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.
Multimodal Fallacy Classification in Political Debates / Eleonora Mancini, Federico Ruggeri, Paolo Torroni. - ELETTRONICO. - (2024), pp. 170-178. (Intervento presentato al convegno 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL) tenutosi a St. Julian’s, Malta nel March 2024).
Multimodal Fallacy Classification in Political Debates
Eleonora Mancini
Primo
;Federico RuggeriSecondo
;Paolo TorroniUltimo
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
