Natural Language Deduction with Incomplete Information
Zayne Sprague, Kaj Bostrom, Swarat Chaudhuri, Greg Durrett · 2022
A growing body of work studies how to answer a question or verify a claim by generating a natural language "proof": a chain of deductive inferences yielding the answer based on a set of premises.However, these methods can only make sound deductions when they follow from evidence that is given.We propose a new system that can handle the underspecified setting where not all premises are stated at the outset; that is, additional assumptions need to be materialized to prove a claim.By using a natural language generation model to abductively infer a premise given another premise and a conclusion, we can impute missing pieces of evidence needed for the conclusion to be true.Our system searches over two fringes in a bidirectional fashion, interleaving deductive (forward-chaining) and abductive (backwardchaining) generation steps.We sample multiple possible outputs for each step to achieve coverage of the search space, at the same time ensuring correctness by filtering lowquality generations with a round-trip validation procedure.Results on a modified version of the EntailmentBank dataset and a new dataset called Everyday Norms: Why Not? show that abductive generation with validation can recover premises across in-and out-of-domain settings.1