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With the rapid development of automatic fake news detection technology, factextraction and verification (FEVER) has been attracting more attention. Thetask aims to extract the most related fact evidences from millions ofopen-domain Wikipedia documents and then verify the credibility ofcorresponding claims. Although several strong models have been proposed for thetask and they have made great progress, we argue that they fail to utilizemulti-view contextual information and thus cannot obtain better performance. Inthis paper, we propose to integrate multi-view contextual information (IMCI)for fact extraction and verification. For each evidence sentence, we define twokinds of context, i.e. intra-document context and inter-document context}.Intra-document context consists of the document title and all the othersentences from the same document. Inter-document context consists of all otherevidences which may come from different documents. Then we integrate themulti-view contextual information to encode the evidence sentences to handlethe task. Our experimental results on FEVER 1.0 shared task show that our IMCIframework makes great progress on both fact extraction and verification, andachieves state-of-the-art performance with a winning FEVER score of 72.97% andlabel accuracy of 75.84% on the online blind test set. We also conduct ablationstudy to detect the impact of multi-view contextual information. Our codes willbe released at https://github.com/phoenixsecularbird/IMCI.

IMCI: Integrate Multi-view Contextual Information for Fact Extraction and Verification