Flee the Flaw: Annotating the Underlying Logic of Fallacious Arguments Through Templates and Slot-filling
This work addresses the challenge of understanding and detecting logical fallacies in arguments for researchers in computational linguistics and AI, representing an incremental advancement by applying template-based annotation to an existing dataset.
The paper tackles the problem of explicating logical errors in computational argumentation by introducing explainable templates for common informal logical fallacies, achieving a high agreement score (Krippendorf's alpha of 0.54) and reasonable coverage (0.83) in an annotation study on 400 fallacious arguments, and finding that state-of-the-art language models struggle with detecting fallacy templates (0.47 accuracy).
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.