FaGANet: An Evidence-Based Fact-Checking Model with Integrated Encoder Leveraging Contextual Information
Weiyao Luo, Junfeng Ran, Zailong Tian, Sujian Li, Zhifang Sui
Abstract
In the face of the rapidly growing spread of false and misleading information in the real world, manual evidence-based fact-checking efforts become increasingly challenging and time-consuming. In order to tackle this issue, we propose FaGANet, an automated and accurate fact-checking model that leverages the power of sentence-level attention and graph attention network to enhance performance. This model adeptly integrates encoder-only models with graph attention network, effectively fusing claims and evidence information for accurate identification of even well-disguised data. Experiment results showcase the significant improvement in accuracy achieved by our FaGANet model, as well as its state-of-the-art performance in the evidence-based fact-checking task. We release our code and data in https://github.com/WeiyaoLuo/FaGANet.- Anthology ID:
- 2024.lrec-main.621
- 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:
- 7082–7088
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.621
- DOI:
- Cite (ACL):
- Weiyao Luo, Junfeng Ran, Zailong Tian, Sujian Li, and Zhifang Sui. 2024. FaGANet: An Evidence-Based Fact-Checking Model with Integrated Encoder Leveraging Contextual Information. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 7082–7088, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- FaGANet: An Evidence-Based Fact-Checking Model with Integrated Encoder Leveraging Contextual Information (Luo et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2024.lrec-main.621.pdf