id stringclasses 179
values | question stringlengths 8.75k 85.9k | answer dict |
|---|---|---|
1909.00279 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What dataset is used for training?
Context: <<<Title>>>
Generating Classical Chinese Poems from Vernacular Chinese
<<<Abstract>>>
Classical Chinese poetry is a jewel in the treasure house o... | {
"references": [
"We collected a corpus of poems and a corpus of vernacular literature from online resources"
],
"type": "extractive"
} |
1909.06762 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What were the evaluation metrics?
Context: <<<Title>>>
Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever
<<<Abstract>>>
Querying the knowledge base (KB) has long ... | {
"references": [
"BLEU,Micro Entity F1,quality of the responses according to correctness, fluency, and humanlikeness on a scale from 1 to 5"
],
"type": "extractive"
} |
1909.06762 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What were the baseline systems?
Context: <<<Title>>>
Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever
<<<Abstract>>>
Querying the knowledge base (KB) has long be... | {
"references": [
"Attn seq2seq,Ptr-UNK,KV Net,Mem2Seq,DSR"
],
"type": "extractive"
} |
1909.06762 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: Which dialog datasets did they experiment with?
Context: <<<Title>>>
Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever
<<<Abstract>>>
Querying the knowledge base ... | {
"references": [
"Camrest,InCar Assistant"
],
"type": "extractive"
} |
1911.00069 | Please answer the following question with yes or no based on the given text. You only need to output 'Yes' or 'No' without any additional explanation.
Question: Do they train their own RE model?
Context: <<<Title>>>
Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping
<<<Abstract>>>
Rela... | {
"references": [
"Yes"
],
"type": "boolean"
} |
1911.00069 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What languages do they experiment on?
Context: <<<Title>>>
Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping
<<<Abstract>>>
Relation extraction (RE) seeks t... | {
"references": [
"English, German, Spanish, Italian, Japanese and Portuguese, English, Arabic and Chinese"
],
"type": "extractive"
} |
1911.00069 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What datasets are used?
Context: <<<Title>>>
Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping
<<<Abstract>>>
Relation extraction (RE) seeks to detect and c... | {
"references": [
"in-house dataset,ACE05 dataset "
],
"type": "extractive"
} |
2001.01589 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How does the word segmentation method work?
Context: <<<Title>>>
Morphological Word Segmentation on Agglutinative Languages for Neural Machine Translation
<<<Abstract>>>
Neural machine tran... | {
"references": [
"morpheme segmentation BIBREF4 and Byte Pair Encoding (BPE) BIBREF5,Zemberek,BIBREF12"
],
"type": "extractive"
} |
2001.01589 | Please answer the following question with yes or no based on the given text. You only need to output 'Yes' or 'No' without any additional explanation.
Question: Is the word segmentation method independently evaluated?
Context: <<<Title>>>
Morphological Word Segmentation on Agglutinative Languages for Neural Machine T... | {
"references": [
"No"
],
"type": "boolean"
} |
1910.05456 | Please answer the following question with yes or no based on the given text. You only need to output 'Yes' or 'No' without any additional explanation.
Question: Are agglutinative languages used in the prediction of both prefixing and suffixing languages?
Context: <<<Title>>>
Acquisition of Inflectional Morphology in ... | {
"references": [
"Yes"
],
"type": "boolean"
} |
1910.05456 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What is an example of a prefixing language?
Context: <<<Title>>>
Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge
<<<Abstract>>>
How does knowledge ... | {
"references": [
"Zulu"
],
"type": "extractive"
} |
1910.05456 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What are the tree target languages studied in the paper?
Context: <<<Title>>>
Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge
<<<Abstract>>>
How do... | {
"references": [
"English, Spanish and Zulu"
],
"type": "extractive"
} |
1909.04625 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What is the size of the datasets employed?
Context: <<<Title>>>
Representation of Constituents in Neural Language Models: Coordination Phrase as a Case Study
<<<Abstract>>>
Neural language ... | {
"references": [
"(about 4 million sentences, 138 million word tokens),one trained on the Billion Word benchmark"
],
"type": "extractive"
} |
1909.04625 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What are the baseline models?
