Fine-grained Fallacy Detection with Human Label Variation

This repository contains materials associated to the paper:

Alan Ramponi, Agnese Daffara, and Sara Tonelli. 2025. Fine-grained Fallacy Detection with Human Label Variation. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 762--784, Albuquerque, New Mexico. Association for Computational Linguistics. [cite] [paper]

Data

Please write an e-mail to the first/corresponding author to request the FAINA dataset. The dataset is provided in an anonymized format and can be used for non-commercial purposes only. The user must declare to avoid redistribution of the dataset to third parties, deanonymization (by any means), and to exclude data misuse.

Code

The full code to replicate the experiments will be uploaded in this repository soon!

Citation

If you use or build on top of this work, please cite our paper as follows:

@inproceedings{ramponi-etal-2025-fine,
    title = "Fine-grained Fallacy Detection with Human Label Variation",
    author = "Ramponi, Alan  and
      Daffara, Agnese  and
      Tonelli, Sara",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.naacl-long.34/",
    doi = "10.18653/v1/2025.naacl-long.34",
    pages = "762--784",
    ISBN = "979-8-89176-189-6"
}