中国科学院自动化研究所机构知识库

To inhibit the spread of rumorous information, fact checking aims to retrieve evidence to verify the truthfulness of a given statement. Fact checking methods typically use knowledge graphs (KGs) as external repositories and develop reasoning methods to retrieve evidence from KGs. As real-world statement is often complex and contains multiple claims, multi-claim fact verification is not only necessary but more important for practical applications. However, existing methods only focus on verifying a single claim (i.e. a single-claim statement). Multiple claims implies rich context information and modeling the interrelations between claims can facilitate better verification of a multi-claim statement as a whole. In this paper, we propose a computational method to model inter-claim interactions for multi-claim fact checking. To focus on relevant claims within a statement, our method first extracts topics from the statement and connects the triple claims in the statement to form a claim graph. It then learns a policy-based agent to sequentially select topic-related triples from the claim graph. To fully exploit information from the statement, our method further employs multiple agents develops a hierarchical attention mechanism to verify multiple claims as a whole. Experimental results on two real-world datasets show the effectiveness of our method for multi-claim fact verification.