Context: <<<Title>>>
Representation of Constituents in Neural Language Models: Coordination Phrase as a Case Study
<<<Abstract>>>
Neural language models have a... | {
"references": [
"Recurrent Neural Network (RNN),ActionLSTM,Generative Recurrent Neural Network Grammars (RNNG)"
],
"type": "extractive"
} |
2002.00652 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How big is improvement in performances of proposed model over state of the art?
Context: <<<Title>>>
How Far are We from Effective Context Modeling ? An Exploratory Study on Semantic Parsin... | {
"references": [
"Compared with the previous SOTA without BERT on SParC, our model improves Ques.Match and Int.Match by $10.6$ and $5.4$ points, respectively."
],
"type": "extractive"
} |
2002.00652 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What two large datasets are used for evaluation?
Context: <<<Title>>>
How Far are We from Effective Context Modeling ? An Exploratory Study on Semantic Parsing in Context
<<<Abstract>>>
Rec... | {
"references": [
"SParC BIBREF2 and CoSQL BIBREF6"
],
"type": "extractive"
} |
2002.00652 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What are two datasets models are tested on?
Context: <<<Title>>>
How Far are We from Effective Context Modeling ? An Exploratory Study on Semantic Parsing in Context
<<<Abstract>>>
Recently... | {
"references": [
"SParC BIBREF2 and CoSQL BIBREF6"
],
"type": "extractive"
} |
1909.00324 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How big is the improvement over the state-of-the-art results?
Context: <<<Title>>>
A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis
<<<Abstract>>>
Aspect base... | {
"references": [
"AGDT improves the performance by 2.4% and 1.6% in the “DS” part of the two dataset,Our AGDT surpasses GCAE by a very large margin (+11.4% and +4.9% respectively) on both datasets,In the “HDS” part, the AGDT model obtains +3.6% higher accuracy than GCAE on the restaurant domain and +4.2% higher ... |
1909.00324 | Please answer the following question with yes or no based on the given text. You only need to output 'Yes' or 'No' without any additional explanation.
Question: Is the model evaluated against other Aspect-Based models?
Context: <<<Title>>>
A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysi... | {
"references": [
"Yes"
],
"type": "boolean"
} |
2004.03034 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What models that rely only on claim-specific linguistic features are used as baselines?
Context: <<<Title>>>
The Role of Pragmatic and Discourse Context in Determining Argument Impact
<<<Ab... | {
"references": [
"SVM with RBF kernel"
],
"type": "extractive"
} |
2004.03034 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How is pargmative and discourse context added to the dataset?
Context: <<<Title>>>
The Role of Pragmatic and Discourse Context in Determining Argument Impact
<<<Abstract>>>
Research in the ... | {
"references": [
"While evaluating the impact of a claim, users have access to the full argument context and therefore, they can assess how impactful a claim is in the given context of an argument."
],
"type": "extractive"
} |
2004.03034 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What annotations are available in the dataset?
Context: <<<Title>>>
The Role of Pragmatic and Discourse Context in Determining Argument Impact
<<<Abstract>>>
Research in the social sciences... | {
"references": [
"5 possible impact labels for a particular claim: no impact, low impact, medium impact, high impact and very high impact"
],
"type": "extractive"
} |
1910.12618 | Please answer the following question with yes or no based on the given text. You only need to output 'Yes' or 'No' without any additional explanation.
Question: Is there any example where geometric property is visible for context similarity between words?
Context: <<<Title>>>
Textual Data for Time Series Forecasting
... | {
"references": [
"Yes"
],
"type": "boolean"
} |
1911.12569 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How is multi-tasking performed?
Context: <<<Title>>>
Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis
<<<Abstract>>>
In this paper, we propose a two-layered mu... | {
"references": [
"The proposed system consists of a Bi-directional Long Short-Term Memory (BiLSTM) BIBREF16, a two-level attention mechanism BIBREF29, BIBREF30 and a shared representation for emotion and sentiment analysis tasks.,Each of the shared representations is then fed to the primary attention mechanism"
... |
1911.12569 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What are the datasets used for training?
Context: <<<Title>>>
Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis
<<<Abstract>>>
In this paper, we propose a two-l... | {
"references": [
"SemEval 2016 Task 6 BIBREF7,Stance Sentiment Emotion Corpus (SSEC) BIBREF15"
],
"type": "extractive"
} |
1911.12569 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What is the previous state-of-the-art model?
