To address this problem, in this paper, we propose a span attention module to enhance chartbased neural constituency parsing by incorporating appropriate n-grams into span representations.

for each text span we extract all its substrings that appear in an n-gram lexicon; the span attention uses the normal attention mechanism to weight them with respect to their contributions to predict the constituency label of the span.


Second, compared with span attention without the category mechanism, in which n-grams are weighted together, models with categorical span attention perform better on both F1 and complete match scores with a relatively small increase of parameter numbers (around 1M ).

for the complete match scores, the span attention with normal attentions does not outperform the baseline models in some cases, whereas the categorical span attention mechanism does in all cases.


In this paper, we proposed span attention to integrate n-gram into span representations to enhance chart-based neural constituency parsing.

for each text span in an input sentence, we firstly extracted n-grams in that span from an ngram lexicon, and then fed them into the span attention to weight them according to their contribution to the parsing process.


The interpreter is a parameter-free function that executes the editing action produced by the programmer.

the interpreter first checks if the action is the termination action.


As a stepping stone in this direction, it is important to study the relationship between spoken language (which also includes acoustic information) and free form gestures (which go beyond just a pre-defined dictionary of gesture animations).

how can we automatically generate human body pose (gestures) from language and acoustic inputs?


Hence, when learning these models, we need to not only be accurate for gesture generation, but also handle coverage of both linguistic and visual distributions (Pelachaud, 2009;Kucherenko et al., 2019).

we need models that can balance precision and coverage.


It offers data for 25 speakers with diverse gestures and linguistic content (Ahuja et al., 2020;Ginosar et al., 2019).

it contains 15 talk show hosts, 5 lecturers, 3 YouTubers, and 2 televangelists, providing a total of 251 hours of video clips, with a mean of 10.7 seconds and a standard deviation of 13.5 seconds per clip.


We see a significantly larger preference for our model as compared to S2G and Gesticulator for all four criteria.

expressivity sees the largest jump, indicating improved coverage in the generated gestures.


This shows that leveraging translation loss to expose various segmentations is more effective than constraining the NMT models to observe limited sets of segmentations.

aDVSR improves 1.6 BLEU over SR and 3.2 BLEU over BaSE in the Czech to English dataset.


We adopt the CNN which has been very successful in text classification tasks (Kim, 2014;Choi et al., 2019).

a carefully designed CNN outperforms other architectures in large-scale  (Kim et al., 2019) with relatively small number of parameters.


We apply the multi-task learning by spotting OOC words and text classification tasks as two, related tasks.

we add the OOC words detection objective to the model that is trained jointly with the text classification for pretraining.


In this paper, we have developed a novel CNNbased pretraining framework to handle large-scale text classification.

we pretrain the proposed CNN-based model, which simultaneously learns both the OOC words detection and the text classification task on unlabeled corpora.


In practice, regression suffers from annotation inconsistencies.

the human scores for some documents might be on average higher than for other documents, which easily confuses the regression.


Similarly, averaging different domains also results in significant improvements.

averaging several non-news domains gives better generalization to the news domain.


This overconfidence does not directly affect classification performance, but it degrades the reliability of the model.

the output probability of the over-confident model does not indicate how uncertain the input example is, even if its classification performance is high.


As mentioned earlier, BERT and BioBERT could understand the deep contextual information of the text through pre-training with huge corpora.

biobERT suggested that the pre-training of bERT in a specific domain may contribute to performance improvement in a downstream task of the domain.


As mentioned earlier, the output distribution of the uncalibrated model is biased towards 0 and 1.

the output of the uncalibrated model has a low entropy value.


Although modern abstractive summarization systems generate relatively fluent summaries, recent work has called attention to the problem they have with factual inconsistency (Kryscinski et al., 2019a).

they produce summaries that contain hallucinated facts that are not supported by the source text.


While our results are still preliminary, they provide some evidence that subjects consider the upranked summaries to be more faithful.

of the 19 trials (other than the three catch trials) where all three subjects agreed on which summary was more faithful, in 12 trials, it was the re-ranked summary (as in Table 8, Article 49), while in only 7 was it the original summary (as in Table 8, Article 4).


Graph convolutional networks (GCNs) work on a graph structure and compute representations for the graph nodes by looking at the node's neighbourhood.

let G = (V, E) denote a directed graph, where V is the set of nodes (let |V | = i) and E is the set of edges.


Event Detection (ED) is an important information extraction task that seeks to recognize events of specific types from given text.

each event in a sentence is marked by a word or phrase called "event trigger".


To model the above ideas, in this paper, we propose a novel neural architecture named Edge-Enhanced Graph Convolutional Networks (EE-GCN), which explicitly takes advantage of the typed dependency labels with dynamic representations.

ee-GCN transforms a sentence to a graph by treating words and dependency labels as nodes and typed edges, respectively.


A generated sentence is verifiable if it can be corroborated or disproved by Wikipedia, and we find that the verifiability of generated text strongly depends on the decoding strategy.

we discover a tradeoff between factuality (i.e., the ability of generating Wikipedia corroborated text) and repetitiveness.


In contrast to both BERT and RoBERTa, it makes heavy use of parameter sharing.

aLBERT ties the weight matrices across all hidden layers effectively applying the same non-linear transformation on every hidden layer.


Instead of sequence labeling, Sequence-toSequence (Seq2Seq) models (Cho et al., 2014) are able to extract an entity multiple times, thus multiple relations can be assigned to one entity, which solves the problem naturally (Zeng et al., 2018, 2019a,b; Nayak and Ng, 2019).

all existing Seq2Seq models pre-define a sequential order for the target triplets, e.g. triplet alphabetical order, and then decode the triplet sequence according to the order autoregressively, which means the current triplet prediction relies on the previous output.


Mono-lingual BERT models (Devlin et al., 2019) have also proven effective in document retrieval (Dai and Callan, 2019;MacAvaney et al., 2019;Li et al., 2020).

In particular, Akkalyoncu Yilmaz et al. (2019) demonstrated that BERT models fine-tuned with passage-level relevance data can transfer across domains: surprisingly, fine-tuning on social media data is effective for relevance classification on newswire documents without any additional modifications.


For the IWSLT Portuguese to English (Pt-En) dataset, we replicate the setup of Tan et al. (2019) for training individual models.

the dataset contains about 167k training pairs.


For the IWSLT Pt-En, we use Transformer Small configuration.

the model hidden-size d model is set to 256, the feedforward hidden size d ff is set to 1024 and the number of layers for the encoder and the decoder was set to 2.


Recently, Chen et al. (2017) proposed multicriteria Chinese word segmentation (MCCWS) to effectively utilize the heterogeneous resources with different segmentation criteria

they regard each segmentation criterion as a single  task under the framework of multi-task learning, where a shared layer is used to extract the criteriainvariant features, and a private layer is used to extract the criteria-specific features.


Recently neural network-based models have demonstrated large gains in this space (Hu et al., 2014;Pang et al., 2016).

the Transformer / BERT family of models (Devlin et al., 2018; Lan et al., 2019;Clark et al., 2020) have set a new bar for these semantic text matching problems.


DiPair aims to combine the best of both worlds: Like dual-encoder models, it leverages common pre-computation, while at the same time modeling the text jointly -with cross-attention -using multiple contextual embeddings for each text.

we extract a small fraction of the output token embeddings from each text, and then jointly model this smaller "sequence" using a transformer head (we use the term head to refer to the component that consumes the outputs of a dual-encoder model, see Figure 2).


Inspired by the idea that POS sequences often reflect the syntactic structure of a language, we propose CURSOR (Cross lingUal paRSing by wOrd Reordering) to overcome the word order difference issue in crosslingual transfer.

we assume we have a treebank in the source language and annotated POS corpus in the target language 1 .


Figure 2b shows that after reordering S, its similarity to T increases, and the corresponding cross-lingual parsing performance will improve.

target languages with greater differences to the source one in their word order will benefit more from our reordering method.


All classifiers had significantly reduced F1scores on SEED, due to major drops in threat recall.

bERT was degenerate, assigning all documents to the majority class in all 30 repetitions.


The RL model is the one trained on related languages from the same family as the TL, and DL is the model that is trained on languages that are more distant from the TL.

given a TL, the RL model refers to the language family that includes the TL, and the DL model refers to the other family that does not include the TL.


Graph2Graph Transformer extends the architecture of the Transformer to accept any arbitrary graph as input.

we input the dependency tree as its set of dependency relations.


In this work, the graph edges are labelled dependency relations, which are predicted as part of the actions of a transition-based dependency parser.

the Relation classifier uses the output embeddings of the top two elements on the stack and predicts the label of their dependency relation, conditioned on its direction.


As the loss function of these two models, we use weighted binary cross entropy.

given high imbalance with many more irrelevant instances than positive ones, we put different weights on the classes in computing the loss according to the target distributions (proportions of negative examples are 87% for REL and 93% for EXT).


The difference between the two attention mechanism is that the supervised selfattention recognizes word-level prediction scores of all named entities while the task-specific attention recognizes word-level prediction scores w.r.t only selective named entities (one which correspond to the ADE sentence and ignores other named entities).

the weights of the supervised self-attention and task-specific attention are calculated as follows: Word-level prediction w.r.t the task-specific named entity (i.e.,) ADE: Task-specific Attention Weight, normalized to sum up to 1 over all values in the sentence, is: Supervised Self-Attention Weight, normalized to sum up to 1 over all values in the sentence: shows the examples of the supervised selfattention and task-specific attention distributions generated from our attention layer.


