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95 - Common sense reasoning, with Yejin Choi
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Manage episode 243940781 series 1452120
Content provided by NLP Highlights and Allen Institute for Artificial Intelligence. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by NLP Highlights and Allen Institute for Artificial Intelligence or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
In this episode, we invite Yejin Choi to talk about common sense knowledge and reasoning, a growing area in NLP. We start by discussing a working definition of “common sense” and the practical utility of studying it. We then talk about some of the datasets and resources focused on studying different aspects of common sense (e.g., ReCoRD, CommonsenseQA, ATOMIC) and contrast implicit vs. explicit modeling of common sense, and what it means for downstream applications. To conclude, Yejin shares her thoughts on some of the open problems in this area and where it is headed in the future. Yejin Choi’s homepage: https://homes.cs.washington.edu/~yejin/ ATOMIC: https://homes.cs.washington.edu/~msap/atomic/ ReCoRD: https://sheng-z.github.io/ReCoRD-explorer/ CommonsenseQA: https://www.tau-nlp.org/commonsenseqa
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145 episodes
MP3•Episode home
Manage episode 243940781 series 1452120
Content provided by NLP Highlights and Allen Institute for Artificial Intelligence. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by NLP Highlights and Allen Institute for Artificial Intelligence or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
In this episode, we invite Yejin Choi to talk about common sense knowledge and reasoning, a growing area in NLP. We start by discussing a working definition of “common sense” and the practical utility of studying it. We then talk about some of the datasets and resources focused on studying different aspects of common sense (e.g., ReCoRD, CommonsenseQA, ATOMIC) and contrast implicit vs. explicit modeling of common sense, and what it means for downstream applications. To conclude, Yejin shares her thoughts on some of the open problems in this area and where it is headed in the future. Yejin Choi’s homepage: https://homes.cs.washington.edu/~yejin/ ATOMIC: https://homes.cs.washington.edu/~msap/atomic/ ReCoRD: https://sheng-z.github.io/ReCoRD-explorer/ CommonsenseQA: https://www.tau-nlp.org/commonsenseqa
…
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145 episodes
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