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Recently, pretrained language models (e.g., BERT) have achieved great success on many downstream natural language understanding tasks and exhibit a certain level of commonsense reasoning ability.
Freebase: a collaboratively created graph database for structuring human knowledge
K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor · 2008
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Commonsense reasoning and commonsense knowledge in artificial intelligence
E. Davis and G. Marcus · 2015
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Answering elementary science questions by constructing coherent scenes using background knowledge
Y. Li and P. Clark · 2015
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Science question answering using instructional materials
M. Sachan, A. Dubey, and E. P. Xing · 2016
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ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
R. Speer, J. Chin, and C. Havasi · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
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SemEval-2018 Task 11: Machine comprehension using commonsense knowledge
S. Ostermann, M. Roth, A. Modi, S. Thater, and M. Pinkal · 2018
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Improving language understanding by generative pre-training
A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever · 2018
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Commonsenseqa: A question answering challenge targeting commonsense knowledge
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Probing prior knowledge needed in challenging chinese machine reading comprehension
K. Sun, D. Yu, D. Yu, and C. Cardie · 2019
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Cosmos qa: Machine reading comprehension with contextual commonsense reasoning
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Encoding knowledge graph with graph cnn for question answering
L. Laugier, A. Wang, C.-S. Foo, T. Guenais, and V. Chandrasekhar · 2019
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