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        <title>Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection</title>
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            <namePart type="given">Paola</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2021.eacl-main.83</identifier>
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%0 Conference Proceedings
%T Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection
%A Vo, Nguyen
%A Lee, Kyumin
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F vo-lee-2021-hierarchical
%X 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.
%R 10.18653/v1/2021.eacl-main.83
%U https://aclanthology.org/2021.eacl-main.83
%U https://doi.org/10.18653/v1/2021.eacl-main.83
%P 965-975

Markdown (Informal)

[Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection](https://aclanthology.org/2021.eacl-main.83) (Vo & Lee, EACL 2021)

• Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection (Vo & Lee, EACL 2021)

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.