@inproceedings{ruiz-dolz-etal-2024-overview,
title = "Overview of {D}ial{AM}-2024: Argument Mining in Natural Language Dialogues",
author = "Ruiz-Dolz, Ramon and
Lawrence, John and
Schad, Ella and
Reed, Chris",
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-1.8",
pages = "83--92",
abstract = "Argumentation is the process by which humans rationally elaborate their thoughts and opinions in written (e.g., essays) or spoken (e.g., debates) contexts. Argument Mining research, however, has been focused on either written argumentation or spoken argumentation but without considering any additional information, e.g., speech acts and intentions. In this paper, we present an overview of DialAM-2024, the first shared task in dialogical argument mining, where argumentative relations and speech illocutions are modelled together in a unified framework. The task was divided into two different sub-tasks: the identification of propositional relations and the identification of illocutionary relations. Six different teams explored different methodologies to leverage both sources of information to reconstruct argument maps containing the locutions uttered in the speeches and the argumentative propositions implicit in them. The best performing team achieved an F1-score of 67.05{\%} in the overall evaluation of the reconstruction of complete argument maps, considering both sub-tasks included in the DialAM-2024 shared task.",
}
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<abstract>Argumentation is the process by which humans rationally elaborate their thoughts and opinions in written (e.g., essays) or spoken (e.g., debates) contexts. Argument Mining research, however, has been focused on either written argumentation or spoken argumentation but without considering any additional information, e.g., speech acts and intentions. In this paper, we present an overview of DialAM-2024, the first shared task in dialogical argument mining, where argumentative relations and speech illocutions are modelled together in a unified framework. The task was divided into two different sub-tasks: the identification of propositional relations and the identification of illocutionary relations. Six different teams explored different methodologies to leverage both sources of information to reconstruct argument maps containing the locutions uttered in the speeches and the argumentative propositions implicit in them. The best performing team achieved an F1-score of 67.05% in the overall evaluation of the reconstruction of complete argument maps, considering both sub-tasks included in the DialAM-2024 shared task.</abstract>
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%0 Conference Proceedings
%T Overview of DialAM-2024: Argument Mining in Natural Language Dialogues
%A Ruiz-Dolz, Ramon
%A Lawrence, John
%A Schad, Ella
%A Reed, Chris
%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 ruiz-dolz-etal-2024-overview
%X Argumentation is the process by which humans rationally elaborate their thoughts and opinions in written (e.g., essays) or spoken (e.g., debates) contexts. Argument Mining research, however, has been focused on either written argumentation or spoken argumentation but without considering any additional information, e.g., speech acts and intentions. In this paper, we present an overview of DialAM-2024, the first shared task in dialogical argument mining, where argumentative relations and speech illocutions are modelled together in a unified framework. The task was divided into two different sub-tasks: the identification of propositional relations and the identification of illocutionary relations. Six different teams explored different methodologies to leverage both sources of information to reconstruct argument maps containing the locutions uttered in the speeches and the argumentative propositions implicit in them. The best performing team achieved an F1-score of 67.05% in the overall evaluation of the reconstruction of complete argument maps, considering both sub-tasks included in the DialAM-2024 shared task.
%U https://aclanthology.org/2024.argmining-1.8
%P 83-92
Markdown (Informal)
[Overview of DialAM-2024: Argument Mining in Natural Language Dialogues](https://aclanthology.org/2024.argmining-1.8) (Ruiz-Dolz et al., ArgMining 2024)
ACL