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Non-extractive commonsense QA remains a challenging AI task, as it requires systems to reason about, synthesize, and gather disparate pieces of information, in order to generate responses to queries.
Improving question answering with external knowledge
Xiaoman Pan, Kai Sun, Dian Yu, Heng Ji, and Dong Yu. 2019a · 1902
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Improving question answering with external knowledge
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Graph-based reasoning over heterogeneous external knowledge for commonsense question answering
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Metanet: Deep semantic automatic metaphor analysis
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Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016 · 2016
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Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2016 · 2016
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Dynamic coattention networks for question answering
Caiming Xiong, Victor Zhong, and Richard Socher. 2016 · 2016
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RACE: large-scale reading comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard H. Hovy. 2017 · 2017
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Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang, and Ming Zhou. 2017 · 2017
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Improving question answering by commonsense-based pre-training
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COMET: Commonsense transformers for automatic knowledge graph construction
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