Verdict Inference with Claim and Retrieved Elements Using RoBERTa

In-Zu Gi, Ting-Yu Fang, Richard Tzong-Han Tsai


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
Venues:
EMNLP | FEVER
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
60–65
Language:
URL:
https://aclanthology.org/2021.fever-1.7
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.fever-1.7.pdf
Data
FEVERFEVEROUS