  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
(two-wing 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
entailment and the evidence identification. Experiments on a benchmark dataset
show state-of-the-art performance. Code: https://github.com/yinwenpeng/FEVER
