Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2019

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.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)

First Page

2561

Last Page

2571

Identifier

10.18653/v1/P19-1244

Publisher

Association for Computational Linguistics

City or Country

Florence, Italy

Additional URL

http://doi.org/10.18653/v1/P19-1244

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