ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language
Year: 2021
Type: conference-paper
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 proofs 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.1
Authors: Oyvind Tafjord, Bhavana Dalvi, Peter Clark
Institution: Allen Institute for Artificial Intelligence
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W3115201256