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{
  "doi": "10.18653/v1/2021.findings-acl.317",
  "abstract": "Transformers have been shown to emulate logical deduction over natural language theories (logical rules expressed in natural language), reliably assigning true/false labels to candidate implications. However, their ability to generate implications of a theory has not yet been demonstrated, and methods for reconstructing proofs of answers are imperfect. In this work we show that a generative model, called ProofWriter, can reliably generate both implications of a theory and the natural language proof(s) that support them. In particular, iterating a 1-step implication generator results in proofs that are highly reliable, and represent actual model decisions (rather than post-hoc rationalizations). On the RuleTaker dataset, the accuracy of ProofWriter's proofs exceed previous methods by +9% absolute, and in a way that generalizes to proof depths unseen in training and on out-of-domain problems. We also show that generative techniques can perform a type of abduction with high precision: Given a theory and an unprovable conclusion, identify a missing fact that allows the conclusion to be proved, along with a proof. These results significantly improve the viability of neural methods for systematically reasoning over natural language.",
  "journal": "Findings",
  "year": 2020,
  "authors": "Oyvind Tafjord, Bhavana Dalvi, Peter Clark",
  "url": "https://aclanthology.org/2021.findings-acl.317.pdf",
  "external_ids": {
    "doi": "10.18653/v1/2021.findings-acl.317",
    "s2_id": "229371222",
    "scientist_author_slugs": [
      "peter-clark"
    ],
    "scientist_author_orcids": [
      "0000-0002-8006-7015"
    ]
  },
  "citation_count": 420
}