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Table entailment, the binary classification task of finding if a sentence is supported or refuted by the content of a table, requires parsing language and table structure as well as numerical and discrete reasoning.
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Recognizing textual entailment: Rationale, evaluation and approaches
Ido Dagan, Bill Dolan, Bernardo Magnini, and Dan Roth. 2010 · 2010
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“ask not what textual entailment can do for you…”
Mark Sammons, V.G.Vinod Vydiswaran, and Dan Roth. 2010 · 2010
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Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger. 2018 · 2018
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Jeremy Howard and Sebastian Ruder. 2018 · 2018
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Qiu Ran, Yankai Lin, Peng Li, Jie Zhou, and Zhiyuan Liu. 2019 · 2019
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Felipe Salvatore, Marcelo Finger, and Roberto Hirata Jr. 2019 · 2019
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A corpus for reasoning about natural language grounded in photographs
Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Huajun Bai, and Yoav Artzi. 2019 · 2019
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Do NLP models know numbers? probing numeracy in embeddings
Eric Wallace, Yizhong Wang, Sujian Li, Sameer Singh, and Matt Gardner. 2019 · 2019
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Wei-Cheng Chang, Felix X. Yu, Yin-Wen Chang, Yiming Yang, and Sanjiv Kumar. 2020 · 2020
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Tabfact: A large-scale dataset for table-based fact verification
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Learning general purpose distributed sentence representations via large scale multi-task learning
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