question stringlengths 21 162 | answer stringlengths 1 892 | pos_doc stringlengths 167 10.8k | pos_doc_ids listlengths 1 14 |
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which multilingual approaches do they compare with? | BIBREF19, BIBREF20 | Table TABREF19 and TABREF26 report zero-shot results on Europarl and Multi-UN evaluation sets, respectively. We compare our approaches with related approaches of pivoting, multilingual NMT (MNMT) BIBREF19, and cross-lingual transfer without pretraining BIBREF16. The results show that our approaches consistently outperf... | [
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what are the pivot-based baselines? | pivoting, pivoting$_{\rm m}$ | Table TABREF19 and TABREF26 report zero-shot results on Europarl and Multi-UN evaluation sets, respectively. We compare our approaches with related approaches of pivoting, multilingual NMT (MNMT) BIBREF19, and cross-lingual transfer without pretraining BIBREF16. The results show that our approaches consistently outperf... | [
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which datasets did they experiment with? | Europarl, MultiUN | We evaluate our cross-lingual pre-training based transfer approach against several strong baselines on two public datatsets, Europarl BIBREF31 and MultiUN BIBREF32, which contain multi-parallel evaluation data to assess the zero-shot performance. In all experiments, we use BLEU as the automatic metric for translation e... | [
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what language pairs are explored? | De-En, En-Fr, Fr-En, En-Es, Ro-En, En-De, Ar-En, En-Ru | The statistics of Europarl and MultiUN corpora are summarized in Table TABREF18. For Europarl corpus, we evaluate on French-English-Spanish (Fr-En-Es), German-English-French (De-En-Fr) and Romanian-English-German (Ro-En-De), where English acts as the pivot language, its left side is the source language, and its right s... | [
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what ner models were evaluated? | Stanford NER, spaCy 2.0 , recurrent model with a CRF top layer | In this section we describe a number of experiments targeted to compare the performance of popular named entity recognition algorithms on our data. We trained and evaluated Stanford NER, spaCy 2.0, and a recurrent model similar to BIBREF13 , BIBREF14 that uses bidirectional LSTM cells for character-based feature extrac... | [
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what is the source of the news sentences? | ilur.am | Test dataset
In order to evaluate the models trained on generated data, we manually annotated a named entities dataset comprising 53453 tokens and 2566 sentences selected from over 250 news texts from ilur.am. This dataset is comparable in size with the test sets of other languages (Table TABREF10 ). Included sentences... | [
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did they use a crowdsourcing platform for manual annotations? | No | Test dataset
In order to evaluate the models trained on generated data, we manually annotated a named entities dataset comprising 53453 tokens and 2566 sentences selected from over 250 news texts from ilur.am. This dataset is comparable in size with the test sets of other languages (Table TABREF10 ). Included sentences... | [
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what are the topics pulled from Reddit? | politics, business, science, and AskReddit, and 1000 additional posts from the Reddit frontpage. | Data collection. Subreddits are sub-communities on Reddit oriented around specific interests or topics, such as technology or politics. Sampling from Reddit as a whole would bias the model towards the most commonly discussed content. But by sampling posts from individual subreddits, we can control the kinds of posts we... | [
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What predictive model do they build? | logistic regression models | We compare the predictions of logistic regression models based on unigram bag-of-words features (BOW), sentiment signals (SENT), the linguistic features from our earlier analyses (LING), and combinations of these features. BOW and SENT provide baselines for the task. We compute BOW features using term frequency-inverse... | [
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What crowdsourcing platform is used? | They did not use any platform, instead they hired undergraduate students to do the annotation. | With the purpose of obtaining evaluation datasets from crowd annotators, we collect the sentences from two domains: Dialog and E-commerce domain. We hire undergraduate students to annotate the sentences. They are required to identify the predefined types of entities in the sentences. Together with the guideline documen... | [
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How do they match words before reordering them? | CFILT-preorder system | We use the CFILT-preorder system for reordering English sentences to match the Indian language word order. It contains two re-ordering systems: (1) generic rules that apply to all Indian languages BIBREF17 , and (2) hindi-tuned rules which improve the generic rules by incorporating improvements found through an error a... | [
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On how many language pairs do they show that preordering assisting language sentences helps translation quality? | 5 | In this section, we describe the languages experimented with, datasets used, the network hyper-parameters used in our experiments.
Languages
We experimented with English INLINEFORM0 Hindi translation as the parent task. English is the assisting source language. Bengali, Gujarati, Marathi, Malayalam and Tamil are the pr... | [
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Which dataset(s) do they experiment with? | IITB English-Hindi parallel corpus BIBREF22, ILCI English-Hindi parallel corpus | In this section, we describe the languages experimented with, datasets used, the network hyper-parameters used in our experiments.