Extension to Multiple Target Language Note that though in this work we focus on HRL and LRL pairs, one can easily extend the framework to multiple (> 2) target languages.

the only language dependent component of DecSDE is the matrices W L i , while the rest of DecSDE parameters as well as transformer encoder-decoder parameters are shared.


In this paper, we address these shortcomings, and propose an accurate, efficient, polyglot model for Neural RDG parsing.

our contributions are as follows: Grammar: In practice, RDGs extracted from training graphs can be large and sparse.


CLTS transfers both translated seed words and their learned weights to initialize a "weak" classifier in L T that considers translated seed words and their relative importance for the target task.

cLTS first translates the B seed words in G S into a set G T with seed words in L T .


Instead of using a matrix, holistically, we can quantify the cross-dataset generalization ability of each summarization system using a scalar.

we propose two views to characterize the crossdataset generalization.


Moreover, the proxy-based annotation mechanisms used to label large social media data sets with mental health status invite the introduction of selfdisclosure bias into the modeling task (Amir et al., 2019).

labels sourced from populations of individuals who self-disclose certain attributes may contain activity-level and thematic biases that cause poor generalization in larger populations (Lippincott and Carrell, 2018).


To measure this effect, we examine differences in the distribution of subreddits that individuals in the depression group of the Topic-Restricted Text data post in relative to individuals in the control group.

we fit a logistic regression model mapping the subreddit distribution of individuals' posts to their mental health status after applying each subreddit filter list (e.g. RSDD, SMHD, Ours).


How far do MLMs look when leveraging context?

what are their effective context window sizes?


Our second approach estimates the impact of an input subword s ij to P (w t |X \t ) by using derivatives.

we adapt the IMPACT score proposed in Falenska and Kuhn (2019) to our questions.


To further compare the behaviors of BERT and BiLSTM when identifying essential context, we count the occurrence of dependency paths based on the disjoint essential words.

given an input sentence, we only count the dependency paths of essential words which are unique to each model, e.g., words essential to BERT but not essential to BiLSTM.


This implies that sometimes words of common dependency paths can be identified by BERT as essential while BiL-STM fails to do so and sometimes it's another way around.

there is a room to make models to be more consistently aware of syntactic structures of an input.


FVN extends the Vector-Quantised VAE (VQ-VAE) (van den Oord et al., 2017), which is non-conditional, to allow conditioning on attributes (content and style).

fVN: (1) models two disjoint codebooks for content and style respectively that memorize input text variations; (2) further controls the conveyance of attributes by using content and style specific encoders and decoders; (3) computes disjoint latent space distributions that are conditional on the content and style respectively, which allows to sample latent representations in a focused way at prediction time


We found that our FVN model performs better than the ground truth on agreeable and conscientiousness, while the ground truth is better for the rest of the three personalities.

53% and 67% of the time, the crowdworkers judge the agreeable and conscientious sentences generated by our model to be better than the ground truth sentences.


In this paper, we address the above issues by proposing a novel supervised multi-modal domain adaptation method for VQA to learn joint feature embeddings across different domains and modalities.

we align the data distributions of the source and target domains by considering those modalities both jointly and separately.


If both triggers were deemed valid, then the annotators evaluated whether or not the candidate precondition event was an actual precondition for the target event.

they check if the candidate event is necessary for the target event to happen.


Rules and guidelines were put into place to help ensure that instructions written by the Stage 1 workers were high quality and written with as few errors as possible.

the guidelines serve to prevent the workers from using other elements of the UI or tools we provided, such as the blue navigation line or guiding arrow (see Figure 15) and other elements that were not part of the true environment in  their instructions.


Our intuition is that by employing a generative adversarial model to produce challenging negative code snippets during training, the code retrieval model will be strengthened to distinguish between positive and negative q, c pairs.

we adapt a generative adversarial sampling technique , whose effectiveness has been shown in a wide range of uni-modal text retrieval tasks.


We use Stack Exchange Data Explorer 3 to collect data for training and evaluating QD relevance prediction.

we collect the question pairs from posts that are manually labeled as duplicate by users, which are related by LinkTypeId=3.


In line with previous work on news SD (Vlachos and Riedel, 2014; Ferreira and Vlachos, 2016), in which data was labeled by professional journalists, we rely on domain experts for annotation.

we provided articles to eight economists 3 in batches and asked them to annotate no more than 100 articles per day 4 ; the annotation process lasted 4 months.


This label should be chosen if the article is supporting the theory that the merger is happening.

after reading the article the reader feels more confident that the two companies will merge.


This label should be chosen if the article is refuting the theory that the merger is happening.

after reading the article the reader feels less confident that the two companies will merge.


On the TAC-2009 generic summarization task in Table 3 our Sup-MMD + MKL model outperforms the state-of-theart ICSI model on both ROUGE-2 and ROUGE-SU4.

supMMD + MKL scores 12.33 in ROUGE-2 while the best ICsI variant scores 12.16 in ROUGE-2.


In this paper, we propose a sequence labeling based neural model to enhance NER by incorporating different types of syntactic information, which is conducted by attentive ensemble with key-value memory networks (KVMN) (Miller et al., 2016), syntax attention and the gate mechanism.

the KVMN is applied to encode the context features and their syntax information from different types, e.g., POS labels, syntactic constituents, or dependency relations; syntax attention is proposed to weight different types of such syntactic information, and the gate mechanism controls the contribution of the results from the context encoding and the syntax attention to the NER process.


For the text input, we use three types of embeddings for each language by default.

for English, we use Glove (100dimension)6 (Pennington et al., 2014), ELMo (Peters et al., 2018), and the BERT-cased large7 (Devlin et al., 2019) (the derived embeddings for each word); for Chinese, we use pre-trained character and bi-gram embeddings8 released by  released by Zhang and Yang (2018) (denoted as Giga), Tencent Embedding9 (Song et al., 2018b), and ZEN10 (Diao et al., 2019).


It is confirmed that different types of embedding do provide complement context information to enhance the understanding of input texts for NER.

although contextualized embeddings (i.e., ELMo, BERT, and ZEN) show significantly better performance than others (especially on Chinese), combining them with static embeddings still provide further improvement on the F 1 score of NER systems.


In this paper, we proposed a neural model following the sequence labeling paradigm to enhance NER through attentive ensemble of syntactic information.

the attentive ensemble consists of three components in a sequence: each type of syntactic information is encoded by key-value memory networks, different information types are then weighted in syntax attention, and the gate mechanism is finally applied to control the contribution of syntax attention outputs to NER for different contexts.


the full model with attentive ensemble of all syntactic information) on the test set of all three Chinese datasets.

either BERT or ZEN is used as one of the three types of embeddings (the others are Giga and Tencent Embedding).


Consequently, in this work, we propose to regulate the hidden vectors of the graph-based models for ABSA using the information from the aspect terms, thereby filtering the irrelevant information for the terms and customizing the representation vectors for ABSA.

we compute a gate vector for each layer of the graph-based model for ABSA leveraging the representation vectors of the aspect terms.


The second limitation of the current graph-based deep learning models is the failure to explicitly exploit the overall importance of the words in the sentences that can be estimated from the dependency trees for the ABSA problem.

a motivation of the graph-based models for ABSA is that the neighbor words of the aspect terms in the dependency trees would be more important for the sentiment of the terms than the other words in the sentence.


Consequently, we propose to inject the knowledge from these syntaxbased importance scores into the graph-based models for ABSA via the consistency with the modelbased importance scores.

using the representation vectors from the graph-based models, we compute a second score for each word in the sentences to reflect the model's perspective on the importance of the word for the sentiment of the aspect terms.


Our focus on contextual constraints is in part motivated by studies that use sentence contexts of varying constraint to study priming in humans.

schwanenflugel and LaCount (1988) found low-constraint contexts to show wider scope of facilitation in lexical decision tasks, as compared to high-constraint ones, which only showed facilitations for the best completions (highest cloze probability).


Thus, for the purposes of this study, we cast events as the premise and the relation targets as the defeasible hypotheses.

we extract a total of 24K event (premise) and relation target (hypothesis) pairs.


For example, a norm like "It is good to respect your parents" might be weakened in certain contexts (e.g., "Your parents are abusive and hurtful towards you") and strengthened in others (e.g., "Your parents want what's right for you").

we consider this set of norms of social behavior as hypotheses capable of being strengthened or weakened.


Elicitation as a method of text data collection has a number of known flaws.

(1) annotators may use label-dependent heuristics or strategies to produce sentences that introduce superficial correlations between text features and labels (Poliak et al., 2018;Gururangan et al., 2018;Tsuchiya, 2018); (2) elicitation may result in repeated responses of salient answers that are a small subset of all possible valid answers (McRae et al., 2005); and (3) elicited responses may contain implicit judgments or stereotypic associations about gender, race, and age, among others .


A GENERALIZATION is a statement that speaks of a socially normative behavior.

it is a generalizing statement about how we expect people to behave in society.


The norm of the vectors of the hyperbolic model are measured according to the hyperbolic distance d D (see Equation 1).

we take the hyperbolic distance from the origin to the point, thus the values are above one.


Our model achieves better accuracy than the three models described in Section 3.2, although the accuracy is lower than with human performance.

our model outperforms TSEL-REF and TSEL-REF-DIAL, which use additional learning, with learning only from standard training data. This result demonstrates the advantages and the high learning efficiency of our architecture


To address this problem, we study automated techniques to improve datasets for training and testing.

we focus on paraphrase identification task, which aims to determine whether two given sentences are semantically equivalent.


Moving from single labeled sentence pairs to a graph provides a better understanding of the sentence relations of the dataset, which can be exploited to infer additional edge labels.

since paraphrases are an equality relation, we can perform a transitive closure on the graph to infer additional labels.


