, Text-to-SQL aims to translate a natural language question into the corresponding SQL query. Under the setup of cross-domain, traditional semantic parsing models struggle to adapt to unseen database schemas. To improve the model generalization capability for rare and unseen schemas, we propose a new architecture, ShadowGNN, which processes schemas at abstract and semantic levels. By ignoring names of semantic items in databases, abstract schemas are exploited in a well-designed graph projection neural network to obtain delexicalized representation of question and schema. Based on the domain-independent representations, a relation-aware transformer is utilized to further extract logical linking between question and schema. Finally, a SQL decoder with context-free grammar is applied. On the challenging Text-to-SQL benchmark Spider, empirical results show that ShadowGNN outperforms state-of-the-art models. When the annotated data is extremely limited (only 10% training set), ShadowGNN gets over absolute 5% performance gain, which shows its powerful generalization ability. Our implementation will be open-sourced athttps://github.com/WowCZ/shadowgnn", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_442", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_442.flac", "type": "audio", "duration": 394.043}, {"from": "Assistant", "value": "We propose a method to learn contextualized and generalized sentence representations using contrastive self-supervised learning. In the proposed method, a model is given a text consisting of multiple sentences. One sentence is randomly selected as a target sentence. The model is trained to maximize the similarity between the representation of the target sentence with its context and that of the masked target sentence with the same context. Simultaneously, the model minimizes the similarity between the latter representation and the representation of a random sentence with the same context. We apply our method to discourse relation analysis in English and Japanese and show that it outperforms strong baseline methods based on BERT, XLNet, and RoBERTa.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_443", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_443.flac", "type": "audio", "duration": 713.062}, {"from": "Assistant", "value": "Abstract Meaning Representation parsing is a sentence-to-graph prediction task where target nodes are not explicitly aligned to sentence tokens. However, since graph nodes are semantically based on one or more sentence tokens, implicit alignments can be derived. Transition-based parsers operate over the sentence from left to right, capturing this inductive bias via alignments at the cost of limited expressiveness. In this work, we propose a transition-based system that combines hard-attention over sentences with a target-side action pointer mechanism to decouple source tokens from node representations and address alignments. We model the transitions as well as the pointer mechanism through straightforward modifications within a single Transformer architecture. Parser state and graph structure information are efficiently encoded using attention heads. We show that our action-pointer approach leads to increased expressiveness and attains large gains (+1.6 points) against the best transition-based AMR parser in very similar conditions. While using no graph re-categorization, our single model yields the second best Smatch score on AMR 2.0 (81.8), which is further improved to 83.4 with silver data and ensemble decoding.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_444", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_444.flac", "type": "audio", "duration": 863.086}, {"from": "Assistant", "value": "We study semantic parsing in an interactive setting in which users correct errors with natural language feedback. We present NL-EDIT, a model for interpreting natural language feedback in the interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors. We show that NL-EDIT can boost the accuracy of existing text-to-SQL parsers by up to 20% with only one turn of correction. We analyze the limitations of the model and discuss directions for improvement and evaluation. The code and datasets used in this paper are publicly available athttp://aka.ms/NLEdit.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_445", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_445.flac", "type": "audio", "duration": 719.052}, {"from": "Assistant", "value": "Measuring document similarity plays an important role in natural language processing tasks. Most existing document similarity approaches suffer from the information gap caused by context and vocabulary mismatches when comparing varying-length texts. In this paper, we propose an unsupervised concept representation learning approach to address the above issues. Specifically, we propose a novel Concept Generation Network (CGNet) to learn concept representations from the perspective of the entire text corpus. Moreover, a concept-based document matching method is proposed to leverage advances in the recognition of local phrase features and corpus-level concept features. Extensive experiments on real-world data sets demonstrate that new method can achieve a considerable improvement in comparing length-varying texts. In particular, our model achieved 6.5% better F1 Score compared to the best of the baseline models for a concept-project benchmark dataset.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_446", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_446.flac", "type": "audio", "duration": 529.694}, {"from": "Assistant", "value": "Knowledge data are massive and widespread in the real-world, which can serve as good external sources to enrich conversations. However, in knowledge-grounded conversations, current models still lack the fine-grained control over knowledge selection and integration with dialogues, which finally leads to the knowledge-irrelevant response generation problems: 1) knowledge selection merely relies on the dialogue context, ignoring the inherent knowledge transitions along with conversation flows; 2) the models often over-fit during training, resulting with incoherent response by referring to unrelated tokens from specific knowledge content in the testing phase; 3) although response is generated upon the dialogue history and knowledge, the models often tend to overlook the selected knowledge, and hence generates knowledge-irrelevant response. To address these problems, we proposed to explicitly model the knowledge transition in sequential multi-turn conversations by abstracting knowledge into topic tags. Besides, to fully utilizing the selected knowledge in generative process, we propose pre-training a knowledge-aware response generator to pay more attention on the selected knowledge. In particular, a sequential knowledge transition model equipped with a pre-trained knowledge-aware response generator (SKT-KG) formulates the high-level knowledge transition and fully utilizes the limited knowledge data. Experimental results on both structured and unstructured knowledge-grounded dialogue benchmarks indicate that our model achieves better performance over baseline models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_447", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_447.flac", "type": "audio", "duration": 420.003}, {"from": "Assistant", "value": "Detecting out-of-domain (OOD) intents is crucial for the deployed task-oriented dialogue system. Previous unsupervised OOD detection methods only extract discriminative features of different in-domain intents while supervised counterparts can directly distinguish OOD and in-domain intents but require extensive labeled OOD data. To combine the benefits of both types, we propose a self-supervised contrastive learning framework to model discriminative semantic features of both in-domain intents and OOD intents from unlabeled data. Besides, we introduce an adversarial augmentation neural module to improve the efficiency and robustness of contrastive learning. Experiments on two public benchmark datasets show that our method can consistently outperform the baselines with a statistically significant margin.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_448", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_448.flac", "type": "audio", "duration": 422.371}, {"from": "Assistant", "value": "Zero-shot cross-domain dialogue state tracking (DST) enables us to handle unseen domains without the expense of collecting in-domain data. In this paper, we propose a slot descriptions enhanced generative approach for zero-shot cross-domain DST. Specifically, our model first encodes a dialogue context and a slot with a pre-trained self-attentive encoder, and generates slot value in auto-regressive manner. In addition, we incorporate Slot Type Informed Descriptions that capture the shared information of different slots to facilitates the cross-domain knowledge transfer. Experimental results on MultiWOZ shows that our model significantly improve existing state-of-the-art results in zero-shot cross-domain setting.