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.
翻译:本文介绍Kernel 图形关注网络(KGAT),该网络进行更精细的证据选择和事实核实任务的推理。根据一项索赔和一套可能的辅助性证据判决,KGAT建立了一个图形关注网络,将证据判决作为其节点,并学习使用其边缘内核和结点内核来核实索赔的完整性,边缘内核学习通过证据图传播信息,结点内核学会将节点级信息与图表级信息合并。KGAT在FEWE上达到了类似的性能(69.4%),这是用于事实核实的大规模基准。我们的实验发现,KGAT在需要多种证据的核查情景上非常活跃。这主要来自我们内核技术的稀疏和精细的注意机制。