Argument Mining (AM) involves identifying and extracting Argumentative Components (ACs) and their corresponding Argumentative Relations (ARs). Most of the prior works have broken down these tasks into multiple sub-tasks. Existing end-to-end setups primarily use the dependency parsing approach. This work introduces a generative paradigm-based end-to-end framework argTANL. argTANL frames the argumentative structures into label-augmented text, called Augmented Natural Language (ANL). This framework jointly extracts both ACs and ARs from a given argumentative text. Additionally, this study explores the impact of Argumentative and Discourse markers on enhancing the model's performance within the proposed framework. Two distinct frameworks, Marker-Enhanced argTANL (ME-argTANL) and argTANL with specialized Marker-Based Fine-Tuning, are proposed to achieve this. Extensive experiments are conducted on three standard AM benchmarks to demonstrate the superior performance of the ME-argTANL.

翻译：论证挖掘（Argument Mining，AM）涉及识别与提取论证成分（Argumentative Components，ACs）及其对应的论证关系（Argumentative Relations，ARs）。现有研究大多将这些任务分解为多个子任务。当前的端到端方法主要采用依存句法分析思路。本文提出了一种基于生成范式的端到端框架 argTANL。argTANL 将论证结构转化为标签增强的文本，称为增强自然语言（Augmented Natural Language，ANL）。该框架能够从给定的论证文本中联合提取 ACs 与 ARs。此外，本研究探讨了论证标记与话语标记在提升所提框架模型性能方面的作用。为此，我们提出了两种不同的框架：标记增强型 argTANL（Marker-Enhanced argTANL，ME-argTANL）以及采用专门基于标记的微调策略的 argTANL。我们在三个标准 AM 基准数据集上进行了大量实验，结果证明了 ME-argTANL 的优越性能。