RST-style Discourse Parsing Guided by Document-level Content Structures
Abstract
Rhetorical Structure Theory based Discourse Parsing (RST-DP) explores how clauses, sentences, and large text spans compose a whole discourse and presents the rhetorical structure as a hierarchical tree. Existing RST parsing pipelines construct rhetorical structures without the knowledge of document-level content structures, which causes relatively low performance when predicting the discourse relations for large text spans. Recognizing the value of high-level content-related information in facilitating discourse relation recognition, we propose a novel pipeline for RST-DP that incorporates structure-aware news content sentence representations derived from the task of News Discourse Profiling. By incorporating only a few additional layers, this enhanced pipeline exhibits promising performance across various RST parsing metrics.
Cite
@article{arxiv.2309.04141,
title = {RST-style Discourse Parsing Guided by Document-level Content Structures},
author = {Ming Li and Ruihong Huang},
journal= {arXiv preprint arXiv:2309.04141},
year = {2023}
}