Context: <<<Title>>>
Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis
<<<Abstract>>>
In this paper, we propose a t... | {
"references": [
"BIBREF7,BIBREF39,BIBREF37,LitisMind,Maximum entropy, SVM, LSTM, Bi-LSTM, and CNN"
],
"type": "extractive"
} |
1911.03243 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How was coverage measured?
Context: <<<Title>>>
Crowdsourcing a High-Quality Gold Standard for QA-SRL
<<<Abstract>>>
Question-answer driven Semantic Role Labeling (QA-SRL) has been proposed... | {
"references": [
"QA pairs per predicate"
],
"type": "extractive"
} |
1911.03243 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How was the corpus obtained?
Context: <<<Title>>>
Crowdsourcing a High-Quality Gold Standard for QA-SRL
<<<Abstract>>>
Question-answer driven Semantic Role Labeling (QA-SRL) has been propos... | {
"references": [
" trained annotators BIBREF4,crowdsourcing BIBREF5 "
],
"type": "extractive"
} |
1911.03243 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How are workers trained?
Context: <<<Title>>>
Crowdsourcing a High-Quality Gold Standard for QA-SRL
<<<Abstract>>>
Question-answer driven Semantic Role Labeling (QA-SRL) has been proposed a... | {
"references": [
"extensive personal feedback"
],
"type": "extractive"
} |
1911.03243 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How was the previous dataset annotated?
Context: <<<Title>>>
Crowdsourcing a High-Quality Gold Standard for QA-SRL
<<<Abstract>>>
Question-answer driven Semantic Role Labeling (QA-SRL) has ... | {
"references": [
"the annotation machinery of BIBREF5"
],
"type": "extractive"
} |
1911.03243 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How big is the dataset?
Context: <<<Title>>>
Crowdsourcing a High-Quality Gold Standard for QA-SRL
<<<Abstract>>>
Question-answer driven Semantic Role Labeling (QA-SRL) has been proposed as... | {
"references": [
"1593 annotations"
],
"type": "extractive"
} |
1910.03467 | Please answer the following question with yes or no based on the given text. You only need to output 'Yes' or 'No' without any additional explanation.
Question: Are synonymous relation taken into account in the Japanese-Vietnamese task?
Context: <<<Title>>>
Overcoming the Rare Word Problem for Low-Resource Language P... | {
"references": [
"Yes"
],
"type": "boolean"
} |
1910.03467 | Please answer the following question with yes or no based on the given text. You only need to output 'Yes' or 'No' without any additional explanation.
Question: Is the supervised morphological learner tested on Japanese?
Context: <<<Title>>>
Overcoming the Rare Word Problem for Low-Resource Language Pairs in Neural M... | {
"references": [
"No"
],
"type": "boolean"
} |
1908.07816 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How does the multi-turn dialog system learns?
Context: <<<Title>>>
A Multi-Turn Emotionally Engaging Dialog Model
<<<Abstract>>>
Open-domain dialog systems (also known as chatbots) have inc... | {
"references": [
"we extract the emotion information from the utterances in $\\mathbf {X}$ by leveraging an external text analysis program, and use an RNN to encode it into an emotion context vector $\\mathbf {e}$, which is combined with $\\mathbf {c}_t$ to produce the distribution"
],
"type": "extractive"
} |
1908.07816 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How is human evaluation performed?
Context: <<<Title>>>
A Multi-Turn Emotionally Engaging Dialog Model
<<<Abstract>>>
Open-domain dialog systems (also known as chatbots) have increasingly d... | {
"references": [
"(1) grammatical correctness,(2) contextual coherence,(3) emotional appropriateness"
],
"type": "extractive"
} |
1908.07816 | Please answer the following question with yes or no based on the given text. You only need to output 'Yes' or 'No' without any additional explanation.
Question: Is some other metrics other then perplexity measured?
Context: <<<Title>>>
A Multi-Turn Emotionally Engaging Dialog Model
<<<Abstract>>>
Open-domain dialog s... | {
"references": [
"No"
],
"type": "boolean"
} |
1908.07816 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What two baseline models are used?
Context: <<<Title>>>
A Multi-Turn Emotionally Engaging Dialog Model
<<<Abstract>>>
Open-domain dialog systems (also known as chatbots) have increasingly d... | {
"references": [
" sequence-to-sequence model (denoted as S2S),HRAN"
],
"type": "extractive"
} |
1911.09483 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What evaluation metric is used?
