Fact Verification requires fine-grained natural language inference capability
that finds subtle clues to identify the syntactical and semantically correct
but not well-supported claims. This paper presents Kernel Graph Attention
Network (KGAT), which conducts more fine-grained fact verification with
kernel-based attentions. Given a claim and a set of potential evidence
sentences that form an evidence graph, KGAT introduces node kernels, which
better measure the importance of the evidence node, and edge kernels, which
conduct fine-grained evidence propagation in the graph, into Graph Attention
Networks for more accurate fact verification. KGAT achieves a 70.38% FEVER
score and significantly outperforms existing fact verification models on FEVER,
a large-scale benchmark for fact verification. Our analyses illustrate that,
compared to dot-product attentions, the kernel-based attention concentrates
more on relevant evidence sentences and meaningful clues in the evidence graph,
which is the main source of KGAT's effectiveness.

本研究提出基于核的图注意力网络 (KGAT)，利用节点核函数和边缘核函数，在证据图中进行更细粒度的证实验证，相比于点积注意力，核心注意力更能集中于相关证据句子和证据图中的有意义的线索，从而在 FEVER 测试数据集上取得了较高的 70.38% FEVER 得分，明显优于现有的事实验证模型。