Languages
We experimented with English INLINEFORM0 Hindi translation as the parent task. English is the assisting source language. Bengali, Gujarati, Marathi, Malayalam and Tamil are the pr... | [
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Which information about text structure is included in the corpus? | paragraphs, lines, Information on physical page segmentation (for PDFs only), paragraph segmentation, and line segmentation | The paper at hand introduces a corpus developed for use in automatic readability assessment and automatic text simplification of German. The focus of this publication is on representing information that is valuable for these tasks but that hitherto has largely been ignored in machine learning approaches centering aroun... | [
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Which information about typography is included in the corpus? | font type, font style, Information on the font type and font style (e.g., italics, bold print) of a token and its position on the physical page | The paper at hand introduces a corpus developed for use in automatic readability assessment and automatic text simplification of German. The focus of this publication is on representing information that is valuable for these tasks but that hitherto has largely been ignored in machine learning approaches centering aroun... | [
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On which benchmarks they achieve the state of the art? | SimpleQuestions, WebQSP | The third block of the table details two ablation tests for the proposed components in our KBQA systems: (1) Removing the entity re-ranking step significantly decreases the scores. Since the re-ranking step relies on the relation detection models, this shows that our HR-BiLSTM model contributes to the good performance ... | [
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What they use in their propsoed framework? | break the relation names into word sequences, relation-level and word-level relation representations, bidirectional LSTMs (BiLSTMs), residual learning method | This paper improves KB relation detection to cope with the problems mentioned above. First, in order to deal with the unseen relations, we propose to break the relation names into word sequences for question-relation matching. Second, noticing that original relation names can sometimes help to match longer question con... | [
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What does KBQA abbreviate for | Knowledge Base Question Answering | Knowledge Base Question Answering (KBQA) systems answer questions by obtaining information from KB tuples BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . For an input question, these systems typically generate a KB query, which can be executed to retrieve the answers from a KB. Figure 1 illustrates the proc... | [
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What is te core component for KBQA? | answer questions by obtaining information from KB tuples | Knowledge Base Question Answering (KBQA) systems answer questions by obtaining information from KB tuples BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . For an input question, these systems typically generate a KB query, which can be executed to retrieve the answers from a KB. Figure 1 illustrates the proc... | [
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What experiments are proposed to test that upper layers produce context-specific embeddings? | They measure self-similarity, intra-sentence similarity and maximum explainable variance of the embeddings in the upper layers. | To analyze contextualized word representations, we need input sentences to feed into our pre-trained models. Our input data come from the SemEval Semantic Textual Similarity tasks from years 2012 - 2016 BIBREF14, BIBREF15, BIBREF16, BIBREF17. We use these datasets because they contain sentences in which the same words ... | [
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What is the performance of BERT on the task? | F1 scores are:
HUBES-PHI: Detection(0.965), Classification relaxed (0.95), Classification strict (0.937)
Medoccan: Detection(0.972), Classification (0.967) | To finish with this experiment set, Table also shows the strict classification precision, recall and F1-score for the compared systems. Despite the fact that, in general, the systems obtain high values, BERT outperforms them again. BERT's F1-score is 1.9 points higher than the next most competitive result in the compar... | [
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What are the other algorithms tested? | NER model, CRF classifier trained with sklearn-crfsuite, classifier has been developed that consists of regular-expressions and dictionary look-up | As the simplest baseline, a sensitive data recogniser and classifier has been developed that consists of regular-expressions and dictionary look-ups. For each category to detect a specific method has been implemented. For instance, the Date, Age, Time and Doctor detectors are based on regular-expressions; Hospital, Sex... | [
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Does BERT reach the best performance among all the algorithms compared? | No | In this experiment set, our BERT implementation is compared to several systems that participated in the MEDDOCAN challenge: a CRF classifier BIBREF18, a spaCy entity recogniser BIBREF18, and NLNDE BIBREF12, the winner of the shared task and current state of the art for sensitive information detection and classification... | [
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What are the clinical datasets used in the paper? | MEDDOCAN, NUBes-PHI | At the same challenge, mao2019hadoken occupied the 8th position among 18 participants using BERT. According to the description of the system, the authors used BERT-Base Multilingual Cased and an output CRF layer. However, their system is $\sim $3 F1-score points below our implementation without the CRF layer.
Materials... | [
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how is model compactness measured? | Using file size on disk | Even if LangID-High does not present a more accurate result, it does present a more compact one: LangID-High is 15.4 MB, while the combined wFST high resource models are 197.5 MB.
