  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.