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_449", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_449.flac", "type": "audio", "duration": 713.045}, {"from": "Assistant", "value": "Generative models for dialog systems have gained much interest because of the recent success of RNN and Transformer based models in tasks like question answering and summarization. Although the task of dialog response generation is generally seen as a sequence to sequence (Seq2Seq) problem, researchers in the past have found it challenging to train dialog systems using the standard Seq2Seq models. Therefore, to help the model learn meaningful utterance and conversation level features, Sordoni et al. (2015b), Serban et al. (2016) proposed Hierarchical RNN architecture, which was later adopted by several other RNN based dialog systems. With the transformer-based models dominating the seq2seq problems lately, the natural question to ask is the applicability of the notion of hierarchy in transformer-based dialog systems. In this paper, we propose a generalized framework for Hierarchical Transformer Encoders and show how a standard transformer can be morphed into any hierarchical encoder, including HRED and HIBERT like models, by using specially designed attention masks and positional encodings. We demonstrate that Hierarchical Encoding helps achieve better natural language understanding of the contexts in transformer-based models for task-oriented dialog systems through a wide range of experiments.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_450", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_450.flac", "type": "audio", "duration": 418.688}, {"from": "Assistant", "value": "We consider the intrinsic evaluation of neural generative dialog models through the lens of Grice’s Maxims of Conversation (1975). Based on the maxim of Quantity (be informative), we propose Relative Utterance Quantity (RUQ) to diagnose the ‘I don’t know’ problem, in which a dialog system produces generic responses. The linguistically motivated RUQ diagnostic compares the model score of a generic response to that of the reference response. We find that for reasonable baseline models, ‘I don’t know’ is preferred over the reference the majority of the time, but this can be reduced to less than 5% with hyperparameter tuning. RUQ allows for the direct analysis of the ‘I don’t know’ problem, which has been addressed but not analyzed by prior work.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_451", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_451.flac", "type": "audio", "duration": 758.062}, {"from": "Assistant", "value": "Static knowledge graph (SKG) embedding (SKGE) has been studied intensively in the past years. Recently, temporal knowledge graph (TKG) embedding (TKGE) has emerged. In this paper, we propose a Recursive Temporal Fact Embedding (RTFE) framework to transplant SKGE models to TKGs and to enhance the performance of existing TKGE models for TKG completion. Different from previous work which ignores the continuity of states of TKG in time evolution, we treat the sequence of graphs as a Markov chain, which transitions from the previous state to the next state. RTFE takes the SKGE to initialize the embeddings of TKG. Then it recursively tracks the state transition of TKG by passing updated parameters/features between timestamps. Specifically, at each timestamp, we approximate the state transition as the gradient update process. Since RTFE learns each timestamp recursively, it can naturally transit to future timestamps. Experiments on five TKG datasets show the effectiveness of RTFE.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_452", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_452.flac", "type": "audio", "duration": 674.261}, {"from": "Assistant", "value": "Open relation extraction (OpenRE) aims to extract novel relation types from open-domain corpora, which plays an important role in completing the relation schemes of knowledge bases (KBs). Most OpenRE methods cast different relation types in isolation without considering their hierarchical dependency. We argue that OpenRE is inherently in close connection with relation hierarchies. To establish the bidirectional connections between OpenRE and relation hierarchy, we propose the task of open hierarchical relation extraction and present a novel OHRE framework for the task. We propose a dynamic hierarchical triplet objective and hierarchical curriculum training paradigm, to effectively integrate hierarchy information into relation representations for better novel relation extraction. We also present a top-down hierarchy expansion algorithm to add the extracted relations into existing hierarchies with reasonable interpretability. Comprehensive experiments show that OHRE outperforms state-of-the-art models by a large margin on both relation clustering and hierarchy expansion.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_453", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_453.flac", "type": "audio", "duration": 717.056}, {"from": "Assistant", "value": "Relational triple extraction is a crucial task for knowledge graph construction. Existing methods mainly focused on explicit relational triples that are directly expressed, but usually suffer from ignoring implicit triples that lack explicit expressions. This will lead to serious incompleteness of the constructed knowledge graphs. Fortunately, other triples in the sentence provide supplementary information for discovering entity pairs that may have implicit relations. Also, the relation types between the implicitly connected entity pairs can be identified with relational reasoning patterns in the real world. In this paper, we propose a unified framework to jointly extract explicit and implicit relational triples. To explore entity pairs that may be implicitly connected by relations, we propose a binary pointer network to extract overlapping relational triples relevant to each word sequentially and retain the information of previously extracted triples in an external memory. To infer the relation types of implicit relational triples, we propose to introduce real-world relational reasoning patterns in our model and capture these patterns with a relation network. We conduct experiments on several benchmark datasets, and the results prove the validity of our method.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_454", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_454.flac", "type": "audio", "duration": 500.019}, {"from": "Assistant", "value": "Although pre-trained big models (e.g., BERT, ERNIE, XLNet, GPT3 etc.) have delivered top performance in Seq2seq modeling, their deployments in real-world applications are often hindered by the excessive computations and memory demand involved. For many applications, including named entity recognition (NER), matching the state-of-the-art result under budget has attracted considerable attention. Drawing power from the recent advance in knowledge distillation (KD), this work presents a novel distillation scheme to efficiently transfer the knowledge learned from big models to their more affordable counterpart. Our solution highlights the construction of surrogate labels through the k-best Viterbi algorithm to distill knowledge from the teacher model. To maximally assimilate knowledge into the student model, we propose a multi-grained distillation scheme, which integrates cross entropy involved in conditional random field (CRF) and fuzzy learning. To validate the effectiveness of our proposal, we conducted a comprehensive evaluation on five NER benchmarks, reporting cross-the-board performance gains relative to competing prior-arts. We further discuss ablation results to dissect our gains.