- Hierarchical top-down RST parsing based on neural networks -
Analyzing a discourse structure behind the document is crucial for context aware Natural Language Processing
(NLP) tasks including machine translation and automatic summarization. We propose a neural discourse parsing
method based on Rhetorical Structure Theory (RST) that regards a document as a constituent tree. Our parser
builds RST trees at different levels of granularity in a document and then replace leaves of upper-level RST trees
with lower-level RST trees that were already constructed. The parsing is performed in a top-down manner for
each granularity level by recursively splitting a larger text span into two smaller ones while predicting nuclearity
labels and rhetorical relations. Unlike previous discourse parsers, our parser can be fully parallelized at each
granularity in a document and does not require any handcrafted features such as syntactic features obtained from
full parse trees of sentences.
 N. Kobayashi, T. Hirao, M. Okumura, M. Nagata, “Top-down RST Parsing Utilizing Granularity Levels in Documents,” in Proc. of 25th Annual
Meeting of Natural Language Processing, pp. 1002-1005, 2019.