Discourse relations among arguments reveal logical structures of a debate
conversation. However, no prior work has explicitly studied how the sequence of
discourse relations influence a claim's impact. This paper empirically shows
that the discourse relations between two arguments along the context path are
essential factors for identifying the persuasive power of an argument. We
further propose DisCOC to inject and fuse the sentence-level structural
discourse information with contextualized features derived from large-scale
language models. Experimental results and extensive analysis show that the
attention and gate mechanisms that explicitly model contexts and texts can
indeed help the argument impact classification task defined by Durmus et al.
(2019), and discourse structures among the context path of the claim to be
classified can further boost the performance.

本文提出 DisCOC 方法，通过注入和融合大规模语言模型派生的上下文化特征和句子结构语义信息，实现通过路径上的语篇结构确定辩论中有效干扰因素的目标。实验证明，显式建立上下文和文本之间的关系以及对上下文路径的话语结构进行建模都有助于提高 Durmus 等人（2019）提出的辩论结构分类任务的性能，并且在实验中人们可以进一步了解要分类的论点语境路径之间的语篇结构，从而提高分类性能。