This paper demonstrates an end-to-end Chinese discourse parser. We propose a unified framework based on recursive neural network (RvNN) to jointly model the subtasks including elementary discourse unit (EDU) segmentation, tree structure construction, center labeling, and sense labeling. Experimental results show our parser achieves the state-of-the-art performance in the Chinese Discourse Treebank (CDTB) dataset. We release the source code with a pre-trained model for the NLP community. To the best of our knowledge, this is the first open source toolkit for Chinese discourse parsing. The standalone toolkit can be integrated into subsequent applications without the need of external resources such as syntactic parser. Title and Abstract in Chinese
REF propose a framework based on recursive neural network that jointly models several subtasks including EDU segmentation, tree structure construction, as well as center and sense labeling.
A Unified RvNN Framework for End-to-End Chinese Discourse Parsing
COLING
Computer Science