  The increasing concern with misinformation has stimulated research efforts on
automatic fact checking. The recently-released FEVER dataset introduced a
benchmark fact-verification task in which a system is asked to verify a claim
using evidential sentences from Wikipedia documents. In this paper, we present
a connected system consisting of three homogeneous neural semantic matching
models that conduct document retrieval, sentence selection, and claim
verification jointly for fact extraction and verification. For evidence
retrieval (document retrieval and sentence selection), unlike traditional
vector space IR models in which queries and sources are matched in some
pre-designed term vector space, we develop neural models to perform deep
semantic matching from raw textual input, assuming no intermediate term
representation and no access to structured external knowledge bases. We also
show that Pageview frequency can also help improve the performance of evidence
retrieval results, that later can be matched by using our neural semantic
matching network. For claim verification, unlike previous approaches that
simply feed upstream retrieved evidence and the claim to a natural language
inference (NLI) model, we further enhance the NLI model by providing it with
internal semantic relatedness scores (hence integrating it with the evidence
retrieval modules) and ontological WordNet features. Experiments on the FEVER
dataset indicate that (1) our neural semantic matching method outperforms
popular TF-IDF and encoder models, by significant margins on all evidence
retrieval metrics, (2) the additional relatedness score and WordNet features
improve the NLI model via better semantic awareness, and (3) by formalizing all
three subtasks as a similar semantic matching problem and improving on all
three stages, the complete model is able to achieve the state-of-the-art
results on the FEVER test set.
