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Fact verification is a challenging task that requires simultaneouslyreasoning and aggregating over multiple retrieved pieces of evidence toevaluate the truthfulness of a claim. Existing approaches typically (i) explorethe semantic interaction between the claim and evidence at differentgranularity levels but fail to capture their topical consistency during thereasoning process, which we believe is crucial for verification; (ii) aggregatemultiple pieces of evidence equally without considering their implicit stancesto the claim, thereby introducing spurious information. To alleviate the aboveissues, we propose a novel topic-aware evidence reasoning and stance-awareaggregation model for more accurate fact verification, with the following fourkey properties: 1) checking topical consistency between the claim and evidence;2) maintaining topical coherence among multiple pieces of evidence; 3) ensuringsemantic similarity between the global topic information and the semanticrepresentation of evidence; 4) aggregating evidence based on their implicitstances to the claim. Extensive experiments conducted on the two benchmarkdatasets demonstrate the superiority of the proposed model over severalstate-of-the-art approaches for fact verification. The source code can beobtained from https://github.com/jasenchn/TARSA.

Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification