  Recent studies constructing direct interactions between the claim and each
single user response (a comment or a relevant article) to capture evidence have
shown remarkable success in interpretable claim verification. Owing to
different single responses convey different cognition of individual users
(i.e., audiences), the captured evidence belongs to the perspective of
individual cognition. However, individuals' cognition of social things is not
always able to truly reflect the objective. There may be one-sided or biased
semantics in their opinions on a claim. The captured evidence correspondingly
contains some unobjective and biased evidence fragments, deteriorating task
performance. In this paper, we propose a Dual-view model based on the views of
Collective and Individual Cognition (CICD) for interpretable claim
verification. From the view of the collective cognition, we not only capture
the word-level semantics based on individual users, but also focus on
sentence-level semantics (i.e., the overall responses) among all users and
adjust the proportion between them to generate global evidence. From the view
of individual cognition, we select the top-$k$ articles with high degree of
difference and interact with the claim to explore the local key evidence
fragments. To weaken the bias of individual cognition-view evidence, we devise
inconsistent loss to suppress the divergence between global and local evidence
for strengthening the consistent shared evidence between the both. Experiments
on three benchmark datasets confirm that CICD achieves state-of-the-art
performance.
