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Despite the success of generative pre-trained language models on a series of text generation tasks, they still suffer in cases where reasoning over underlying commonsense knowledge is required during generation.
Zhi-Xiu Ye, Qian Chen, Wen Wang, and Zhen-Hua Ling. 2019 · 1908
Earlier work this paper cites.
Graph-based reasoning over heterogeneous external knowledge for commonsense question answering
Shangwen Lv, Daya Guo, Jingjing Xu, Duyu Tang, Nan Duan, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, and Songlin Hu. 2019 · 1909
Earlier work this paper cites.
Measuring nominal scale agreement among many raters
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Cited alongside, same era.
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Cited alongside, same era.
Commonsense for generative multi-hop question answering tasks
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Nicola De Cao, Wilker Aziz, and Ivan Titov. 2019 · 2019
Later among the works it cites.
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ATOMIC: an atlas of machine commonsense for if-then reasoning
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