Identifying such mention pairs, detected as actually being used to refer to the same event, can provide a strong signal for identifying these predicates as paraphrastic (vs. the quite noisy corpus-level signal of distributional similarity).

we utilize the Chirps paraphrase acquisition method and resource, which follows this approach as described next in some detail.


While the original Chirps method did not utilize the content of the linked article, we find it useful to retrieve more information about the event.

it might help mitigating errors in Chirps' argument matching mechanism, which relies on argument alignment considering only the text of the two tweets.


We consider the ranking evaluation as more informative, as we expect the ranking to reflect the number of contexts in which a pair of predicates may be coreferring.

predicate pairs that are coreferring in many contexts will be ranked higher than those that are coreferring in just a few contexts.


For our evaluation, we present the first multidomain, executable semantic parsing dataset in three languages and an additional locale for a single-domain dataset.

we extend ATIS (Dahl et al., 1994), pairing Chinese (ZH) utterances from Susanto and Lu (2017a) to SQL queries and create a parallel German (DE) humantranslation of the full dataset.


In this work, we aim at addressing the fewshot challenge and improving relation reasoning performance.

we propose a novel model called FIRE for few-shot multi-hop relation learning over KB.


Note that we do not assume a vocabulary.

we do not distinguish words from arbitrary strings made out of the alphabet.


Even with that, PBoS is able to achieve the best POS tagging accuracy in all but one language regardless of morphological types, OOV rates, and the number of training instances (Appendix Table 12).

pBoS improvement accuracy by greater than 0.1 for 9 languages.


Figure 1 depicts the general process of our method, where the target task needs to additionally recognize job titles, which are annotated as the Other (O) class in the source task.

for the job title type, an entity lexicon of the type is collected.


We apply the normal multi-label assignment mechanism for performing T t , instead of the prevalent BIO or BIOES mechanism.

the constituted words of a mention of the entity type e i are all classified to class i without distinction of their positions in the mention.


we use the trained classifier to perform label prediction for words of D t u .


For these methods, we combined their recognition result with that of SourceBERT to perform entity recognition.

for a query sentence, we first perform label inference using SourceBERT and then apply these methods to words being predicted to be the "O" class by SourceBERT to further identity mentions of the new entity types.


We are taking into account the average ranking to make such comparison clearer, with the assumption that all metrics have the same weight.

since there are three different evaluation metrics, i.e., STAcc, BLEU, and Perplexity, and seven different methods (the two proposed seq2seq and five from the literature), the methods are ranked from 1 to 7 in each metric, where 1 is the best and 7 is the worst.


The model is fine-tuned to produce the tokens "true" or "false" depending on whether the document is relevant or not to the query.

"true" and "false" are the target tokens (i.e., ground truth predictions in the sequence-to-sequence transformation).


Note that results from our T5 models have lower proportions of judged documents in the top-20 (Jdg@20) than BM25 and BM25+RM3.

our models are retrieving documents that have never been evaluated, for which we have no relevance labels.


Why is our approach more data-efficient than BERT?

why does T5 significantly outperform BERT when fine-tuned with far fewer training examples?


Embedded in that neural machinery is latent knowledge about semantics, linguistic relations, and lexical features that are necessary to generate fluent text.

t5 has access to an additional source of knowledge that BERt does not.


In contrast, in a low-data setting, T5 can "fall back" on pretrained neural machinery for generating fluent textual output.

the pretraining objective in T5 seems to transfer well to generating relevance labels.


As a whole, OTE-MTL surpasses all its variants.

oTE-MTL is slightly better than oTE-MTL-Inter, however, oTE-MTL exceeds other variants by large margins.


In doing so, we expect to alleviate issues brought by the unified tagging scheme.

we exploit sequence tagging strategies (Lample et al., 2016) for extraction of aspects and opinions, whilst taking advantage of a biaffine scorer (Dozat and Manning, 2017) to obtain word-level sentiment dependencies.


To alleviate the above problem, we propose AirConcierge, an end-to-end trainable text-to-SQL guided framework to learn a neural agent that interacts with KBs using the generated SQL queries.

the neural agent first learns to ask and confirm the customer's intent during the multi-turn interactions, then dynamically determining when to ground the user constraints into executable SQL queries so as to fetch relevant information from KBs.


However, such symbolic operations break the differentiability of the system and prevent end-to-end training of neural dialogue agents.

it is unclear if end-to-end models can completely replace and perform better than pipeline methods in a task-directed setting.


Besides, during the dialogue with a user, AirConcierge actively prompts and guides the user for key information, and responds with informative and humancomprehensible sentences based on the retrieved results from the KBs.

the "dialogueto-SQL-to-dialogue" approach, which we implement in AirConcierge allows it to integrate with large-scale, real-world KBs.


We treat insertion as the inversion of deletion.

we produce the source graph x via a DELETE operation on G, where the target graph z is set to G. Like the deletion operator, the insertion query y is generated by either the MTurk workers, or by templates.


We refer to this gating mechanism as late fusion since it does not let the information from the graph and text interact in their respective lower level encoders.

the fusion happens after the contextualized information has already been learned.


Recall that the parameters of the graph and query encoders are shared to enable encoding of the two sources in the same semantic space.

we use the same transformer encoder for both sources.


However, the decision for such a structured system has the consequence that there are aspects of narratological theory which are not clearly reflected in our annotation.

we decided to handle thought representation parallel to speech representation, treating thought essentially as 'silent speech'.


In fact, many of these repositories can be useful to the community not only for the automatic creation of textual corpora but also for retrieving crucial meta-information about texts.

the use of metadata provides the reader with a wealth of information that is often not identifiable in the texts themselves.


the use of meta-information provides the reader with a wealth of information that is often not identifiable in the texts themselves.


Built on the DHTK library (Picca and Egloff, 2017), WeDH is written in Python and proposes similar objectives which are detailed in section 3..

dHTK's main purpose is to provide the human scientist with a tool that leverages on the main semantic repositories as dBpedia (Auer et al., 2007) to complete annotation and search for metadata (e.g., the year of the first edition, main characters, book categories, etc.).


The user interface is constantly improving and some features are already planned.

the option to select specific metadata for download as well as the option to download texts in TEI format, are already in production.


At the same time, existing corpora with quotation annotation vary greatly in whether, how, and to what extent they cover the interpersonal structure (i.e., who communicates what to whom -see Section 2. for details).

we are not aware of any large-scale, publicly available corpora which mark both speakers and addressees for quotations in literary text.


Note that cues are not necessary for speech events.

literary renderings of dialogue can omit cues and just let quotations stand: "How are you?"


VGG consists of 99 texts for a total of more than 22,000 pages that were written in Italian during the period of the First World War or shortly afterwards.

they go from 1914 up to 1923, in order to cover not only the years of the war, but also the cultural and social environment leading to the war and the aftermath of the Great War.


EXTra includes various parameters that allow users to optimize the extracted terms with respect to the target corpus and domain.

users can specify the set of structured patterns that guide the extraction process, a list of stopwords, the association measure to be used by the weighting algorithm (in VGG, local mutual information, LMI), as well as the thresholds for the association measure and the n-gram frequency.


While they shed much light on the complex issue of translated literature, the method of stylometry by most frequent words does not allow direct comparison of original texts and their translations.

an attempt to hang, say, novels in English and their multiple translations from the same cluster analysis tree would produce the trivial effect of separating the texts by the two languages, and little more.


We replaced the form and lemma in the CoNLL-U document with the first match in the CS column.

each occurrence of quotes, question mark, and �b�t� in a Czech CoNLL-U file was replaced with pseudolemma L00009, L00010, and L00011, respectively.


While most prior work extended proposition types based on the needs of the task at hand, our taxonomy has been motivated mainly by argumentation theory.

the argumentation schemes of Walton et al. (2008) are a set of reasoning types commonly used in daily life.


This requires researchers to make a trade-off between, on one hand, the expressiveness and fidelity of the linguistic construct they are attempting to capture, and on the other the potential for operationalization and quantification in coding manuals and fully automated systems.

in imbalanced tasks, these choices can have the effect of producing an inaccurate picture of the minority class and producing datasets that are no longer a valid representation of the original construct (Corbett-Davies and Goel, 2018).


When compared to Trump, the Democratic candidates make much greater use of normative language.

language from the two Democratic candidates uses normative propositions and expresses desires a lot more than Trump, often to make the case for specific policies based on normative values.


We also explored a simpler binary version of our classification task (Row 2), which reduced the class skew while still making a pedagogically useful distinction.

during discussions with teachers where we visualized the collaboration annotations in the corpus that came from their particular classrooms, we found that teachers were very curious about whether students were introducing new information into the discussion or building off of what was previously said.


New in the 2.2 version of the corpus are two additional annotation layers for coherence relations following the Penn Discourse TreeBank framework.

we add relation senses to an already existing layer of discourse connectives and their arguments, and we introduce a new layer with additional coherence relation types, resulting in a German corpus that mirrors the annotation scheme of the PDTB (which is a much larger corpus, though).


Experiments are performed in different languages, such as English, Dutch, German, Portuguese Brazilian and Basque to highlight the cross-lingual effectiveness of the segmenter.

the model achieves a state-of-the-art F-score of 96.7 for the RST-DT corpus (Carlson et al., 2003) improving on the previous best model by 7.2%.


While modeling segmentation as token classification helped remove these errors, injecting syntax helped remove these errors further.

we observed that jointly training for part-of-speech tags helped remove punctuation errors and resolve confusions between infinitival complements and clauses.


Results obtained and analysis performed show how injecting syntax into the model helped achieve better results.

the joint learning of syntactic features allowed the model uncover complex syntactic patterns that could not be captured by simply fine-tuning BERT.


