  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.
