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Language technologies that accurately model the dynamics of events must perform commonsense reasoning.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Natural language processing with Python: analyzing text with the natural language toolkit
Steven Bird, Ewan Klein, and Edward Loper. 2009 · 2009
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell. 2011 · 2011
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A dynamic oracle for arc-eager dependency parsing
Yoav Goldberg and Joakim Nivre. 2012 · 2012
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A corpus and cloze evaluation for deeper understanding of commonsense stories
Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 2016a · 2016
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John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
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Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Social IQa: Commonsense reasoning about social interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi. 2019 · 2019
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
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Does it make sense? and why? a pilot study for sense making and explanation
Cunxiang Wang, Shuailong Liang, Yue Zhang, Xiaonan Li, and Tian Gao. 2019 · 2019
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Abductive commonsense reasoning
Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Wen tau Yih, and Yejin Choi. 2020 · 2020
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Autoregressive knowledge distillation through imitation learning
Alexander Lin, Jeremy Wohlwend, Howard Chen, and Tao Lei. 2020a · 2020
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CommonGen: A constrained text generation challenge for generative commonsense reasoning
Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, and Xiang Ren. 2020b · 2020
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Thinking like a skeptic: Defeasible inference in natural language
Rachel Rudinger, Vered Shwartz, Jena D. Hwang, Chandra Bhagavatula, Maxwell Forbes, Ronan Le Bras, Noah A. Smith, and Yejin Choi. 2020 · 2020
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022 · 2022
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Katherine M Collins, Catherine Wong, Jiahai Feng, Megan Wei, and Josh Tenenbaum. 2022 · 2022
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e-CARE: a new dataset for exploring explainable causal reasoning
Li Du, Xiao Ding, Kai Xiong, Ting Liu, and Bing Qin. 2022 · 2022
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GENIE: Toward reproducible and standardized human evaluation for text generation
Daniel Khashabi, Gabriel Stanovsky, Jonathan Bragg, Nicholas Lourie, Jungo Kasai, Yejin Choi, Noah A. Smith, and Daniel Weld. 2022 · 2022
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Taxonomy of risks posed by language models
Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, et al. 2022 · 2022
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CaTeRS: Causal and temporal relation scheme for semantic annotation of event structures
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WinoWhy: A deep diagnosis of essential commonsense knowledge for answering Winograd schema challenge
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