A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing

Hang Yan, Xipeng Qiu, Xuanjing Huang


Abstract
Chinese word segmentation and dependency parsing are two fundamental tasks for Chinese natural language processing. The dependency parsing is defined at the word-level. Therefore word segmentation is the precondition of dependency parsing, which makes dependency parsing suffer from error propagation and unable to directly make use of character-level pre-trained language models (such as BERT). In this paper, we propose a graph-based model to integrate Chinese word segmentation and dependency parsing. Different from previous transition-based joint models, our proposed model is more concise, which results in fewer efforts of feature engineering. Our graph-based joint model achieves better performance than previous joint models and state-of-the-art results in both Chinese word segmentation and dependency parsing. Additionally, when BERT is combined, our model can substantially reduce the performance gap of dependency parsing between joint models and gold-segmented word-based models. Our code is publicly available at https://github.com/fastnlp/JointCwsParser
Anthology ID:
2020.tacl-1.6
Volume:
Transactions of the Association for Computational Linguistics, Volume 8
Month:
Year:
2020
Address:
Cambridge, MA
Editors:
Mark Johnson, Brian Roark, Ani Nenkova
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
78–92
Language:
URL:
https://aclanthology.org/2020.tacl-1.6
DOI:
10.1162/tacl_a_00301
Bibkey:
Cite (ACL):
Hang Yan, Xipeng Qiu, and Xuanjing Huang. 2020. A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing. Transactions of the Association for Computational Linguistics, 8:78–92.
Cite (Informal):
A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing (Yan et al., TACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.tacl-1.6.pdf
Code
 fastnlp/JointCwsParser