In this work, we are concerned with identifying the first type of near-duplicate document.

we require a system that is capable of receiving documents in a stream, and for each document, can answer two questions: is this document a near-duplicate of one previously seen, and if so, which?


In this study, we created an automated essay scoring (AES) system for nonnative Japanese learners using an essay dataset with annotations for a holistic score and multiple trait scores, including content, organization, and language scores.

we developed AES systems using two different approaches: a feature-based approach and a neural-network-based approach.


The essays were annotated by six annotators.

each essay was annotated by three out of these six annotators.


Although the scoring criteria were applied as per the evaluation flowchart, some of the annotators were severe; therefore, the resulting agreement scores were moderate.

between the most severe and the gentlest of the six annotators, there was an average difference of about 0.5 points on a one to six-point rating.


Intuitively, "Number of common morphemes in the prompt and the essay" appears in holistic features.

a story related to the prompt is more highly rated than one not related to the prompt.


This may lead to a large spreading of hatred or abusive messages which have to be moderated.

theses messages may express threats, harassment, intimidation or "disparage a person or a group on the basis of some characteristic such as race, color, ethnicity, gender, sexual orientation, nationality, religion, or other characteristics" (Nockleby, 2000).


Our study pursues a different line of analysis, whereby speech acts bearing on derogatory content are ranked according to their perlocutionary force and assertions are classified as more or less direct.

in order to make emerge different degrees of downgrading tones, we have chosen to distinguish cases where the addressee is directly addressed from those in which she is not, as done in hate speech analysis (ElSherief et al., 2018; Ousidhoum et al., 2019).


We use all 1,000 articles (200 titles on five complexity levels) as our dataset, and apply ten-fold cross-validation setup with ten repetitions (in Weka Experimenter) without controlling for which titles and text versions end up in which fold.

we behave as we had 1,000 independent articles on five different complexity levels, but choose to have 200 titles on five complexity levels, in order to: (1) allow for learning subtle differences between different complexity levels of texts treating the same topic; and (2) avoid bias that might arise from topic differences if we used corpora with five complexity levels, in which each level has different topics (as in the case of typical language learners corpora).


Objective news maintains almost the same values throughout the frames, while column news presents a peak in the second frame.

regarding the sentiment lexicon, the objective news is more unstable throughout the text, and newspaper columns present more instability in the middle of the text.


While opinion mining is a well-established task with many standard datasets and well-defined methodologies, emotion mining has received less attention due to its complexity.

the annotated gold standard resources available are not enough.


This corpus contains manual annotations of eight emotional categories: expectation, joy, love, surprise, anxiety, sorrow, anger and hate.

we found only a few resources annotated with emotions in Spanish and even most of the English emotion datasets have not been fully annotated manually.


In order to retrieve tweets for each event, we select the trending topic that may contain affective content.

we choose the following events that occurred during April 2019:


In order to download the tweets, we used the Twitter Search API 1 .

we used an easy-to-use Python library to access the Twitter API: Tweetpy 2 .


For example, it can be observed that the number of tweets for fear, disgust and surprise are noticeably lower compared to others (joy, sadness, anger).

fear and surprise are the most difficult emotions to label.


In this section, we describe the different experiments we carried out to test the validity of the dataset.

we trained a classifier based on machine learning.


For this reason, we applied pre-processing techniques in order to prepare the data for the text classification.

we preprocessed the tweets following these steps: The tweets were tokenized using NLTK TweetTokenizer 4 and all hashtags were removed.


We use the most popular: The Term Frequency Inverse Document Frequency scheme (TF-IDF).

using this scheme each tweet is represented as a vector of unigrams.


For this reason we decide to employ a machine learning algorithm in order to classify the tweets by emotions.

we selected the Support Vector Machine (SVM).


While most research on emotion analysis focuses on emotion classification, including predicting the emotions of the writer as well as those of the reader of a text (Chang et al., 2015), a few studies so far have focused on identifying what might have triggered that emotion (Sailunaz et al., 2018).

the task has been framed as Emotion Cause Extraction (Lee et al., 2010a) from news and microblogs, where the cause of an emotion is usually a single clause (Chen et al., 2010) connected by a discourse relation to another clause that explicitly expresses a given emotion (Cheng et al., 2017), as in this example from Gui et al.


They might contain multiple keywords as well.

in this task, the dataset is provided with the sentiment (positive or negative) of the text, and our goal is to find the emotion carriers for that sentiment.


The authors thank coders and co-workers who participated in elaborating protocols and annotating emotional states.

Corinne Bignon and her staff for the implication in the annotation process.


Using these two settings, we aim at obtaining a better understanding of the advantages and disadvantages of an expert vs. crowdsourcing setting in this novel annotation task.

we are interested in estimating the potential of a crowdsourcing environment for the task of self-perceived emotion annotation in poetry, given time and cost overhead associated with in-house annotation process (that usually involve training and close supervision of the annotators).


For consistency and to simplify the task for the annotators, we opt for a trade-off between completeness and granularity of the annotation.

we subselect stanzas composed of four verses from the corpus of 64 hand selected English poems.


In this paper, we construct the first empathy lexicon.

we learn ratings for two kinds of empathy - empathic concern (feeling for someone) and personal distress (suffering with someone)-for words given existing document-level ratings from the recently published Empathic Reactions dataset (Buechel et al., 2018).


In contrast, resources from psychologists tend to focus on valence and arousal (or other representations of affective states (Ekman, 1992)).

this includes the Affective Norms for English Words (ANEW; (Bradley and Lang, 1999)) which have been adopted to many languages (Redondo et al., 2007;Montefinese et al., 2014), and their extension by Warriner et al.


Abdul-Mageed et al. (2017), in contrast, focus on trait empathy, a temporally more stable personal attribute.

they studied the detection of "pathogenic empathy", marked by selffocused distress, a potentially detrimental form of empathy associated with health risks, in social media language using a wide array of features, including n-grams and demographic information.


We can now train the model to predict the document gold ratings Y d using a gradient descent-based method.

for a document d i , the embedding centroid of tokens present in d i is used as input a (0)  where len(d i ) is the number of tokens in d i .


Thus, if the sentence selection in the process of creating the dataset fails to make pairs of sentences with the same communicative functions, the accuracy will be low but the agreement will be high.

a low accuracy and high agreement indicate that the dataset is of low quality.


The homogeneity metric is proposed to summarize the uniformity of a cluster distribution.

how uniformly the embedding vectors of the samples in a group of texts are distributed in the embedding space.


Afterwards we discuss two different implementations of NLI databases.

we will introduce the databasecentric approach based on SODA (Blunschi et al. 2012) as well as the information retrieval-centric approach based on the Terrier system (Macdonald and He, 2008)


It is often thought to involve effects related to co-activation between word representations, a theoretical position mainly inspired by the Interactive Activation model (McClelland and Rumelhart, 1981).

the neighborhood effect elicited by a word such as PLEAD is a function of the orthographic similarity of PLEAD to other words in the lexicon.


An open ngram encoding is defined as the set of n-combinations of letters a word, where the letters within a combination are ordered by their occurrence in the word.

the word SWAN generates the following open 2-gram (or bigram) features: {SW, SA, SN, WA, WN, AN}.


The material is robust in that it allows obtaining multiple samples of each speech sound so that each list is equally representative of the French sound system.

each consonant appears at least twice in each position, as measure of speech intelligibility based on acoustic properties of the speech signal.


In this paper, we provide an extensive evaluation of 14 spelling correction tools on a common benchmark.

the evaluation provides a detailed comparison with respect to 12 error categories.


Most algorithms and tools in the field simply assume that the given text is free from errors such as the above and they fail if this is not the case.

this is true for basic problems such as POS-tagging, sentence parsing and entity recognition, as well as for more complex problems such as question answering (QA).


Over the recent years, endeavours have been devoted to designing accurate and efficient EL systems.

english eL has undergone continuous development, with the aid of up-to-date KBs and evaluation benchmarks.


In contrast to the advancement in English, however, Chinese EL systems suffer from lagged development, partially due to the lack of appropriate Chinese KBs and evaluation benchmarks.

almost all existing publicly available Chinese EL datasets are based on short text, such as microblogs (NLPCC 2013 1 , NLPCC 2014 2 , NLPCC 2015 3 ) and news headings (Chen et al., 2018) 4 .


In this work, we propose CLEEK, a Chinese long-text corpus for entity linking, which comprises 100 documents and 2,786 mentions, along with a measure for characterizing corpus difficulty.

we first elaborate the process of corpus construction and annotation, and then provide an in-depth analysis of corpus properties, in particular the difficulty of dataset.


The knowledge base population (KBP) track 7 includes Chinese EL as a component since 2015.

for the Chinese EL dataset in KBP2016 task (Ji et al., 2016), there are 8,845 mentions and 167 documents in evaluation data and 15,000 documents in the source data.


As is illustrated in Figure 2, the work flow of corpus construction initiates from mining news and commentaries from websites.

we crawl approximately 10,000 pieces of long texts from Sohu News 8 and China Newsweek 9 , which cover five domains, namely, Sport, Travelling, Economy, Film Review and Politics.


Although the performance of using prior probability can be regarded as an advisable measure, it neglects the semantic similarities between mentions and true entities.

provided that a mention and its corresponding entity are close in the semantic space, the linking process could be easily realized via EL solutions based on neural networks and embeddings.


The inferior outcome, to a certain degree, can be attributed to the deficiency of generating candidate entities.

according to the observation of results, the candidate entities generation strategies for the two approaches fail to generate candidate entities for the majority of mentions.


