@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://preview.aclanthology.org/mtsummit-25-ingestion/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."
}