@inproceedings{cantin-larumbe-chust-vendrell-2025-argumentative,
title = "Argumentative Fallacy Detection in Political Debates",
author = "Cant{\'i}n Larumbe, Eva and
Chust Vendrell, Adriana",
editor = "Chistova, Elena and
Cimiano, Philipp and
Haddadan, Shohreh and
Lapesa, Gabriella and
Ruiz-Dolz, Ramon",
booktitle = "Proceedings of the 12th Argument mining Workshop",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.argmining-1.36/",
doi = "10.18653/v1/2025.argmining-1.36",
pages = "369--373",
ISBN = "979-8-89176-258-9",
abstract = "Building on recent advances in Natural Language Processing (NLP), this work addresses the task of fallacy detection in political debates using a multimodal approach combining text and audio, as well as text-only and audio-only approaches. Although the multimodal setup is novel, results show that text-based models consistently outperform both audio-only and multimodal models, confirming that textual information remains the most effective for this task. Transformer-based and few-shot architectures were used to detect fallacies. While fine-tuned language models demonstrate strong performance, challenges such as data imbalance, audio processing, and limited dataset size persist."
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%0 Conference Proceedings
%T Argumentative Fallacy Detection in Political Debates
%A Cantín Larumbe, Eva
%A Chust Vendrell, Adriana
%Y Chistova, Elena
%Y Cimiano, Philipp
%Y Haddadan, Shohreh
%Y Lapesa, Gabriella
%Y Ruiz-Dolz, Ramon
%S Proceedings of the 12th Argument mining Workshop
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-258-9
%F cantin-larumbe-chust-vendrell-2025-argumentative
%X Building on recent advances in Natural Language Processing (NLP), this work addresses the task of fallacy detection in political debates using a multimodal approach combining text and audio, as well as text-only and audio-only approaches. Although the multimodal setup is novel, results show that text-based models consistently outperform both audio-only and multimodal models, confirming that textual information remains the most effective for this task. Transformer-based and few-shot architectures were used to detect fallacies. While fine-tuned language models demonstrate strong performance, challenges such as data imbalance, audio processing, and limited dataset size persist.
%R 10.18653/v1/2025.argmining-1.36
%U https://aclanthology.org/2025.argmining-1.36/
%U https://doi.org/10.18653/v1/2025.argmining-1.36
%P 369-373
Markdown (Informal)
[Argumentative Fallacy Detection in Political Debates](https://aclanthology.org/2025.argmining-1.36/) (Cantín Larumbe & Chust Vendrell, ArgMining 2025)
ACL