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_455", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_455.flac", "type": "audio", "duration": 659.052}, {"from": "Assistant", "value": "Keyphrases, that concisely summarize the high-level topics discussed in a document, can be categorized into present keyphrase which explicitly appears in the source text and absent keyphrase which does not match any contiguous subsequence but is highly semantically related to the source. Most existing keyphrase generation approaches synchronously generate present and absent keyphrases without explicitly distinguishing these two categories. In this paper, a Select-Guide-Generate (SGG) approach is proposed to deal with present and absent keyphrases generation separately with different mechanisms. Specifically, SGG is a hierarchical neural network which consists of a pointing-based selector at low layer concentrated on present keyphrase generation, a selection-guided generator at high layer dedicated to absent keyphrase generation, and a guider in the middle to transfer information from selector to generator. Experimental results on four keyphrase generation benchmarks demonstrate the effectiveness of our model, which significantly outperforms the strong baselines for both present and absent keyphrases generation. Furthermore, we extend SGG to a title generation task which indicates its extensibility in natural language generation tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_456", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_456.flac", "type": "audio", "duration": 739.021}, {"from": "Assistant", "value": "Speech Act Classification determining the communicative intent of an utterance has been investigated widely over the years as a standalone task. This holds true for discussion in any fora including social media platform such as Twitter. But the emotional state of the tweeter which has a considerable effect on the communication has not received the attention it deserves. Closely related to emotion is sentiment, and understanding of one helps understand the other. In this work, we firstly create a new multi-modal, emotion-TA (‘TA’ means tweet act, i.e., speech act in Twitter) dataset calledEmoTAcollected from open-source Twitter dataset. We propose a Dyadic Attention Mechanism (DAM) based multi-modal, adversarial multi-tasking framework. DAM incorporates intra-modal and inter-modal attention to fuse multiple modalities and learns generalized features across all the tasks. Experimental results indicate that the proposed framework boosts the performance of the primary task, i.e., TA classification (TAC) by benefitting from the two secondary tasks, i.e., Sentiment and Emotion Analysis compared to its uni-modal and single task TAC (tweet act classification) variants.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_457", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_457.flac", "type": "audio", "duration": 595.011}, {"from": "Assistant", "value": "There are common semantics shared across text and images. Given a sentence in a source language, whether depicting the visual scene helps translation into a target language? Existing multimodal neural machine translation methods (MNMT) require triplets of bilingual sentence - image for training and tuples of source sentence - image for inference. In this paper, we propose ImagiT, a novel machine translation method via visual imagination. ImagiT first learns to generate visual representation from the source sentence, and then utilizes both source sentence and the “imagined representation” to produce a target translation. Unlike previous methods, it only needs the source sentence at the inference time. Experiments demonstrate that ImagiT benefits from visual imagination and significantly outperforms the text-only neural machine translation baselines. Further analysis reveals that the imagination process in ImagiT helps fill in missing information when performing the degradation strategy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_458", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_458.flac", "type": "audio", "duration": 595.058}, {"from": "Assistant", "value": "Non-autoregressive Transformer is a promising text generation model. However, current non-autoregressive models still fall behind their autoregressive counterparts in translation quality. We attribute this accuracy gap to the lack of dependency modeling among decoder inputs. In this paper, we propose CNAT, which learns implicitly categorical codes as latent variables into the non-autoregressive decoding. The interaction among these categorical codes remedies the missing dependencies and improves the model capacity. Experiment results show that our model achieves comparable or better performance in machine translation tasks than several strong baselines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_459", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_459.flac", "type": "audio", "duration": 402.053}, {"from": "Assistant", "value": "Machine translation of user-generated code-mixed inputs to English is of crucial importance in applications like web search and targeted advertising. We address the scarcity of parallel training data for training such models by designing a strategy of converting existing non-code-mixed parallel data sources to code-mixed parallel data. We present an m-BERT based procedure whose core learnable component is a ternary sequence labeling model, that can be trained with a limited code-mixed corpus alone. We show a 5.8 point increase in BLEU on heavily code-mixed sentences by training a translation model using our data augmentation strategy on an Hindi-English code-mixed translation task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_460", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_460.flac", "type": "audio", "duration": 721.049}, {"from": "Assistant", "value": "We often use perturbations to regularize neural models. For neural encoder-decoders, previous studies applied the scheduled sampling (Bengio et al., 2015) and adversarial perturbations (Sato et al., 2019) as perturbations but these methods require considerable computational time. Thus, this study addresses the question of whether these approaches are efficient enough for training time. We compare several perturbations in sequence-to-sequence problems with respect to computational time. Experimental results show that the simple techniques such as word dropout (Gal and Ghahramani, 2016) and random replacement of input tokens achieve comparable (or better) scores to the recently proposed perturbations, even though these simple methods are faster.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_461", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_461.flac", "type": "audio", "duration": 698.062}, {"from": "Assistant", "value": "Although many end-to-end context-aware neural machine translation models have been proposed to incorporate inter-sentential contexts in translation, these models can be trained only in domains where parallel documents with sentential alignments exist. We therefore present a simple method to perform context-aware decoding with any pre-trained sentence-level translation model by using a document-level language model. Our context-aware decoder is built upon sentence-level parallel data and target-side document-level monolingual data. From a theoretical viewpoint, our core contribution is the novel representation of contextual information using point-wise mutual information between context and the current sentence. We demonstrate the effectiveness of our method on English to Russian translation, by evaluating with BLEU and contrastive tests for context-aware translation.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_462", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_462.flac", "type": "audio", "duration": 413.056}, {"from": "Assistant", "value": "Translated texts have been used for malicious purposes, i.e., plagiarism or fake reviews. Existing detectors have been built around a specific translator (e.g., Google) but fail to detect a translated text from a strange translator. If we use the same translator, the translated text is similar to its round-trip translation, which is when text is translated into another language and translated back into the original language. However, a round-trip translated text is significantly different from the original text or a translated text using a strange translator. Hence, we propose a detector using text similarity with round-trip translation (TSRT). TSRT achieves 86.9% accuracy in detecting a translated text from a strange translator. It outperforms existing detectors (77.9%) and human recognition (53.3%).", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_463", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_463.flac", "type": "audio", "duration": 429.058}, {"from": "Assistant", "value": "Existing pre-trained language models (PLMs) are often computationally expensive in inference, making them impractical in various resource-limited real-world applications. To address this issue, we propose a dynamic token reduction approach to accelerate PLMs’ inference, named TR-BERT, which could flexibly adapt the layer number of each token in inference to avoid redundant calculation. Specially, TR-BERT formulates the token reduction process as a multi-step token selection problem and automatically learns the selection strategy via reinforcement learning. The experimental results on several downstream NLP tasks show that TR-BERT is able to speed up BERT by 2-5 times to satisfy various performance demands. Moreover, TR-BERT can also achieve better performance with less computation in a suite of long-text tasks since its token-level layer number adaption greatly accelerates the self-attention operation in PLMs. The source code and experiment details of this paper can be obtained fromhttps://github.com/thunlp/TR-BERT.