README.md

---
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."
}
```