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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/K17-3014.pdf
- Code
- datquocnguyen/jPTDP
- Data
- Universal Dependencies