According to their results, both filled and unfilled hesitation pauses cause extra activation of the primary auditory cortex (PAC) and the motor components of the speech system in the brain.

for filled pauses, a more expressed activation was detected in the additional motor cortex (SMA), which is known to be involved into the initiation of utterances.


The basic vocal annotation and the additional disfluency annotation were imported into the eaf files used in ELAN.

time boundaries and type code of every annotated disfluency were imported into the intervals of the "N/C/R-Dislf" tiers (where N, C or R stands for Narrator, Commentator, and Reteller, respectively).


Since the document frequency of entities varies a lot from high frequency entities to low frequency entities (power law distribution), we focus on the scale of the document frequency of entities.

we put entities whose log document frequency under the same scale into the same group, and present the log of the number of entities in each group.


To make the comparison possible, article matching and entity alignment are necessary.

we first identify a common set of articles by title matching, i.e., only articles with the exactly same title are selected


We investigate this issue by measuring the impact of LRR.

we extract subsets with decreasing LRR and present ESD results of the subsets.


We follow the work of Dunietz and Gillick (2014).

we use some hand-crafted features to train a binary classifier to identify whether an entity is salient in a document.


On the other hand, the number of false positives (FP) in the NMT system was considerably larger than that in the SMT system.

the NMT system changed many points that did not need to be changed.


This flexibility was demonstrated to be useful in the biomedical domain, where syntactic parsers are brittle.

a state-of-the-art Odin grammar for this domain had to backoff to surface information for 38% of its patterns (Valenzuela-Escárcega et al., 2018).


Odinson was designed to have a flexible index schema, and so few fields are mandatory.

the only mandatory field is the "raw" field, which encodes unmodified tokens.


Odinson also supports event queries, which make it easier to specify complex patterns involving many entities interacting through a trigger.

each event query is a structured query that contain several sub-queries: one for the event trigger, and one for each of the required arguments.


Therefore the TNE leverages Adaptive Computation Time (Graves, 2016) to dynamically allocate more computational resources for the encoding of some words compared to others in the same EEG report.

the TNE consists of an Adaptive Universal Transformer (Dehghani et al., 2018) with 12 recurrent blocks as described in (Maldonado and Harabagiu, 2019).


Thus microtask crowdsourcing has been widely used for quick and easy, isolated tasks such as image tagging, or print document digitization (Kittur et al., 2011), several researchers have attempted to explore crowdsourcing for challenging and expert tasks such as programming, product design, or NLP tasks (Kittur et al., 2013;Valentine et al., 2017).

empirical examination of numerous NLP tasks such as image recognition, sentiment analysis, and assess-ment of the performance of machine translation systems via crowdsourcing has shown that collective responses of crowd workers may provide gold standard data sets with quality approaching those generated by experts (Snow et al., 2008; Callison-Burch, 2009; Nowak and Ruger, 2010).


Inspired by these findings, we suggest using micro-task crowdsourcing to evaluate the extrinsic and intrinsic quality of query-based extractive forum summarization to overcome these time and cost barriers of summary quality evaluation.

when the naive end-users viewpoint is needed to evaluate an automatic summarization application or any summarization method, the subjective quality evaluation plays an important role.


Meanwhile, the natural language processing (NLP) community has observed significant advances in both machine translation (Sutskever et al., 2014; Cho et al., 2014; Vaswani et al., 2017) and speech synthesis (Oord et al., 2016; Wang et al., 2017; Shen et al., 2018), especially driven by deep learning in recent years.

there has been growing attention towards lowresource scenarios (Zoph et al., 2016;Gu et al., 2018), which pose a unique challenge for existing deep learning methods.


We assess the accuracy of the temple corpus by evaluating the context-question-answer data using C/Q and Q/A classifier.

we evaluate on a set of 50 temples, randomly selecting 26 temples (264 Question:Answer pairs) with wikipedia article and 24 temples (359 Question:Answer pairs) without wikipedia article.


In this paper, we consider whether sub-word embeddings can be leveraged to form cross-lingual embeddings for OOV words.

we consider a novel bilingual lexicon induction task focused on OOV words, for language pairs covering several language families.


In this paper we evaluate whether sub-word embeddings can be leveraged in cross-lingual models.

we consider a novel bilingual lexicon induction task in which an in-vocabulary target language translation is found for an OOV source language word, where the representation of the source language word is constructed from subword embeddings.


Because of the previous findings that fastText embeddings outperform BPE, we only consider fastText embeddings in these experiments.

we use fastText embeddings pretrained on Cherokee Wikipedia.


In this paper we considered whether sub-word embeddings can be leveraged in cross-lingual word embedding models.

we evaluated sub-word embeddings in a novel bilingual lexicon induction task in which we identify target language translations for OOV source language words.


We have contributed to the field by researching methods that are applicable to less-resourced languages, with Basque as a case study.

we have compared a training data projection approach with direct model transfer strategies.


We do this by enforcing a label distribution 6 among test questions and excluding test questions which are ambiguous.

we leave in difficult cases, but only if they are unambiguously so and clearly covered by the instructions.


However, we also found that for a minority of challenging cases, agreement is very low, usually < 50%.

we see a clear two-way split between straightforward cases (100% agreement) and ambiguous cases (< 50% agreement).


Besides providing additional data, we designed the recording sessions in ways that allow the elicitation of linguistic patterns of interest.

we replicated part of the setting used in the HCRC MapTask corpus (Anderson et al., 1991).


Thus, we employed regular expressions to detect and delete sentences holding this kind of issue.

we look for sentences with three or more sequential tokens composed of one or two characters at most.


We perform a downstream task to evaluate the quality of the corpora after the filtering process.

we train an open-vocabulary language model at character-level (Mielke et al., 2019) and measure the results with character-level perplexity (Mielke, 2019).


our scenario relates to using robotics and autonomous systems on an offshore energy platform to resolve an emergency and is part of the EPSRC ORCA Hub project (Hastie et al., 2018).


The time that robots would take to perform actions was based on simulations run on a Digital Twin of the offshore facility implemented in Gazebo (Pairet et al., 2019).

we pre-simulated typical robot actions, with the robot's progress and position reflected in the Wizard interface with up-to-date dialogue options for the Emergency Assistant.


In the domain of machine-readable lexicons, different projects have tackled the difficult issue of unifying features to describe lexical entries, allowing a meaningful semantic interoperability.

projects have aimed to unify the descriptors used to label specific forms in a lexical entry.


The work presented in this paper is part of a project on Simplification of Arabic Masterpieces for Extensive Reading (SAMER) (Al Khalil et al., 2017; Al Khalil et al., 2018).

we discuss the challenges of, and solutions to, the development of a large-scale leveled readability lexicon for Modern Standard Arabic (MSA).


In this article, we will introduce two parts of the new multi-part version of the Lexical Markup Framework (LMF) International Organization for Standardization (ISO) standard, namely Part 3 of the standard (ISO/DIS 24613-3), which deals with etymological and diachronic data, and Part 4 (ISO/DIS 24613-4), which consists of a TEI serialisation of all of the prior parts of the model (in what follows we will refer to the TEI-XML serialisation of LMF data as LMF in TEI).

we will show how LMF and especially parts 3 and 4 can be used to encode etymological data by taking example encodings of entries from an important reference dictionary for the Portuguese language, the Grande Dicion´ario Houaiss da L´ıngua Portuguesa (Houaiss, 2015), from now on Houaiss


Our approach to modelling etymologies in LMF has been heavily influenced by prior work in etymology representation both in the 2008 version of LMF (Salmon-Alt, 2006) as well as in other standards such as TEI (Bowers and Romary, 2017) and Ontolex-lemon (Khan, 2018).

we have sought to emphasise the following three aspects of etymologies when representing them as computational resources: 1. their status as descriptions of abstract graphs, i.e., data structures of the kind that computer scientists are (exceedingly) used to working with; 2. the fact that etymologies usually describe the genealogy of a given lexical phenomenon through the use of a narrative (simple in most cases); and finally, 3. the fact that individual etymologies also describe hypotheses (often of a scholarly nature) and tend to include references to other texts in justification of these hypotheses.


However since etymologies usually represent an ordering of etymons (and, of course, etymons can be associated with more than one etymology and even more than one etymology for the same entry), we opted to create indirect rather than direct associations between etymologies and etymons.

we define etymologies as containers for an ordered series of etymological links, see Figure 3 below.


By reconstructing a phylogenetic tree from an etymological database, it is possible to evaluate its quality while being an interesting task per se.

it can be used to validate and enhance said resource, which we intend to do for our own.


LARA, which uses a crowdsourcing/online community approach where content creators and content users interact in a shared online environment, adapts and extends many of the ideas used in these earlier platforms and adds new ones.

LARA texts are organised so that, when the user accesses them through the online portal (Habibi, 2019), a personalised concordance is built up which associates each word in the text with previous occurrences in the learner’s own reading history


The distance to the prototypical subjectivity vector is used as a measure of deviation from the norm.

we sort essays according to the distance between the subjectivity vector and the corresponding centroid.


We implemented AES models using different machine learning algorithms.

we learn AES models using Support Vector Regression (SVR), Random Forests (RF), Logistic Regression (LR), Gradient Boosting (GB), and Multi-Layer Perceptron (MLP).


At the moment, we provide a generic RDF conversion for each of the dictionary collections, we do not harmonize external vocabularies beyond the application of the OntoLex-Lemon vocabulary.

linguistic categories are not yet normalized against the LexInfo ontology.


Over the years, a number of language databases and tools for Danish have been created based on public and private initiatives, but the linguistic resources for Danish are still somewhat scattered and efforts have not been sufficiently coordinated.

insufficient attention has been paid to the fact that these building blocks, often costly to produce, generally need upscaling, validation, and maintenance, and last but not least that they should be made freely available.


