@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",
pages = "4810--4845",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification
%A Helwe, Chadi
%A Calamai, Tom
%A Paris, Pierre-Henri
%A Clavel, Chloé
%A Suchanek, Fabian
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F helwe-etal-2024-mafalda
%X 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.
%U https://aclanthology.org/2024.naacl-long.270
%P 4810-4845
Markdown (Informal)
[MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification](https://aclanthology.org/2024.naacl-long.270) (Helwe et al., NAACL 2024)
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.