{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/abductionrules-training-transformers-to-1","title":"AbductionRules: Training Transformers to Explain Unexpected Inputs","arxiv_id":"2203.12186","date":"2022-03-23","proceeding":"Findings (ACL) 2022 5","authors":["Nathan Young","Qiming Bao","Joshua Bensemann","Michael Witbrock"],"abstract":"Transformers have recently been shown to be capable of reliably performing logical reasoning over facts and rules expressed in natural language, but abductive reasoning - inference to the best explanation of an unexpected observation - has been underexplored despite significant applications to scientific discovery, common-sense reasoning, and model interpretability. 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