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_464", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_464.flac", "type": "audio", "duration": 622.016}, {"from": "Assistant", "value": "Recently Graph Neural Network (GNN) has been used as a promising tool in multi-hop question answering task. However, the unnecessary updations and simple edge constructions prevent an accurate answer span extraction in a more direct and interpretable way. In this paper, we propose a novel model of Breadth First Reasoning Graph (BFR-Graph), which presents a new message passing way that better conforms to the reasoning process. In BFR-Graph, the reasoning message is required to start from the question node and pass to the next sentences node hop by hop until all the edges have been passed, which can effectively prevent each node from over-smoothing or being updated multiple times unnecessarily. To introduce more semantics, we also define the reasoning graph as a weighted graph with considering the number of co-occurrence entities and the distance between sentences. Then we present a more direct and interpretable way to aggregate scores from different levels of granularity based on the GNN. On HotpotQA leaderboard, the proposed BFR-Graph achieves state-of-the-art on answer span prediction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_465", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_465.flac", "type": "audio", "duration": 712.939}, {"from": "Assistant", "value": "Multilingual question answering over knowledge graph (KGQA) aims to derive answers from a knowledge graph (KG) for questions in multiple languages. To be widely applicable, we focus on its zero-shot transfer setting. That is, we can only access training data in a high-resource language, while need to answer multilingual questions without any labeled data in target languages. A straightforward approach is resorting to pre-trained multilingual models (e.g., mBERT) for cross-lingual transfer, but there is a still significant gap of KGQA performance between source and target languages. In this paper, we exploit unsupervised bilingual lexicon induction (BLI) to map training questions in source language into those in target language as augmented training data, which circumvents language inconsistency between training and inference. Furthermore, we propose an adversarial learning strategy to alleviate syntax-disorder of the augmented data, making the model incline to both language- and syntax-independence. Consequently, our model narrows the gap in zero-shot cross-lingual transfer. Experiments on two multilingual KGQA datasets with 11 zero-resource languages verify its effectiveness.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_466", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_466.flac", "type": "audio", "duration": 631.36}, {"from": "Assistant", "value": "In open-domain question answering, dense passage retrieval has become a new paradigm to retrieve relevant passages for finding answers. Typically, the dual-encoder architecture is adopted to learn dense representations of questions and passages for semantic matching. However, it is difficult to effectively train a dual-encoder due to the challenges including the discrepancy between training and inference, the existence of unlabeled positives and limited training data. To address these challenges, we propose an optimized training approach, called RocketQA, to improving dense passage retrieval. We make three major technical contributions in RocketQA, namely cross-batch negatives, denoised hard negatives and data augmentation. The experiment results show that RocketQA significantly outperforms previous state-of-the-art models on both MSMARCO and Natural Questions. We also conduct extensive experiments to examine the effectiveness of the three strategies in RocketQA. Besides, we demonstrate that the performance of end-to-end QA can be improved based on our RocketQA retriever.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_467", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_467.flac", "type": "audio", "duration": 414.058}, {"from": "Assistant", "value": "Recent QA with logical reasoning questions requires passage-level relations among the sentences. However, current approaches still focus on sentence-level relations interacting among tokens. In this work, we explore aggregating passage-level clues for solving logical reasoning QA by using discourse-based information. We propose a discourse-aware graph network (DAGN) that reasons relying on the discourse structure of the texts. The model encodes discourse information as a graph with elementary discourse units (EDUs) and discourse relations, and learns the discourse-aware features via a graph network for downstream QA tasks. Experiments are conducted on two logical reasoning QA datasets, ReClor and LogiQA, and our proposed DAGN achieves competitive results. The source code is available athttps://github.com/Eleanor-H/DAGN.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_468", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_468.flac", "type": "audio", "duration": 402.216}, {"from": "Assistant", "value": "In open-domain question answering (QA), retrieve-and-read mechanism has the inherent benefit of interpretability and the easiness of adding, removing, or editing knowledge compared to the parametric approaches of closed-book QA models. However, it is also known to suffer from its large storage footprint due to its document corpus and index. Here, we discuss several orthogonal strategies to drastically reduce the footprint of a retrieve-and-read open-domain QA system by up to 160x. Our results indicate that retrieve-and-read can be a viable option even in a highly constrained serving environment such as edge devices, as we show that it can achieve better accuracy than a purely parametric model with comparable docker-level system size.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_469", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_469.flac", "type": "audio", "duration": 755.415}, {"from": "Assistant", "value": "Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive. We explore the possibility to train a well-performed multi-hop QA model without referencing any human-labeled multi-hop question-answer pairs, i.e., unsupervised multi-hop QA. We propose MQA-QG, an unsupervised framework that can generate human-like multi-hop training data from both homogeneous and heterogeneous data sources. MQA-QG generates questions by first selecting/generating relevant information from each data source and then integrating the multiple information to form a multi-hop question. Using only generated training data, we can train a competent multi-hop QA which achieves 61% and 83% of the supervised learning performance for the HybridQA and the HotpotQA dataset, respectively. We also show that pretraining the QA system with the generated data would greatly reduce the demand for human-annotated training data. Our codes are publicly available athttps://github.com/teacherpeterpan/Unsupervised-Multi-hop-QA.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_470", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_470.flac", "type": "audio", "duration": 412.897}, {"from": "Assistant", "value": "Neural-based summarization models suffer from the length limitation of text encoder. Long documents have to been truncated before they are sent to the model, which results in huge loss of summary-relevant contents. To address this issue, we propose the sliding selector network with dynamic memory for extractive summarization of long-form documents, which employs a sliding window to extract summary sentences segment by segment. Moreover, we adopt memory mechanism to preserve and update the history information dynamically, allowing the semantic flow across different windows. Experimental results on two large-scale datasets that consist of scientific papers demonstrate that our model substantially outperforms previous state-of-the-art models. Besides, we perform qualitative and quantitative investigations on how our model works and where the performance gain comes from.