Context: <<<Title>>>
MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning
<<<Abstract>>>
In sequence to sequence learning, the self-attenti... | {
"references": [
"The BLEU metric "
],
"type": "extractive"
} |
1911.09483 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What datasets are used?
Context: <<<Title>>>
MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning
<<<Abstract>>>
In sequence to sequence learning, the self-attention mecha... | {
"references": [
"WMT14 En-Fr and En-De datasets,IWSLT De-En and En-Vi datasets"
],
"type": "extractive"
} |
1911.09483 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What are three main machine translation tasks?
Context: <<<Title>>>
MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning
<<<Abstract>>>
In sequence to sequence learning, t... | {
"references": [
"De-En, En-Fr and En-Vi translation tasks"
],
"type": "extractive"
} |
1911.09483 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How big is improvement in performance over Transformers?
Context: <<<Title>>>
MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning
<<<Abstract>>>
In sequence to sequence l... | {
"references": [
"2.2 BLEU gains"
],
"type": "extractive"
} |
1909.05358 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What is the average number of turns per dialog?
Context: <<<Title>>>
Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset
<<<Abstract>>>
A significant barrier to progress in data-dri... | {
"references": [
"The average number of utterances per dialog is about 23 "
],
"type": "extractive"
} |
1909.05358 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What baseline models are offered?
Context: <<<Title>>>
Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset
<<<Abstract>>>
A significant barrier to progress in data-driven approaches... | {
"references": [
"3-gram and 4-gram conditional language model,Convolution,LSTM models BIBREF27 with and without attention BIBREF28,Transformer,GPT-2"
],
"type": "extractive"
} |
1909.05358 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: Which six domains are covered in the dataset?
Context: <<<Title>>>
Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset
<<<Abstract>>>
A significant barrier to progress in data-drive... | {
"references": [
"ordering pizza, creating auto repair appointments, setting up ride service, ordering movie tickets, ordering coffee drinks and making restaurant reservations"
],
"type": "extractive"
} |
2004.03744 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: Is model explanation output evaluated, what metric was used?
Context: <<<Title>>>
e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanations
<<<Abstract>>>
The re... | {
"references": [
"balanced accuracy, i.e., the average of the three accuracies on each class"
],
"type": "extractive"
} |
2004.03744 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How many annotators are used to write natural language explanations to SNLI-VE-2.0?
Context: <<<Title>>>
e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanation... | {
"references": [
"2,060 workers"
],
"type": "extractive"
} |
2004.03744 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How much is performance difference of existing model between original and corrected corpus?
Context: <<<Title>>>
e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Exp... | {
"references": [
"73.02% on the uncorrected SNLI-VE test set, achieves 73.18% balanced accuracy when tested on the corrected test set"
],
"type": "extractive"
} |
2004.03744 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What is the class with highest error rate in SNLI-VE?
Context: <<<Title>>>
e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanations
<<<Abstract>>>
The recently ... | {
"references": [
"neutral class"
],
"type": "extractive"
} |
1911.12579 | Please answer the following question with yes or no based on the given text. You only need to output 'Yes' or 'No' without any additional explanation.
Question: Are trained word embeddings used for any other NLP task?
Context: <<<Title>>>
A New Corpus for Low-Resourced Sindhi Language with Word Embeddings
<<<Abstract... | {
"references": [
"No"
],
"type": "boolean"
} |
1911.12579 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How is the data collected, which web resources were used?
Context: <<<Title>>>
A New Corpus for Low-Resourced Sindhi Language with Word Embeddings
<<<Abstract>>>
Representing words and phra... | {
"references": [
"daily Kawish and Awami Awaz Sindhi newspapers,Wikipedia dumps,short stories and sports news from Wichaar social blog,news from Focus Word press blog,historical writings, novels, stories, books from Sindh Salamat literary website,novels, history and religious books from Sindhi Adabi Board, tweet... |
2004.02929 | Please answer the following question with yes or no based on the given text. You only need to output 'Yes' or 'No' without any additional explanation.
Question: Does the paper mention other works proposing methods to detect anglicisms in Spanish?
Context: <<<Title>>>
An Annotated Corpus of Emerging Anglicisms in Span... | {
"references": [
"Yes"
],
"type": "boolean"
} |
2004.02929 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What is the performance of the CRF model on the task described?
