Argument Mining (AM) is a crucial aspect of computational argumentation,
which deals with the identification and extraction of Argumentative Components
(ACs) and their corresponding Argumentative Relations (ARs). Most prior works
have solved these problems by dividing them into multiple subtasks. And the
available end-to-end setups are mostly based on the dependency parsing
approach. This work proposes a unified end-to-end framework based on a
generative paradigm, in which the argumentative structures are framed into
label-augmented text, called Augmented Natural Language (ANL). Additionally, we
explore the role of different types of markers in solving AM tasks. Through
different marker-based fine-tuning strategies, we present an extensive study by
integrating marker knowledge into our generative model. The proposed framework
achieves competitive results to the state-of-the-art (SoTA) model and
outperforms several baselines.

基于生成式范式，本研究提出了一个统一的端到端框架，将论证结构构建为标签增强文本，称之为增强自然语言（ANL），并且通过探索不同类型标记的作用，将标记知识融入生成模型中，实现了与最新模型相媲美的成果并超过了几个基准结果。