Incorporating External Knowledge for Evidence-based Fact Verification

Existing fact verification methods employ pre-trained language models such as BERT for the contextual representation of evidence sentences. However, such representations do not take into account commonsense knowledge and these methods often conclude that there is not enough information to predict whether a claim is supported or refuted by the evidence sentences. In this work, we propose a framework called CGAT that incorporates external knowledge from ConceptNet to enrich the contextual representations of evidence sentences. We employ graph attention models to propagate the information among the evidence sentences before predicting the veracity of the claim. Experiment results on the benchmark FEVER dataset and UKP Snopes Corpus indicate that the proposed approach leads to higher accuracy and FEVER score compared to state-of-the-art claim verification methods.