Fact verification tasks aim to identify the integrity of textual contents
according to the truthful corpus. Existing fact verification models usually
build a fully connected reasoning graph, which regards claim-evidence pairs as
nodes and connects them with edges. They employ the graph to propagate the
semantics of the nodes. Nevertheless, the noisy nodes usually propagate their
semantics via the edges of the reasoning graph, which misleads the semantic
representations of other nodes and amplifies the noise signals. To mitigate the
propagation of noisy semantic information, we introduce a Confidential Graph
Attention Network (CO-GAT), which proposes a node masking mechanism for
modeling the nodes. Specifically, CO-GAT calculates the node confidence score
by estimating the relevance between the claim and evidence pieces. Then, the
node masking mechanism uses the node confidence scores to control the noise
information flow from the vanilla node to the other graph nodes. CO-GAT
achieves a 73.59% FEVER score on the FEVER dataset and shows the generalization
ability by broadening the effectiveness to the science-specific domain.

Fact verification tasks using a Confidential Graph Attention Network (CO-GAT) with a node masking mechanism achieve a 73.59% FEVER score on the FEVER dataset and show generalization ability to science-specific domains.