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The state-of-the-art pre-trained language representation models, such as Bidirectional Encoder Representations from Transformers (BERT), rarely incorporate commonsense knowledge or other knowledge explicitly.
Exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge
Yu-Ping Ruan, Xiaodan Zhu, Zhen-Hua Ling, Zhan Shi, Quan Liu, and Si Wei. 2019 · 1904
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Probing prior knowledge needed in challenging chinese machine reading comprehension
Kai Sun, Dian Yu, Dong Yu, and Claire Cardie. 2019a · 1904
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Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, and Hua Wu. 2019b · 1904
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A surprisingly robust trick for winograd schema challenge
Vid Kocijan, Ana-Maria Cretu, Oana-Maria Camburu, Yordan Yordanov, and Thomas Lukasiewicz. 2019 · 1905
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ERNIE: Enhanced Language Representation with Informative Entities
Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. 2019 · 1905
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Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 1906
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XLNet: Generalized Autoregressive Pretraining for Language Understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le. 2019 · 1906
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RoBERTa: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Evaluating commonsense in pre-trained language models
Xuhui Zhou, Yue Zhang, Leyang Cui, and Dandan Huang. 2019 · 1911
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Distant supervision for relation extraction without labeled data
Mike Mintz, Steven Bills, Rion Snow, and Daniel Jurafsky. 2009 · 2009
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Modeling relations and their mentions without labeled text
Sebastian Riedel, Limin Yao, and Andrew McCallum. 2010 · 2010
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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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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 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 simple method for commonsense reasoning
Trieu H Trinh and Quoc V Le. 2018 · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
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Improving question answering by commonsense-based pre-training
Wanjun Zhong, Duyu Tang, Nan Duan, Ming Zhou, Jiahai Wang, and Jian Yin. 2018 · 2018
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Semi-supervised sequence tagging with bidirectional language models
Matthew Peters, Waleed Ammar, Chandra Bhagavatula, and Russell Power. 2017 · 2017
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Robert Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
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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 · 2018
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Jeremy Howard and Sebastian Ruder. 2018 · 2018
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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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Knowledge enhanced contextual word representations
Matthew E. Peters, Mark Neumann, Robert L Logan, Roy Schwartz, Vidur Joshi, Sameer Singh, and Noah A. Smith. 2019 · 2019
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How reasonable are common-sense reasoning tasks: A case-study on the Winograd schema challenge and SWAG
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