Leveraging Context for Multimodal Fallacy Classification in Political Debates
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 18 references that are in the map. First posted on arXiv 21 Jul 2025.
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References · 9
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