IMCI : Integrate Multi-view Contextual Information for Fact Extraction and Verification (2022)

Abstract: With the rapid development of automatic fake news detection technology, fact extraction and verification (FEVER) has been attracting more attention. The task aims to extract the most related fact evidences from millions of open-domain Wikipedia documents and then verify the credibility of corresponding claims. Although several strong models have been proposed for the task and they have made great process, we argue that they fail to utilize multi-view contextual information and thus cannot obtain better performance. In this paper, we propose to integrate multi-view contextual information (IMCI) for fact extraction and verification. For each evidence sentence, we define two kinds of context, i.e. intra-document context and inter-document context. Intra-document context consists of the document title and all the other sentences from the same document. Inter-document context consists of all other evidences which may come from different documents. Then we integrate the multi-view contextual information to encode the evidence sentences to handle the task. Our experimental results on FEVER 1.0 shared task show that our IMCI framework makes great progress on both fact extraction and verification, and achieves state-of-the-art performance with a winning FEVER score of 73.96% and label accuracy of 77.25% on the online blind test set. We also conduct ablation study to detect the impact of multi-view contextual information.

## introduction fake news propagation is a severe social problem, which may cause great loss and lead to serious consequence, e.g. panic, quarrel, opposition and even war. the situation has become a general concern since brexit and the u.s. president campaign in * *corresponding author 2016 and gets far more intense due to covid-19 pandemic @xcite . in this condition, automatic fake news detection has been developing rapidly. according to @xcite , automatic fake news detection mainly include textualcontent based methods @xcite @xcite , userrole based methods @xcite , multi-modal approaches @xcite and detection of bots and trolls @xcite . among textual-content based methods, fact extraction and verification (fever) @xcite has been attracting developing attention. as shown in figure 1 , for a given claim, the task aims to select at most 5 most related sentences as evidences from millions of open-domain wikipedia documents for fact extraction, and combine the selected evidences to judge the claim as supports, refutes or not enough info (nei) for fact verification. recently, several strong models @xcite @xcite @xcite have been proposed for the task. although they have made great progress and obtained excellent performance on the task, we argue that they fail to utilize multi-view contextual information and thus cannot obtain better performance. specifically, we define two kinds of context for each evidence sentence, i.e. intra-document context and inter-document context. intra-document context consists of the document title and all the other sentences from the same document. inter-document context consists of all other evidences which may come from different documents. multi-view contextual information is of great importance for fact extraction and verification. for instance, as shown in figure 1 , intra-document context information can help to clarify the relationship between different entities, e.g. "edar wright" and its alias name "edar howard wright" in the first evidence, and "gemini" and it coreference "this sign" in the second sentence. besides, the two evidence sentences can be regarded as inter-document context of each other, and the information interaction and fusion between them is essential to verify the claim in this multihop sample. to this end, we propose to integrate multi-view contextual information (imci) for fact extraction and fact verification, where we introduce the multiview contextual information to encode the evidence sentences to handle the task. in summary, our contributions are as follows: ## iterative multi-view fact extraction our fact extraction model iteratively conducts document retrieval and sentence retrieval in two iterations to obtain corresponding candidate evidence sentences, and then reranks the candidates of different iterations for better performance. ## conclusion in this paper, we propose to integrate multi-view contextual information for fact extraction and verification. our experimental results show that our imci model can obtain state-of-the-art performance on the task. moreover, the ablation study results indicate that multi-view contextual information is essential for both fact extraction and fact verification. in the future, we will explore much stronger model to utilize contextual information in a more efficient way.