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Winograd Schema Challenge (WSC) was proposed as an AI-hard problem in testing computers' intelligence on common sense representation and reasoning.
Understanding natural language
Terry Winograd · 1972
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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The winograd schema challenge
Hector Levesque, Ernest Davis, and Leora Morgenstern · 2012
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Resolving complex cases of definite pronouns: the winograd schema challenge
Altaf Rahman and Vincent Ng · 2012
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Tackling winograd schemas by formalizing relevance theory in knowledge graphs
Peter Schüller · 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
Earlier work this paper cites.
The winograd schema challenge: evaluating progress in commonsense reasoning
Leora Morgenstern and Charles Ortiz · 2015
Cited alongside, same era.
Solving hard coreference problems
Haoruo Peng, Daniel Khashabi, and Dan Roth · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Beyond the turing test
Gary Marcus, Francesca Rossi, and Manuela Veloso · 2016
Cited alongside, same era.
Unsupervised machine translation using monolingual corpora only
Guillaume Lample, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato · 2017
Cited alongside, same era.
Cause-effect knowledge acquisition and neural association model for solving a set of winograd schema problems
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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A knowledge hunting framework for common sense reasoning
Ali Emami, Noelia De La Cruz, Adam Trischler, Kaheer Suleman, and Jackie Chi Kit Cheung · 2018
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
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The importance of being recurrent for modeling hierarchical structure
Ke Tran, Arianna Bisazza, and Christof Monz · 2018
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Quan Liu, Hui Jiang, Andrew Evdokimov, Zhen-Hua Ling, Xiaodan Zhu, Si Wei, and Yu Hu · 2017
Cited alongside, same era.
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
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
Cited alongside, same era.
Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, and Łukasz Kaiser · 2018
Cited alongside, same era.
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
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A simple method for commonsense reasoning
Trieu H Trinh and Quoc V Le · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amapreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 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
Later among the works it cites.