Results on Unseen Languages
Finally, we report our models' results on unseen languages in Table TABREF28 . The unseen languages are any that... | [
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what was the baseline? | system presented by deri2016grapheme | It would be interesting to compute error metrics that incorporate phoneme similarity, such as those proposed by hixon2011phonemic. PER weights all phoneme errors the same, even though some errors are more harmful than others: and are usually contrastive, whereas and almost never are. Such statistics would be especially... | [
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what evaluation metrics were used? | Phoneme Error Rate (PER), Word Error Rate (WER), Word Error Rate 100 (WER 100) | Experiments
We present experiments with two versions of our sequence-to-sequence model. LangID prepends each training, validation, and test sample with an artificial token identifying the language of the sample. NoLangID omits this token. LangID and NoLangID have identical structure otherwise. To translate the test cor... | [
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what datasets did they use? | the Carnegie Mellon Pronouncing Dictionary BIBREF12, the multilingual pronunciation corpus collected by deri2016grapheme , ranscriptions extracted from Wiktionary | The artificial token is treated simply as an element of the grapheme sequence. This is similar to the approach taken by johnson2016google in their zero-shot NMT system. However, their source-side artificial tokens identify the target language, whereas ours identify the source language. An alternative approach, used by ... | [
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Who were the human evaluators used? | 20 evaluators were recruited from our institution and asked to each perform 20 annotations | Human Evaluation Results. While automated evaluation metrics like ROUGE measure lexical similarity between machine and human summaries, humans can better measure how coherent and readable a summary is. Our evaluation study investigates whether tuning the PG-net model increases summary coherence, by asking evaluators to... | [
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Is the template-based model realistic? | Yes | Hypothesis 2 (H2) : Domain transfer helps abstractive models even if in-domain and out-of-domain data are very different and the amount of in-domain data is very small.
Hypothesis 3 (H3) : Enriching abstractive training data with synthetic data helps overcome in-domain data scarcity.
Hypothesis 4 (H4) : The proposed te... | [
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Is the student reflection data very different from the newspaper data? | Yes | To improve performance in low resource domains, we explore three directions. First, we explore domain transfer for abstractive summarization. While domain transfer is not new, compared to prior summarization studies BIBREF6, BIBREF7, our training (news) and tuning (student reflection) domains are quite dissimilar, and ... | [
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What is the recent abstractive summarization method in this paper? | pointer networks with coverage mechanism (PG-net) | To overcome the size issue of the student reflection dataset, we first explore the effect of incorporating domain transfer into a recent abstractive summarization model: pointer networks with coverage mechanism (PG-net)BIBREF0. To experiment with domain transfer, the model was pretrained using the CNN/DM dataset, then ... | [
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Why are prior knowledge distillation techniques models are ineffective in producing student models with vocabularies different from the original teacher models? | While there has been existing work on reducing NLP model vocabulary sizes BIBREF15, distillation techniques cannot utilize these, since they require the student and teacher models to share the same vocabulary and output space. This profoundly limits their potential to further reduce model sizes. | However, a significant bottleneck that has been overlooked by previous efforts is the input vocabulary size and its corresponding word embedding matrix, often accounting for a significant proportion of all model parameters. For instance, the embedding table of the BERTBASE model, comprising over 30K WordPiece tokens BI... | [
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What state-of-the-art compression techniques were used in the comparison? | baseline without knowledge distillation (termed NoKD), Patient Knowledge Distillation (PKD) | For the language modeling evaluation, we also evaluate a baseline without knowledge distillation (termed NoKD), with a model parameterized identically to the distilled student models but trained directly on the teacher model objective from scratch. For downstream tasks, we compare with NoKD as well as Patient Knowledge... | [
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What is the size of the dataset? | 30,000 | Stereotyping and Bias in the Flickr30K Dataset
An untested assumption behind the crowdsourced descriptions of the images in the Flickr30K dataset (Young et al., 2014) is that they"focus only on the information that can be obtained from the image alone"(Hodosh et al., 2013, p. 859). This paper presents some evidence aga... | [
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Which methods are considered to find examples of biases and unwarranted inferences?? | spot patterns by just looking at a collection of images, tag all descriptions with part-of-speech information, I applied Louvain clustering | It may be difficult to spot patterns by just looking at a collection of images. Another method is to tag all descriptions with part-of-speech information, so that it becomes possible to see e.g. which adjectives are most commonly used for particular nouns. One method readers may find particularly useful is to leverage ... | [
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What biases are found in the dataset? | Ethnic bias | Annotators will often guess the status or occupation of people in an image. Sometimes these guesses are relatively general (e.g. college-aged people being called students in image 36979), but other times these are very specific (e.g. a man in a workshop being called a graphics designer, 5867606).
