Joint Learning of POS and Dependencies for Multilingual Universal Dependency Parsing
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
- 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)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/K18-2006.pdf
- Code
- bcmi220/joint_stackptr
- Data
- Universal Dependencies