@inproceedings{mezza-etal-2024-exploiting,
title = "Exploiting Dialogue Acts and Context to Identify Argumentative Relations in Online Debates",
author = "Mezza, Stefano and
Wobcke, Wayne and
Blair, Alan",
editor = "Ajjour, Yamen and
Bar-Haim, Roy and
El Baff, Roxanne and
Liu, Zhexiong and
Skitalinskaya, Gabriella",
booktitle = "Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.argmining-1.4",
pages = "36--45",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Exploiting Dialogue Acts and Context to Identify Argumentative Relations in Online Debates
%A Mezza, Stefano
%A Wobcke, Wayne
%A Blair, Alan
%Y Ajjour, Yamen
%Y Bar-Haim, Roy
%Y El Baff, Roxanne
%Y Liu, Zhexiong
%Y Skitalinskaya, Gabriella
%S Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F mezza-etal-2024-exploiting
%X 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.
%U https://aclanthology.org/2024.argmining-1.4
%P 36-45
Markdown (Informal)
[Exploiting Dialogue Acts and Context to Identify Argumentative Relations in Online Debates](https://aclanthology.org/2024.argmining-1.4) (Mezza et al., ArgMining 2024)
ACL