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It remains an open question whether incorporating external knowledge benefits commonsense reasoning while maintaining the flexibility of pretrained sequence models.
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A simple method for commonsense reasoning
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COMET: Commonsense transformers for automatic knowledge graph construction
Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi. 2019 · 2019
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Commonsense knowledge mining from pretrained models
Joe Davison, Joshua Feldman, and Alexander Rush. 2019 · 2019
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KagNet: Knowledge-aware graph networks for commonsense reasoning
Bill Yuchen Lin, Xinyue Chen, Jamin Chen, and Xiang Ren. 2019 · 2019
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Towards generalizable neuro-symbolic systems for commonsense question answering
Kaixin Ma, Jonathan Francis, Quanyang Lu, Eric Nyberg, and Alessandro Oltramari. 2019 · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
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Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 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. 2019b · 2019
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Unsupervised commonsense question answering with self-talk
Vered Shwartz, Peter West, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2020 · 2020
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Designing templates for eliciting commonsense knowledge from pretrained sequence-to-sequence models
Jheng-Hong Yang, Sheng-Chieh Lin, Rodrigo Nogueira, Ming-Feng Tsai, Chuan-Ju Wang, and Jimmy Lin. 2020 · 2020
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Benchmarking knowledge-enhanced commonsense question answering via knowledge-to-text transformation
Ning Bian, Xianpei Han, Bo Chen, and Le Sun. 2021 · 2021
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Dynamic neuro-symbolic knowledge graph construction for zero-shot commonsense question answering
Antoine Bosselut, Ronan Le Bras, and Yejin Choi. 2021 · 2021
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Unicorn on rainbow: A universal commonsense reasoning model on a new multitask benchmark
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Improving question answering by commonsense-based pre-training
Wanjun Zhong, Duyu Tang, Nan Duan, Ming Zhou, Jiahai Wang, and Jian Yin. 2019 · 2019
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Incorporating commonsense knowledge graph in pretrained models for social commonsense tasks
Ting-Yun Chang, Yang Liu, Karthik Gopalakrishnan, Behnam Hedayatnia, Pei Zhou, and Dilek Hakkani-Tur. 2020 · 2020
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How can we know what language models know?
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020 · 2020
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Qasc: A dataset for question answering via sentence composition
Tushar Khot, Peter Clark, Michal Guerquin, Peter Jansen, and Ashish Sabharwal. 2020 · 2020
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Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-Trained Language Models
Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xiang Ren. 2020 · 2020
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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. 2020 · 2020
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Knowledge-driven data construction for zero-shot evaluation in commonsense question answering
Kaixin Ma, Filip Ilievski, Jonathan Francis, Yonatan Bisk, Eric Nyberg, and Alessandro Oltramari. 2021 · 2021
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Prompting contrastive explanations for commonsense reasoning tasks
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Exploiting cloze-questions for few-shot text classification and natural language inference
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Usc ink submission on numersense
Jun Yan. 2021 · 2021
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Stanford submission on numersense
Yuhui Zhang. 2021 · 2021
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