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