A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing

Dat Quoc Nguyen, Mark Dras, Mark Johnson


Abstract
We present a novel neural network model that learns POS tagging and graph-based dependency parsing jointly. Our model uses bidirectional LSTMs to learn feature representations shared for both POS tagging and dependency parsing tasks, thus handling the feature-engineering problem. Our extensive experiments, on 19 languages from the Universal Dependencies project, show that our model outperforms the state-of-the-art neural network-based Stack-propagation model for joint POS tagging and transition-based dependency parsing, resulting in a new state of the art. Our code is open-source and available together with pre-trained models at: https://github.com/datquocnguyen/jPTDP
Anthology ID:
K17-3014
Volume:
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
134–142
Language:
URL:
https://aclanthology.org/K17-3014
DOI:
10.18653/v1/K17-3014
Bibkey:
Cite (ACL):
Dat Quoc Nguyen, Mark Dras, and Mark Johnson. 2017. A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 134–142, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing (Nguyen et al., CoNLL 2017)
Copy Citation:
PDF:
https://aclanthology.org/K17-3014.pdf
Code
 datquocnguyen/jPTDP
Data
Universal Dependencies