Detecting stereotype-d... | [
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What discourse relations does it work best/worst for? | explicit discourse relations | The second row shows the performance of our basic paragraph-level model which predicts both implicit and explicit discourse relations in a paragraph. Compared to the variant system (the first row), the basic model further improved the classification performance on the first three implicit relations. Especially on the c... | [
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How much does this model improve state-of-the-art? | the basic model yields good performance for recognizing explicit discourse relations as well, which is comparable with previous best result (92.05% macro F1-score and 93.09% accuracy as reported in BIBREF11 )., full paragraph-level neural network model achieves the best macro-average F1-score of 48.82% in predicting im... | The second row shows the performance of our basic paragraph-level model which predicts both implicit and explicit discourse relations in a paragraph. Compared to the variant system (the first row), the basic model further improved the classification performance on the first three implicit relations. Especially on the c... | [
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Where is a question generation model used? | The question generation model provides each candidate answer with a score by measuring semantic relevance between the question and the generated question based on the semantics of the candidate answer. | We implement a framework consisting of both a question answering model and a question generation model, both of which take the knowledge extracted from the document as well as relevant facts from an external knowledge base such as Freebase/ProBase/Reverb/NELL. The question answering model gives each candidate answer a ... | [
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Were any of these tasks evaluated in any previous work? | Yes | Recent work examines the extent to which RNN-based models capture syntax-sensitive phenomena that are traditionally taken as evidence for the existence in hierarchical structure. In particular, in BIBREF1 we assess the ability of LSTMs to learn subject-verb agreement patterns in English, and evaluate on naturally occur... | [
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Do they build a model to automatically detect demographic, lingustic or psycological dimensons of people? | No | LIWC. In addition to individual words, we can also create maps for word categories that reflect a certain psycholinguistic or semantic property. Several lexical resources, such as Roget or Linguistic Inquiry and Word Count BIBREF9 , group words into categories. Examples of such categories are Money, which includes word... | [
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Which demographic dimensions of people do they obtain? | occupation, industry, profile information, language use, gender | Our premise is that we can generate informative maps using geolocated information available on social media; therefore, we guide the blog collection process with the constraint that we only accept blogs that have specific location information. Moreover, we aim to find blogs belonging to writers from all 50 U.S. states,... | [
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How do they obtain psychological dimensions of people? | using the Meaning Extraction Method | Values. We also measure the usage of words related to people's core values as reported by Boyd et al. boyd2015. The sets of words, or themes, were excavated using the Meaning Extraction Method (MEM) BIBREF10 . MEM is a topic modeling approach applied to a corpus of texts created by hundreds of survey respondents from t... | [
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What is the baseline? | QA PGNet, Multi-decoder QA PGNet with lookup table embedding | For the MR extraction task, we measure the ROUGE-1 scores BIBREF14 for both the Dosage and Frequency extraction tasks. It should be noted that since Dosage is a single word token (after processing), both the reference and hypothesis are a single token, making its ROUGE-1 F1, Precision and Recall scores equal and be equ... | [
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Is the data de-identified? | Yes | Our dataset consists of a total of 6,693 real doctor-patient conversations recorded in a clinical setting using distant microphones of varying quality. The recordings have an average duration of 9min 28s and have a verbatim transcript of 1,500 words on average (written by the experts). Both the audio and the transcript... | [
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What embeddings are used? | simple lookup table embeddings learned from scratch, using high-performance contextual embeddings, which are ELMo BIBREF11, BERT BIBREF16 and ClinicalBERT BIBREF13 | Apart from learning-based baselines, we also create two naive baselines, one each for the Dosage and Frequency extraction tasks. For Dosage extraction, the baseline we consider is `Nearest Number', where we take the number nearest to the Medication Name as the prediction, and `none' if no number is mentioned or if the ... | [
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What datasets did they use for evaluation? | CSAT dataset, 20 newsgroups, Fisher Phase 1 corpus | Given that Transformers' edge over recurrent networks is their ability to effectively capture long distance relationships between words in a sequence BIBREF0, we experiment with replacing the LSTM recurrent layer in favor of a small Transformer model (2 layers of transformer building block containing self-attention, fu... | [
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On top of BERT does the RNN layer work better or the transformer layer? | Transformer over BERT (ToBERT) | In this paper, we propose a method that builds upon BERT's architecture. We split the input text sequence into shorter segments in order to obtain a representation for each of them using BERT. Then, we use either a recurrent LSTM BIBREF10 network, or another Transformer, to perform the actual classification. We call th... | [
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How was this data collected? | CrowdFlower | A perfect data collection scenario for our dataset would use real running dialog system providing general information from the knowledge base to real users. This system could then ask for explanations and answers for questions which it is not able to answer.
However, getting access to systems with real users is usually... | [
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What is the average length of dialog? | 4.49 turns | We collected the dataset with 1900 dialogs and 8533 turns. Topics discussed in dialogs are questions randomly chosen from training examples of Simple questions BIBREF7 dataset. From this dataset we also took the correct answers in form of Freebase entities.
