Sentence-Level Evidence Embedding for Claim Verification with
Hierarchical Attention Networks
Abstract
Claim verification is generally a task of verifying the veracity of a given claim, which
is critical to many downstream applications.
It is cumbersome and inefficient for human
fact-checkers to find consistent pieces of evidence, from which solid verdict could be inferred against the claim. In this paper, we propose a novel end-to-end hierarchical attention
network focusing on learning to represent coherent evidence as well as their semantic relatedness with the claim. Our model consists of
three main components: 1) A coherence-based
attention layer embeds coherent evidence considering the claim and sentences from relevant articles; 2) An entailment-based attention
layer attends on sentences that can semantically infer the claim on top of the first attention; and 3) An output layer predicts the verdict based on the embedded evidence. Experimental results on three public benchmark
datasets show that our proposed model outperforms a set of state-of-the-art baselines