Context: <<<Title>>>
An Annotated Corpus of Emerging Anglicisms in Spanish Newspaper Headlines
<<<Abstract>>>
The extraction ... | {
"references": [
"the results obtained on development and test set (F1 = 89.60, F1 = 87.82) and the results on the supplemental test set (F1 = 71.49)"
],
"type": "extractive"
} |
2004.02929 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: Does the paper motivate the use of CRF as the baseline model?
Context: <<<Title>>>
An Annotated Corpus of Emerging Anglicisms in Spanish Newspaper Headlines
<<<Abstract>>>
The extraction of... | {
"references": [
"the task of detecting anglicisms can be approached as a sequence labeling problem where only certain spans of texts will be labeled as anglicism (in a similar way to an NER task). The chosen model was conditional random field model (CRF), which was also the most popular model in both Shared Tas... |
2004.02929 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What are the handcrafted features used?
Context: <<<Title>>>
An Annotated Corpus of Emerging Anglicisms in Spanish Newspaper Headlines
<<<Abstract>>>
The extraction of anglicisms (lexical b... | {
"references": [
"Bias feature,Token feature,Uppercase feature (y/n),Titlecase feature (y/n),Character trigram feature,Quotation feature (y/n),Word suffix feature (last three characters),POS tag (provided by spaCy utilities),Word shape (provided by spaCy utilities),Word embedding (see Table TABREF26)"
],
"ty... |
1910.00825 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What automatic and human evaluation metrics are used to compare SPNet to its counterparts?
Context: <<<Title>>>
Abstractive Dialog Summarization with Semantic Scaffolds
<<<Abstract>>>
The d... | {
"references": [
"ROUGE and CIC,relevance, conciseness and readability on a 1 to 5 scale, and rank the summary pair"
],
"type": "extractive"
} |
1910.00825 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How does SPNet utilize additional speaker role, semantic slot and dialog domain annotations?
Context: <<<Title>>>
Abstractive Dialog Summarization with Semantic Scaffolds
<<<Abstract>>>
The... | {
"references": [
"Our encoder-decoder framework employs separate encoding for different speakers in the dialog.,We integrate semantic slot scaffold by performing delexicalization on original dialogs.,We integrate dialog domain scaffold through a multi-task framework."
],
"type": "extractive"
} |
1910.00825 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What are previous state-of-the-art document summarization methods used?
Context: <<<Title>>>
Abstractive Dialog Summarization with Semantic Scaffolds
<<<Abstract>>>
The demand for abstracti... | {
"references": [
"Pointer-Generator,Transformer"
],
"type": "extractive"
} |
1910.00825 | Please answer the following question with yes or no based on the given text. You only need to output 'Yes' or 'No' without any additional explanation.
Question: Is new evaluation metric extension of ROGUE?
Context: <<<Title>>>
Abstractive Dialog Summarization with Semantic Scaffolds
<<<Abstract>>>
The demand for abst... | {
"references": [
"No"
],
"type": "boolean"
} |
1910.00458 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How big are improvements of MMM over state of the art?
Context: <<<Title>>>
MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
<<<Abstract>>>
Machine Reading Compre... | {
"references": [
"test accuracy of 88.9%, which exceeds the previous best by 16.9%"
],
"type": "extractive"
} |
1910.00458 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What out of domain datasets authors used for coarse-tuning stage?
Context: <<<Title>>>
MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
<<<Abstract>>>
Machine Rea... | {
"references": [
"MultiNLI BIBREF15 and SNLI BIBREF16 "
],
"type": "extractive"
} |
1910.00458 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What four representative datasets are used for bechmark?
Context: <<<Title>>>
MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
<<<Abstract>>>
Machine Reading Comp... | {
"references": [
"DREAM, MCTest, TOEFL, and SemEval-2018 Task 11"
],
"type": "extractive"
} |
2001.11268 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What baselines did they consider?
Context: <<<Title>>>
Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering Tasks
<<<Abstract>>>
This research on data ... | {
"references": [
"LSTM,SCIBERT"
],
"type": "extractive"
} |
1909.08824 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: Which models do they use as baselines on the Atomic dataset?