Language tags serve to identify the language of a resource and the linguistic entities therein.

language tagging is a key aspect when modeling linguistic resources in Resource Description Framework (RDF) (Manola and Miller, 2014) following the principles of Linked Data (LD) (Berners-Lee, 2006).


Therefore, our Hadith corpus relies on the source.

missing values or inconsistencies with the original book are dependent on Sunnah.com.


For CLARIN, the clustering in SSHOC is yet another context in which the ongoing effort towards optimizing the service we offer for comparative research based on the CLARIN Resource Families (FiÃ…Â¡er et al., 2018) can be coordinated.

for the work on better integration of parliamentary data and the support for the development of methodologies for working with heterogeneous data sets, the SSHOC project has a big potential for impact, as it is calling for collaboration with political scientists and linguists, the coupling of parliamentary corpora with other political research data sets, such as the party manifestos, 31 and the integration of textual data and quantitative data from polls.


The taxonomy was based upon a review of schema previously developed for META-SHARE and the ISOCat Data Category Registry and then tried against the LDC Catalog.

the authors reviewed the entries for each of the 574 corpora then included in the LDC Catalog.


They were transcribed and during the transcription process new abstract attributes of description were identified (see Section 4.3).

the transcription implied extensively completing the designed questionnaire in continuous prose and, where possible, adding links to existent institutional websites.


The basic idea behind this is that each time, when the model predicts an output word, it only uses the parts of input where the most relevant information is concentrated instead of the whole sentence.

it only pays attention to some weighted words.


Such methods may yield pairs sharing little common content, thus giving alignment errors.

these errors often occur when the sentence order differs greatly between the source and target.


Unfortunately, the target-side sentences of each corpus came from different resources.

the target-side sentences of the content-equivalent corpus came from the content-equivalent news, while those of the back-translated corpus (CE-NMT) came from the original news, as listed in Table 1.


The technology used within the task was developed within FP7 projects XLike 26 and XLime 27 focused on cross-lingual knowledge-extraction.

for this task we will use the systems and components from Wikifier (Brank et al., 2017), XLing (Rupnik et al., 2016) and EventRegistry (Leban et al., 2014) all dealing with statistical and semantic cross-lingual annotations and alignments.


A large comparable corpus for Basque-Spanish was prepared, on the basis of independently-produced news by the Basque public broadcaster , and we discuss the impact of various techniques to exploit the original data in order to determine optimal variants of the corpus.

we show that filtering in terms of alignment thresholds and length-difference outliers has a significant impact on translation quality.


The accuracy of LST depends on the coverage and quality of the linguistic resources used to train them.

when the coverage of the training resources are poor such as for rare words, named entities or neologisms, the accuracy of tokenisation of out of vocabulary (OOV) words can be low.


Similar to th, for tr we see that the LIT methods trained with vocabularies of sizes 50K and 100K perform better than other settings.

for tr LIT consistently outperforms LST.


This dataset classifies word-pairs according to POS category of the two words being compared.

both words in a word-pair belong to the same POS category, which makes it an ideal candidate for studying the effect of tokenisation on different POS categories.


Therefore, we use SIF for creating word embeddings from subword embeddings in this experiment.

we use the GloVe embeddings for Japanese subtokens/tokens obtained by a particular tokenisation method and use SIF to create the word embeddings for each word in word-pairs in the Japanese semantic similarity dataset.


We select prefixes or suffixes that have known inflectional roles and compute the cosine similarity between each prefix/suffix and all other tokens in the vocabulary using the unweighted embedding method to find the nearest neighbours in the embedding space.

we conduct this nearest neighbour analysis for the three languages: English, Japanese and Turkish.


This appendix contains additional experimental results not included in the main body of the paper.

it contains supplementary results for the dictionary induction (Table 6) and cross-lingual word similarity (Table 7) tasks, using all sources of supervision: no supervision, dictionary of identical words, and dictionaries containing 100, 1K and 8K translation pairs.


Before we introduce our own bilingual Vietnamese-English corpus, it is also important to note that previous work has been done to create a similar corpus for the same language pair.

tuc (2003) collected a corpus of 60 hours of speech, comprising both sociolinguistic interviews and speakers' self-recorded speech, over twenty years ago in Victoria, Australia.


This is likely because Vietnamese POS taggers are not only typically trained on much less data than English POS taggers, but they are also unlikely to be well-suited to speech data (Plank et al., 2016).

spoken Vietnamese is characterised by extensive use of discourse markers and lexicon variation due to regional dialects and so is significantly different to written Vietnamese.


Table 8 thus illustrates contrasting occasions when the pronoun was translated incorrectly and correctly in a monolingual Vietnamese and mixed clause respectively.

the first person subject con (kin term meaning 'child') was erroneously translated as a 3SG common noun in the monolingual Vietnamese clause, but accurately translated as a 1SG subject pronoun in the mixed clause.


SLING also in-  corporates an attention mechanism based on neuro-science models of attention and awareness in (Nelson et al., 2017) and (Graziano, 2013).

the attention mechanism focuses on encoding the frame representation that the parser has created rather than encoding the tokens themselves.


Though not provided with the annotated dataset for obvious copyright reasons 12 , the textual content of every speech turn has been revised, based on the output of the OCR tool we used to retrieve the subtitles.

we restored a few missing words, mostly for BB, the subtitles sometimes containing some deletions.


Yet, most of the Information Retrieval or Information Extraction tasks still mainly rely on textual data to automatically extract meaningful knowledge from unstructured text.

over the last decade, the Twitter platform was a major source of information for natural language processing (NLP) applications such as information retrieval, sentiment analysis, or topic modeling.


Preliminary experiments on a dataset built using our approach show its interest.

it offers the opportunity to investigate the benefit of exploiting related visual information to improve the Entity linking task.


Deep Learning models make use of neural networks with many layers and units (neurons) to explore the data and extract features to be used in the learning process.

while a Classic method works with the features provided beforehand only, a Deep Learning model uses all of them to generate many more features itself, which is done by using internal (hidden) layers of the model.


The results demonstrate that flood-related news articles do not consistently report on a single, currently unfolding flooding event and we should also not assume that a flood-related image will directly relate to a flooding-event described in the corresponding article.


Event information from online sources provides an important complement to information that can be derived from remote sensing imagery.

it is not dependent on factors such as the periodicity of the satellite or cloud-cover conditions.


However, multimedia research often makes highly rigid assumptions about the relationship among the media.

in research that deals with combinations of text and images, the text is usually assumed to describe the image.


We highlight the presence of multiple modalities and its real-life nature, which differentiates it from prior work.

lifeQA is the only existing Video QA dataset that focuses on real-life understanding and is carefully constructed from hand-picked in-the-wild videos.


Deep neural language models have recently evolved to a successful method for representing text.

bidirectional Encoder Representations from Transformers (bERT) outperformed previous state-of-the-art methods by a large margin on various NLP tasks (Devlin et al., 2019).


To improve lists of entity-to-entity PMI, a cutoff based on the raw counts is dynamically determined for each list.

all counts are sorted from highest to lowest counts and the count limit is determined by when a total share of 80% has been reached, or a minimum of 2 if there are too few entries.


The archaeology domain, like other scientific fields, produces large amounts of textual data.

a large amount of excavation reports are available, which are created whenever an excavation is completed, detailing everything that has been found together with an interpretation of the site (Richards et al., 2015).


In this paper, we have explored the use of SOTA Transformer models for the purposes of medical text augmentation.

we focused on the vanilla Transformer and GPT-2 models to generate discharge summaries from the MIMIC-III dataset, modelled as a seq2seq task.


To verify the efficacy of our methodology, we conduct both an intrinsic and an extrinsic task which are word similarity and sentence classification, respectively.

we apply our method to various languages which are rooted from different language families since subword information is a language-dependent characteristic.


Although the domain where the pretrained word embeddings are trained is not quite related to the medical domain, the performance is largely increased.

the performance in the UMN dataset is increased as much as 82% in terms of Spearman's correlation.


As can be seen from the table, again, our method outperforms other baselines by a large margin.

the proposed model achieves as much as 8.1% improvement on average in test accuracy compared to strong baselines in the CR dataset.


This result is consistent with previous studies (see section 1.3).

both the presence and the mean smiling intensity seem to be a marker of humor.


Considering that these results tend to indicate that both intensity mean (figure 6) and presence/absence of smiling (figure 7) are markers of humor, the impact of smiling on the success or failure of humor was then investigated.

does smiling reduce the risk that humor will fail?


However, comparatively little has been done to explore the integration of vetted terminology and MT processing, with the goal of improving overall results.

manipulating the translation output of NMT systems to adhere to user-provided terminology specifications, despite the impressive quality improvements of NMT, remains an open problem (Hasler et al., 2018).


In both Figures 1b and 2a, the highest value in the column under the source token "report," a value of 0.8274, triggers the target token selection.

the token "informe" is the correct target equivalent of the source token "report" with an 82.74% confidence.


It can be used to extract high-quality language features from any text data, as well as to fine-tune the model on a specific task like classification, entity recognition, or question answering.

we use BERT to extract word and sentence embedding vectors, again calculating cosine similarities.


In this work, we want to know if the type of training corpora affects SA task performance.

is it better to train embeddings with task-specific (polar) corpora in SA framework?


Essentially, there are a dual purpose expression.

the meaning of what a speaker wants to express is very different from the superficial meaning of what he/she says, and even in most cases the two meanings are completely opposite.


