TwoWingOS: A Two-Wing Optimization Strategy for Evidential Claim Verification
Wenpeng Yin, Dan Roth · 2018
Determining whether a given claim is supported by evidence is a fundamental NLP problem that is best modeled as Textual Entailment.However, given a large collection of text, finding evidence that could support or refute a given claim is a challenge in itself, amplified by the fact that different evidence might be needed to support or refute a claim.Nevertheless, most prior work decouples evidence identification from determining the truth value of the claim given the evidence.We propose to consider these two aspects jointly.We develop TWOWINGOS (twowing optimization strategy), a system that, while identifying appropriate evidence for a claim, also determines whether or not the claim is supported by the evidence.Given the claim, TWOWINGOS attempts to identify a subset of the evidence candidates; given the predicted evidence, it then attempts to determine the truth value of the corresponding claim.We treat this challenge as coupled optimization problems, training a joint model for it.TWOWINGOS offers two advantages: (i) Unlike pipeline systems, it facilitates flexible-size evidence set, and (ii) Joint training improves both the claim verification and the evidence identification.Experiments on a benchmark dataset show state-of-the-art performance.1