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.
Anthology ID:
2024.naacl-long.270
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4810–4845
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.naacl-long.270/
DOI:
10.18653/v1/2024.naacl-long.270
Bibkey:
Cite (ACL):
Chadi Helwe, Tom Calamai, Pierre-Henri Paris, Chloé Clavel, and Fabian Suchanek. 2024. MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4810–4845, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification (Helwe et al., NAACL 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.naacl-long.270.pdf
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