A Unified RvNN Framework for End-to-End Chinese Discourse Parsing

Lin Chuan-An, Hen-Hsen Huang, Zi-Yuan Chen, Hsin-Hsi Chen


Abstract
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.
Anthology ID:
C18-2016
Volume:
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
73–77
Language:
URL:
https://www.aclweb.org/anthology/C18-2016
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/C18-2016.pdf