Leveraging Context for Multimodal Fallacy Classification in Political Debates
This work addresses the problem of detecting logical fallacies in political debates for argument mining researchers, but it is incremental as it builds on existing shared task frameworks.
The paper tackled multimodal fallacy classification in political debates by leveraging context with pretrained Transformers, achieving macro F1-scores of 0.4444 for text, 0.3559 for audio, and 0.4403 for multimodal models.
In this paper, we present our submission to the MM-ArgFallacy2025 shared task, which aims to advance research in multimodal argument mining, focusing on logical fallacies in political debates. Our approach uses pretrained Transformer-based models and proposes several ways to leverage context. In the fallacy classification subtask, our models achieved macro F1-scores of 0.4444 (text), 0.3559 (audio), and 0.4403 (multimodal). Our multimodal model showed performance comparable to the text-only model, suggesting potential for improvements.