Unleashing the Power of Neural Discourse Parsers -- A Context and Structure Aware Approach Using Large Scale Pretraining
This work addresses discourse parsing for NLP applications like summarization and machine translation, representing an incremental improvement with strong specific gains.
The paper tackled the problem of RST-based discourse parsing by introducing a simple yet highly accurate parser that incorporates contextual language models, achieving new state-of-the-art performance on RST-DT and Instr-DT datasets.
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.