{"type":"ScholarlyArticle","title":"Evidence-Aware Hierarchical Interactive Attention Networks for Explainable Claim Verification","authors":[{"name":"Lianwei Wu","slug":"lianwei-wu","given":"Lianwei","family":"Wu","affiliation":"Lab of Social Intelligence and Complexity Data Processing , School of Software Engineering , Xi'an Jiaotong University , China"},{"name":"Yuan Rao","slug":"yuan-rao","given":"Yuan","family":"Rao","affiliation":"Lab of Social Intelligence and Complexity Data Processing , School of Software Engineering , Xi'an Jiaotong University , China"},{"name":"Xiong Yang","slug":"xiong-yang","given":"Xiong","family":"Yang","affiliation":"Lab of Social Intelligence and Complexity Data Processing , School of Software Engineering , Xi'an Jiaotong University , China; Research Institute of Xi'an Jiaotong University , Shenzhen , China"},{"name":"Wanzhen Wang","slug":"wanzhen-wang","given":"Wanzhen","family":"Wang","affiliation":"Shannxi Joint Key Laboratory for Artifact Intelligence(Sub-Lab of Xi'an Jiaotong University) , China"},{"name":"Ambreen Nazir","slug":"ambreen-nazir","given":"Ambreen","family":"Nazir","affiliation":"Shannxi Joint Key Laboratory for Artifact Intelligence(Sub-Lab of Xi'an Jiaotong University) , China"}],"abstract":"Exploring evidence from relevant articles to confirm the veracity of claims is a trend towards explainable claim verification. However, most strategies capture the top-k check-worthy articles or salient words as evidence, but this evidence is difficult to focus on the questionable parts of unverified claims. Besides, they utilize relevant articles indiscriminately, ignoring the source credibility of these articles, which may cause quiet a few unreliable articles to interfere with the assessment results. In this paper, we propose Evidence-aware Hierarchical Interactive Attention Networks (EHIAN) by considering the capture of evidence fragments and the fusion of source credibility to explore more credible evidence semantics discussing the questionable parts of claims for explainable claim verification. EHIAN first designs internal interaction layer (IIL) to strengthen deep interaction and matching between claims and relevant articles for obtaining key evidence fragments, and then proposes global inference layer (GIL) that fuses source features of articles and interacts globally with the average semantics of all articles and finally earns the more credible evidence semantics discussing the questionable parts of claims. Experiments on two datasets demonstrate that EHIAN not only achieves the state-of-the-art performance but also secures effective evidence to explain the results.","doi":"10.24963/ijcai.2020/193","url":"https://ijcai.org/proceedings/2020/193","pdf":"https://ijcai.org/proceedings/2020/0193.pdf","markdown":"https://ijcai.org/proceedings/2020/193.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/193","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Data Mining","track":"Main track","number":193,"pages":{"first":1388,"last":1394},"keywords":["Natural Language Processing: Text Classification","Data Mining: Mining Text, Web, Social Media","Natural Language Processing: Sentiment Analysis and Text Mining"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25001/file_25001.m3u8","length_sec":286,"provider":"hls"}],"references":[{"position":1,"raw":"[Ajao et al., 2019] Oluwaseun Ajao, Deepayan Bhowmik, and Shahrzad Zargari. 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