论文标题
DTCA：基于决策树的共同注意网络，用于可解释的索赔验证
DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification
论文作者
论文摘要
最近，许多方法通过适当的神经网络从可靠来源中发现有效的证据，以进行可解释的索赔验证，并被广泛认可。但是，在这些方法中，证据的发现过程是非透明且无法解释的。同时，发现的证据只针对整个主张顺序的解释性，而不足以专注于索赔的错误部分。在本文中，我们提出了一个基于决策树的共同注意模型（DTCA），以发现可解释索赔验证的证据。具体而言，我们首先构建基于决策树的证据模型（DTE），以透明和可解释的方式选择具有高信誉的评论作为证据。然后，我们设计了共同注意的自我注意力网络（CASA），以使所选证据与主张相互作用，这是1）培训DTE来确定最佳决策阈值并获得更有力的证据； 2）利用证据在索赔中找到虚假部分。在两个公共数据集（Rumoureval和Pheme）上进行的实验表明，DTCA不仅为索赔验证结果提供了解释，而且还实现了最新的性能，将F1得分分别提高了3.11％，2.41％。
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 state-of-the-art performance, boosting the F1-score by 3.11%, 2.41%, respectively.
