Abstract
The widespread of fake news and misinformation in various domains ranging from politics, economics to public health has posed an urgent need to automatically fact-check information. A recent trend in fake news detection is to utilize evidence from external sources. However, existing evidence-aware fake news detection methods focused on either only word-level attention or evidence-level attention, which may result in suboptimal performance. In this paper, we propose a Hierarchical Multi-head Attentive Network to fact-check textual claims. Our model jointly combines multi-head word-level attention and multi-head document-level attention, which aid explanation in both word-level and evidence-level. Experiments on two real-word datasets show that our model outperforms seven state-of-the-art baselines. Improvements over baselines are from 6% to 18%. Our source code and datasets are released at https://github.com/nguyenvo09/EACL2021.- Anthology ID:
- 2021.eacl-main.83
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 965–975
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.83
- DOI:
- 10.18653/v1/2021.eacl-main.83
- Cite (ACL):
- Nguyen Vo and Kyumin Lee. 2021. Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 965–975, Online. Association for Computational Linguistics.
- Cite (Informal):
- Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection (Vo & Lee, EACL 2021)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2021.eacl-main.83.pdf
- Code
- nguyenvo09/EACL2021
- Data
- PolitiFact, Snopes