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 (translated)
论辩挖掘（Argument Mining, AM）旨在揭示文本中的论辩结构。以往方法需执行多个子任务（如跨度识别、组件分类与关系分类），因此依赖基于规则的后处理步骤，从各子任务输出中推导出论辩结构。该方法增加了模型复杂性并扩大了超参数搜索空间。为应对此挑战，我们提出一种基于文本到文本生成框架的简洁而有效的方法，利用预训练编码器-解码器语言模型实现。该方法可同时生成标注跨度、组件及关系的论文化文本，从而无需任务特定的后处理与超参数调优。此外，由于采用直接的文本到文本生成方式，该方法能便捷适配多种类型的论辩结构。实验结果表明，本方法在三个不同基准数据集上均取得最先进性能：论文化作文语料库（Argument-annotated Essays Corpus, AAEC）、AbstRCT及康奈尔电子立法语料库（Cornell eRulemaking Corpus, CDCP）。
URL
https://arxiv.org/abs/2603.23949
