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Large crowdsourced datasets are widely used for training and evaluating neural models on natural language inference (NLI).
Determiners and logic
Johan van Benthem. 1983 · 1983
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Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone
Michael Lesk. 1986 · 1986
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FraCaS–a framework for computational semantics
Robin Cooper, Richard Crouch, Jan van Eijck, Chris Fox, Josef van Genabith, Jan Jaspers, Hans Kamp, Manfred Pinkal, Massimo Poesio, Stephen Pulman, et al. 1994 · 1994
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WordNet: An Electronic Lexical Database
Christiane Fellbaum. 1998 · 1998
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The Syntactic Process
Mark Steedman. 2000 · 2000
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Light-weight entailment checking for computational semantics
Christof Monz and Maarten de Rijke. 2001 · 2001
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Monotonicity and processing load
Bart Geurts and Frans van der Slik. 2005 · 2005
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Recognizing Textual Entailment: Models and Applications
Ido Dagan, Dan Roth, Mark Sammons, and Fabio Massimo Zanzotto. 2013 · 2013
Earlier work this paper cites.
Recent progress in monotonicity
Thomas Icard and Lawrence Moss. 2014 · 2014
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A SICK cure for the evaluation of compositional distributional semantic models
Marco Marelli, Stefano Menini, Marco Baroni, Luisa Bentivogli, Raffaella Bernardi, and Roberto Zamparelli. 2014 · 2014
Cited alongside, same era.
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015a · 2015
Cited alongside, same era.
The Parallel Meaning Bank: Towards a multilingual corpus of translations annotated with compositional meaning representations
Lasha Abzianidze, Johannes Bjerva, Kilian Evang, Hessel Haagsma, Rik van Noord, Pierre Ludmann, Duc-Duy Nguyen, and Johan Bos. 2017 · 2017
Cited alongside, same era.
Towards universal semantic tagging
Lasha Abzianidze and Johan Bos. 2017 · 2017
Cited alongside, same era.
Enhanced lstm for natural language inference
Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018 · 2018
Later among the works it cites.
Stress test evaluation for natural language inference
Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, and Graham Neubig. 2018 · 2018
Later among the works it cites.
Collecting diverse natural language inference problems for sentence representation evaluation
Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, and Benjamin Van Durme. 2018a · 2018
Later among the works it cites.
Testing the generalization power of neural network models across NLI benchmarks
Aarne Talman and Stergios Chatzikyriakidis. 2018 · 2018
Later among the works it cites.
Performance impact caused by hidden bias of training data for recognizing textual entailment
Masatoshi Tsuchiya. 2018 · 2018
Later among the works it cites.
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Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2017 · 2017
Cited alongside, same era.
Stress-testing neural models of natural language inference with multiply-quantified sentences
Atticus Geiger, Ignacio Cases, Lauri Karttunen, and Christopher Potts. 2018 · 2018
Cited alongside, same era.
Breaking NLI systems with sentences that require simple lexical inferences
Max Glockner, Vered Shwartz, and Yoav Goldberg. 2018 · 2018
Cited alongside, same era.
Recursive neural networks can learn logical semantics
Samuel R. Bowman, Christopher Potts, and Christopher D. Manning. 2015b
Cited in the paper.
Hypothesis only baselines in natural language inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018b
Cited in the paper.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Later among the works it cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Closest in time.
GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019 · 2019
Closest in time.