用自回归方法端到端预测论点结构，提升自动论点挖掘效果。
End-to-End Argument Mining through Autoregressive Argumentative Structure Prediction
- 将论点组件与关系联合建模为逐步生成的动作序列。
- 在三个标准数据集上达到当前最佳性能，两个达新纪录。
- 适合需要完整论点结构解析的文本分析任务。
论点挖掘（AM）旨在自动化提取论证文本中的复杂结构，如前提、主张等论点组件（ACs），以及支持、攻击等论证关系（ARs）。由于该任务涉及复杂的推理过程，建模组件与关系之间的依赖关系极具挑战。现有方法多采用扁平化生成范式，而本文提出端到端的自回归论点结构预测（AASP）框架，通过条件预训练语言模型，将论证结构建模为受约束的预定义动作集合，以自回归方式逐步构建结构，有效捕捉论证推理流程。在三个标准AM基准上的大量实验表明，AASP在两个基准上所有任务均达到当前最佳（SoTA）结果，在一个基准上也表现强劲。
原文摘要 · Abstract (English)
Argument Mining (AM) helps in automating the extraction of complex argumentative structures such as Argument Components (ACs) like Premise, Claim etc. and Argumentative Relations (ARs) like Support, Attack etc. in an argumentative text. Due to the inherent complexity of reasoning involved with this task, modelling dependencies between ACs and ARs is challenging. Most of the recent approaches formulate this task through a generative paradigm by flattening the argumentative structures. In contrast to that, this study jointly formulates the key tasks of AM in an end-to-end fashion using Autoregressive Argumentative Structure Prediction (AASP) framework. The proposed AASP framework is based on the autoregressive structure prediction framework that has given good performance for several NLP tasks. AASP framework models the argumentative structures as constrained pre-defined sets of actions with the help of a conditional pre-trained language model. These actions build the argumentative structures step-by-step in an autoregressive manner to capture the flow of argumentative reasoning in an efficient way. Extensive experiments conducted on three standard AM benchmarks demonstrate that AASP achieves state-of-theart (SoTA) results across all AM tasks in two benchmarks and delivers strong results in one benchmark.
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