Kernel Graph Attention Network for Fact Verification
Year: 2019
Type: preprint
Abstract: This paper presents Kernel Graph Attention Network (KGAT), which conductsmore 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.
Source: arXiv (Cornell University)
Authors: Zhenghao Liu, Chenyan Xiong, Maosong Sun
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2982120026