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Know What You Don't Know: Unanswerable Questions for SQuAD
TL;DR: SQuadRUn is a new dataset that combines the existing Stanford Question Answering Dataset (SQuAD) with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones.
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Abstract: Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for which the correct answer is not stated in the context. Existing datasets either focus exclusively on answerable questions, or use automatically generated unanswerable questions that are easy to identify. To address these weaknesses, we present SQuAD 2.0, the latest version of the Stanford Question Answering Dataset (SQuAD). SQuAD 2.0 combines existing SQuAD data with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD 2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. SQuAD 2.0 is a challenging natural language understanding task for existing models: a strong neural system that gets 86% F1 on SQuAD 1.1 achieves only 66% F1 on SQuAD 2.0.
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Learning from Task Descriptions
TL;DR: The authors proposed a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area, and instantiate this framework with a new English language dataset, ZEST, structured for task-oriented evaluation on unseen tasks.
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Text Alignment Is An Efficient Unified Model for Massive NLP Tasks
TL;DR: The authors propose text alignment as an efficient unified model for a wide range of crucial tasks involving text entailment, similarity, question answering (and answerability), factual consistency, and so forth.
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Ensemble ALBERT and RoBERTa for Span Prediction in Question Answering
Sony Bachina,Spandana Balumuri,Sowmya Kamath S +2 more
- 01 Aug 2021
TL;DR: In this article, the authors proposed models designed as part of their submission to the DialDoc21 Shared Task (Document-grounded Dialogue and Conversational Question Answering) for span prediction in question answering, which leveraged the superior predictive power of pretrained transformer models like RoBERTa, ALBERT and ELECTRA.
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EQuANt (Enhanced Question Answer Network).
TL;DR: This work presents Enhanced Question Answer Network (EQuANt), an MRC model which extends the successful QANet architecture of Yu et al. to cope with unanswerable questions and demonstrates the utility of multi-task learning in the MRC context.
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A Call for Clarity in Contemporary Authorship Attribution Evaluation.
Allen Riddell,Haining Wang,Patrick Juola +2 more
- 01 Sep 2021
TL;DR: The authors contributed materials for five closed-set authorship identification experiments involving contemporary non-fiction American English prose, including texts from 106 distinct authors, and provided the foundation for comparable and reproducible authorship attribution research involving contemporary writing.
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References
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SQuAD: 100,000+ Questions for Machine Comprehension of Text
TL;DR: The Stanford Question Answering Dataset (SQuAD) as mentioned in this paper is a reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage.
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•Proceedings Article
Teaching machines to read and comprehend
Karl Moritz Hermann,Tomáš Kočiský,Edward Grefenstette,Lasse Espeholt,Will Kay,Mustafa Suleyman,Phil Blunsom +6 more
- 07 Dec 2015
TL;DR: A new methodology is defined that resolves this bottleneck and provides large scale supervised reading comprehension data that allows a class of attention based deep neural networks that learn to read real documents and answer complex questions with minimal prior knowledge of language structure to be developed.
•Proceedings Article
Bidirectional Attention Flow for Machine Comprehension
Minjoon Seo,Aniruddha Kembhavi,Ali Farhadi,Hannaneh Hajishirzi +3 more
- 04 Nov 2016
TL;DR: The BIDAF network is introduced, a multi-stage hierarchical process that represents the context at different levels of granularity and uses bi-directional attention flow mechanism to obtain a query-aware context representation without early summarization.
2.1K
•Proceedings Article
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.
Tri Nguyen,Mir Rosenberg,Xia Song,Jianfeng Gao,Saurabh Tiwary,Rangan Majumder,Li Deng +6 more
- 04 Nov 2016
TL;DR: MS MARCO as mentioned in this paper is a large scale dataset for reading comprehension and question answering, where all questions are sampled from real anonymized user queries and context passages from which answers in the dataset are derived from real web documents using the most advanced version of the Bing search engine.
WikiQA: A Challenge Dataset for Open-Domain Question Answering
Yi Yang,Wen-tau Yih,Christopher Meek +2 more
- 21 Sep 2015
TL;DR: The WIKIQA dataset is described, a new publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering, which is more than an order of magnitude larger than the previous dataset.