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.
BibTeX
@inproceedings{luo-etal-2024-faganet,
title = "{F}a{GAN}et: An Evidence-Based Fact-Checking Model with Integrated Encoder Leveraging Contextual Information",
author = "Luo, Weiyao and
Ran, Junfeng and
Tian, Zailong and
Li, Sujian and
Sui, Zhifang",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.621/",
pages = "7082--7088"
}