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_471", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_471.flac", "type": "audio", "duration": 689.052}, {"from": "Assistant", "value": "State-of-the-art abstractive summarization models generally rely on extensive labeled data, which lowers their generalization ability on domains where such data are not available. In this paper, we present a study of domain adaptation for the abstractive summarization task across six diverse target domains in a low-resource setting. Specifically, we investigate the second phase of pre-training on large-scale generative models under three different settings: 1) source domain pre-training; 2) domain-adaptive pre-training; and 3) task-adaptive pre-training. Experiments show that the effectiveness of pre-training is correlated with the similarity between the pre-training data and the target domain task. Moreover, we find that continuing pre-training could lead to the pre-trained model’s catastrophic forgetting, and a learning method with less forgetting can alleviate this issue. Furthermore, results illustrate that a huge gap still exists between the low-resource and high-resource settings, which highlights the need for more advanced domain adaptation methods for the abstractive summarization task.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_472", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_472.flac", "type": "audio", "duration": 751.125}, {"from": "Assistant", "value": "Meetings are a key component of human collaboration. As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed. However, it is hard to create a single short summary that covers all the content of a long meeting involving multiple people and topics. In order to satisfy the needs of different types of users, we define a new query-based multi-domain meeting summarization task, where models have to select and summarize relevant spans of meetings in response to a query, and we introduce QMSum, a new benchmark for this task. QMSum consists of 1,808 query-summary pairs over 232 meetings in multiple domains. Besides, we investigate a locate-then-summarize method and evaluate a set of strong summarization baselines on the task. Experimental results and manual analysis reveal that QMSum presents significant challenges in long meeting summarization for future research. Dataset is available athttps://github.com/Yale-LILY/QMSum.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_473", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_473.flac", "type": "audio", "duration": 407.812}, {"from": "Assistant", "value": "Multimodal summarization becomes increasingly significant as it is the basis for question answering, Web search, and many other downstream tasks. However, its learning materials have been lacking a holistic organization by integrating resources from various modalities, thereby lagging behind the research progress of this field. In this study, we release a full-scale multimodal dataset comprehensively gathering documents, summaries, images, captions, videos, audios, transcripts, and titles in English from CNN and Daily Mail. To our best knowledge, this is the first collection that spans all modalities and nearly comprises all types of materials available in this community. In addition, we devise a baseline model based on the novel dataset, which employs a newly proposed Jump-Attention mechanism based on transcripts. The experimental results validate the important assistance role of the external information for multimodal summarization.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_474", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_474.flac", "type": "audio", "duration": 390.049}, {"from": "Assistant", "value": "This paper introduces MediaSum, a large-scale media interview dataset consisting of 463.6K transcripts with abstractive summaries. To create this dataset, we collect interview transcripts from NPR and CNN and employ the overview and topic descriptions as summaries. Compared with existing public corpora for dialogue summarization, our dataset is an order of magnitude larger and contains complex multi-party conversations from multiple domains. We conduct statistical analysis to demonstrate the unique positional bias exhibited in the transcripts of televised and radioed interviews. We also show that MediaSum can be used in transfer learning to improve a model’s performance on other dialogue summarization tasks.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_475", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_475.flac", "type": "audio", "duration": 417.054}, {"from": "Assistant", "value": "Despite significant progress in neural abstractive summarization, recent studies have shown that the current models are prone to generating summaries that are unfaithful to the original context. To address the issue, we study contrast candidate generation and selection as a model-agnostic post-processing technique to correct the extrinsic hallucinations (i.e. information not present in the source text) in unfaithful summaries. We learn a discriminative correction model by generating alternative candidate summaries where named entities and quantities in the generated summary are replaced with ones with compatible semantic types from the source document. This model is then used to select the best candidate as the final output summary. Our experiments and analysis across a number of neural summarization systems show that our proposed method is effective in identifying and correcting extrinsic hallucinations. We analyze the typical hallucination phenomenon by different types of neural summarization systems, in hope to provide insights for future work on the direction.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_476", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_476.flac", "type": "audio", "duration": 411.063}, {"from": "Assistant", "value": "How to generate summaries of different styles without requiring corpora in the target styles, or training separate models? We present two novel methods that can be deployed during summary decoding on any pre-trained Transformer-based summarization model. (1) Decoder state adjustment instantly modifies decoder final states with externally trained style scorers, to iteratively refine the output against a target style. (2) Word unit prediction constrains the word usage to impose strong lexical control during generation. In experiments of summarizing with simplicity control, automatic evaluation and human judges both find our models producing outputs in simpler languages while still informative. We also generate news headlines with various ideological leanings, which can be distinguished by humans with a reasonable probability.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-main_477", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-main_477.flac", "type": "audio", "duration": 723.766}, {"from": "Assistant", "value": "Adversarial Examples (AEs) generated by perturbingining examples are useful in improving the robustness of Deep Learning (DL) based models. Most prior works generate AEs that are either unconscionable due to lexical errors or semantically and functionally deviant from original examples. In this paper, we present ReinforceBug, a reinforcement learning framework, that learns a policy that is transferable on unseen datasets and generates utility-preserving and transferable (on other models) AEs. Our experiments show that ReinforceBug is on average 10% more successful as compared to the state-of the-art attack TextFooler. Moreover, the target models have on average 73.64% confidence in wrong prediction, the generated AEs preserve the functional equivalence and semantic similarity (83.38%) to their original counterparts, and are transferable on other models with an average success rate of 46%", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-demos_1", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-demos_1.flac", "type": "audio", "duration": 560.299}, {"from": "Assistant", "value": "We present the first multi-task learning model – named PhoNLP – for joint Vietnamese part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing. Experiments on Vietnamese benchmark datasets show that PhoNLP produces state-of-the-art results, outperforming a single-task learning approach that fine-tunes the pre-trained Vietnamese language model