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• W3127495942 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 texttt{url{this https URL}}." @default.
• W3127495942 created "2021-02-15" @default.
• W3127495942 creator A5076188177 @default.
• W3127495942 creator A5082286754 @default.
• W3127495942 date "2021-02-04" @default.
• W3127495942 modified "2023-09-27" @default.
• W3127495942 title "Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection" @default.
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• W3127495942 hasPublicationYear "2021" @default.
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