Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining
Grigorii Guz, Patrick Huber, Giuseppe Carenini · 2020
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.