Our dataset consists of standard data split into training, dev... | [
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How are models evaluated in this human-machine communication game? | by training an autocomplete system on 500K randomly sampled sentences from Yelp reviews | We perform joint updates on $(\alpha , \beta , \lambda )$, where $\beta $ and $\lambda $ are updated via standard gradient computations, while $\alpha $ uses an unbiased, stochastic gradient estimate where we approximate the expectation in Eq (DISPLAY_FORM9). We use a single sample from $q_{\alpha }(z\mid x)$ and movin... | [
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How many participants were trying this communication game? | 100 | We recruited 100 crowdworkers on Amazon Mechanical Turk (AMT) and measured completion times and accuracies for typing randomly sampled sentences from the Yelp corpus. Each user was shown alternating autocomplete and writing tasks across 50 sentences (see Appendix for user interface). For the autocomplete task, we gave ... | [
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What user variations have been tested? | completion times and accuracies | We recruited 100 crowdworkers on Amazon Mechanical Turk (AMT) and measured completion times and accuracies for typing randomly sampled sentences from the Yelp corpus. Each user was shown alternating autocomplete and writing tasks across 50 sentences (see Appendix for user interface). For the autocomplete task, we gave ... | [
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What are the baselines used? | Unif and Stopword | We quantify the efficiency-accuracy tradeoff compared to two rule-based baselines: Unif and Stopword. The Unif encoder randomly keeps tokens to generate keywords with the probability $\delta $. The Stopword encoder keeps all tokens but drops stop words (e.g. `the', `a', `or') all the time ($\delta =0$) or half of the t... | [
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How does the IPA label data after interacting with users? | It defined a sequence labeling task to extract custom entities from user input and label the next action (out of 13 custom actions defined). | Named Entity Recognition: We defined a sequence labeling task to extract custom entities from user input. We assumed seven (7) possible entities (see Table TABREF43) to be recognized by the model: topic, subtopic, examination mode and level, question number, intent, as well as the entity other for remaining words in th... | [
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What kind of repetitive and time-consuming activities does their assistant handle? | What kind of topic (or sub-topic) a student has a problem with, At which examination mode (i.e., quiz, chapter level training or exercise, section level training or exercise, or final examination) the student is working right now, the exact question number and exact problem formulation | In general, student questions can be grouped into three main categories: organizational questions (e.g., course certificate), contextual questions (e.g., content, theorem) and mathematical questions (e.g., exercises, solutions). To assist a student with a mathematical question, a tutor has to know the following regular... | [
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How was the audio data gathered? | Through the All India Radio new channel where actors read news. | In this paper, we explore multiple pooling strategies for language identification task. Mainly we propose Ghost-VLAD based pooling method for language identification. Inspired by the recent work by W. Xie et al. [9] and Y. Zhong et al. [10], we use Ghost-VLAD to improve the accuracy of language identification task for ... | [
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What is the GhostVLAD approach? | extension of the NetVLAD, adds Ghost clusters along with the NetVLAD clusters | GhostVLAD is an extension of the NetVLAD approach, which we discussed in the previous section. The GhostVLAD model was proposed for face recognition by Y. Zhong [10]. GhostVLAD works exactly similar to NetVLAD except it adds Ghost clusters along with the NetVLAD clusters. So, now we will have a K+G number of clusters i... | [
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What datasets do they evaluate on? | Wall Street Journal (WSJ) portion of the Penn Treebank | For both POS tagging and dependency parsing, we run experiments on the Wall Street Journal (WSJ) portion of the Penn Treebank. To create the observed data embeddings, we train skip-gram word embeddings BIBREF7 that are found to capture syntactic properties well when trained with small context window BIBREF8 , BIBREF9 .... | [
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Do they evaluate only on English datasets? | Yes | For both POS tagging and dependency parsing, we run experiments on the Wall Street Journal (WSJ) portion of the Penn Treebank. To create the observed data embeddings, we train skip-gram word embeddings BIBREF7 that are found to capture syntactic properties well when trained with small context window BIBREF8 , BIBREF9 .... | [
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What is the invertibility condition? | The neural projector must be invertible. | In this section, we introduce an invertibility condition for our neural projector to tackle the optimization challenge. Specifically, we constrain our neural projector with two requirements: (1) INLINEFORM0 and (2) INLINEFORM1 exists. Invertible transformations have been explored before in independent components analys... | [
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Do they show on which examples how conflict works better than attention? | Yes | We also show the training loss curve for both the models having attention and attention combined with conflict respectively. Figure 4 and 5 shows these curves for Task 1 and Task 2 respectively. The curves are smoothed using moving average having an window size of 8. We notice that the conflict model has much steeper s... | [
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Which neural architecture do they use as a base for their attention conflict mechanisms? | GRU-based encoder, interaction block, and classifier consisting of stacked fully-connected layers. | We create two models both of which constitutes of three main parts: encoder, interaction and classifier and take two sequences as input. Except interaction, all the other parts are exactly identical between the two models. The encoder is shared among the sequences simply uses two stacked GRU layers. The interaction par... | [
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On which tasks do they test their conflict method? | Task 1: Quora Duplicate Question Pair Detection, Task 2: Ranking questions | We create two models both of which constitutes of three main parts: encoder, interaction and classifier and take two sequences as input. Except interaction, all the other parts are exactly identical between the two models. The encoder is shared among the sequences simply uses two stacked GRU layers. The interaction par... | [
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What are the sources of the datasets? | rupnik2016news | More recently, crosslingual linking of clusters has been discussed by rupnik2016news in the context of linking existing clusters from the Event Registry BIBREF7 in a batch fashion, and by steinberger2016mediagist who also present a batch clustering linking system. However, these are not “truly” online crosslingual clus... | [
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What in-domain text did they use? | PubMed+PMC | We train Word2Vec with vector size $d_\mathrm {W2V} = d_\mathrm {LM} = 768$ on PubMed+PMC (see Appendix for details). Then, we follow the procedure described in Section SECREF3 to update the wordpiece embedding layer and tokenizer of general-domain BERT.
Experiment 1: Biomedical NER ::: Finetuning
We finetune GreenBioB... | [
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Which languages do they test on for the under-resourced scenario? | English, German | Under-resourced Translation
First, we consider the translation for an under-resourced pair of languages. Here a small portion of the large parallel corpus for English-German is used as a simulation for the scenario where we do not have much parallel data: Translating texts in English to German. We perform language-spec... | [
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Are the automatically constructed datasets subject to quality control? | No | The use of lexical resources and knowledge graphs such as WordNet to construct datasets has a long history, and has recently appeared in work on adversarial attacks BIBREF14, BIBREF29 and general task construction BIBREF30, BIBREF31. In the area of MCQA, there is related work on constructing questions from tuples BIBRE... | [
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Do they focus on Reading Comprehension or multiple choice question answering? | MULTIPLE CHOICE QUESTION ANSWERING | Automatically answering questions, especially in the open-domain setting (i.e., where minimal or no contextual knowledge is explicitly provided), requires bringing to bear considerable amount of background knowledge and reasoning abilities. For example, knowing the answers to the two questions in Figure FIGREF1 require... | [
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After how many hops does accuracy decrease? | 1-hop links to 2-hops | Our comprehensive assessment reveals several interesting nuances to the overall positive trend. For example, the performance of even the best QA models degrades substantially on our hyponym probes (by 8-15%) when going from 1-hop links to 2-hops. Further, the accuracy of even our best models on the WordNetQA probe drop... | [
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How do they control for annotation artificats? | we use several of the MCQA baseline models first introduced in BIBREF0 | which is run through the pre-trained BERT encoder to generate a representation for $ q^{(j)}_{a_{i}}$ using the hidden state representation for CLS (i.e., the classifier token) $\textbf {c}_{i}$:
The probability of a given answer $p^{(j)}_{i}$ is then computed as $p^{(j)}_{i} \propto e^{\textbf {v}\cdot \textbf {c}^{(j... | [
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Is WordNet useful for taxonomic reasoning for this task? | Yes | While our methodology is amenable to any knowledge source and set of models/benchmark tasks, we focus on probing state-of-the-art transformer models BIBREF7, BIBREF9 in the domain of science MCQA. For sources of expert knowledge, we use WordNet, a comprehensive lexical ontology, and other publicly available dictionary ... | [
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How do they perform multilingual training? | Multilingual training is performed by randomly alternating between languages for every new minibatch | As MSD tags are only available in Track 1, this augmentation only applies to this track.
The parameters of the entire MSD (auxiliary-task) decoder are shared across languages.
Since a grouping of the languages based on language family would have left several languages in single-member groups (e.g. Russian is the sole r... | [
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Does the model have attention? | Yes | Our system is a modification of the provided CoNLL–SIGMORPHON 2018 baseline system, so we begin this section with a reiteration of the baseline system architecture, followed by a description of the three augmentations we introduce.
Baseline
The CoNLL–SIGMORPHON 2018 baseline is described as follows:
The system is an en... | [
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What architecture does the decoder have? | LSTM | Our system is a modification of the provided CoNLL–SIGMORPHON 2018 baseline system, so we begin this section with a reiteration of the baseline system architecture, followed by a description of the three augmentations we introduce.
Baseline
The CoNLL–SIGMORPHON 2018 baseline is described as follows:
The system is an en... | [
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What architecture does the encoder have? | LSTM | Our system is a modification of the provided CoNLL–SIGMORPHON 2018 baseline system, so we begin this section with a reiteration of the baseline system architecture, followed by a description of the three augmentations we introduce.