PhoBERT (Nguyen and Nguyen, 2020) for each task independently. We publicly release PhoNLP as an open-source toolkit under the Apache License 2.0. Although we specify PhoNLP for Vietnamese, our PhoNLP training and evaluation command scripts in fact can directly work for other languages that have a pre-trained BERT-based language model and gold annotated corpora available for the three tasks of POS tagging, NER and dependency parsing. We hope that PhoNLP can serve as a strong baseline and useful toolkit for future NLP research and applications to not only Vietnamese but also the other languages. Our PhoNLP is available athttps://github.com/VinAIResearch/PhoNLP", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-demos_2", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-demos_2.flac", "type": "audio", "duration": 321.813}, {"from": "Assistant", "value": "We describe Machine-Aided Script Curator (MASC), a system for human-machine collaborative script authoring. Scripts produced with MASC include (1) English descriptions of sub-events that comprise a larger, complex event; (2) event types for each of those events; (3) a record of entities expected to participate in multiple sub-events; and (4) temporal sequencing between the sub-events. MASC automates portions of the script creation process with suggestions for event types, links to Wikidata, and sub-events that may have been forgotten. We illustrate how these automations are useful to the script writer with a few case-study scripts.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-demos_3", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-demos_3.flac", "type": "audio", "duration": 669.056}, {"from": "Assistant", "value": "We present NAMER, an open-domain Chinese knowledge base question answering system based on a novel node-based framework that better grasps the structural mapping between questions and KB queries by aligning the nodes in a query with their corresponding mentions in question. Equipped with techniques including data augmentation and multitasking, we show that the proposed framework outperforms the previous SoTA on CCKS CKBQA dataset. Moreover, we develop a novel data annotation strategy that facilitates the node-to-mention alignment, a dataset (https://github.com/ridiculouz/CKBQA) with such strategy is also published to promote further research. An online demo of NAMER (http://kbqademo.gstore.cn) is provided to visualize our framework and supply extra information for users, a video illustration (https://youtu.be/yetnVye_hg4) of NAMER is also available.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-demos_7", "conversations": [{"from": "User", "value": "Provide the abstract of this scientific conference presentation.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-demos_7.flac", "type": "audio", "duration": 679.021}, {"from": "Assistant", "value": "We present EventPlus, a temporal event understanding pipeline that integrates various state-of-the-art event understanding components including event trigger and type detection, event argument detection, event duration and temporal relation extraction. Event information, especially event temporal knowledge, is a type of common sense knowledge that helps people understand how stories evolve and provides predictive hints for future events. EventPlus as the first comprehensive temporal event understanding pipeline provides a convenient tool for users to quickly obtain annotations about events and their temporal information for any user-provided document. Furthermore, we show EventPlus can be easily adapted to other domains (e.g., biomedical domain). We make EventPlus publicly available to facilitate event-related information extraction and downstream applications.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-demos_8", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-demos_8.flac", "type": "audio", "duration": 673.856}, {"from": "Assistant", "value": "To combat COVID-19, both clinicians and scientists need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract fine-grained multimedia knowledge elements (entities, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence. All of the data, KGs, reports.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-demos_10", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-demos_10.flac", "type": "audio", "duration": 563.061}, {"from": "Assistant", "value": "Automated fact-checking on a large-scale is a challenging task that has not been studied systematically until recently. Large noisy document collections like the web or news articles make the task more difficult. We describe a three-stage automated fact-checking system, named Quin+, using evidence retrieval and selection methods. We demonstrate that using dense passage representations leads to much higher evidence recall in a noisy setting. We also propose two sentence selection approaches, an embedding-based selection using a dense retrieval model, and a sequence labeling approach for context-aware selection. Quin+ is able to verify open-domain claims using results from web search engines.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-demos_11", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-demos_11.flac", "type": "audio", "duration": 469.056}, {"from": "Assistant", "value": "We present an interactive Plotting Agent, a system that enables users to directly manipulate plots using natural language instructions within an interactive programming environment. The Plotting Agent maps language to plot updates. We formulate this problem as a slot-based task-oriented dialog problem, which we tackle with a sequence-to-sequence model. This plotting model while accurate in most cases, still makes errors, therefore, the system allows a feedback mode, wherein the user is presented with a top-k list of plots, among which the user can pick the desired one. From this kind of feedback, we can then, in principle, continuously learn and improve the system. Given that plotting is widely used across data-driven fields, we believe our demonstration will be of interest to both practitioners such as data scientists broadly defined, and researchers interested in natural language interfaces.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-demos_12", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-demos_12.flac", "type": "audio", "duration": 696.043}, {"from": "Assistant", "value": "ActiveAnno is an annotation tool focused on document-level annotation tasks developed both for industry and research settings. It is designed to be a general-purpose tool with a wide variety of use cases. It features a modern and responsive web UI for creating annotation projects, conducting annotations, adjudicating disagreements, and analyzing annotation results. ActiveAnno embeds a highly configurable and interactive user interface. The tool also integrates a RESTful API that enables integration into other software systems, including an API for machine learning integration. ActiveAnno is built with extensible design and easy deployment in mind, all to enable users to perform annotation tasks with high efficiency and high-quality annotation results.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-demos_13", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-demos_13.flac", "type": "audio", "duration": 550.059}, {"from": "Assistant", "value": "Embeddings of words and concepts capture syntactic and semantic regularities of language; however, they have seen limited use as tools to study characteristics of different corpora and how they relate to one another. We introduce TextEssence, an interactive system designed to enable comparative analysis of corpora using embeddings. TextEssence includes visual, neighbor-based, and similarity-based modes of embedding analysis in a lightweight, web-based interface. We further propose a new measure of embedding confidence based on nearest neighborhood overlap, to assist in identifying high-quality embeddings for corpus analysis. A case study on COVID-19 scientific literature illustrates the utility of the system. TextEssence can be found athttps://textessence.github.io.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-demos_14", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-demos_14.flac", "type": "audio", "duration": 704.896}, {"from": "Assistant", "value": "We present a tool that provides automated feedback to students studying Spanish writing. The feedback is given for four categories: topic development, coherence, writing conventions, and essay organization. The tool is made freely available via a Google Docs add-on. A small user study with third-level students in Mexico shows that students found the tool generally helpful and that most of them plan to continue using it as they work to improve their writing skills.