Argument mining is to analyze argument structure and extract important argument information from unstructured text. An argument mining system can help people automatically gain causal and logical information behind the text. As argumentative corpus gradually increases, like more people begin to argue and debate on social media, argument mining from them is becoming increasingly critical. However, argument mining is still a big challenge in natural language tasks due to its difficulty, and relative techniques are not mature. For example, research on non-tree argument mining needs to be done more. Most works just focus on extracting tree structure argument information. Moreover, current methods cannot accurately describe and capture argument relations and do not predict their types. In this paper, we propose a novel neural model called AutoAM to solve these problems. We first introduce the argument component attention mechanism in our model. It can capture the relevant information between argument components, so our model can better perform argument mining. Our model is a universal end-to-end framework, which can analyze argument structure without constraints like tree structure and complete three subtasks of argument mining in one model. The experiment results show that our model outperforms the existing works on several metrics in two public datasets.

翻译：论点挖掘旨在分析论元结构并从非结构化文本中提取重要论点信息。一个论点挖掘系统能帮助人们自动获取文本背后的因果与逻辑信息。随着论辩性语料逐渐增加（例如越来越多人在社交媒体上展开论辩），从中进行论点挖掘变得日益关键。然而，由于论元挖掘的难度，其在自然语言任务中仍是一大挑战，相关技术尚未成熟。例如，非树结构论点挖掘的研究有待深化，现有多数工作仅聚焦于提取树结构的论点信息。此外，当前方法无法准确描述和捕捉论元关系，也不对其类型进行预测。本文提出一种名为AutoAM的新型神经模型以解决这些问题。我们首先在模型中引入论元组件注意力机制，该机制能捕捉论元组件间的关联信息，从而使模型更有效地执行论点挖掘。AutoAM是一个通用的端到端框架，可在不受树结构等约束的情况下分析论点结构，并在单一模型中完成论点挖掘的三个子任务。实验结果表明，在两个公开数据集的多个指标上，本模型优于现有方法。