DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1024–1035 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 1024 DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification Lianwei Wu, Yuan Rao, Yongqiang Zhao, Hao Liang, Ambreen Nazir Lab of Social Intelligence and Complexity Data Processing, School of Software Engineering, Xi’an Jiaotong University, China Shannxi Joint Key Laboratory for Artifact Intelligence(Sub-Lab of Xi’an Jiaotong University), China Research Institute of Xi’an Jiaotong University, Shenzhen, China {stayhungry,yongqiang1210,favorablelearner,ambreen.nazir}@stu.xjtu.edu.cn raoyuan@mail.xjtu.edu.cn Abstract Recently, many methods discover effective ev- idence from reliable sources by appropriate neural networks for explainable claim verifica- tion, which has been widely recognized. How- ever, in these methods, the discovery process of evidence is nontransparent and unexplained. Simultaneously, the discovered evidence only roughly aims at the interpretability of the whole sequence of claims but insufficient to focus on the false parts of claims. In this paper, we propose a Decision Tree-based Co- Attention model (DTCA) to discover evidence for explainable claim verification. Specifi- cally, we first construct Decision Tree-based Evidence model (DTE) to select comments with high credibility as evidence in a transpar- ent and interpretable way. Then we design Co-attention Self-attention networks (CaSa) to make the selected evidence interact with claims, which is for 1) training DTE to de- termine the optimal decision thresholds and obtain more powerful evidence; and 2) utiliz- ing the evidence to find the false parts in the claim. Experiments on two public datasets, RumourEval and PHEME, demonstrate that DTCA not only provides explanations for the results of claim verification but also achieves the state-of-the-art performance, boosting the F1-score by 3.11%, 2.41%, respectively. 1 Introduction The increasing popularity of social media has brought unprecedented challenges to the ecology of information dissemination, causing rampancy of a large volume of false or unverified claims, like extreme news, hoaxes, rumors, fake news, etc. Re- search indicates that during the US presidential election (2016), fake news accounts for nearly 6% of all news consumption, where 1% of users are ex- posed to 80% of fake news, and 0.1% of users are responsible for sharing 80% of fake news (Grinberg et al., 2019), and democratic elections are vulnera- ble to manipulation of the false or unverified claims on social media (Aral and Eckles, 2019), which ren- ders the automatic verification of claims a crucial problem. Currently, the methods for automatic claim veri- fication could be divided into two categories: the first is that the methods relying on deep neural net- works learn credibility indicators from claim con- tent and auxiliary relevant articles or comments (i.e., responses) (Volkova et al., 2017; Rashkin et al., 2017; Dungs et al., 2018). Despite their effec- tiveness, these methods are difficult to explain why claims are true or false in practice. To overcome the weakness, a trend in recent studies (the sec- ond category) is to endeavor to explore evidence- based verification solutions, which focuses on cap- turing the fragments of evidence obtained from reli- able sources by appropriate neural networks …

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