Context: <<<Title>>>
Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder
<<<... | {
"references": [
"RNN-based Seq2Seq,Variational Seq2Seq,VRNMT ,CWVAE-Unpretrained"
],
"type": "extractive"
} |
1909.02480 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What non autoregressive NMT models are used for comparison?
Context: <<<Title>>>
FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow
<<<Abstract>>>
Most sequenc... | {
"references": [
"NAT w/ Fertility,NAT-IR,NAT-REG,LV NAR,CTC Loss,CMLM"
],
"type": "extractive"
} |
1909.02480 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What are three neural machine translation (NMT) benchmark datasets used for evaluation?
Context: <<<Title>>>
FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow... | {
"references": [
"WMT2014, WMT2016 and IWSLT-2014"
],
"type": "extractive"
} |
1910.02754 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What is result of their attention distribution analysis?
Context: <<<Title>>>
On Leveraging the Visual Modality for Neural Machine Translation
<<<Abstract>>>
Leveraging the visual modality ... | {
"references": [
"visual attention is very sparse, visual component of the attention hasn't learnt any variation over the source encodings"
],
"type": "extractive"
} |
1910.02754 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What is result of their Principal Component Analysis?
Context: <<<Title>>>
On Leveraging the Visual Modality for Neural Machine Translation
<<<Abstract>>>
Leveraging the visual modality eff... | {
"references": [
"existing visual features aren't sufficient enough to expect benefits from the visual modality in NMT"
],
"type": "extractive"
} |
1910.02754 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What are 3 novel fusion techniques that are proposed?
Context: <<<Title>>>
On Leveraging the Visual Modality for Neural Machine Translation
<<<Abstract>>>
Leveraging the visual modality eff... | {
"references": [
"Step-Wise Decoder Fusion,Multimodal Attention Modulation,Visual-Semantic (VS) Regularizer"
],
"type": "extractive"
} |
2004.02393 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What are two models' architectures in proposed solution?
Context: <<<Title>>>
Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games
<<<Abstract>>>
We p... | {
"references": [
"Reasoner model, also implemented with the MatchLSTM architecture,Ranker model"
],
"type": "extractive"
} |
2004.02393 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How do two models cooperate to select the most confident chains?
Context: <<<Title>>>
Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games
<<<Abstract... | {
"references": [
"Reasoner learns to extract the linking entity from chains selected by a well-trained Ranker, and it benefits the Ranker training by providing extra rewards"
],
"type": "extractive"
} |
2004.01694 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What empricial investigations do they reference?
Context: <<<Title>>>
A Set of Recommendations for Assessing Human-Machine Parity in Language Translation
<<<Abstract>>>
The quality of machi... | {
"references": [
"empirically test to what extent changes in the evaluation design affect the outcome of the human evaluation"
],
"type": "extractive"
} |
2004.01694 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What languages do they investigate for machine translation?
Context: <<<Title>>>
A Set of Recommendations for Assessing Human-Machine Parity in Language Translation
<<<Abstract>>>
The quali... | {
"references": [
"English ,Chinese "
],
"type": "extractive"
} |
2004.01694 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What recommendations do they offer?
Context: <<<Title>>>
A Set of Recommendations for Assessing Human-Machine Parity in Language Translation
<<<Abstract>>>
The quality of machine translatio... | {
"references": [
" Choose professional translators as raters, Evaluate documents, not sentences,Evaluate fluency in addition to adequacy,Do not heavily edit reference translations for fluency,Use original source texts"
],
"type": "extractive"
} |
2003.00576 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: By how much they improve over the previous state-of-the-art?
Context: <<<Title>>>
StructSum: Incorporating Latent and Explicit Sentence Dependencies for Single Document Summarization
<<<Abs... | {
"references": [
"1.08 points in ROUGE-L over our base pointer-generator model ,0.6 points in ROUGE-1"
],
"type": "extractive"
} |
2003.00576 | Please answer the following question with yes or no based on the given text. You only need to output 'Yes' or 'No' without any additional explanation.
Question: Is there any evidence that encoders with latent structures work well on other tasks?
Context: <<<Title>>>
StructSum: Incorporating Latent and Explicit Senten... | {
"references": [
"Yes"
],
"type": "boolean"
} |
2004.00139 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How many words are coded in the dictionary?
