Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks

Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong


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.
Anthology ID:
P19-1244
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2561–2571
Language:
URL:
https://aclanthology.org/P19-1244
DOI:
10.18653/v1/P19-1244
Bibkey:
Cite (ACL):
Jing Ma, Wei Gao, Shafiq Joty, and Kam-Fai Wong. 2019. Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2561–2571, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks (Ma et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1244.pdf
Data
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