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-demos_15", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-demos_15.flac", "type": "audio", "duration": 531.648}, {"from": "Assistant", "value": "Traditional goal-oriented dialogue systems rely on various components such as natural language understanding, dialogue state tracking, policy learning and response generation. Training each component requires annotations which are hard to obtain for every new domain, limiting scalability of such systems. Similarly, rule-based dialogue systems require extensive writing and maintenance of rules and do not scale either. End-to-End dialogue systems, on the other hand, do not require module-specific annotations but need a large amount of data for training. To overcome these problems, in this demo, we present Alexa Conversations, a new approach for building goal-oriented dialogue systems that is scalable, extensible as well as data efficient. The components of this system are trained in a data-driven manner, but instead of collecting annotated conversations for training, we generate them using a novel dialogue simulator based on a few seed dialogues and specifications of APIs and entities provided by the developer. Our approach provides out-of-the-box support for natural conversational phenomenon like entity sharing across turns or users changing their mind during conversation without requiring developers to provide any such dialogue flows. We exemplify our approach using a simple pizza ordering task and showcase its value in reducing the developer burden for creating a robust experience. Finally, we evaluate our system using a typical movie ticket booking task integrated with live APIs and show that the dialogue simulator is an essential component of the system that leads to over 50% improvement in turn-level action signature prediction accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-demos_16", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-demos_16.flac", "type": "audio", "duration": 667.133}, {"from": "Assistant", "value": "We present a new information extraction system that can automatically construct temporal event graphs from a collection of news documents from multiple sources, multiple languages (English and Spanish for our experiment), and multiple data modalities (speech, text, image and video). The system advances state-of-the-art from two aspects: (1) extending from sentence-level event extraction to cross-document cross-lingual cross-media event extraction, coreference resolution and temporal event tracking; (2) using human curated event schema library to match and enhance the extraction output. We have made the dockerlized system publicly available for research purpose at GitHub, with a demo video.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-demos_17", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-demos_17.flac", "type": "audio", "duration": 756.042}, {"from": "Assistant", "value": "The interest in offensive content identification in social media has grown substantially in recent years. Previous work has dealt mostly with post level annotations. However, identifying offensive spans is useful in many ways. To help coping with this important challenge, we present MUDES, a multilingual system to detect offensive spans in texts. MUDES features pre-trained models, a Python API for developers, and a user-friendly web-based interface. A detailed description of MUDES’ components is presented in this paper.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_1", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_1.flac", "type": "audio", "duration": 546.18}, {"from": "Assistant", "value": "In most of neural machine translation distillation or stealing scenarios, the highest-scoring hypothesis of the target model (teacher) is used to train a new model (student). If reference translations are also available, then better hypotheses (with respect to the references) can be oversampled and poor hypotheses either removed or undersampled. This paper explores the sampling method landscape (pruning, hypothesis oversampling and undersampling, deduplication and their combination) with English to Czech and English to German MT models using standard MT evaluation metrics. We show that careful oversampling and combination with the original data leads to better performance when compared to training only on the original or synthesized data or their direct combination.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_2", "conversations": [{"from": "User", "value": "Summarize the key findings presented in this scientific talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_2.flac", "type": "audio", "duration": 565.058}, {"from": "Assistant", "value": "Automatic Text Summarization (ATS) is the task of generating concise and fluent summaries from one or more documents. In this paper, we present IceSum, the first Icelandic corpus annotated with human-generated summaries. IceSum consists of 1,000 online news articles and their extractive summaries. We train and evaluate several neural network-based models on this dataset, comparing them against a selection of baseline methods. We find that an encoder-decoder model with a sequence-to-sequence based extractor obtains the best results, outperforming all baseline methods. Furthermore, we evaluate how the size of the training corpus affects the quality of the generated summaries. We release the corpus and the models with an open license.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_3", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_3.flac", "type": "audio", "duration": 663.046}, {"from": "Assistant", "value": "Negation is a linguistic universal that poses difficulties for cognitive and computational processing. Despite many advances in text analytics, negation resolution remains an acute and continuously researched question in Natural Language Processing. Reliable negation parsing affects results in biomedical text mining, sentiment analysis, machine translation, and many other fields. The availability of multilingual pre-trained general representation models makes it possible to experiment with negation detection in languages that lack annotated data. In this work we test the performance of two state-of-the-art contextual representation models, Multilingual BERT and XLM-RoBERTa. We resolve negation scope by conducting zero-shot transfer between English, Spanish, French, and Russian. Our best result amounts to a token-level F1-score of 86.86% between Spanish and Russian. We correlate these results with a linguistic negation typology and lexical capacity of the models.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_4", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_4.flac", "type": "audio", "duration": 719.377}, {"from": "Assistant", "value": "Neural networks are the state-of-the-art method of machine learning for many problems in NLP. Their success in machine translation and other NLP tasks is phenomenal, but their interpretability is challenging. We want to find out how neural networks represent meaning. In order to do this, we propose to examine the distribution of meaning in the vector space representation of words in neural networks trained for NLP tasks. Furthermore, we propose to consider various theories of meaning in the philosophy of language and to find a methodology that would enable us to connect these areas.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_5", "conversations": [{"from": "User", "value": "Briefly describe the contributions of the research paper given in the clip.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_5.flac", "type": "audio", "duration": 719.772}, {"from": "Assistant", "value": "In this thesis proposal, we explore the application of event extraction to literary texts. Considering the lengths of literary documents modeling events in different granularities may be more adequate to extract meaningful information, as individual elements contribute little to the overall semantics. We adapt the concept of schemas as sequences of events all describing a single process, connected through shared participants extending it to for multiple schemas in a document. Segmentation of event sequences into schemas is approached by modeling event sequences, on such task as the narrative cloze task, the prediction of missing events in sequences. We propose building on sequences of event embeddings to form schema embeddings, thereby summarizing sections of documents using a single representation. This approach will allow for the comparisons of different sections of documents and entire literary works. Literature is a challenging domain based on its variety of genres, yet the representation of literary content has received relatively little attention.