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.- Anthology ID:
- 2024.eacl-short.16
- Volume:
- Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian’s, Malta
- Editors:
- Yvette Graham, Matthew Purver
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 170–178
- Language:
- URL:
- https://aclanthology.org/2024.eacl-short.16
- DOI:
- Cite (ACL):
- Eleonora Mancini, Federico Ruggeri, and Paolo Torroni. 2024. Multimodal Fallacy Classification in Political Debates. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 170–178, St. Julian’s, Malta. Association for Computational Linguistics.
- Cite (Informal):
- Multimodal Fallacy Classification in Political Debates (Mancini et al., EACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.eacl-short.16.pdf