AbductionRules: Training Transformers to Explain Unexpected Inputs (2022.findings-acl)
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| Challenge: | AbductionRules is a set of natural language datasets designed to train and test generalisable abduction over natural-language knowledge bases. |
| Approach: | They propose to train and test generalisable abduction over natural-language knowledge bases by using natural language datasets to fine tune pre-trained Transformers. |
| Outcome: | The proposed models learned generalisable abduction techniques but also exploited the structure of the datasets. |
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Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, Alexander Rush
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