Abstract
Because the most common transition systems are projective, training a transition-based dependency parser often implies to either ignore or rewrite the non-projective training examples, which has an adverse impact on accuracy. In this work, we propose a simple modification of dynamic oracles, which enables the use of non-projective data when training projective parsers. Evaluation on 73 treebanks shows that our method achieves significant gains (+2 to +7 UAS for the most non-projective languages) and consistently outperforms traditional projectivization and pseudo-projectivization approaches.- Anthology ID:
- N18-2066
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 413–419
- Language:
- URL:
- https://aclanthology.org/N18-2066
- DOI:
- 10.18653/v1/N18-2066
- Cite (ACL):
- Lauriane Aufrant, Guillaume Wisniewski, and François Yvon. 2018. Exploiting Dynamic Oracles to Train Projective Dependency Parsers on Non-Projective Trees. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 413–419, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Exploiting Dynamic Oracles to Train Projective Dependency Parsers on Non-Projective Trees (Aufrant et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/N18-2066.pdf