Argument mining automatically identifies and extracts the structure of inference and reasoning conveyed in natural language arguments. To the best of our knowledge, most of the state-of-the-art works in this field have focused on using tree-like structures and linguistic modeling. But, these approaches are not able to model more complex structures which are often found in online forums and real world argumentation structures. In this paper, a novel methodology for argument mining is proposed which employs attention-based embeddings for link prediction to model the causational hierarchies in typical argument structures prevalent in online discourse.

翻译：论点挖掘旨在自动识别并提取自然语言论点中所蕴含的推理与论证结构。据我们所知，该领域现有的大多数前沿研究集中于采用树状结构及语言建模方法。然而，这些方法无法有效建模在线论坛及现实世界论证结构中常见的更复杂架构。本文提出了一种新颖的论点挖掘方法，通过采用基于注意力的嵌入进行链接预测，从而对在线话语中典型论点结构内普遍存在的因果层级关系进行建模。