Argument Mining (AM) is a relatively recent discipline, which concentrates on
extracting claims or premises from discourses, and inferring their structures.
However, many existing works do not consider micro-level AM studies on
discussion threads sufficiently. In this paper, we tackle AM for discussion
threads. Our main contributions are follows: (1) A novel combination scheme
focusing on micro-level inner- and inter- post schemes for a discussion thread.
(2) Annotation of large-scale civic discussion threads with the scheme. (3)
Parallel constrained pointer architecture (PCPA), a novel end-to-end technique
to discriminate sentence types, inner-post relations, and inter-post
interactions simultaneously. The experimental results demonstrate that our
proposed model shows better accuracy in terms of relations extraction, in
comparison to existing state-of-the-art models.

本文介绍了 Argument Mining 领域的最新研究，专注于针对讨论串的微观层面进行研究，提出了一种新的组合方案并使用 PCPA 技术进行了结构推断，同时通过大规模的注释和实验验证表明该方法优于现有的最先进模型。