In order to go in the direction of a deeper and finer-grained analysis on the relationship between semantics and syntax in the case of ironic expressions, we performed a preliminary analysis on the new gold standard.

we wanted to investigate whether there exist some cooccurrencies, related to PoS tags and dependency relations, that are more frequent than other.


As Table 2 shows, mistakes made by the system in assigning the wrong phrase structure contributed the largest number of errors: 71%.

some notorious issues were caused by the morphological ambiguity between bound/free relative pronouns and interrogative pronouns and their mismatch with determiners and adverbials.


In the PDT-C project, we aim to provide all these included treebanks with full manual annotation at the lower layers and unify and correct annotation at all layers.

the data in PDT-C 1.0 is (mainly) enhanced with a manual annotation at the morphological layer, consistently across all the four original treebanks (see Sect.


In this work, we follow the simple intuition that any component that does not explicitly depend on a treebank annotation model is shared between the parsing models for the different treebanks.

the representation models for words (word embedding and bidirectional LSTM) and the buffer are shared, while the representations for constituents and the stack, as well as the action selector, are kept separate for each treebank.


We propose a simple yet accurate method for dependency parsing that treats parsing as tagging (PaT).

our approach addresses the parsing of dependency trees with a sequence model implemented with a bidirectional LSTM over BERT embeddings, where the "tag" to be predicted at each token position is the relative position of the corresponding head.


We propose an extremely simple method for dependency parsing that treats parsing as tagging (PaT).

our approach addresses the parsing of dependency trees with a sequence model, where the "tag" to be predicted at each token position is the relative position of the corresponding head.


Since the FTB contains exclusively written texts, the parser inevitably encounters difficulties to parse the spontaneous speech turns of our corpus.

the concept of sentence, which is helpful to the parser, is not operative on spoken language.


To investigate how AlloVera improves multilingual speech recognition, we implemented three multilingual models mentioned above and compared their performance.

we selected 11 languages from AlloVera taking into consideration the availability of those languages in our training speech corpus.


The quality of the collected data, however, is highly dependent on the way the task is presented to the participants.

the data collection method should be carefully designed to (1) elicit naturalistic data in a controlled setting, and (2) avoid undesired biases, for example prompting the participants to repeat the same syntactic structures or lexical choices that are in the task description, which could happen if participants are just asked to paraphrase a command.


In this section we describe a methodology to categorise gold standards according to linguistic complexity, required reasoning and background knowledge, and their factual correctness.

we use those dimensions as highlevel categories of a qualitative annotation schema for annotating question, expected answer and the corresponding context.


Our error analysis observed that while this substantially increases QC performance, it changes the distribution of errors made by the system.

25% of errors become highly correlated with an incorrect answer candidate, which (we show in Section 5.)


We incorporate QC information into the QA process by implementing a variant of a query expansion model (Qiu and Frei, 1993).

for a given {question, QC label} pair, we expand the question text by concatenating the definition text of the question classification label to the start of the question.


Because each question can have up to two labels, we treat each label for a given question as a separate evaluation of interannotator agreement.

for questions where both annotators labeled each question as having 1 or 2 labels, we treat this as 1 or 2 separate evaluations of interannotator agreement.


To address such absence, we release SQuAD2-CR dataset, which contains annotations on unanswerable questions from the SQuAD 2.0 dataset, to enable an explanatory analysis of the model prediction.

we annotate (1) explanation on why the most plausible answer span cannot be the answer and (2) which part of the question causes unanswerability.


An intuitive way is to provide pseudolabels to unlabeled data using our annotations and semi-supervised approaches.

we apply tri-training (Zhou and Li, 2005), which is one of the strong baselines for neural semi-supervised learning for natural language processing (Ruder and Plank, 2018).


All of the videos include spoken instructions which are transcribed and manually segmented into multiple segments.

we asked the annotators to manually divide each video into multiple segments such that each of the segments can serve as an answer to any question.


We present our choices of data sets for training and testing the components, and present the experimental results that helped us optimize the parameters of the chatbot.

we discuss the appropriateness of using the SQuAD dataset for evaluating end-to-end QA, in the light of our system's behavior.


We found that web browsers' traditional text span selection lacks speediness, usability, and regularly shows buggy mobile interaction.

during our preliminary tests, we found several cases of highlighting of incomplete words, and thus incomplete answers, which is a situation we needed to avoid.


The dataset created (i.e., ScholarlyRead dataset) could be served as the dataset for building Question Answering (QA) models on scholarly articles.

it could be a benchmark dataset for span-of-words-based MRC systems in the domain of scholarly articles.


A third set of QA methods draw answers from patient-specific sources.

the answer is not a solution for the patient (e.g., what is the best treatment for this patient?), but rather directly from the patient’s records (e.g., what treatments have been given to this patient?).


emrQA (Pampari et al., 2018) is a large medical QA dataset automatically constructed from the i2b2 challenge datasets.

they utilize the existing NLP annotations to populate pre-defined question and paraphrase templates and associate them with the corresponding clinical notes.


We select a variety of models to analyze the effect of using different pre-training datasets on clinical QA.

we use 4 variants of Transformer language models pre-trained on different open-domain, biomedical, and clinical datasets.


We perform an array of experiments to analyze the effect of different fine-tuning datasets on clinical QA performance.

we run the above models by fine-tuning on different combinations of the included datasets and evaluate their performance on the held out test sets from CliCR and emrQA datasets.


It can be noted that fine-tuning on another medical dataset (different than the one for which the task is predicted) consistently performs worse than fine-tuning on the SQuAD dataset.

fine-tuning on an open-domain dataset performs better than fine-tuning on another dataset in the same domain.


Only some acts have been annotated in parallel by multiple annotators.

these are: Three annotators for the first act of text #9 and two annotators for act I and II of text #6.


Even though CR remains a challenging task for the time being, it is clear that coreference chains are an important component for the analysis of character-driven texts such as theatre plays.

they allow insight into indirect presentations of characters, e.g., through the speech of other characters.


We describe a resource and first studies towards answering this question.

we create wikiHowToImprove, a collection of revision histories for about 2.7 million sentences from about 246 000 wikiHow articles.


For simplicity, we focus on edits on the sentence level.

we consider all articles in wiki-How for which a revision history is available and examine each original sentence, henceforth base version, and how it is changed at subsequent points in time, henceforth revised versions.


Instead of the well-known “one sense per discourse“ assumption made by Gale et al. (1992), this work makes a more relaxed hypotesis, i.e., "one sense per Wikipedia Category".

a noun is used always with the same meaning within a Wikipedia Category.


Surprisingly, the performance drops significantly on every dataset as the weight of the original word vectors is decreased.

these existing benchmarks may favor dominating senses in the training corpus, making it unnecessary for the sense embedding models to deal with different senses separately.


The ratio of multi-sense words is 79.6%.

less than 21% of the words in MSD-1030 are single-sense.


The proposed network consists of two components: (1) The Attribute Layers, implemented by a multilayer perceptron (MLP), initially learn the mapping from word embedding vectors to their corresponding attribute vectors presented by a human-generated visual attributes dataset.

we use the VisA dataset (Silberer et al., 2013); (2) The Semantic Task Layer then fine-tunes the pretrained attribute vectors through supervised lexical entailment tasks (Section 2).


The rationale behind the present study is in line with this approach.

we expect that many of the attributes associated with a hyponym form a subset of the hypernym's attribute set.


It would thus be possible to employ lexical entailment directionality detection task as a training task for predicting an adequate set of semantic attributes.

if we are given an initial set of attribute vectors for the vocabulary of concern, such a directionality task can be utilized as a task for fine-tuning the attribute vectors.


This supervised task can be considered as an independent learning task in its own, but it is considered as a pretraining step in the whole learning process.

the learned parameters (W and b) are used to initialize the corresponding parameters in the lexical entailment fine-tuning tasks.


Instead of learning to predict the degree of the entailment relation, we concentrate on training the network with the entailment directionality tasks.

given a pair of words (w 1 , w 2 ), the network simply tries to assign a label, either of hyper or hypo.


On the other hand, text classification results provided new insights on the most predictive features for distinguishing abusive and not-abusive swear words.

we found that a wide range of features can actually improve the models performance.


The term oral history, in historical research, refers to conducting and analyzing interviews with contemporary witnesses.

in Germany, this kind of research focuses above all on the period of the Second World War and National Socialism.


We do not only transfer the hidden layers in this stage but we apply a full weight transfer of the entire source model for initialization of the target model.

we do not replace the output layer in this stage, since we use the same set of phonemes and the same phonetic decision tree both in Stage 2 and Stage 3.


In the first ablation study setup, we remove the adaption from the English model trained in Stage 1.

we randomly initialize the model in Stage 2 for training on German broadcast data and then adapted to the oral history data in Stage 3.


Thus, adapting from a rich-resourced language directly to the target language and domain is a reasonable approach if no other data is available for training in the target language.

this is shown in this experiment for state-of-the-art LF-MMI acoustic models with an LSTM-TDNN topology, which usually require a lot of data during training.


CRF take into account contextual information from previous labels to make predictions.

CRF models have been shown to be well-suited for the formulation of segmentation as sequence labelling that we use here for other tasks such as chunking (EshkolTaravella et al., 2019; Tellier et al., 2012, 2013, 2014) or Named Entity Recognition (Dupont & Tellier 2014).


This includes localized digital assistants, improved accessibility to the visually impaired to listen to text in the languages they are comfortable with and so on.

for the visually impaired, the local language books can now be converted to a large audio book library for their consumption.


Although the number of speakers was insufficient, a mild positive correlation was found between age and WER (correlation coefficient: 0.59).

the accuracy of speech recognition tended to decrease as the age of the speaker increased.


