Exploiting Dialogue Acts and Context to Identify Argumentative Relations in Online Debates

Stefano Mezza, Wayne Wobcke, Alan Blair

Abstract
Argumentative Relation Classification is the task of determining the relationship between two contributions in the context of an argumentative dialogue. Existing models in the literature rely on a combination of lexical features and pre-trained language models to tackle this task; while this approach is somewhat effective, it fails to take into account the importance of pragmatic features such as the illocutionary force of the argument or the structure of previous utterances in the discussion; relying solely on lexical features also produces models that over-fit their initial training set and do not scale to unseen domains. In this work, we introduce ArguNet, a new model for Argumentative Relation Classification which relies on a combination of Dialogue Acts and Dialogue Context to improve the representation of argument structures in opinionated dialogues. We show that our model achieves state-of-the-art results on the Kialo benchmark test set, and provide evidence of its robustness in an open-domain scenario.
Anthology ID:
2024.argmining-1.4
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:
36–45
Language:
URL:
https://aclanthology.org/2024.argmining-1.4
DOI:
10.18653/v1/2024.argmining-1.4
Bibkey:
Cite (ACL):
Stefano Mezza, Wayne Wobcke, and Alan Blair. 2024. Exploiting Dialogue Acts and Context to Identify Argumentative Relations in Online Debates. In Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024), pages 36–45, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Exploiting Dialogue Acts and Context to Identify Argumentative Relations in Online Debates (Mezza et al., ArgMining 2024)
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