DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification
Lianwei Wu Affiliation / Address line 1 Affiliation / Address line 2 Affiliation / Address line 3、Second Author Affiliation / Address line 1 Affiliation / Address line 2 Affiliation / Address line 3 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
Abstract
Recently, many methods discover effective evidence from reliable sources by appropriate neural networks for explainable claim verification, which has been widely recognized. However, 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. Specifically, we first construct Decision Tree-based Evidence model (DTE) to select comments with high credibility as evidence in a transparent 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 determine the optimal decision thresholds and obtain more powerful evidence; and 2) utilizing 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 sta
中文速览
社交媒体上虚假信息泛滥，自动核查谣言、假新闻等"声索"（claim verification）已成为迫切需求，但现有基于神经网络的方法存在两个痛点：筛选证据的过程像"黑盒"无法解释，且找到的证据只能笼统指向整条声索，无法精准定位其中的虚假片段。为此，作者提出了 DTCA（Decision Tree-based Co-Attention）模型，先用决策树（Decision Tree）以透明、可解释的方式从用户评论中筛选高可信度评论作为证据——决策条件涵盖语义相似度、评论者可信度和评论本身可信度等因素；再设计协同注意力自注意力网络（Co-attention Self-attention Networks，CaSa）让证据与声索进行深层语义交互，既反过来优化决策树的筛选阈值以获得更强的证据，又能将注意力精准落在声索中的虚假片段上。在 RumourEval 和 PHEME 两个公开数据集上的实验表明，DTCA 的 F1 值分别比此前最优方法提升了 3.11% 和 2.41%，同时提供了对验证结果和证据筛选过程的双重可解释性。这项工作的重要意义在于，它将决策树的内在透明性与深度学习的强表达能力结合起来，迈出了让假新闻检测既"能干"又"说得清楚"的关键一步。
原文 arXiv:2004.13455;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2004.13455v1