RST-based discourse parsing is an important NLP task with numerous downstream
applications, such as summarization, machine translation and opinion mining. In
this paper, we demonstrate a simple, yet highly accurate discourse parser,
incorporating recent contextual language models. Our parser establishes the new
state-of-the-art (SOTA) performance for predicting structure and nuclearity on
two key RST datasets, RST-DT and Instr-DT. We further demonstrate that
pretraining our parser on the recently available large-scale "silver-standard"
discourse treebank MEGA-DT provides even larger performance benefits,
suggesting a novel and promising research direction in the field of discourse
analysis.

这篇论文提出了一个简单但高精度的 RST 语篇分析器，采用最近的上下文语言模型，表现出两个重要数据集，RST-DT 和 Instr-DT 的最新技术性能。研究人员还表明，在最近可用的大规模 “银标准” 话语树库 MEGA-DT 上预训练我们的分析器可以提供更大的性能改进，这为话语分析领域提供了一种新的有前途的研究方向。