Recent advances in NLP suggest that some tasks, such as argument detection and relation classification, are better framed in a multimodal perspective. We propose multimodal argument mining for argumentative fallacy classification in political debates. To this end, we release the first corpus for multimodal fallacy classification. Our experiments show that the integration of the audio modality leads to superior classification performance. Our findings confirm that framing fallacy classification as a multimodal task is essential to capture paralinguistic aspects of fallacious arguments.

Eleonora Mancini, F.R. (2024). Multimodal Fallacy Classification in Political Debates. Association for Computational Linguistics.

Multimodal Fallacy Classification in Political Debates

Eleonora Mancini
Primo
;
Federico Ruggeri
Secondo
;
Paolo Torroni
Ultimo
2024

Abstract

Recent advances in NLP suggest that some tasks, such as argument detection and relation classification, are better framed in a multimodal perspective. We propose multimodal argument mining for argumentative fallacy classification in political debates. To this end, we release the first corpus for multimodal fallacy classification. Our experiments show that the integration of the audio modality leads to superior classification performance. Our findings confirm that framing fallacy classification as a multimodal task is essential to capture paralinguistic aspects of fallacious arguments.
2024
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
170
178
Eleonora Mancini, F.R. (2024). Multimodal Fallacy Classification in Political Debates. Association for Computational Linguistics.
Eleonora Mancini, Federico Ruggeri, Paolo Torroni
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/966856
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