Academic

Argument Mining as a Text-to-Text Generation Task

arXiv:2603.23949v1 Announce Type: new 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. Experimenta

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Masayuki Kawarada, Tsutomu Hirao, Wataru Uchida, Masaaki Nagata · · 1 min read · 7 views
#cs.CL

arXiv:2603.23949v1 Announce Type: new 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)

Executive Summary

This article proposes a novel text-to-text generation approach for Argument Mining (AM), a task that involves uncovering the argumentative structures within a text. The authors present a simple yet effective method using a pretrained encoder-decoder language model to simultaneously generate argumentatively annotated text for spans, components, and relations. This approach eliminates the need for task-specific postprocessing and hyperparameter tuning, making it a more efficient and scalable solution. Experimental results demonstrate its effectiveness on three benchmark datasets, achieving state-of-the-art performance. The proposed method has significant implications for the field of Natural Language Processing and Argumentation Theory, as it enables the application of AM to various types of argumentative structures. This research has the potential to revolutionize the way we analyze and understand complex texts, particularly in the realms of law, politics, and philosophy.

Key Points

  • ▸ Argument Mining as a text-to-text generation task eliminates the need for subtasks and postprocessing
  • ▸ The proposed method uses a pretrained encoder-decoder language model for simultaneous generation of argumentatively annotated text
  • ▸ Experimental results demonstrate state-of-the-art performance on three benchmark datasets

Merits

Strength

The proposed method is simple, efficient, and scalable, making it a significant improvement over existing approaches. It also enables the application of AM to various types of argumentative structures, which is a major breakthrough in the field.

Demerits

Limitation

The proposed method may not generalize well to out-of-domain datasets or texts with complex argumentative structures. Additionally, the reliance on a pretrained language model may introduce biases and limitations that need to be addressed.

Expert Commentary

The proposed method is a significant contribution to the field of Argument Mining and Natural Language Processing. It offers a more efficient and scalable solution to the AM task, which is a major breakthrough. However, it is essential to address the limitations and potential biases introduced by the reliance on a pretrained language model. Additionally, further research is needed to explore the application of this method to out-of-domain datasets and texts with complex argumentative structures. With proper development and refinement, this method has the potential to revolutionize the way we analyze and understand complex texts, with significant implications for various domains and policy-making.

Recommendations

  • ✓ Further research is needed to address the limitations and potential biases introduced by the reliance on a pretrained language model.
  • ✓ The proposed method should be explored in various domains, including law, politics, and philosophy, to analyze and understand complex texts and arguments.

Sources

Original: arXiv - cs.CL

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