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The recently introduced BERT model exhibits strong performance on several language understanding benchmarks.
A knowledge hunting framework for common sense reasoning
Ali Emami, Noelia De La Cruz, Adam Trischler, Kaheer Suleman, and Jackie Chi Kit Cheung. 2018 · 1958
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Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S Gordon. 2011 · 2011
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The winograd schema challenge
Hector Levesque, Ernest Davis, and Leora Morgenstern. 2012 · 2012
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Resolving complex cases of definite pronouns: the winograd schema challenge
Altaf Rahman and Vincent Ng. 2012 · 2012
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
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Tackling winograd schemas by formalizing relevance theory in knowledge graphs
Peter Schüller. 2014 · 2014
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The winograd schema challenge and reasoning about correlation
Daniel Bailey, Amelia J Harrison, Yuliya Lierler, Vladimir Lifschitz, and Julian Michael. 2015 · 2015
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Solving hard coreference problems
Haoruo Peng, Daniel Khashabi, and Dan Roth. 2015 · 2015
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Towards addressing the winograd schema challenge—building and using a semantic parser and a knowledge hunting module
Arpit Sharma, Nguyen H Vo, Somak Aditya, and Chitta Baral. 2015 · 2015
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Probabilistic reasoning via deep learning: Neural association models
Quan Liu, Hui Jiang, Andrew Evdokimov, Zhen-Hua Ling, Xiaodan Zhu, Si Wei, and Yu Hu. 2016 · 2016
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Planning, executing, and evaluating the winograd schema challenge
Leora Morgenstern, Ernest Davis, and Charles L Ortiz. 2016 · 2016
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Dissecting contextual word embeddings: Architecture and representation
Matthew Peters, Mark Neumann, Luke Zettlemoyer, and Wen-tau Yih. 2018a · 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 simple machine learning method for commonsense reasoning? A short commentary on trinh & le (2018)
Walid S. Saba. 2018 · 2018
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Why self-attention? a targeted evaluation of neural machine translation architectures
Gongbo Tang, Mathias Müller, Annette Rios, and Rico Sennrich. 2018 · 2018
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On the evaluation of common-sense reasoning in natural language understanding
Paul Trichelair, Ali Emami, Jackie Chi Kit Cheung, Adam Trischler, Kaheer Suleman, and Fernando Diaz. 2018 · 2018
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End-to-end neural coreference resolution
Kenton Lee, Luheng He, Mike Lewis, and Luke Zettlemoyer. 2017 · 2017
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Combing context and commonsense knowledge through neural networks for solving winograd schema problems
Quan Liu, Hui Jiang, Zhen-Hua Ling, Xiaodan Zhu, Si Wei, and Yu Hu. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 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.
Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018b
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A simple method for commonsense reasoning
Trieu H Trinh and Quoc V Le. 2018 · 2018
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Swag: A large-scale adversarial dataset for grounded commonsense inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018 · 2018
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A surprisingly robust trick for the winograd schema challenge
Vid Kocijan, Ana-Maria Cretu, Oana-Maria Camburu, Yordan Yordanov, and Thomas Lukasiewicz. 2019 · 2019
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