  Fact verification is a challenging task that requires simultaneously
reasoning and aggregating over multiple retrieved pieces of evidence to
evaluate the truthfulness of a claim. Existing approaches typically (i) explore
the semantic interaction between the claim and evidence at different
granularity levels but fail to capture their topical consistency during the
reasoning process, which we believe is crucial for verification; (ii) aggregate
multiple pieces of evidence equally without considering their implicit stances
to the claim, thereby introducing spurious information. To alleviate the above
issues, we propose a novel topic-aware evidence reasoning and stance-aware
aggregation model for more accurate fact verification, with the following four
key properties: 1) checking topical consistency between the claim and evidence;
2) maintaining topical coherence among multiple pieces of evidence; 3) ensuring
semantic similarity between the global topic information and the semantic
representation of evidence; 4) aggregating evidence based on their implicit
stances to the claim. Extensive experiments conducted on the two benchmark
datasets demonstrate the superiority of the proposed model over several
state-of-the-art approaches for fact verification. The source code can be
obtained from https://github.com/jasenchn/TARSA.