Baseline
The CoNLL–SIGMORPHON 2018 baseline is described as follows:
The system is an en... | [
776
] |
Do they use attention? | Yes | Contextual Encoding Layer. A shared two-layers BiLSTM is used on the top to encode the contextual information of both passages and questions. To avoid overfitting, we concatenate a pre-trained 600-dimensional CoVe vectors BIBREF12 trained on German-English machine translation dataset, with the aforementioned lexicon em... | [
793
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What other models do they compare to? | SAN Baseline, BNA, DocQA, R.M-Reader, R.M-Reader+Verifier and DocQA+ELMo | The results in terms of EM and F1 is summarized in Table TABREF20 . We observe that Joint SAN outperforms the SAN baseline with a large margin, e.g., 67.89 vs 69.27 (+1.38) and 70.68 vs 72.20 (+1.52) in terms of EM and F1 scores respectively, so it demonstrates the effectiveness of the joint optimization. By incorporat... | [
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What is the architecture of the span detector? | adopt a multi-turn answer module for the span detector BIBREF1 | where INLINEFORM0 means that we only drop diagonal elements on the similarity matrix (i.e., attention with itself). At last, INLINEFORM1 and INLINEFORM2 are concatenated and are passed through a BiLSTM to form the final memory: INLINEFORM3 .
Span detector. We adopt a multi-turn answer module for the span detector BIBRE... | [
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What evaluation metric do they use? | Accuracy | Comparing to 3 , it adds the term $\lambda * L$ , where $L$ is the identity matrix of the size equal to the number of features, with 0 at the top left cell, and $\lambda $ is a real number used to tune the influence of regularization term (if $\lambda = 0$ , there is no regularization).
To test all the possible combina... | [
816
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What are the baselines? | a score of 40 | BIBREF6 introduce the KG-A2C, which uses a knowledge graph based state-representation to aid in the section of actions in a combinatorially-sized action-space—specifically they use the knowledge graph to constrain the kinds of entities that can be filled in the blanks in the template action-space. They test their appro... | [
823,
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What are the two new strategies? | a method that detects bottlenecks in text-games using the overall reward gained and the knowledge graph state, to leverage knowledge graphs to improve existing exploration algorithms for dealing with combinatorial action-space | More efficient exploration strategies are required to pass bottlenecks. Our contributions are two-fold. We first introduce a method that detects bottlenecks in text-games using the overall reward gained and the knowledge graph state. This method freezes the policy used to reach the bottleneck and restarts the training ... | [
825
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How much better than the baseline is LiLi? | In case of Freebase knowledge base, LiLi model had better F1 score than the single model by 0.20 , 0.01, 0.159 for kwn, unk, and all test Rel type. The values for WordNet are 0.25, 0.1, 0.2.
| Given a relation INLINEFORM0 and an observed triple ( INLINEFORM1 , INLINEFORM2 , INLINEFORM3 ) in training or testing, the pair ( INLINEFORM4 , INLINEFORM5 ) is regarded as a +ve instance for INLINEFORM6 . Following BIBREF18 , for each +ve instance ( INLINEFORM7 , INLINEFORM8 ), we generate two negative ones, one by r... | [
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What baseline is used in the experiments? | versions of LiLi | Given a relation INLINEFORM0 and an observed triple ( INLINEFORM1 , INLINEFORM2 , INLINEFORM3 ) in training or testing, the pair ( INLINEFORM4 , INLINEFORM5 ) is regarded as a +ve instance for INLINEFORM6 . Following BIBREF18 , for each +ve instance ( INLINEFORM7 , INLINEFORM8 ), we generate two negative ones, one by r... | [
878,
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] |
In what way does LiLi imitate how humans acquire knowledge and perform inference during an interactive conversation? | newly acquired facts are retained in the KB and used in inference for future queries, and that the accumulated knowledge in addition to the updated KB including past inference performances are leveraged to guide future interaction and learning | We solve the OKBC problem by mimicking how humans acquire knowledge and perform reasoning in an interactive conversation. Whenever we encounter an unknown concept or relation while answering a query, we perform inference using our existing knowledge. If our knowledge does not allow us to draw a conclusion, we typically... | [
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What metrics are used to establish that this makes chatbots more knowledgeable and better at learning and conversation? | Coverage, Avg. MCC and avg. +ve F1 score | BG: The missing or connecting links (when the user does not respond) are filled with “@-RelatedTo-@" blindly, no guessing mechanism.
w/o PTS: LiLi does not ask for additional clues via past task selection for skillset improvement.
Evaluation Metrics. To evaluate the strategy formulation ability, we introduce a measure ... | [
879
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What are the components of the general knowledge learning engine? | Answer with content missing: (list)
LiLi should have the following capabilities:
1. to formulate an inference strategy for a given query that embeds processing and interactive actions.
2. to learn interaction behaviors (deciding what to ask and when to ask the user).
