@inproceedings{vo-lee-2021-hierarchical,
    title = "Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection",
    author = "Vo, Nguyen  and
      Lee, Kyumin",
    editor = "Merlo, Paola  and
      Tiedemann, Jorg  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.eacl-main.83/",
    doi = "10.18653/v1/2021.eacl-main.83",
    pages = "965--975",
    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 \url{https://github.com/nguyenvo09/EACL2021}."
}