Context: <<<Title>>>
A Swiss German Dictionary: Variation in Speech and Writing
<<<Abstract>>>
We introduce a dictionary containing forms of comm... | {
"references": [
"11'248"
],
"type": "extractive"
} |
2004.00139 | Please answer the following question with yes or no based on the given text. You only need to output 'Yes' or 'No' without any additional explanation.
Question: Is the model evaluated on the graphemes-to-phonemes task?
Context: <<<Title>>>
A Swiss German Dictionary: Variation in Speech and Writing
<<<Abstract>>>
We i... | {
"references": [
"Yes"
],
"type": "boolean"
} |
1911.01188 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What translationese effects are seen in the analysis?
Context: <<<Title>>>
Analysing Coreference in Transformer Outputs
<<<Abstract>>>
We analyse coreference phenomena in three neural machi... | {
"references": [
"potentially indicating a shining through effect,explicitation effect"
],
"type": "extractive"
} |
1911.01188 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What languages are seen in the news and TED datasets?
Context: <<<Title>>>
Analysing Coreference in Transformer Outputs
<<<Abstract>>>
We analyse coreference phenomena in three neural machi... | {
"references": [
"English,German"
],
"type": "extractive"
} |
1911.01188 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How are the (possibly incorrect) coreference chains in the MT outputs annotated?
Context: <<<Title>>>
Analysing Coreference in Transformer Outputs
<<<Abstract>>>
We analyse coreference phen... | {
"references": [
"allows the annotator to define each markable as a certain mention type (pronoun, NP, VP or clause),The mentions referring to the same discourse item are linked between each other.,chain members are annotated for their correctness"
],
"type": "extractive"
} |
1911.01188 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: Which three neural machine translation systems are analyzed?
Context: <<<Title>>>
Analysing Coreference in Transformer Outputs
<<<Abstract>>>
We analyse coreference phenomena in three neura... | {
"references": [
"first two systems are transformer models trained on different amounts of data,The third system includes a modification to consider the information of full coreference chains"
],
"type": "extractive"
} |
1911.01188 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: Which coreference phenomena are analyzed?
Context: <<<Title>>>
Analysing Coreference in Transformer Outputs
<<<Abstract>>>
We analyse coreference phenomena in three neural machine translati... | {
"references": [
"shining through,explicitation"
],
"type": "extractive"
} |
1910.06701 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: what are the existing models they compared with?
Context: <<<Title>>>
NumNet: Machine Reading Comprehension with Numerical Reasoning
<<<Abstract>>>
Numerical reasoning, such as addition, su... | {
"references": [
"Syn Dep,OpenIE,SRL,BiDAF,QANet,BERT,NAQANet,NAQANet+"
],
"type": "extractive"
} |
2001.10179 | Please answer the following question with yes or no based on the given text. You only need to output 'Yes' or 'No' without any additional explanation.
Question: Is their implementation on CNN-DSA compared to GPU implementation in terms of power consumption, accuracy and speed?
Context: <<<Title>>>
Multi-modal Sentime... | {
"references": [
"No"
],
"type": "boolean"
} |
2001.10179 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How is Super Character method modified to handle tabular data also?
Context: <<<Title>>>
Multi-modal Sentiment Analysis using Super Characters Method on Low-power CNN Accelerator Device
<<<... | {
"references": [
"simply split the image into two parts. One for the text input, and the other for the tabular data"
],
"type": "extractive"
} |
1911.03842 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What baseline is used to compare the experimental results against?
Context: <<<Title>>>
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation
<<<Abstract>>>
Models often ea... | {
"references": [
"Transformer generation model"
],
"type": "extractive"
} |
1911.06191 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How does soft contextual data augmentation work?
Context: <<<Title>>>
Microsoft Research Asia's Systems for WMT19
<<<Abstract>>>
We Microsoft Research Asia made submissions to 11 language d... | {
"references": [
"softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words,replacing the one-hot representation of a word by a distribution provided by a language model over the vocabulary"
],
"type": "extractive"
} |
1911.06191 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How does muli-agent dual learning work?
Context: <<<Title>>>
Microsoft Research Asia's Systems for WMT19
<<<Abstract>>>
We Microsoft Research Asia made submissions to 11 language directions... | {
"references": [
"MADL BIBREF0 extends the dual learning BIBREF1, BIBREF2 framework by introducing multiple primal and dual models."
],
"type": "extractive"
} |
1911.06191 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: Which language directions are machine translation systems of WMT evaluated on?