3. to leverage the acquired knowledge in the current... | We solve the OKBC problem by mimicking how humans acquire knowledge and perform reasoning in an interactive conversation. Whenever we encounter an unknown concept or relation while answering a query, we perform inference using our existing knowledge. If our knowledge does not allow us to draw a conclusion, we typically... | [
844
] |
What is the architecture of the model? | one-layer CNN structure from previous works BIBREF22 , BIBREF4 | For the proposed model, we denote INLINEFORM0 parameterized by INLINEFORM1 as a neural-based feature encoder that maps documents from both domains to a shared feature space, and INLINEFORM2 parameterized by INLINEFORM3 as a fully connected layer with softmax activation serving as the sentiment classifier. We aim to lea... | [
895,
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What are the baseline methods? | (1) Naive, (2) mSDA BIBREF7, (3) NaiveNN, (4) AuxNN BIBREF4, (5) ADAN BIBREF16, (6) MMD | On the small-scale datasets and the Aamzon benchmark, INLINEFORM0 and INLINEFORM1 are set to 200 and 1, respectively, tuned on the development set of task E INLINEFORM2 BK under setting 1. On the large-scale datasets, INLINEFORM3 and INLINEFORM4 are set to 500 and 0.2, respectively, tuned on I INLINEFORM5 Y. We use a G... | [
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What are the source and target domains? | Book, electronics, beauty, music, IMDB, Yelp, cell phone, baby, DVDs, kitchen | Most previous works BIBREF0 , BIBREF1 , BIBREF6 , BIBREF7 , BIBREF29 carried out experiments on the Amazon benchmark released by Blitzer et al. ( BIBREF0 ). The dataset contains 4 different domains: Book (B), DVDs (D), Electronics (E), and Kitchen (K). Following their experimental settings, we consider the binary class... | [
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Did they use a crowdsourcing platform for annotations? | No | When it comes to feature selection, the most unexpected phenomenon observed in this study is low usefulness of the interpretation-based features. According to Table TABREF22 , adding interpretations of neighbouring words ( INLINEFORM0 ) yields very little improvement, while this type of information regarding replacemen... | [
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How do they deal with unknown distribution senses? | The Näive-Bayes classifier is corrected so it is not biased to most frequent classes | Monosemous relatives have been employed multiple times (see Section 2), but results remain unsatisfactory. The aim of my study is to explore the limitations of this technique by implementing and evaluating such a tool for Polish. Firstly, the method is expanded by waiving the requirement of monosemy and proposing sever... | [
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Do they report results only on English data? | Yes | With their ability to operate freely on social media now curtailed, ISIS recruiters and propagandists increased their attentiveness to another longstanding tool–English language online magazines targeting western audiences. Al Hayat, the media wing of ISIS, published multiple online magazines in different languages inc... | [
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What conclusions do the authors draw from their finding that the emotional appeal of ISIS and Catholic materials are similar? | both corpuses used words that aim to inspire readers while avoiding fear, actual words that lead to these effects are very different in the two contexts, our findings indicate that, using proper methods, automated analysis of large bodies of textual data can provide novel insight insight into extremist propaganda | Comparing these topics with those that appeared on a Catholic women forum, it seems that both ISIS and non-violent groups use topics about motherhood, spousal relationship, and marriage/divorce when they address women. Moreover, we used Depechemood methods to analyze the emotions that these materials are likely to elic... | [
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How id Depechemood trained? | By multiplying crowd-annotated document-emotion matrix with emotion-word matrix. | Depechemood is a lexicon-based emotion detection method gathered from crowd-annotated news BIBREF24. Drawing on approximately 23.5K documents with average of 500 words per document from rappler.com, researchers asked subjects to report their emotions after reading each article. They then multiplied the document-emotion... | [
970
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How are similarities and differences between the texts from violent and non-violent religious groups analyzed? | By using topic modeling and unsupervised emotion detection on ISIS materials and articles from Catholic women forum | One such approach was the publication of online magazines in different languages including English. Although discontinued now, these online resources provided a window into ISIS ideology, recruitment, and how they wanted the world to perceive them. For example, after predominantly recruiting men, ISIS began to also inc... | [
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How are prominent topics idenified in Dabiq and Rumiyah? | LDA, non-negative matrix factorization (NMF) | Topic modeling methods are the more powerful technique for understanding the contents of a corpus. These methods try to discover abstract topics in a corpus and reveal hidden semantic structures in a collection of documents. The most popular topic modeling methods use probabilistic approaches such as probabilistic late... | [
967,
968
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Are the images from a specific domain? | Yes | Practical evaluation of GTD is currently only possible on synthetic data. We construct a range of datasets designed for image captioning evaluation. We call this diagnostic evaluation benchmark ShapeWorldICE (ShapeWorld for Image Captioning Evaluation). We illustrate the evaluation of specific image captioning models o... | [
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Which datasets are used? | Existential (OneShape, MultiShapes), Spacial (TwoShapes, Multishapes), Quantification (Count, Ratio) datasets are generated from ShapeWorldICE | Language constructions here correspond to reduced caption representations which only record whether an object is described by shape (e.g., “square”), color (e.g., “red shape”) or color-shape combination (e.g., “red square”). So the statement “A square is red” and “A circle is blue” are considered the same, while “A sha... | [
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