Sentence-level evidence embedding for claim verification with hierarchical attention networks

• Main Authors: MA, Jing, GAO, Wei, JOTY, Shafiq, WONG, Kam-Fai
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/4557; https://ink.library.smu.edu.sg/context/sis_research/article/5560/viewcontent/P19_1244.pdf
• Institution: Singapore Management University

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.