research-article

EvidenceNet: Evidence Fusion Network for Fact Verification

Authors:

Siu Cheung Hui,

Jiaqi LiAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 2636 - 2645

https://doi.org/10.1145/3485447.3512135

Published: 25 April 2022 Publication History

Abstract

Fact verification is a challenging task that requires the retrieval of multiple pieces of evidence from a reliable corpus for verifying the truthfulness of a claim. Although the current methods have achieved satisfactory performance, they still suffer from one or more of the following three problems: (1) unable to extract sufficient contextual information from the evidence sentences; (2) containing redundant evidence information and (3) incapable of capturing the interaction between claim and evidence. To tackle the problems, we propose an evidence fusion network called EvidenceNet. The proposed EvidenceNet model captures global contextual information from various levels of evidence information for deep understanding. Moreover, a gating mechanism is designed to filter out redundant information in evidence. In addition, a symmetrical interaction attention mechanism is also proposed for identifying the interaction between claim and evidence. We conduct extensive experiments based on the FEVER dataset. The experimental results have shown that the proposed EvidenceNet model outperforms the current fact verification methods and achieves the state-of-the-art performance.

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Cited By

Arriba-Pérez FGarcía-Méndez SLeal FMalheiro BBurguillo J(2025)Identification and explanation of disinformation in wiki data streamsIntegrated Computer-Aided Engineering10.1177/10692509241306580Online publication date: 2-Feb-2025
https://doi.org/10.1177/10692509241306580
Qian ZLi PZhu QZhou G(2025)Document-Level Event Factuality Identification via Reinforced Semantic Learning NetworkJournal of Computer Science and Technology10.1007/s11390-024-2655-139:6(1248-1268)Online publication date: 16-Jan-2025
https://doi.org/10.1007/s11390-024-2655-1
Zhu JGao CYin ZLi XKurths JBaeza-Yates RBonchi F(2024)Propagation Structure-Aware Graph Transformer for Robust and Interpretable Fake News DetectionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672024(4652-4663)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672024
Show More Cited By

Index Terms

EvidenceNet: Evidence Fusion Network for Fact Verification
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
    2. Natural language processing
  2. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
      2. Neural networks
2. Information systems
  1. Information retrieval

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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the National Key R\&D Program of China under Grant

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Arriba-Pérez FGarcía-Méndez SLeal FMalheiro BBurguillo J(2025)Identification and explanation of disinformation in wiki data streamsIntegrated Computer-Aided Engineering10.1177/10692509241306580Online publication date: 2-Feb-2025
https://doi.org/10.1177/10692509241306580
Qian ZLi PZhu QZhou G(2025)Document-Level Event Factuality Identification via Reinforced Semantic Learning NetworkJournal of Computer Science and Technology10.1007/s11390-024-2655-139:6(1248-1268)Online publication date: 16-Jan-2025
https://doi.org/10.1007/s11390-024-2655-1
Zhu JGao CYin ZLi XKurths JBaeza-Yates RBonchi F(2024)Propagation Structure-Aware Graph Transformer for Robust and Interpretable Fake News DetectionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672024(4652-4663)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672024
Zhang FLiu JXie JZhang QXu YZha ZChua TNgo CKa-Wei Lee RKumar RLauw H(2024)ESCNet: Entity-enhanced and Stance Checking Network for Multi-modal Fact-CheckingProceedings of the ACM Web Conference 202410.1145/3589334.3645455(2429-2440)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645455
Wu JXu WLiu QWu SWang L(2024)Adversarial Contrastive Learning for Evidence-Aware Fake News Detection With Graph Neural NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.334164036:11(5591-5604)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2023.3341640
Chen ZHui SZhuang FLiao LJia MLi JHuang H(2024)A syntactic evidence network model for fact verificationNeural Networks10.1016/j.neunet.2024.106424178:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106424
Chen CChen WZheng JLuo ACai FZhang Y(2024)Input-oriented demonstration learning for hybrid evidence fact verificationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123191246:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123191
Senouci AMeziane HBenbernou S(2024)Claim polarity analysis from conflicting sourcesInternational Journal of Data Science and Analytics10.1007/s41060-024-00634-6Online publication date: 7-Oct-2024
https://doi.org/10.1007/s41060-024-00634-6
Wu JHooi BSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)DECOR: Degree-Corrected Social Graph Refinement for Fake News DetectionProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599298(2582-2593)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599298
Liu GMa YLiang X(2023)Cross-Domain Fake News Detection Based on Coarse-Fine Grained Environments Reflecting Public Expectation2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386913(403-412)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386913

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