Abstract
Claim verification is challenging because it requires first to find textual evidence and then apply claim-evidence entailment to verify a claim. Previous works evaluate the entailment step based on the retrieved evidence, whereas we hypothesize that the entailment prediction can provide useful signals for evidence retrieval, in the sense that if a sentence supports or refutes a claim, the sentence must be relevant. We propose a novel model that uses the entailment score to express the relevancy. Our experiments verify that leveraging entailment prediction improves ranking multiple pieces of evidence.- Anthology ID:
- 2021.ranlp-1.174
- Volume:
- Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
- Month:
- September
- Year:
- 2021
- Address:
- Held Online
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 1553–1558
- Language:
- URL:
- https://aclanthology.org/2021.ranlp-1.174
- DOI:
- Cite (ACL):
- Fan Yang, Eduard Dragut, and Arjun Mukherjee. 2021. Improving Evidence Retrieval with Claim-Evidence Entailment. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1553–1558, Held Online. INCOMA Ltd..
- Cite (Informal):
- Improving Evidence Retrieval with Claim-Evidence Entailment (Yang et al., RANLP 2021)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2021.ranlp-1.174.pdf
- Data
- FEVER