Leveraging Context for Multimodal Fallacy Classification in Political Debates

Alessio Pittiglio

doi:10.18653/v1/2025.argmining-1.39

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arXiv · 2507.15641

Leveraging Context for Multimodal Fallacy Classification in Political Debates

Alessio Pittiglio

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Abstract

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.

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BibTeX RIS

Alessio Pittiglio. 2025-07-21. Leveraging Context for Multimodal Fallacy Classification in Political Debates. https://doi.org/10.18653/v1%2F2025.argmining-1.39

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