Top-down Discourse Parsing via Sequence Labelling
Fajri Koto, Jey Han Lau, Timothy J. Baldwin · 2021
We introduce a top-down approach to discourse parsing that is conceptually simpler than its predecessors (Kobayashi et al., 2020;Zhang et al., 2020).By framing the task as a sequence labelling problem where the goal is to iteratively segment a document into individual discourse units, we are able to eliminate the decoder and reduce the search space for splitting points.We explore both traditional recurrent models and modern pre-trained transformer models for the task, and additionally introduce a novel dynamic oracle for top-down parsing.Based on the Full metric, our proposed LSTM model sets a new state-of-the-art for RST parsing. 1