Abstract
We present a new method for transition-based parsing where a solution is a pair made of a dependency tree and a derivation graph describing the construction of the former. From this representation we are able to derive an efficient parsing algorithm and design a neural network that learns vertex representations and arc scores. Experimentally, although we only train via local classifiers, our approach improves over previous arc-hybrid systems and reach state-of-the-art parsing accuracy.- Anthology ID:
- K19-1023
- Volume:
- Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Mohit Bansal, Aline Villavicencio
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 238–248
- Language:
- URL:
- https://aclanthology.org/K19-1023
- DOI:
- 10.18653/v1/K19-1023
- Cite (ACL):
- Joseph Le Roux, Antoine Rozenknop, and Mathieu Lacroix. 2019. Representation Learning and Dynamic Programming for Arc-Hybrid Parsing. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 238–248, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Representation Learning and Dynamic Programming for Arc-Hybrid Parsing (Le Roux et al., CoNLL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/K19-1023.pdf