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Pretrained language models, such as BERT and RoBERTa, have shown large improvements in the commonsense reasoning benchmark COPA.
Multi-task deep neural networks for natural language understanding
Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019a · 1901
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Socialiqa: Commonsense reasoning about social interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi. 2019 · 1904
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SuperGLUE: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019 · 1905
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Oskar Pfungst. 1911 · 1911
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The argument reasoning comprehension task: Identification and reconstruction of implicit warrants
Ivan Habernal, Henning Wachsmuth, Iryna Gurevych, and Benno Stein. 2018 · 1940
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Joseph L. Fleiss. 1981 · 1981
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Eric W Noreen. 1989 · 1989
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Support-vector networks
Corinna Cortes and Vladimir Vapnik. 1995 · 1995
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Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S Gordon. 2011 · 2011
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Travis Goodwin, Bryan Rink, Kirk Roberts, and Sanda Harabagiu. 2012 · 2012
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Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Adina Williams, Nikita Nangia, and Samuel Bowman. 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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What bert is not: Lessons from a new suite of psycholinguistic diagnostics for language models
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