用文本生成统一解决论点挖掘任务，省去复杂后处理
Argument Mining as a Text-to-Text Generation Task
- 将论点挖掘转为文本到文本生成，一次输出所有结构信息
- 在三个数据集上达到当前最优性能，无需额外调参
- 方法简洁通用，可轻松适配不同论点结构类型
论点挖掘（AM）旨在揭示文本中的论证结构。传统方法需分步完成片段识别、成分分类和关系分类等多个子任务，且需规则后处理整合结果，增加模型复杂度并扩大超参数搜索空间。为此，本文提出一种基于预训练编码器-解码器语言模型的文本到文本生成方法，可同时生成带有论点标注的文本片段、成分和关系，无需任务特定后处理与超参数调优。该方法结构简单，易于扩展至多种论证结构。实验表明，该方法在三个基准数据集——论点标注作文语料库（AAEC）、AbstRCT 和康奈尔电子规则制定语料库（CDCP）上均取得当前最优表现。
原文摘要 · Abstract (English)
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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