  We introduce MAFALDA, a benchmark for fallacy classification that merges and
unites previous fallacy datasets. It comes with a taxonomy that aligns,
refines, and unifies existing classifications of fallacies. We further provide
a manual annotation of a part of the dataset together with manual explanations
for each annotation. We propose a new annotation scheme tailored for subjective
NLP tasks, and a new evaluation method designed to handle subjectivity. We then
evaluate several language models under a zero-shot learning setting and human
performances on MAFALDA to assess their capability to detect and classify
fallacies.
