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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 Value-aware Approximate Attention ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Berant, Jonathan; Gupta, Ankit. - : Underline Science Inc., 2021 BASE Show details 2 Memory-efficient Transformers via Top-k Attention ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Shaya; Berant, Jonathan. - : Underline Science Inc., 2021 BASE Show details 3 ESL Students' Reading Reactions on Multiple-Choice Modules at Varied Proficiency Standards. An Eye-Tracking Research Gupta, Ankit [Verfasser]. - München : GRIN Verlag, 2020 DNB Subject Category Language Show details 4 Break It Down: A Question Understanding Benchmark ... Wolfson, Tomer; Geva, Mor; Gupta, Ankit; Gardner, Matt; Goldberg, Yoav; Deutch, Daniel; Berant, Jonathan. - : arXiv, 2020 Abstract: Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning Representation (QDMR) for questions. QDMR constitutes the ordered list of steps, expressed through natural language, that are necessary for answering a question. We develop a crowdsourcing pipeline, showing that quality QDMRs can be annotated at scale, and release the Break dataset, containing over 83K pairs of questions and their QDMRs. We demonstrate the utility of QDMR by showing that (a) it can be used to improve open-domain question answering on the HotpotQA dataset, (b) it can be deterministically converted to a pseudo-SQL formal language, which can alleviate annotation in semantic parsing applications. Last, we use Break to train a sequence-to-sequence model with copying that parses questions into QDMR structures, and show that it substantially outperforms several natural baselines. ... : Accepted for publication in Transactions of the Association for Computational Linguistics (TACL), 2020. Author's final version ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2001.11770 https://arxiv.org/abs/2001.11770 BASE Hide details 5 Injecting Numerical Reasoning Skills into Language Models ... Geva, Mor; Gupta, Ankit; Berant, Jonathan. - : arXiv, 2020 BASE Show details 6 ESL Students' Reading Reactions on Multiple-Choice Modules at Varied Proficiency Standards. An Eye-Tracking Research Gupta, Ankit [Verfasser]. - München : GRIN Verlag, 2019 DNB Subject Category Language Show details

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