MAMKit: A Comprehensive Multimodal Argument Mining Toolkit

Eleonora Mancini, Federico Ruggeri, Stefano Colamonaco, Andrea Zecca, Samuele Marro, Paolo Torroni

Abstract
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.
Anthology ID:
2024.argmining-1.7
Volume:
Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Yamen Ajjour, Roy Bar-Haim, Roxanne El Baff, Zhexiong Liu, Gabriella Skitalinskaya
Venue:
ArgMining
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
69–82
Language:
URL:
https://aclanthology.org/2024.argmining-1.7
DOI:
Bibkey:
Cite (ACL):
Eleonora Mancini, Federico Ruggeri, Stefano Colamonaco, Andrea Zecca, Samuele Marro, and Paolo Torroni. 2024. MAMKit: A Comprehensive Multimodal Argument Mining Toolkit. In Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024), pages 69–82, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
MAMKit: A Comprehensive Multimodal Argument Mining Toolkit (Mancini et al., ArgMining 2024)
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