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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2507.15641 |
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| _version_ | 1866908480049250304 |
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| author | Pittiglio, Alessio |
| author_facet | Pittiglio, Alessio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15641 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging Context for Multimodal Fallacy Classification in Political Debates Pittiglio, Alessio Computation and Language Artificial Intelligence 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. |
| title | Leveraging Context for Multimodal Fallacy Classification in Political Debates |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2507.15641 |