Prior research in computational argumentation has mainly focused on scoring the quality of arguments, with less attention on explicating logical errors. In this work, we introduce four sets of explainable templates for common informal logical fallacies designed to explicate a fallacy's implicit logic. Using our templates, we conduct an annotation study on top of 400 fallacious arguments taken from LOGIC dataset and achieve a high agreement score (Krippendorf's alpha of 0.54) and reasonable coverage (0.83). Finally, we conduct an experiment for detecting the structure of fallacies and discover that state-of-the-art language models struggle with detecting fallacy templates (0.47 accuracy). To facilitate research on fallacies, we make our dataset and guidelines publicly available.

在这项研究中，我们引入了四套可解释的模板，用于常见非正式逻辑谬误的解释，通过我们的模板，在LOGIC数据集的400个谬误论证中，进行了注释研究，并取得了高的一致性得分（Krippendorf's alpha为0.54）和合理的涵盖范围（0.83）。最后，我们进行了一项关于谬误结构的实验，并发现现有的语言模型在检测谬误模板方面存在困难（准确率为0.47）。为了促进谬误研究，我们公开了我们的数据集和指南。

逃离缺陷：通过模板和槽填充标注谬误论证的基本逻辑