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The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning.
Multi-task deep neural networks for natural language understanding
Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019 · 1901
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The Winograd Schema Challenge
Hector J. Levesque, Ernest Davis, and Leora Morgenstern. 2011 · 2011
Earlier work this paper cites.
The Winograd Schema Challenge
Hector J. 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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The Stanford CoreNLP natural language processing toolkit
Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014 · 2014
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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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The first Winograd Schema Challenge at IJCAI-16
Ernest Davis, Leora Morgenstern, and Charles L. Ortiz. 2017 · 2017
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Deep pyramid convolutional neural networks for text categorization
Rie Johnson and Tong Zhang. 2017 · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz 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.
A knowledge hunting framework for common sense reasoning
Ali Emami, Noelia De La Cruz, Adam Trischler, Kaheer Suleman, and Jackie Chi Kit Cheung. 2018 · 2018
Cited alongside, same era.
Achieving human parity on automatic Chinese to English news translation
A conservative human baseline estimate for GLUE: People still (mostly) beat machines
Nikita Nangia and Samuel R. Bowman. 2018 · 2018
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Addressing the Winograd Schema Challenge as a sequence ranking task
Juri Opitz and Anette Frank. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Later among the works it cites.
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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A Simple Method for Commonsense Reasoning
T. H. Trinh and Q. V. Le. 2018 · 2018
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Hany Hassan, Anthony Aue, Chang Chen, Vishal Chowdhary, Jonathan Clark, Christian Federmann, Xuedong Huang, Marcin Junczys-Dowmunt, William Lewis, Mu Li, Shujie Liu, Tie-Yan Liu, Renqian Luo, Arul Menezes, Tao Qin, Frank Seide, Xu Tan, Fei Tian, Lijun Wu, Shuangzhi Wu, Yingce Xia, Dongdong Zhang, Zhirui Zhang, and Ming Zhou. 2018 · 2018
Cited alongside, same era.
Fine-tuned language models for text classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
Cited alongside, same era.
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
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