A CDT-styled end-to-end Chinese discourse parser

Abstract

Discourse parsing is a challenging task and plays a critical role in discourse analysis. Since the release of the Rhetorical Structure Theory Discourse Treebank (RST-DT) and the Penn Discourse Treebank (PDTB), the research on English discourse parsing has attracted increasing attention and achieved considerable success in recent years. At the same time, some preliminary research on certain subtasks about discourse parsing for other languages, such as Chinese, has been conducted. In this paper, the Connective-driven Dependency Treebank (CDTB) corpus is introduced. Then an end-to-end Chinese discourse parser to parse free texts into the Connective-driven Dependency Tree (CDT) style is presented. The parser consists of multiple components including elementary discourse unit detector, discourse relation recognizer, discourse parse tree generator and attribution labeler. In particular, attribution labeler determines two attributions (sense and centering) for every non-terminal node in the discourse parse trees. Effective feature sets are proposed for every component respectively. Comprehensive experiments are conducted on the Connective-driven Dependency Treebank (CDTB) corpus with an overall F1 score of 20.0%.