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configs:
- config_name: default
data_files:
- split: test
path: dataset.jsonl
task_categories:
- text-classification
language:
- en
pretty_name: MAFALDA
size_categories:
- 1K<n<10K
---
# Dataset Card for MAFALDA-fallacies
<!-- Provide a quick summary of the dataset. -->
This is a unified fallacy classification dataset originally publishde in [github](https://github.com/ChadiHelwe/MAFALDA/tree/main) and [NAACL 2024](https://aclanthology.org/2024.naacl-long.270/).
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
[MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification](https://aclanthology.org/2024.naacl-long.270/) (Helwe et al., NAACL 2024)
**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",
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."
}
``` |