@inproceedings{kawarada-etal-2024-argument,
title = "Argument Mining as a Text-to-Text Generation Task",
author = "Kawarada, Masayuki and
Hirao, Tsutomu and
Uchida, Wataru and
Nagata, Masaaki",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-long.121",
pages = "2002--2014",
abstract = "Argument Mining (AM) aims to uncover the argumentative structures within a text. Previous methods require several subtasks, such as span identification, component classification, and relation classification. Consequently, these methods need rule-based postprocessing to derive argumentative structures from the output of each subtask. This approach adds to the complexity of the model and expands the search space of the hyperparameters. To address this difficulty, we propose a simple yet strong method based on a text-to-text generation approach using a pretrained encoder-decoder language model. Our method simultaneously generates argumentatively annotated text for spans, components, and relations, eliminating the need for task-specific postprocessing and hyperparameter tuning. Furthermore, because it is a straightforward text-to-text generation method, we can easily adapt our approach to various types of argumentative structures.Experimental results demonstrate the effectiveness of our method, as it achieves state-of-the-art performance on three different types of benchmark datasets: the Argument-annotated Essays Corpus (AAEC), AbstRCT, and the Cornell eRulemaking Corpus (CDCP).",
}
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<abstract>Argument Mining (AM) aims to uncover the argumentative structures within a text. Previous methods require several subtasks, such as span identification, component classification, and relation classification. Consequently, these methods need rule-based postprocessing to derive argumentative structures from the output of each subtask. This approach adds to the complexity of the model and expands the search space of the hyperparameters. To address this difficulty, we propose a simple yet strong method based on a text-to-text generation approach using a pretrained encoder-decoder language model. Our method simultaneously generates argumentatively annotated text for spans, components, and relations, eliminating the need for task-specific postprocessing and hyperparameter tuning. Furthermore, because it is a straightforward text-to-text generation method, we can easily adapt our approach to various types of argumentative structures.Experimental results demonstrate the effectiveness of our method, as it achieves state-of-the-art performance on three different types of benchmark datasets: the Argument-annotated Essays Corpus (AAEC), AbstRCT, and the Cornell eRulemaking Corpus (CDCP).</abstract>
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%0 Conference Proceedings
%T Argument Mining as a Text-to-Text Generation Task
%A Kawarada, Masayuki
%A Hirao, Tsutomu
%A Uchida, Wataru
%A Nagata, Masaaki
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F kawarada-etal-2024-argument
%X Argument Mining (AM) aims to uncover the argumentative structures within a text. Previous methods require several subtasks, such as span identification, component classification, and relation classification. Consequently, these methods need rule-based postprocessing to derive argumentative structures from the output of each subtask. This approach adds to the complexity of the model and expands the search space of the hyperparameters. To address this difficulty, we propose a simple yet strong method based on a text-to-text generation approach using a pretrained encoder-decoder language model. Our method simultaneously generates argumentatively annotated text for spans, components, and relations, eliminating the need for task-specific postprocessing and hyperparameter tuning. Furthermore, because it is a straightforward text-to-text generation method, we can easily adapt our approach to various types of argumentative structures.Experimental results demonstrate the effectiveness of our method, as it achieves state-of-the-art performance on three different types of benchmark datasets: the Argument-annotated Essays Corpus (AAEC), AbstRCT, and the Cornell eRulemaking Corpus (CDCP).
%U https://aclanthology.org/2024.eacl-long.121
%P 2002-2014
Markdown (Informal)
[Argument Mining as a Text-to-Text Generation Task](https://aclanthology.org/2024.eacl-long.121) (Kawarada et al., EACL 2024)
ACL
- Masayuki Kawarada, Tsutomu Hirao, Wataru Uchida, and Masaaki Nagata. 2024. Argument Mining as a Text-to-Text Generation Task. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2002–2014, St. Julian’s, Malta. Association for Computational Linguistics.