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Neural network models have been very successful in natural language inference, with the best models reaching 90% accuracy in some benchmarks.
Wordnet: A lexical database for english
George A. Miller. 1995 · 1995
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Semeval-2012 task 6: A pilot on semantic textual similarity
Eneko Agirre, Mona Diab, Daniel Cer, and Aitor Gonzalez-Agirre. 2012 · 2012
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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
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. 2014 · 2014
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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An overview of natural language inference data collection: The way forward?
Stergios Chatzikyriakidis, Robin Cooper, Simon Dobnik, and Staffan Larsson. 2017 · 2017
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Enhanced lstm for natural language inference
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2017 · 2017
Earlier work this paper cites.
Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes. 2017 · 2017
Cited alongside, same era.
Neural natural language inference models enhanced with external knowledge
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Diana Inkpen, and Si Wei. 2018 · 2018
Cited alongside, same era.
AllenNLP: A deep semantic natural language processing platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke S. Zettlemoyer. 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.
Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018 · 2018
Cited alongside, same era.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Hypothesis only baselines in natural language inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018 · 2018
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Natural language inference with hierarchical bilstm max pooling architecture
Aarne Talman, Anssi Yli-Jyrä, and Jörg Tiedemann. 2018 · 2018
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Performance Impact Caused by Hidden Bias of Training Data for Recognizing Textual Entailment
Masatoshi Tsuchiya. 2018 · 2018
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Haohan Wang, Da Sun, and Eric P. Xing. 2018 · 2018
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Adversarial training for textual entailment with knowledge-guided examples
Dongyeop Kang, Tushar Khot, Ashish Sabharwal, and Eduard Hovy. 2018 · 2018
Cited alongside, same era.
Scitail: A textual entailment dataset from science question answering
Tushar Khot, Ashish Sabharwal, and Peter Clark. 2018 · 2018
Cited alongside, same era.
Semantic sentence matching with densely-connected recurrent and co-attentive information
Seonhoon Kim, Jin-Hyuk Hong, Inho Kang, and Nojun Kwak. 2018 · 2018
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R. Bowman. 2018 · 2018
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What kind of natural language inference are nlp systems learning: Is this enough?
Jean-Philippe Bernardy and Stergios Chatzikyriakidis. 2019 · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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