Abstract
The rise of online social media platforms has made them a popular source of news. However, they are also prone to misinformation and fake news. To combat this, fact-checking is essential to verify the accuracy of claims made on these platforms. However, the existing methods in this field often lack the use of external sources and human-understandable explanations for system decisions. In this paper, we introduce a framework called Triple-R (Retriever, Ranker, Reasoner) that addresses these challenges. The framework uses the Web as an external knowledge source to retrieve relevant evidence for claims and includes a method to generate reasons based on the retrieved evidence for datasets lacking explanations. We then use this modified dataset to fine-tune a causal language model that generates natural language explanations and labels for pairs of retrieved evidence and claims. Our approach aims to improve the transparency and interpretability of fact-checking systems by providing understandable explanations for decision-making processes. We evaluated our method on a popular dataset and demonstrated its performance through an ablation study. The modified dataset is available on the Canadian Institute for Cybersecurity datasets webpage at https://www.unb.ca/cic/datasets/index.html.- Anthology ID:
- 2024.lrec-main.1463
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 16831–16840
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1463
- DOI:
- Cite (ACL):
- Mohammadamin Kanaani, Sajjad Dadkhah, and Ali A. Ghorbani. 2024. Triple-R: Automatic Reasoning for Fact Verification Using Language Models. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 16831–16840, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Triple-R: Automatic Reasoning for Fact Verification Using Language Models (Kanaani et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1463.pdf