Joint Learning of POS and Dependencies for Multilingual Universal Dependency Parsing

Zuchao Li, Shexia He, Zhuosheng Zhang, Hai Zhao


Abstract
This paper describes the system of team LeisureX in the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system predicts the part-of-speech tag and dependency tree jointly. For the basic tasks, including tokenization, lemmatization and morphology prediction, we employ the official baseline model (UDPipe). To train the low-resource languages, we adopt a sampling method based on other richresource languages. Our system achieves a macro-average of 68.31% LAS F1 score, with an improvement of 2.51% compared with the UDPipe.
Anthology ID:
K18-2006
Volume:
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Month:
October
Year:
2018
Address:
Brussels, Belgium
Editors:
Daniel Zeman, Jan Hajič
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
65–73
Language:
URL:
https://aclanthology.org/K18-2006
DOI:
10.18653/v1/K18-2006
Bibkey:
Cite (ACL):
Zuchao Li, Shexia He, Zhuosheng Zhang, and Hai Zhao. 2018. Joint Learning of POS and Dependencies for Multilingual Universal Dependency Parsing. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 65–73, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Joint Learning of POS and Dependencies for Multilingual Universal Dependency Parsing (Li et al., CoNLL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-bitext-workshop/K18-2006.pdf
Code
 bcmi220/joint_stackptr
Data
Universal Dependencies