@inproceedings{tahir-etal-2025-prompt,
    title = "Prompt-Guided Augmentation and Multi-modal Fusion for Argumentative Fallacy Classification in Political Debates",
    author = "Tahir, Abdullah  and
      Ibrar, Imaan  and
      Ameer, Huma  and
      Fatima, Mehwish  and
      Latif, Seemab",
    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/transition-to-people-yaml/2025.argmining-1.38/",
    doi = "10.18653/v1/2025.argmining-1.38",
    pages = "381--387",
    ISBN = "979-8-89176-258-9",
    abstract = "Classifying argumentative fallacies in political discourse is challenging due to their subtle, persuasive nature across text and speech. In our MM-ArgFallacy Shared Task submission, Team NUST investigates uni-modal (text/audio) and multi-modal (text+audio) setups using pretrained models{---}RoBERTa for text and Whisper for audio. To tackle severe class imbalance, we introduce Prompt-Guided Few-Shot Augmentation (PG-FSA) to generate synthetic samples for underrepresented fallacies. We further propose a late fusion architecture combining linguistic and paralinguistic cues, enhanced with balancing techniques like SMOTE and Focal Loss. Our approach achieves top performance across modalities, ranking 1st in text-only and multi-modal tracks, and 3rd in audio-only, on the official leaderboard. These results underscore the effectiveness of targeted augmentation and modular fusion in multi-modal fallacy classification."
}