NAACL 2024long12 citations
MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification
Chadi Helwe, Tom Calamai, Pierre-Henri Paris, Chloé Clavel, Fabian Suchanek
Abstract
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.
BibTeX
@inproceedings{helwe-etal-2024-mafalda,
title = "{MAFALDA}: A Benchmark and Comprehensive Study of Fallacy Detection and Classification",
author = "Helwe, Chadi and
Calamai, Tom and
Paris, Pierre-Henri and
Clavel, Chlo{\'e} and
Suchanek, Fabian",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.270/",
doi = "10.18653/v1/2024.naacl-long.270",
pages = "4810--4845"
}