Abstract
The influence of fake news in the perception of reality has become a mainstream topic in the last years due to the fast propagation of misleading information. In order to help in the fight against misinformation, automated solutions to fact-checking are being actively developed within the research community. In this context, the task of Automated Claim Verification is defined as assessing the truthfulness of a claim by finding evidence about its veracity. In this work we empirically demonstrate that enriching a BERT model with explicit semantic information such as Semantic Role Labelling helps to improve results in claim verification as proposed by the FEVER benchmark. Furthermore, we perform a number of explainability tests that suggest that the semantically-enriched model is better at handling complex cases, such as those including passive forms or multiple propositions.- Anthology ID:
- 2022.fever-1.5
- Volume:
- Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- 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:
- 37–48
- Language:
- URL:
- https://aclanthology.org/2022.fever-1.5
- DOI:
- 10.18653/v1/2022.fever-1.5
- Cite (ACL):
- Blanca Calvo Figueras, Montse Cuadros, and Rodrigo Agerri. 2022. A Semantics-Aware Approach to Automated Claim Verification. In Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER), pages 37–48, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- A Semantics-Aware Approach to Automated Claim Verification (Calvo Figueras et al., FEVER 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.fever-1.5.pdf
- Data
- FEVER