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_6", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_6.flac", "type": "audio", "duration": 491.056}, {"from": "Assistant", "value": "Text simplification is a growing field with many potential useful applications. Training text simplification algorithms generally requires a lot of annotated data, however there are not many corpora suitable for this task. We propose a new unsupervised method for aligning text based on Doc2Vec embeddings and a new alignment algorithm, capable of aligning texts at different levels. Initial evaluation shows promising results for the new approach. We used the newly developed approach to create a new monolingual parallel corpus composed of the works of English early modern philosophers and their corresponding simplified versions.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_7", "conversations": [{"from": "User", "value": "What are the main results discussed in this paper presentation?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_7.flac", "type": "audio", "duration": 447.054}, {"from": "Assistant", "value": "We present a simple method for extending transformers to source-side trees. We define a number of masks that limit self-attention based on relationships among tree nodes, and we allow each attention head to learn which mask or masks to use. On translation from English to various low-resource languages, and translation in both directions between English and German, our method always improves over simple linearization of the source-side parse tree and almost always improves over a sequence-to-sequence baseline, by up to +2.1 BLEU.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_8", "conversations": [{"from": "User", "value": "Summarize the research presented in this talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_8.flac", "type": "audio", "duration": 715.732}, {"from": "Assistant", "value": "One of the ways blind people understand their surroundings is by clicking images and relying on descriptions generated by image-captioning systems. Current work on captioning images for the visually impaired do not use the textual data present in the image when generating captions. This problem is critical as many visual scenes contain text, and 21% of the questions asked by blind people about the images they click pertain to the text present in them. In this work, we propose altering AoANet, a state-of-the-art image-captioning system, to leverage text detected in the image as an input feature. In addition, we use a pointer-generator network to copy detected text to the caption when tokens need to be reproduced accurately. Our model outperforms AoANet on the benchmark dataset VizWiz, giving a 35% and 16.2% performance improvement on CIDEr and SPICE scores, respectively.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_9", "conversations": [{"from": "User", "value": "Write the abstract for this academic conference talk.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_9.flac", "type": "audio", "duration": 707.814}, {"from": "Assistant", "value": "Open-domain question answering aims at locating the answers to user-generated questions in massive collections of documents. Retriever-readers and knowledge graph approaches are two big families of solutions to this task. A retriever-reader first applies information retrieval techniques to locate a few passages that are likely to be relevant, and then feeds the retrieved text to a neural network reader to extract the answer. Alternatively, knowledge graphs can be constructed and queried to answer users’ questions. We propose an algorithm with a novel reader-retriever design that differs from both families. Our reader-retriever first uses an offline reader to read the corpus and generate collections of all answerable questions associated with their answers, and then uses an online retriever to respond to user queries by searching the pre-constructed question spaces for answers that are most likely to be asked in the given way. We further combine one retriever-reader and two reader-retrievers into a hybrid model called R6 for the best performance. Experiments with two large-scale public datasets show that R6 achieves state-of-the-art accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_10", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_10.flac", "type": "audio", "duration": 689.052}, {"from": "Assistant", "value": "Meeting minutes record any subject matter discussed, decisions reached and actions taken at the meeting. The importance of automatic minuting cannot be overstated. In this paper, we present a sliding window approach to automatic generation of meeting minutes. It aims at addressing issues pertaining to the nature of spoken text, including the lengthy transcript and lack of document structure, which make it difficult to identify salient content to be included in meeting minutes. Our approach combines a sliding-window approach and a neural abstractive summarizer to navigate through the raw transcript to find salient content. The approach is evaluated on transcripts of natural meeting conversations, where we compare results obtained for human transcripts and two versions of automatic transcripts and discuss how and to what extent the summarizer succeeds at capturing salient content.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_11", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_11.flac", "type": "audio", "duration": 462.681}, {"from": "Assistant", "value": "We propose semantic visualization as a linguistic visual analytic method. It can enable exploration and discovery over large datasets of complex networks by exploiting the semantics of the relations in them. This involves extracting information, applying parameter reduction operations, building hierarchical data representation and designing visualization. We also present the accompanying COVID-SemViz a searchable and interactive visualization system for knowledge exploration of COVID-19 data to demonstrate the application of our proposed method. In the user studies, users found that semantic visualization-powered COVID-SemViz is helpful in terms of finding relevant information and discovering unknown associations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_12", "conversations": [{"from": "User", "value": "Provide a concise summary of the research presentation provided in the audio.", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_12.flac", "type": "audio", "duration": 386.055}, {"from": "Assistant", "value": "State-of-the-art transformer models have achieved robust performance on a variety of NLP tasks. Many of these approaches have employed domain agnostic pre-training tasks to train models that yield highly generalized sentence representations that can be fine-tuned for specific downstream tasks. We propose refining a pre-trained NLP model using the objective of detecting shuffled tokens. We use a sequential approach by starting with the pre-trained RoBERTa model and training it using our approach. Applying random shuffling strategy on the word-level, we found that our approach enables the RoBERTa model achieve better performance on 4 out of 7 GLUE tasks. Our results indicate that learning to detect shuffled tokens is a promising approach to learn more coherent sentence representations.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}
{"id": "2021_naacl-srw_13", "conversations": [{"from": "User", "value": "What research problem does this paper address in the audio?", "type": "text"}, {"from": "User", "value": "audio/speech/nutshell_en/2021_naacl-srw_13.flac", "type": "audio", "duration": 720.608}, {"from": "Assistant", "value": "In this work, we explore generating morphologically enhanced word embeddings for Tamil, a highly agglutinative South Indian language with rich morphology that remains low-resource with regards to NLP tasks. We present here the first-ever word analogy dataset for Tamil, consisting of 4499 hand-curated word tetrads across 10 semantic and 13 morphological relation types. Using a rules-based segmenter to capture morphology as well as meta-embedding techniques, we train meta-embeddings that outperform existing baselines by 16% on our analogy task and appear to mitigate a previously observed trade-off between semantic and morphological accuracy.", "type": "text"}], "language": "en", "extra": "{\"dataset_name\": \"nutshell\", \"split\": \"train\"}"}