Abstract
Automatic fact verification has attracted recent research attention as the increasing dissemination of disinformation on social media platforms. The FEVEROUS shared task introduces a benchmark for fact verification, in which a system is challenged to verify the given claim using the extracted evidential elements from Wikipedia documents. In this paper, we propose our 3rd place three-stage system consisting of document retrieval, element retrieval, and verdict inference for the FEVEROUS shared task. By considering the context relevance in the fact extraction and verification task, our system achieves 0.29 FEVEROUS score on the development set and 0.25 FEVEROUS score on the blind test set, both outperforming the FEVEROUS baseline.- Anthology ID:
- 2021.fever-1.7
- Volume:
- Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)
- Month:
- November
- Year:
- 2021
- Address:
- Dominican Republic
- Editors:
- Rami Aly, Christos Christodoulopoulos, Oana Cocarascu, Zhijiang Guo, Arpit Mittal, Michael Schlichtkrull, James Thorne, Andreas Vlachos
- Venue:
- FEVER
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 60–65
- Language:
- URL:
- https://aclanthology.org/2021.fever-1.7
- DOI:
- 10.18653/v1/2021.fever-1.7
- Cite (ACL):
- In-Zu Gi, Ting-Yu Fang, and Richard Tzong-Han Tsai. 2021. Verdict Inference with Claim and Retrieved Elements Using RoBERTa. In Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER), pages 60–65, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Verdict Inference with Claim and Retrieved Elements Using RoBERTa (Gi et al., FEVER 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2021.fever-1.7.pdf
- Data
- FEVER, FEVEROUS