Context: <<<Title>>>
Microsoft Research Asia's Systems for WMT19
<<<Abstract>>>
We Microsoft Research Asia mad... | {
"references": [
"German$\\leftrightarrow $English, German$\\leftrightarrow $French, Chinese$\\leftrightarrow $English, English$\\rightarrow $Lithuanian, English$\\rightarrow $Finnish, and Russian$\\rightarrow $English,Lithuanian$\\rightarrow $English, Finnish$\\rightarrow $English, and English$\\rightarrow $Kaz... |
2002.12328 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What was the criteria for human evaluation?
Context: <<<Title>>>
Few-shot Natural Language Generation for Task-Oriented Dialog
<<<Abstract>>>
As a crucial component in task-oriented dialog ... | {
"references": [
"to judge each utterance from 1 (bad) to 3 (good) in terms of informativeness and naturalness"
],
"type": "extractive"
} |
2002.12328 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What automatic metrics are used to measure performance of the system?
Context: <<<Title>>>
Few-shot Natural Language Generation for Task-Oriented Dialog
<<<Abstract>>>
As a crucial componen... | {
"references": [
"BLEU scores and the slot error rate (ERR)"
],
"type": "extractive"
} |
2002.12328 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What existing methods is SC-GPT compared to?
Context: <<<Title>>>
Few-shot Natural Language Generation for Task-Oriented Dialog
<<<Abstract>>>
As a crucial component in task-oriented dialog... | {
"references": [
"$({1})$ SC-LSTM BIBREF3,$({2})$ GPT-2 BIBREF6 ,$({3})$ HDSA BIBREF7"
],
"type": "extractive"
} |
1908.09951 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What is the baseline?
Context: <<<Title>>>
An Emotional Analysis of False Information in Social Media and News Articles
<<<Abstract>>>
Fake news is risky since it has been created to manipu... | {
"references": [
"Majority Class baseline (MC) ,Random selection baseline (RAN)"
],
"type": "extractive"
} |
1908.09951 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What datasets did they use?
Context: <<<Title>>>
An Emotional Analysis of False Information in Social Media and News Articles
<<<Abstract>>>
Fake news is risky since it has been created to ... | {
"references": [
"News Articles,Twitter"
],
"type": "extractive"
} |
1911.11698 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: How better are results for pmra algorithm than Doc2Vec in human evaluation?
Context: <<<Title>>>
Doc2Vec on the PubMed corpus: study of a new approach to generate related articles
<<<Abst... | {
"references": [
"The D2V model has been rated 80 times as \"bad relevance\" while the pmra returned only 24 times badly relevant documents."
],
"type": "extractive"
} |
1911.11698 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What Doc2Vec architectures other than PV-DBOW have been tried?
Context: <<<Title>>>
Doc2Vec on the PubMed corpus: study of a new approach to generate related articles
<<<Abstract>>>
PubMed ... | {
"references": [
"PV-DM"
],
"type": "extractive"
} |
1911.11698 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What four evaluation tasks are defined to determine what influences proximity?
Context: <<<Title>>>
Doc2Vec on the PubMed corpus: study of a new approach to generate related articles
<<<Abs... | {
"references": [
"String length,Words co-occurrences,Stems co-occurrences,MeSH similarity"
],
"type": "extractive"
} |
1911.11698 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What six parameters were optimized with grid search?
Context: <<<Title>>>
Doc2Vec on the PubMed corpus: study of a new approach to generate related articles
<<<Abstract>>>
PubMed is the big... | {
"references": [
"window_size,alpha,sample,dm,hs,vector_size"
],
"type": "extractive"
} |
2001.06354 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What metrics are used in challenge?
Context: <<<Title>>>
Modality-Balanced Models for Visual Dialogue
<<<Abstract>>>
The Visual Dialog task requires a model to exploit both image and conver... | {
"references": [
"NDCG,MRR,recall@k,mean rank"
],
"type": "extractive"
} |
2001.06354 | Please extract a concise answer without any additional explanation for the following question based on the given text.
Question: What model was winner of the Visual Dialog challenge 2018?
Context: <<<Title>>>
Modality-Balanced Models for Visual Dialogue
<<<Abstract>>>
The Visual Dialog task requires a model to exploi... | {
"references": [
"DL-61"
],
"type": "extractive"
} |
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