In order to build robust ASR for such settings, which can act as the foundation for CAPT systems, it is necessary to train these systems on appropriate utterances.

the data must reflect the interaction between learners and the CAPT system.


To overcome the data sparsity problem, we use a pre-trained language model for the encoding part of the encoder-decoder setup, which creates a contextualized representation of the input sequence.

we use BERT due to its bi-directional context conditioning, multilingualism and state-of-the-art scores on many other tasks (Devlin et al., 2019).


This strengthens dependencies among neighboring elements and makes the model distance-aware when it searches for lowlevel patterns in a sequence.

it restricts the attention scope to the window of neighboring elements.


We assume that the difference in performance on the CNN/Daily Mail datasets reflects the difference in distribution of the useful information within the text.

that in the SwissText dataset, it is spread more uniformly than in the CNN/Daily Mail dataset.


We adopt and make key extensions to Grusky et al.’s (2018) methodology for the development of their Newsroom dataset to the Danish language.

our clarifications, extensions, and associated code presented here permit researchers to easily develop similar automatic summarisation datasets for other non-English languages.


We extend the work of Newsroom (Grusky et al., 2018) and use The Internet Archive 6 , a non-profit archiver.

we use the Wayback Machine 7 , a sort of automatic archive system and a product of The Internet Archive.


The scores reflect how difficult it is for a QA system to choose the correct answer.

if the IR scores of Stem + Correct Answer are much higher than those of Stem + Options, then it is easy for the QA system to answer that item correctly by picking the option that has the highest scores.


In this work, we manage to derive adversarial examples in terms of the hypothesis-only bias and explore eligible ways to mitigate such bias.

we extract various phrases from the hypotheses (artificial patterns) in the training sets, and show that they have been strong indicators to the specific labels.


This dataset was designed to be as realistic as possible, so that the fact checkers trained on this dataset can handle real claims effectively.

the claims are real, often requiring a context to be fully comprehended, and the evidence is embedded in real documents from various domains.


Thus, by enabling integration of these techniques into the training pipeline, we hope to control lexicalization in the datasets which the NN methods possibly depend upon.

the contributions of our work are: (1) To motivate further research in using delexicalized datasets, we present the delexicalized versions of several benchmark datasets used in NLI (e.g., FEVER, Fake News Challenge, SNLI, and MNLI), along with the corresponding software for the delexicalization.


Next, to capture the entity overlap between premise and hypothesis sentences, we uniquely enumerate the named entities.

in the claim (c) the first instance of an entity is tagged with c1.


Note that in some cases of out-ofdomain experiments, the label space of the source domain did not match with that of the target domain.

while the FEVER dataset consisted of data belonging to 3 classes, the FNC dataset had data points belonging to 4 classes.


The transformation phase makes changes to the data format, organises data in structures, accumulates data with linguistic information, analyses data and provides explicit links between different data segments.

the Bulgarian data is web crawled, extracted from the original HTML format, filtered by document type, tokenised, sentence split, tagged and lemmatised with a fine-grained version of the Bulgarian Language Processing Chain 2 and dependency parsed with NLP-Cube 3 .


As mentioned above, this data has five listeners' responses to the same narrative speech.

there are four more attentive listening responses in Figure 3.


Therefore, contrary to the versatility, degree of empathy shown by these responses to narrative tends to become low.

the versatility and degree of empathy of the response are in a trade-off relation.


On the basis of a number of previous works (Dell’Orletta et al., 2011; Xiaobin and Meurers, 2016; Grego Bolli et al., 2017; Brezina and Pallotti, 2016; Gyllstad et al., 2014; Norris and Ortega, 2009), we defined a set of 139 linguistic features and implemented them by relying on well known NLP tools for the Italian language.

UDPipe (Straka and Strakova, 2017) was used for tokenization, lemmatisation, POS tagging and to build dependency trees, while OpenNER (Garcıa-Pablos et al., 2013) was adopted in order to compute the constituent trees.


Diachronic, or historical, linguistics has been developing in the Western humanities since the 18 th century because of growing availability of large diachronic digital corpora.

new computational linguistic methods have been developed to analyze semantic change (see Tang, 2018, for an up-to-date review).


Gradient judgments would account for the fact that bwick is typically judged to be a possible English word like blick but not as good

bwick is better than bnick but not as good as blick.


Making use of data that are concept-aligned across the languages provides a certain amount of control (to the extent possible) of the influence of linguistic content on the forms that we are modeling.

these forms should be largely comparable across the languages in terms of how common they are in the active vocabulary of adult speakers.


First, we calculated the bitsper-phoneme for just the first three positions in the word, and then looked at the correlation between this word-onset bits per phoneme and the average (full) word length in phoneme segments.

for the purpose of calculating bits-perphoneme, we truncated all words to a maximum of three phonemes, and in such a way explicitly eliminated the contribution of positions later in any word.


In order to explore the variance of the model performance induced by initialization effects, we fix the hyperparameter configurations and train models initialized with various random seeds.

we select five hyperparameter configurations, 20 and retrained them for additional five times each with different random initializations.


But looking at Equations 8 and 9, we can consider the circumstances under which this inequality will be tight, in which case we can recover the parameters directly.

if the grammar is unambiguous (i.e., if every string has at most one derivation tree) then if the left-hand side of the inequality is nonzero we can immediately see that the inequality will become an equality.


Here, we are exploring an alternative or perhaps complementary hypothesis: namely, that the acquisition of the syntactic categories and rules of the language can to a certain extent be learned using only information derived from the surface strings without any appeal to external information about the hierarchical structure of the language that is being learned.

the initial phases of language acquisition are based on purely syntactic information rather than the semantic bootstrapping discussed above.


Although trust is an important component in relationships of all kinds, it has its limitations.

it falls short of a well-known security maxim, originating in a Russian proverb that translates as, Trust, but verify.


Another approach to exploit external resources is to use a language model pre-trained on a large amount of text.

we used BERT (Devlin et al., 2019), which has shown competitive results in many NLP tasks.


Therefore, we constructed two sets of sentences including correct and wrong knowledge respectively based on the test set of ConceptNet.

the correct sentences are produced with a synonymous template whose relation tokens are replaced by synonyms (e.g., causes can also be translated to leads to), while the wrong sentences with a random template whose relation tokens are randomly replaced by another one.


The ratio of layman to expert according to each metric denotes the gap between the two styles, and a higher value implies smaller differences except that for #Sentence.

any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.


Style accuracy remains similar among these medical PCIO elements, but there are significant differences among the models in their performance for preserving content.

this is markedly demonstrated by the larger difference between their (concepts) vocabulary sizes (0.62/0.81 vs. 0.85 in ratio of layman to expert), and between the readability indices (0.81/0.81 vs. 0.84 on average).


(2) MSD is more distinct between the two styles, which is critical for style transfer.

available datasets rarely contain such cases.


The purpose of the rescaling operation in Eq. (7)is to indicate the focused aspect of the current PD step for each WD step.

We show the present and absent keyphrase prediction results in Table 2 and Table 3 correspondingly.


WOS dataset is randomly splitted into training, validation and test subsets.

the results present that our proposed global model HiAGM has the advanced capability of enhancing text features for HtC.


Those models mainly focus on improving decoders based on the constraint of hierarchical paths.

we rescale and normalize the prior probabilities of child nodes v child(k) to sum total to 1.


We apply the proposed methodology to the aligned PUD.

for example, a preposition accompanying a verb may be dropped in translation if the corresponding verb is transitive (cf. went around the world in En vs. oboshelwent.around mirworld in Ru).


Due to the difficulty in finding annotators proficient in pairs of these languages, our annotation takes English as the source language.

only content words are aligned, so as to sidestep the inherently ambiguous nature of aligning function words across divergent constructions.


The POS and edge-type confusion matrices built from this experiment are very similar to the ones reported in this paper (save for compound, which is not produced by the Stanford Zh parser), and are not reproduced here (they can be found in the Supplementary Materials).

in constructions like he could do something the direct edge between the subject and the verb of the dependent clause is replaced with two edges going through the modal predicate.


This adds a large memory and computational overhead and makes the approach impractical in real-life applications.

by utilizing ConveRT instead, we arrive at a much more lightweight and efficient model.


The French translation is poor (cumulative-split is, for instance, not translated) as the low gold MQM score shows.

existing work has only explored textual context.


Additionally, we propose and experiment with BERT-BiRNN, a variant of the BiRNN model.

as the corresponding product image shows, this product is an item of clothing, and thus the machine translation is incorrect.


In this paper, we showed that the current headline generation model yields unexpected words.

in Section 4, we build a binary classifier that predicts an entailment relation between an article and its headline and use the classifier to filter out untruthful instances in the training data.


So we need to find the most related word from the other sequence for each word in the pair.

similar to the previous works (Mikolov et al., 2013b; Pennington et al., 2014), we also learn the embeddings based on word co-occurrence.


Their encodings are then concatenated and passed through a network which scores how appropriate the response is given the utterance.

we introduce Reinforced Fine-tune Learning, an extension to DQfD, enabling us to overcome the domain gap between the datasets and the environment.


A Full Label Expert is trained on fully-annotated in-domain data, but this is lacking for many domains, and is expensive to collect and label from scratch  (Shah et al., 2018).

we believe our work is the first to explore the use of weak demonstrations for DQfD in a dialog environment.


To decode a transition sequence during the testing stage, the standard method is to choose the action that has the maximum probability of the current time step as the input for the next time step.

the textual form of each of these three discourse units will be fed into the BERt encoder for representing as Enc(s 1 ), Enc(s 2 ), and Enc(q).