Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks
Jing Ma, Wei Guang Gao, Shafiq Joty, Kam‐Fai Wong · 2019
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.