Prompt-Guided Augmentation and Multi-modal Fusion for Argumentative Fallacy Classification in Political Debates

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 textonly and multi-modal tracks, and 3rd in audioonly, on the official leaderboard.These results underscore the effectiveness of targeted augmentation and modular fusion in multi-modal fallacy classification.