Eleonora Mancini


2024

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Multimodal Fallacy Classification in Political Debates
Eleonora Mancini | Federico Ruggeri | Paolo Torroni
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)

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.

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MAMKit: A Comprehensive Multimodal Argument Mining Toolkit
Eleonora Mancini | Federico Ruggeri | Stefano Colamonaco | Andrea Zecca | Samuele Marro | Paolo Torroni
Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)

Multimodal Argument Mining (MAM) is a recent area of research aiming to extend argument analysis and improve discourse understanding by incorporating multiple modalities. Initial results confirm the importance of paralinguistic cues in this field. However, the research community still lacks a comprehensive platform where results can be easily reproduced, and methods and models can be stored, compared, and tested against a variety of benchmarks. To address these challenges, we propose MAMKit, an open, publicly available, PyTorch toolkit that consolidates datasets and models, providing a standardized platform for experimentation. MAMKit also includes some new baselines, designed to stimulate research on text and audio encoding and fusion for MAM tasks. Our initial results with MAMKit indicate that advancements in MAM require novel annotation processes to encompass auditory cues effectively.

2022

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Multimodal Argument Mining: A Case Study in Political Debates
Eleonora Mancini | Federico Ruggeri | Andrea Galassi | Paolo Torroni
Proceedings of the 9th Workshop on Argument Mining

We propose a study on multimodal argument mining in the domain of political debates. We collate and extend existing corpora and provide an initial empirical study on multimodal architectures, with a special emphasis on input encoding methods. Our results provide interesting indications about future directions in this important domain.