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.
Eleonora Mancini
Postdoctoral Research Fellow
Her research concerns artificial intelligence and in particular multimodal deep learning, natural language processing, image and speech recognition.
Federico Ruggeri
Postdoctoral Research Fellow
His research aims to devise Natural Language Processing (NLP) systems that learn to generate, distill, and use knowledge from unstructured text.
Paolo Torroni
Associate Professor
Head of the Language Technologies lab. His main research focus is in artificial intelligence, and in particular natural language processing, multi-agent systems, and computational logics.