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Natural language inference (NLI) is among the most challenging tasks in natural language understanding.
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Earlier work this paper cites.
A fast unified model for parsing and sentence understanding
Samuel R. Bowman, Jon Gauthier, Abhinav Rastogi, Raghav Gupta, Christopher D. Manning, and Christopher Potts. 2016 · 2016
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Distraction-based neural networks for modeling document
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, and Hui Jiang. 2016 · 2016
Earlier work this paper cites.
A decomposable attention model for natural language inference
Ankur P. Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit. 2016 · 2016
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Reading and thinking: Re-read LSTM unit for textual entailment recognition
Lei Sha, Baobao Chang, Zhifang Sui, and Sujian Li. 2016 · 2016
Earlier work this paper cites.
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.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman. 2017 · 2017
Earlier work this paper cites.
Neural tree indexers for text understanding
Hong Yu and Tsendsuren Munkhdalai. 2017 · 2017
Cited alongside, same era.
Exploring question understanding and adaptation in neural-network-based question answering
Junbei Zhang, Xiaodan Zhu, Qian Chen, Lirong Dai, Si Wei, and Hui Jiang. 2017 · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 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.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
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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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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A compare-propagate architecture with alignment factorization for natural language inference
Yi Tay, Luu Anh Tuan, and Siu Cheung Hui. 2018 · 2018
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Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R. Bowman, and Noah A. Smith. 2018 · 2018
Cited alongside, same era.
Stress test evaluation for natural language inference
Aakanksha Naik, Abhilasha Ravichander, Norman M. Sadeh, Carolyn Penstein Rosé, and Graham Neubig. 2018 · 2018
Cited alongside, same era.
Enhancing sentence embedding with generalized pooling
Qian Chen, Zhen-Hua Ling, and Xiaodan Zhu. 2018a
Cited in the paper.
Neural natural language inference models enhanced with external knowledge
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Diana Inkpen, and Si Wei. 2018b
Cited in the paper.
Haohan Wang, Da Sun, and Eric P. Xing. 2018 · 2018
Later among the works it cites.
Dynamic self-attention : Computing attention over words dynamically for sentence embedding
Deunsol Yoon, Dongbok Lee, and SangKeun Lee. 2018 · 2018
Later among the works it cites.