- Zhenghao Liu, Chenyan Xiong, Maosong Sun. 2019. Kernel Graph Attention Network for Fact Verification. Abstract: This paper presents Kernel Graph Attention Network ( KGAT ), which conducts more ﬁne-grained evidence selection and reasoning for the fact veriﬁcation task. Given a claim and a set of potential supporting evidence sentences, KGAT constructs a graph attention network using the evidence sentences as its nodes and learns to verify the claim integrity using its edge kernels and node kernels, where the edge kernels learn to propagate information across the evidence graph, and the node kernels learn to merge node level information to the graph level. KGAT reaches a comparable performance (69.4%) on FEVER, a large-scale benchmark for fact veriﬁcation. Our experiments ﬁnd that KGAT thrives on veriﬁcation scenarios where multiple evidence pieces are required. This advantage mainly comes from the sparse and ﬁne-grained attention mechanisms from our kernel technique.
